docs: update stale latest version claims

MCP's stdio transport uses stdout for JSON-RPC framing, so anything else written there corrupts every response after it. The original #1134 bug was progress-tracker output landing on stdout during tool calls that construct a `ContextGraph`, which is exactly what happens on any request that triggers reasoning or extraction. This PR closes out the remaining pieces of that fix: loading now goes through `load_from_file()` instead of the older `load()` path on the root graph, and mutations, `record_decision`, `add_entity`, `add_relationship`, now persist back to `SEMANTICA_KG_PATH` when it's configured, in both MCP server implementations (the root `mcp/` package and the packaged `semantica.mcp_server`), not just one.

Four things came out of review on top of that.

The stdio regression test originally exercised `get_graph_summary`, which doesn't touch the progress tracker at all, so it couldn't have caught the original bug. Swapped it for `run_reasoning`: `Reasoner.infer_with_results()` calls `progress_tracker.start_tracking()` directly, the exact call site that corrupted stdout before, so this is the minimal path that actually proves the fix. The test now spawns a real `python -m mcp` subprocess, sends it a `tools/call` for `run_reasoning`, and asserts every single line on stdout parses as JSON.

Loading a corrupt or unreadable `SEMANTICA_KG_PATH` used to fail silently and fall through to an empty graph, which meant the next mutation would happily save that empty graph over the original file. Both implementations now track whether the initial load actually succeeded. If it didn't, every mutation handler refuses to save and returns an error instead, so a broken file on disk stays broken rather than getting silently replaced with nothing. An empty file is treated differently: that's a fresh destination, not a corrupt one, and starts a normal empty graph without tripping the guard.

`save_to_file` used to `open(path, 'w')` and `json.dump` directly into the destination, so a crash or disk-full error mid-write could leave a truncated file as the only copy of the graph. It now writes to a temp file in the same directory, flushes, fsyncs, and only then `os.replace`s the destination, so the destination is always either the old contents or the new contents, never a partial write. The temp file gets cleaned up if anything fails before the replace.

And since a mutation is applied to the in-memory graph before the save happens, a save failure used to leave the in-memory graph ahead of what's on disk, an entity or decision the client thinks succeeded but that never made it to the file. `record_decision`, `add_entity`, and `add_relationship` all roll back the in-memory mutation now if `save_to_file` raises, so the client-visible state and the persisted state never diverge: either both hold the change or neither does.

104 tests passing across the MCP, persistence, and progress-tracking suites.

.dockerignore

@@ -18,6 +18,9 @@
 .git/**
 .github
 .github/**
+!.github/requirements/
+!.github/requirements/explorer-extra-py313.txt
+!.github/requirements/pep517-build.txt
 .claude
 .claude/**
 .codex

.github/actions/setup-semantica/action.yml

@@ -0,0 +1,56 @@
+name: 'Setup Semantica'
+description: 'Install Python, cache pip, and install the semantica package into a workflow'
+author: 'Semantica'
+
+inputs:
+  python-version:
+    description: 'Python version to set up'
+    required: false
+    default: '3.11'
+  version:
+    description: 'Version constraint to append to the pip spec, e.g. "==0.6.7" or ">=0.6,<0.7". Leave empty for the latest release.'
+    required: false
+    default: ''
+  extras:
+    description: 'Comma-separated extras to install, e.g. "explorer,all"'
+    required: false
+    default: ''
+  cache:
+    description: 'Pip cache mode passed straight to actions/setup-python ("pip" to enable). Left empty (disabled) by default because this action is meant to run standalone in any caller repo, and actions/setup-python errors out if it cannot find a requirements.txt/pyproject.toml/setup.py/poetry.lock to key the cache on. Opt in only when the caller repo has one of those files.'
+    required: false
+    default: ''
+
+outputs:
+  version:
+    description: 'The installed semantica version'
+    value: ${{ steps.verify.outputs.version }}
+
+runs:
+  using: 'composite'
+  steps:
+    - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7
+      with:
+        python-version: ${{ inputs.python-version }}
+        cache: ${{ inputs.cache }}
+
+    - name: Install semantica
+      shell: bash
+      env:
+        SEMANTICA_EXTRAS: ${{ inputs.extras }}
+        SEMANTICA_VERSION: ${{ inputs.version }}
+      run: |
+        python -m pip install --upgrade pip
+        if [ -n "$SEMANTICA_EXTRAS" ]; then
+          spec="semantica[$SEMANTICA_EXTRAS]$SEMANTICA_VERSION"
+        else
+          spec="semantica$SEMANTICA_VERSION"
+        fi
+        python -m pip install -- "$spec"
+
+    - name: Verify install
+      id: verify
+      shell: bash
+      run: |
+        VERSION=$(python -c "import semantica; print(semantica.__version__)")
+        echo "Installed semantica $VERSION"
+        echo "version=$VERSION" >> "$GITHUB_OUTPUT"

.github/dependabot.yml

@@ -101,6 +101,29 @@ updates:
     allow:
       - dependency-type: "production"
 
+  # Explorer frontend (npm)
+  - package-ecosystem: "npm"
+    directory: "/explorer"
+    schedule:
+      interval: "weekly"
+      day: "monday"
+      time: "03:30"  # 3:30 AM UTC (9:00 AM IST)
+    open-pull-requests-limit: 10
+    reviewers:
+      - "KaifAhmad1"
+    assignees:
+      - "KaifAhmad1"
+    commit-message:
+      prefix: "security"
+      include: "scope"
+    labels:
+      - "dependencies"
+      - "javascript"
+      - "security"
+    allow:
+      - dependency-type: "production"
+      - dependency-type: "development"
+
   # Docker dependencies (if you use Docker)
   - package-ecosystem: "docker"
     directory: "/"

.github/requirements/README.md

@@ -0,0 +1,58 @@
+# CI tool requirements
+
+Hash-pinned `pip install` targets for CI/release/Dockerfile steps that install
+something other than the project's own audited `requirements-ci.txt` set.
+These exist because OpenSSF Scorecard's Pinned-Dependencies check flags any
+`pip install` in a workflow or Dockerfile that isn't hash-verified, and
+`requirements-ci.txt` alone doesn't cover build/release/security tooling or
+the project's own local-source install.
+
+Each `.txt` was generated from the adjacent `.in` (or, for `explorer-extra-py311.txt`,
+`explorer-extra-py313.txt`, and `base-deps.txt`, from `pyproject.toml` directly) with:
+
+```
+uv pip compile <input> --python-version 3.11 --python-platform linux \
+  --constraint requirements-ci.txt --generate-hashes -o <output>.txt
+```
+
+(`--constraint requirements-ci.txt` is omitted for `bootstrap.txt`,
+`build-tools.txt`, `uv-tool.txt`, `twine.txt`, `pip-audit.txt`, and
+`security-scan-tools.txt`, since those install standalone tooling with no
+version relationship to the project's own dependency tree.)
+
+Regenerate a file the same way after bumping a pinned version, and re-run it
+whenever `requirements-ci.txt` changes if the file used `--constraint` (see
+each file's own autogenerated header comment for its exact command).
+
+| File | Used by | Installs |
+| --- | --- | --- |
+| `bootstrap.txt` | security-scan.yml, benchmark.yml | pip, setuptools (upgrade before anything else) |
+| `pep517-build.txt` | ci.yml, benchmark.yml, Dockerfile | exact `[build-system] requires` from `pyproject.toml` (setuptools, wheel) - installed with `--no-build-isolation` before any `pip install -e .` / `pip install .`, since `--no-deps` alone doesn't stop pip's PEP 517 build isolation from fetching those two *unhashed* |
+| `explorer-extra-py311.txt` | ci.yml | semantica's base deps + the `explorer` extra, resolved for python 3.11 |
+| `explorer-extra-py313.txt` | Dockerfile | the same, resolved for python 3.13 (the image's actual interpreter) |
+| `pytest-tool.txt` | ci.yml | pytest, for the pre-all-extras deterministic test |
+| `uv-tool.txt` | ci.yml | uv, to verify requirements-ci.txt is current |
+| `build-tools.txt` | ci.yml, release.yml | build, wheel |
+| `twine.txt` | release.yml | twine |
+| `pip-audit.txt` | security-scan.yml | pip-audit |
+| `security-scan-tools.txt` | security-scan.yml | bandit, semgrep, jq |
+| `base-deps.txt` | benchmark.yml | semantica's base deps (no extras) |
+| `benchmark-extra.txt` | benchmark.yml | the benchmark-only libs (neo4j, pdfplumber, etc.) |
+
+`explorer-extra-py31{1,3}.txt` and `base-deps.txt` are large (they mirror
+most of `requirements-ci.txt`) because semantica's `dependencies` list in
+`pyproject.toml` isn't extras-gated - installing the package at all pulls
+the full base set. That's expected, not a mistake.
+
+`explorer-extra-py311.txt` and `explorer-extra-py313.txt` are **not**
+interchangeable, and can't be collapsed into one file compiled for either
+version: `librosa`'s `audioread` dependency needs `standard-aifc` /
+`standard-sunau` only under `python_version >= "3.13"` (Python 3.13 dropped
+`aifc`/`sunau` from stdlib). A file resolved for 3.11 simply omits those
+packages' hashes, so installing it with `--require-hashes` on a real 3.13
+interpreter (the Dockerfile's base image) fails outright rather than
+silently under-pinning. Any other file shared across a 3.11 and 3.13
+consumer would need the same split if it hits a similar stdlib-removal
+edge case - check for `ERROR: In --require-hashes mode, all requirements
+must have their versions pinned` on the *other* Python version before
+assuming one `--python-version` covers every consumer.

.github/requirements/benchmark-extra.in

@@ -0,0 +1,14 @@
+rdflib
+neo4j
+faiss-cpu
+torch
+pyarrow
+pdfplumber
+python-pptx
+openpyxl
+lxml
+python-docx
+beautifulsoup4
+chardet
+langdetect
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.8.0/en_core_web_sm-3.8.0-py3-none-any.whl

.github/requirements/bootstrap.in

@@ -0,0 +1,2 @@
+pip
+setuptools

.github/requirements/bootstrap.txt

@@ -0,0 +1,10 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/bootstrap.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/bootstrap.txt
+pip==26.2.1 \
+    --hash=sha256:71138adf1f4ca900cdb7d289c21b7494329f2332b6d85f0e1c42108c0384ed3e \
+    --hash=sha256:f6ad667e89a1fe78046c8f13232b247200f5258d7828f3f7883d660878e0813f
+    # via -r .github/requirements/bootstrap.in
+setuptools==84.0.0 \
+    --hash=sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670 \
+    --hash=sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73
+    # via -r .github/requirements/bootstrap.in

.github/requirements/build-tools.in

@@ -0,0 +1,2 @@
+build==1.6.0
+wheel==0.48.0

.github/requirements/build-tools.txt

@@ -0,0 +1,20 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/build-tools.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/build-tools.txt
+build==1.6.0 \
+    --hash=sha256:bd2c8afc603e7a2e0ce70e2ea85f0a6d02043bafbd307f5bada0f98669eca5af \
+    --hash=sha256:f7aaf1ebbb79178a02ba248bb524f2176b256017e17e8e4bd4289c7b38cc2bad
+    # via -r .github/requirements/build-tools.in
+packaging==26.3 \
+    --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \
+    --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c
+    # via
+    #   build
+    #   wheel
+pyproject-hooks==1.2.0 \
+    --hash=sha256:1e859bd5c40fae9448642dd871adf459e5e2084186e8d2c2a79a824c970da1f8 \
+    --hash=sha256:9e5c6bfa8dcc30091c74b0cf803c81fdd29d94f01992a7707bc97babb1141913
+    # via build
+wheel==0.48.0 \
+    --hash=sha256:3217dcc807155e45db462d7ef2431f5ddda0d7273b700d05a67b271ceb1287ab \
+    --hash=sha256:94800765601e9171bf5d58d066e640662842bcedcbab982b2c90787a2c987322
+    # via -r .github/requirements/build-tools.in

.github/requirements/checkov.in

@@ -0,0 +1 @@
+checkov==3.3.16

.github/requirements/pep517-build.in

@@ -0,0 +1,2 @@
+setuptools==84.0.0
+wheel==0.48.0

.github/requirements/pep517-build.txt

@@ -0,0 +1,14 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/pep517-build.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/pep517-build.txt
+packaging==26.3 \
+    --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \
+    --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c
+    # via wheel
+setuptools==84.0.0 \
+    --hash=sha256:51a52592b3b99e102b609654876bd65f19f999935166d1352678931132b0c670 \
+    --hash=sha256:f4695c21257f0d9b537ec2692c941d02ee143b7cc1276941349a546573b2ef73
+    # via -r .github/requirements/pep517-build.in
+wheel==0.48.0 \
+    --hash=sha256:3217dcc807155e45db462d7ef2431f5ddda0d7273b700d05a67b271ceb1287ab \
+    --hash=sha256:94800765601e9171bf5d58d066e640662842bcedcbab982b2c90787a2c987322
+    # via -r .github/requirements/pep517-build.in

.github/requirements/pip-audit.in

@@ -0,0 +1 @@
+pip-audit==2.10.1

.github/requirements/pip-audit.txt

@@ -0,0 +1,423 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/pip-audit.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/pip-audit.txt
+boolean-py==5.0 \
+    --hash=sha256:60cbc4bad079753721d32649545505362c754e121570ada4658b852a3a318d95 \
+    --hash=sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9
+    # via license-expression
+cachecontrol==0.14.4 \
+    --hash=sha256:b7ac014ff72ee199b5f8af1de29d60239954f223e948196fa3d84adaffc71d2b \
+    --hash=sha256:e6220afafa4c22a47dd0badb319f84475d79108100d04e26e8542ef7d3ab05a1
+    # via pip-audit
+certifi==2026.7.22 \
+    --hash=sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775 \
+    --hash=sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55
+    # via requests
+charset-normalizer==3.5.1 \
+    --hash=sha256:00668ebb0609751758682eb0b5857e7c35b9f00e84dfdef062e103244ec94d45 \
+    --hash=sha256:012a22b88a77ca2e59b98ac5889b0deb604147666032f45e6d6e217634d2550d \
+    --hash=sha256:01e93745f7f219b703b60ba7afead36cfc4242782be5af484673fc500df12da5 \
+    --hash=sha256:04368edf83514385ffc3e1cfd4546e595f4f1272dd23ba437a93a9cc3741d47b \
+    --hash=sha256:0722590aabf9dc6a6c0343d523c05458fa2b5047dbe6302fd526bb570600753f \
+    --hash=sha256:07ffd07412fc5d5e84cd8952acf9ff7e4ed7a708e69d1bada19d8ba91711353f \
+    --hash=sha256:09a7bba9f739468c8e78c36a75c33768e53cb1959fc638f510454c14683f00d5 \
+    --hash=sha256:0b2b1b3fa5670c127b246df1d0c059defd41f689a868a3b9d79df9b1cac42d22 \
+    --hash=sha256:0c6dfb5ca6723eeed15aa8e564a014d69fcb8812f94eef11fe3631e0508199f5 \
+    --hash=sha256:0d929fc574b4d6fd9e7c0f5c2ede8716a41911923aa7fa5fce38e0818aa4a1ac \
+    --hash=sha256:13e3afe97712e8887cd516e960c63f0b93122971e5b5e4b2622fe7701771e838 \
+    --hash=sha256:15f024313246a4ed976c60f440bb8d257815513a681d212ff74fd46f7d715a90 \
+    --hash=sha256:195ce897c6153c0700078142cf8efe3e6454ca4cf4357499e4078dfd83396626 \
+    --hash=sha256:19a3dd5aa73cef1c99687c4fc57db016a9c17104ae1185da88ba566a5d3bebe4 \
+    --hash=sha256:1d1c7a53a6c2103925cdd6d7229f8c567379f211c869793df679f2e9f738c369 \
+    --hash=sha256:1f5883d77fd409a261abb5dc8ccbe335720d798b1de4abb3b1d47ccbbc76b53b \
+    --hash=sha256:21b82d8082f6f5e7f456ef0bd16323d08de1266efbfeb476e64b2a91d1471a4e \
+    --hash=sha256:252d099029bcbea642f2a06c4ed5046bdf8b5a8150b64afa5e027e88b106e5ee \
+    --hash=sha256:256dd4d85d9e4dc595e2bc983c980e73f62ddeb3165c58b4c3dfe78c5c8548c1 \
+    --hash=sha256:26422d45fd13551cf564c58932f7d72b4f58b93b0fcf18c35ba6be12b46bb102 \
+    --hash=sha256:2679de311c7946dde5d3b6f44941844133ff5c7cb86099c0061ab1e8901c20a8 \
+    --hash=sha256:29880d17a8eb0b5cfdfd8944b468322928059aa35f1f5fa8ff22b149ec0b42f8 \
+    --hash=sha256:2bced4061f000f7187254a02ad3433ae17eaf991747ceea2f478422590a5bba9 \
+    --hash=sha256:2e9cf9253119d8e5d111f05d71626786fd3d6193817316eab1ca088cdb8593cf \
+    --hash=sha256:2f06b7eae9dbe77fe1d644ca244dad508de8d302870a43f3c559b521270938a0 \
+    --hash=sha256:2f293479cce755c75f1697e87c409b7ae4c555c7dfecb6e988ad13abba943031 \
+    --hash=sha256:329fc3ccb63ad22d867d84c2adea759a64079a37ba4a343433b02c7a2816871e \
+    --hash=sha256:343fb4f2821043bd87095f7b08a1a181febc8e36ac64212143bbfd0a0e1bc235 \
+    --hash=sha256:3588e376b3ea2eea84976f67273d679f229e24c66dce7b82ae45aef04ff6e072 \
+    --hash=sha256:35aea775dc2bd5f54cd84a1cd2696cc3207c479cb9cf0bd346f0d343e4300ddb \
+    --hash=sha256:35fe081843b35aad20ffeccec3eeffbe637b15d14f3fb22cc1b59cd8ec17e93c \
+    --hash=sha256:36047af20e17097c3bb9476c2b7655f2f7aa51322c0ba58c07695bedf755a950 \
+    --hash=sha256:3617ac3cfd8b9888f145ad89dd6e692285834b0201c6074a5eeaad3fd4d668c2 \
+    --hash=sha256:366ec70f5547c640d3ce1985722490f23faf4eb5216a7eeba78277490e78dacb \
+    --hash=sha256:394fea06235c8543390050ed5f529187074b029fb027213f6c46ac11ab5d950e \
+    --hash=sha256:3d27167433c0d5f18dc850f07d0b3816221984fecdc405d6c157a6f0b8f8e9e6 \
+    --hash=sha256:3e5e1224c0a6a90e05843e07adfec669edebec17801c67072f51e59561d63c0b \
+    --hash=sha256:41876ee62a3dddf48ff1121ad8f0798032aa03f2fd35f21f34a4cab14f18d8d2 \
+    --hash=sha256:433c5a81eade63b47e522303bad236f59dba55ea6951746f5558355eeed8c75d \
+    --hash=sha256:4582c27e8c889d64811987b5967fbd3ae0c823fe1fd933b543d55ac20bb475fa \
+    --hash=sha256:485a0d363cafefcd2538a73c7c838daa2035f09b2c9f9b5e3133f80c6aeb84c2 \
+    --hash=sha256:494b70049a4d69aec6e8137c13af4cf8db8c9f9820a1392ac293b0dd2987a818 \
+    --hash=sha256:496846868fea80e479324862fa877f02411f2fd0f83b79ccee2607aa68b2a032 \
+    --hash=sha256:4abdc5f9ad448c1ecbfae2974b820535d6bc6e7eef63babbab3d81cf46968c71 \
+    --hash=sha256:4b599739b93b2cbeded49645ae3c8d1405c29ddfbceac1545c87a3f9580a9e96 \
+    --hash=sha256:4bea7f8ebe90bbd7f0e4a2de42ca6924ba23e3e76418c408ff82f1d46fabd687 \
+    --hash=sha256:4c4fb141a727957c93edfe5c32a26ceb6b5f6461d67146e2d39f51e16170bea8 \
+    --hash=sha256:4c9548dc78002099910abaebc0a72ac58b7d30931869e0351c09b507dff4ece3 \
+    --hash=sha256:4d26f14f041e83dd8edfd61f4cd4fa7285d31798b5bf1f28e70c367ba6c41d61 \
+    --hash=sha256:4f298bdadb8f0b9e5672877f647d1be9373ef5320c9e2f049795e26cad28b6a9 \
+    --hash=sha256:52ec005752a56ae79547a05c0139ca2501a0c866390b6115008456b9f0e7cde1 \
+    --hash=sha256:55261ac0d2941c42f196dd576f543d87a8ee03cd6f5e30dfb4d807b2e3b9121a \
+    --hash=sha256:56490c595a28b1bb27dfc583e816152a9767721ef58b2c03b13f954d2f707420 \
+    --hash=sha256:58d3e12c88e0950bca850ae1f7c256055c097639c2edb9eb123af9807d8b15e4 \
+    --hash=sha256:58d4aa13a59c969dbfdf9e6a9560e242cbfd9e8a8f50c2747714df1a423adf65 \
+    --hash=sha256:59171c6e45bf07d0d5cab3b0bf81d945035530f6873398b3b531c31184d46663 \
+    --hash=sha256:5b6d1386bf0096d26d3a863dc0a487a5b4eb9aa93cf5ba69683d29dde6b9d60f \
+    --hash=sha256:5c0ea61a470e070686aa30892fed79e297d2c8d0ab46b8bcdf027d38c51da591 \
+    --hash=sha256:5c84bec0ab5ae0c64bfe73a7d2adcb5ce73b467523fc27fd6a28ab2aa6cbe35a \
+    --hash=sha256:5ca0555312ae2fe82715cada7fac375530c2f3349e1eaa1bcb33d0283ac79a18 \
+    --hash=sha256:5d8531a6569d025f68e2321e7638fb7978f23db58e5f69f56913837aae03816e \
+    --hash=sha256:5e2d0e146dcb57034f8b97dc58d2d512cb90aba253960ce449f695fec6a82c6f \
+    --hash=sha256:5fc45d653ea8c9a20479167e11d4a0f8cb2fa3470737ab6f9c827532313187b7 \
+    --hash=sha256:6117b84ea48435e5356dc737f5121485c30920ba43375fa7b434fd753df0eac3 \
+    --hash=sha256:6199d5606e2bbf2b096cf64d03f8b6790c91081d5ac866b8e7bb6422738cc60c \
+    --hash=sha256:62b55f6722735a6c472f88361cde6640608773d9443cebdbb51abf436a1fcdd3 \
+    --hash=sha256:687c9ca3035544b113bea2055e180af96fb63c0c476e22a9180f51925186e7b7 \
+    --hash=sha256:6b7430cf5728e68f6c462254009a6ef4086e1bea43cf2f57aa9c55fb4f50ff96 \
+    --hash=sha256:6ba32c4d2abf1d2fe7cf27d280f4cca5664233b0f885549c7761719eb977f486 \
+    --hash=sha256:6c9cdde8becb25a7fde49924511aa2644d6f8081cc8df8e9452724303348d8e3 \
+    --hash=sha256:6df0ec430f9a831772c23ca5a224cba36517a58a84bb32c32bb59a9fa67c47f6 \
+    --hash=sha256:6e2912d4babbc65196ac13c2f53468dc57fb8b9c25ef913e8c59ddf7c6dc0e1b \
+    --hash=sha256:6e5e4d73d588ca5ed09df1b7dcd1b203d1df3c542e3f50d126c947d432b10731 \
+    --hash=sha256:70055ff39b97c99e7ae40ea3e393fb62aa2e44dbd9b29f8d14f42fb0025c3959 \
+    --hash=sha256:706bfd38730a5ac7a365793269a00f4e988178cec121391f4248d84ad8c972e9 \
+    --hash=sha256:7235dc28fc6dd9d832ac7c7bce95367dedb85929f17368a0c2bee1e080b9acbf \
+    --hash=sha256:774d157f112367ff4abd29019f38f023c24e00e56edc7829c20e358a5a913ad8 \
+    --hash=sha256:77efcff2b23071c349402ac1066667a3d011f62398d81408c9b88ad991747c9e \
+    --hash=sha256:789b8982559ae28dad2356519f841655756cdcd96616410590ae0b17454ee64f \
+    --hash=sha256:7ac76cf9afd34929d76eb7fcb63be476a4853d8a96f0dcf2d0db68a0cbdf9885 \
+    --hash=sha256:7c0c10730342b0c9b35dd1d619beb8214e520bd96a1f870f452680b238aab3e0 \
+    --hash=sha256:823f82903d189af463d7df250ef1f7f696f3cee08cc8d91deb565e8d425f6506 \
+    --hash=sha256:838648accb3a7fd9803fd45c87bce8509648eb0c11bc34e216141300977244f2 \
+    --hash=sha256:854066be00447fa8de2ccbbe893e2ffc4b123ef16d897af794c1e18bd4a714b0 \
+    --hash=sha256:85d5855daafc240cc045c026d7a15fd198a09b0fc8ff6f5ecbb5297b509cb11e \
+    --hash=sha256:85de3134b5379856e323ba37c19c9256d39425f7b76a63af52b09fb4664c2e8f \
+    --hash=sha256:87e4f41d375c0b9be2fb5251aee4b8a689169e134535aed81bf085c3b647451e \
+    --hash=sha256:88ca277405c2d3b71c4e1c2ee0e7966e807bcba86a69d11e19ba199d18ae4491 \
+    --hash=sha256:88e85ab89cb822c1e635f51d6d32e488f94e002e70e2f492bdb8b945543f345a \
+    --hash=sha256:8ac8c94b6539074e0f40899301273ac8402b9b3e01c7b7ba269ff30340aaaf20 \
+    --hash=sha256:8fe532b3c966d1fb794e0698e4589d0444017ae77fc0b31edea13c0e35bcc449 \
+    --hash=sha256:9085f87b0e38a2b92b8923059b4e8789fe40d9279712d15dcc670048d77079af \
+    --hash=sha256:90b7481fb62fbe172c558bc6fd1c4c98d82004a54a7551f20e11ac9bf0b8708c \
+    --hash=sha256:92caef967d287a407085d61176fce4012b1dd62daed4eb6d5ceb26d3d2538712 \
+    --hash=sha256:9362dd90aa7dab48c0054a21187791ccf05473f7dba5d92b8033ae62164675e7 \
+    --hash=sha256:94d78ecec2605a8d0398b0f365d5f12a63248438516f5dac536a5eff7337df4a \
+    --hash=sha256:94fbf1c0c6cc0d3d5e50f9a9313a8cdca90dd696d34b381cd1704f8c9e939f20 \
+    --hash=sha256:950f23cb393f85543777b0433f082cddd25b51ab398eac7971146495679efe5f \
+    --hash=sha256:96eefc178f8636b9c760c5829345307fd81cfae9ab1e80997dbddeb0f54ee9a3 \
+    --hash=sha256:96fef3e886d6a9874b14f27fc193fbdc69d5d8035783d86aa4e1cea594e695f9 \
+    --hash=sha256:977cdbd483a9cff38179bea4fd754289a6f2195c7abd414aba85410b3e66cc5e \
+    --hash=sha256:978eab16f55b4ab2c2a745be9a0a840bf8f09a7f227d9c76eb30214d078865a5 \
+    --hash=sha256:994e883d17c559cdfd38c84003c8b27d25424a1077272a17e7cd27bfe0bf57b2 \
+    --hash=sha256:9ac4444d8d4fd4c4bd08bf451ed3167aa9e7ec6cdb41b648794f1d1103652e36 \
+    --hash=sha256:9b5db6052055d34d41230fb78d7c439c23dc536a9896f6cb039e8dd92cfc1263 \
+    --hash=sha256:9d9a0dc7cbe9bec24c3f767c9122c41fe5a1bc43f47cd099d00d393e09769de4 \
+    --hash=sha256:9dbdd9205662134957cf0c324f639bdc5031c0ca056e2369e238db75187c0f11 \
+    --hash=sha256:9eea3ab2597a5e65fe65296e2d6a84570845a6b55532d90333d740d48bbc850a \
+    --hash=sha256:a2028475ba855475b8b4d3cfeb4994269c967aea8b9892dfba907f4263a863a3 \
+    --hash=sha256:a3a370082ce34d0612f421e15fe011c53bb1feff21a26d06ad4fb244dab5a375 \
+    --hash=sha256:a545775cfe815855ea32d7c27731d79da358ef2055b4a25830231b1622dd18aa \
+    --hash=sha256:a5cbd90ecf0fc62e64726917ad083b73001f0563657a87ec3c0b504e277dc90d \
+    --hash=sha256:a6d095662e73e74f0a49988e0593373e243e3a52e27bfeea0a859e88acf4a0f5 \
+    --hash=sha256:a6dac12ff6b846103483683f60c5f8fee205121adc58ffd87e90a90a3af69e99 \
+    --hash=sha256:a951ad59cad9145664a730d3036b40b844e74d2d3683da40111463cd3a83845d \
+    --hash=sha256:aa1099b956fb795e686d073568f6dc002a0bb89765ea6d5b055dd7d9bf1b116c \
+    --hash=sha256:aa2bb0b37202dca27175591f761108b5d34096ade1191ffe4808bdf6b1571488 \
+    --hash=sha256:aae2ee51122d3ae968a3837d97dc24a0aeebb0dea23694422cd172bd30017cd6 \
+    --hash=sha256:ab743e9bc90c1f73552ec33e10e3331315acd2c397b36065b591b0181de533cc \
+    --hash=sha256:ac00177c4831ffa650f8609e4bdddd5fe09c03b1c0c47acece7e6ea20421598b \
+    --hash=sha256:ac13b004224fb341e1e25a1ed5e19d32f57cdb2a403e01f003b46f051a550f6f \
+    --hash=sha256:acaf604462bf330b0d07e7a07c1d6e4adac79e5fb13e9c5140590542cafacc00 \
+    --hash=sha256:ae31a1a1db2ee6cc2942fccaf695c934bc7f3db9f2133a3fef1f367cf1a4ab10 \
+    --hash=sha256:ae4a097991662cd4fff0ddc74e0fe7874f82e00042fa0ea00855645ed0c79598 \
+    --hash=sha256:aea996a6aba25260827c9ea511d1addfde2da9eb686ac961838509086188b7e6 \
+    --hash=sha256:b39b69b347e5e47a3b5b8cfc005c68c1ba347474e3960236c4944a8ecd174962 \
+    --hash=sha256:b54e7e13267d49ffbfe68e25b3cbd774dab38fa37238f71265e91b36146eb21c \
+    --hash=sha256:b9af956078716df40d985fb0dfeb2c2120c5ca92ba4ff4b388acfd01cdc14d08 \
+    --hash=sha256:ba2f37ee79e6338845261a3c5b1784e5d1acdff2c0785b284f1b633033d136ab \
+    --hash=sha256:ba501e667c17d8411f98e67a022d9604ef179aff0e459b7e292c796837c13573 \
+    --hash=sha256:baf3775a2635e5a11fbd5e4e64ee69c7e86875d224a5c72aca4c141064589a90 \
+    --hash=sha256:bb57753e36e4855b8ca375069482250a6246372331a3e4f3407eaebb007443f5 \
+    --hash=sha256:bd6c173f04743d483881bffa1478d5a4624475b8cd1d2194956a75548e191c18 \
+    --hash=sha256:be47f99644b208bff7766314013f9acf57b056b04191d570d68ad14022cf5b1d \
+    --hash=sha256:c010f5581d9c612804cc59fcf7b524b707fbcb72828551237ab545bb5c7034af \
+    --hash=sha256:c1dcc36dcb96abc02236e182d17e0f71430152a6c2c7447421da2d2dc144edea \
+    --hash=sha256:c428c6c31eb5f4277d7f8eccaf767fbd548ddd5ce3c8b4f4cbbfab3d96b5904c \
+    --hash=sha256:c658c50ac0c98cd755a2dd50b7977d3bca7df401dcc47fbdfa87db53ef7d4e8b \
+    --hash=sha256:c71fb0d56c920c269cd3e2e3fe7c610e3f1fdb21a6ce60efa6430ff63676cea6 \
+    --hash=sha256:c7b742bf31c88566b4bb6335a7f393bb322e580b6bb98df7bd0c25e6e3519ce8 \
+    --hash=sha256:cc0329df4caaceb950d2f580b5ac716a377f7059624a0bafaeaf8a218c6ed774 \
+    --hash=sha256:cc5d36d96478aa9c60654bd932525bf32964c62a7281eafdf16d85003a8d6004 \
+    --hash=sha256:ce854f5f478050ade5a238731c4ca985a7d3b3cb53ff600a9b5c3b689b5f0a7a \
+    --hash=sha256:ced3fdd71aaa83ce593746c2edb42b7a59cb4c19c8b5c407781c72e493aae55a \
+    --hash=sha256:cee5dd7c6fb5dd52a0fe2a740f9bc6e3593f5f8b1788bde49de02086f30182b2 \
+    --hash=sha256:cfa1c0cc3a8f9f53f1243a5a99ac36fd003880199383b37672e86ddda9cb07e2 \
+    --hash=sha256:d1ee1e296209fdce05b81b663250eefa02213a2da7b41bf26f7829b8ba3545aa \
+    --hash=sha256:d59b75732e9b6f27388e10c14b0259cc5f2e48c78627d185e6a177b58ad3cffe \
+    --hash=sha256:d63600d620ad0064c3a748b950ac5ea38a80190e5498532efefa4b7b3f1da1f3 \
+    --hash=sha256:dd732602a7009217f658d5863d12d79d373a4de0eebc111094bcdd3bb8e0a6cc \
+    --hash=sha256:e06efa066f7dbadbc84ebc126a97c452a6451dfcf589d89d788484949e1cf795 \
+    --hash=sha256:e199fb99720074809a7720f1c0b4d919eea8b87e88713e0f8f602f7bef543d9d \
+    --hash=sha256:e4b018dc5a0eee4676e38fe84a47a427816c590b93b55d9025274ec4d6ffc2dc \
+    --hash=sha256:e6621fb2a4988d6e53eedc455e5903e2679f3967b8acb3d639f1b63c14a2e893 \
+    --hash=sha256:e71c909f353863b2b89c83de2ebed71ea6d0df8a6ef65a128193c5e650766bef \
+    --hash=sha256:e90251c0c7bdd54a100a0dce3c07b7e637278c93af29dbf78ebb89a58c4bac7d \
+    --hash=sha256:e9fbdce1e47394b09bc9f26ab117dfc8d6491977a11d86f592bb42c779db2fda \
+    --hash=sha256:eb12fb2ba69ffa05f8695f61c69e591dc4b4a12ac3757ac8af8adb259bf56d17 \
+    --hash=sha256:eda059b6bc8bc0812d626fd91a7ce01bf583df0a61296eff390fd94141a34e30 \
+    --hash=sha256:f03ac127268b43ef4fe9e6ab6794a6794b49485a0cc0c1db79876d2f33f75bc7 \
+    --hash=sha256:f298e218441525d3794428b4c8b8fb8662c6d3ea79925d4807ee6b9a96a3bca5 \
+    --hash=sha256:f5542f9b941279d82d41eb0aa9f98eba36fe4df5c7086c651df7944935b37182 \
+    --hash=sha256:f6f7deae3feb4edfa2efaf7c574fe88cbf055038a6abdb40188e4fff66d5699f \
+    --hash=sha256:f9b1e28d0e8dbfa858abdba91d6b547beaf2df1a59bec6da6faae7b96a4991a9 \
+    --hash=sha256:f9f8405c2c758532c74fed975dbee57be1f31a6e865c031870c79a6ed3212ada \
+    --hash=sha256:fa48b1b63d639f9483e0633e092f5851e2348c352f1f9bb6c8182f87884ef876 \
+    --hash=sha256:fb78f6e7fcd8ad785d28cd577168bc1aaee827b25bb8755638f694794ea98f0a \
+    --hash=sha256:fbc597639158fd7c14d55e808718848319540f51b0e6746e3eefa59723a4a348 \
+    --hash=sha256:fce8cbd4997efeb450bd298b54f755dcdff18d496f7a5ddbb4867c6d7c88fdc3 \
+    --hash=sha256:fd0350afdc3aabd5576f60ea109228bd5538139713c7b094c5cd27c73a98bc6f \
+    --hash=sha256:fd0a274c0e5f9a21565cd9d3dd749b61f96b7aa1e20a93aa1ba4029518f2e5c0 \
+    --hash=sha256:fdb8a068947befafba9952162645dc2fecaeb400e64584829ed5e9b2fbe21a7f
+    # via requests
+cyclonedx-python-lib==11.12.0 \
+    --hash=sha256:0e807521a921a5c3cb8ce1153f8a61d29eedfe76a46aac2796b7c6b573391a54 \
+    --hash=sha256:16767c4039de90c04e9f03348f8f0ed4b8ff842eaa7eefcad3a95685f970dacf
+    # via pip-audit
+defusedxml==0.7.1 \
+    --hash=sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69 \
+    --hash=sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61
+    # via py-serializable
+filelock==3.32.4 \
+    --hash=sha256:22e58ca3b1ae3b98993b762d7338367ae64fe50252bf78d59da3bfebcdf1cedd \
+    --hash=sha256:2bde2e4cf732e0153406d8a7bc80620ecf5e621fe0d25e41143c4e3b4733ff30
+    # via cachecontrol
+idna==3.19 \
+    --hash=sha256:5e0811a4383b21dc5838069f801c4fb62113b7447663d2530d2bd6e77b49bf15 \
+    --hash=sha256:815e7be7a7806d54abb586dc943addc79e8b2ee16915059658cbeff4b1b43bf4
+    # via requests
+license-expression==30.4.4 \
+    --hash=sha256:421788fdcadb41f049d2dc934ce666626265aeccefddd25e162a26f23bcbf8a4 \
+    --hash=sha256:73448f0aacd8d0808895bdc4b2c8e01a8d67646e4188f887375398c761f340fd
+    # via cyclonedx-python-lib
+markdown-it-py==4.2.0 \
+    --hash=sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49 \
+    --hash=sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a
+    # via rich
+mdurl==0.1.2 \
+    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+    # via markdown-it-py
+msgpack==1.2.2 \
+    --hash=sha256:06d95f61de7afe4f4ff908a6feebfcb070d0582ac87c9cf3cedf8551cf634516 \
+    --hash=sha256:0708afbf6a9587f0bfe479a9825c141d14d91e2f6a5c8103cf28bc96f4edb5d9 \
+    --hash=sha256:0883a1578168929fd1640fbbc4614773f1a130e419a8a817dc2918d9af1b651c \
+    --hash=sha256:0a652ceeededf71d3fa40c303a02a149d42338d310162367b91c539d4bd6e0a3 \
+    --hash=sha256:0dd9173c5ebaf5ecc5ca86e7ae1db92934e1d57b856f3dd90698941431f4fd77 \
+    --hash=sha256:0e3315de5a4b2920ccef48d96b4448025e064a10d0f5a250f6584477d839c8d4 \
+    --hash=sha256:0e91332144f69bc3018c91232fac26da580ef748fb8eaddd7914d4458001cc4f \
+    --hash=sha256:0fbc1bed8a535389b41882cfae66376e248cd1680eaa94fd83193c73e1d24986 \
+    --hash=sha256:11e8c421e117d1c36728b423d0402555cccbf0c6f53e288f0e75b6b12100d70f \
+    --hash=sha256:1510f24612d4b983dff6935d9273e02c320cfd525727fbcb58836a75f589fdbc \
+    --hash=sha256:1814f92306ae7862908e9ece7cfd90e0dc87ded3e89b6ae7ffdd1175d6376fdc \
+    --hash=sha256:1e8cdd1f3e7cc52c751092a9bf740e81e6919ab109cd376ae2d965dad0bbae34 \
+    --hash=sha256:1f3af0baafd184436501004828bb3df64eeb2fc49dfe9d89abcf604956094563 \
+    --hash=sha256:1f6b6f8deb07d49090e1808c6ef9cb7d23ca17bef3aa6ed3e5e03df16606e60c \
+    --hash=sha256:226a62ffe99fe54c5c61d910ec64c3449b7766c3280bd286bf6c94838dde239a \
+    --hash=sha256:29cc2d5291711a52956a79a51f41c732329df39ad727c886bd8f0b5b9237a808 \
+    --hash=sha256:336525cc2688e43ea77dfb1a4ce012c8cde561835913801dbfcfdcf4111d8abb \
+    --hash=sha256:34e83e345194a2a51d8bd447dea9de2104f91e75b247f4735f14f04529f0746b \
+    --hash=sha256:352ed831042549cca8be23780e1fe7c9177e65ff02bf183509c4b4d33f671782 \
+    --hash=sha256:3e915d390d7068b257ca8b62f3fc59fad135c8631d1017ab03b0b924b07c5367 \
+    --hash=sha256:419a45c67a5c04213172a14b1864657e014665b77d7081b107a51707923dd39e \
+    --hash=sha256:42fd9260416885b4815caca5bdd14dfd5dda6cdade732d6c09104ef8f6228761 \
+    --hash=sha256:46ec851571d8f1b6e29794ebb9dd36f785008da6d14f57c702e60781d6caf648 \
+    --hash=sha256:4710d881d8fb047deed2485707409116722af2b992d3fefd73c7667c4e350839 \
+    --hash=sha256:4955accbd87f27beebef5f3ecc27503aa74cb016fb4f640868e749fd93194a35 \
+    --hash=sha256:4a4348705be86e029d04e741cf9ed0dfe03e942d7d3b92e838fa80d3aa2c3ebc \
+    --hash=sha256:4b554d8164ebb526892194f71dcd96ef1fefe0c250087498785d3ffc04a80be3 \
+    --hash=sha256:4d9a562aec0a92fe536da2e533d313b3d2a6b929157b1dec7ff623446dc0a8ab \
+    --hash=sha256:51dd39d23cfdea0400ed3ff2d29d1e83bd951d3aea79dc89be5b701a09edfe23 \
+    --hash=sha256:53679573c75cce5f82359e0bd4e6a97809a6b9a9b7a48fd1ba592f4a82cddc84 \
+    --hash=sha256:55faa6f8395e23b848c535ad5dcb96b3462f37f5e7f4ac500d500434f7345da7 \
+    --hash=sha256:58ce37a4a54577115922385d37201d9a44d66d0167dfbbf4770a2e9bf8ea7ba3 \
+    --hash=sha256:59d5b93efa45fd09f620d0c9ba81cde339a2c9937af3eea42ee9653094ce6640 \
+    --hash=sha256:6195257a107bf25872ef84aab7295078271eea3ac6413f0506b631f6c9586ed5 \
+    --hash=sha256:652d1bf13d01bac8fd569def0fe76745e55bcda01e30aa6332d5947ea3788839 \
+    --hash=sha256:682804bf31e43d46e51a9a33bd575b51e839d715ce6bd5612c055f7b28ad637b \
+    --hash=sha256:68df2947921d449f6dcfeafd86cb2cdde13327a8b447534bbe4ee5aaf32a5695 \
+    --hash=sha256:6f53285f20d592ed309ee19e509cc4c77a3bda1db02ad67e8a0949bb227a5a6d \
+    --hash=sha256:73b0e05c32c3cfc3cd84994908e57430c0ebc6813abf905d3f18ff115d54df3f \
+    --hash=sha256:77c2e018417dc1d66f235e383877ee885b60ade9d29e494dd581e08af2cb1923 \
+    --hash=sha256:7826f16edc763e768404f55605ef85dfcf5857e729c1ed29e0d7c180be4fe6d8 \
+    --hash=sha256:7afa5431f6f3487c584187ca6c8e2a34e9b106529893b3e720eabb068f6ac970 \
+    --hash=sha256:7d095df2627e5dd59ac7b0c5ad627a671c76e6020171e03cbe4621a61f0562c3 \
+    --hash=sha256:7fe374ba76eb0ecca13a1703daa8fa85825a6ddddbb52d4c1a732fa524194683 \
+    --hash=sha256:82b1bdf293267afaadcc608b125e7fc6576bb0785a60c4fa7d07c7ab76ed76ec \
+    --hash=sha256:86f173a584f72f6164801f31866d22a581f60c991572cf922aed9ab8eb422b77 \
+    --hash=sha256:8b1415d02e9bf722672af8a90f90813265a0cd0b14163187261e54a5592bc949 \
+    --hash=sha256:8b2a281b556f120a43e591ea39915741b7ad54d4727b9c4350a0a11692252533 \
+    --hash=sha256:8c6321a414f8b4a8dc43976b2fa8349156434ca9adedd9a187b796f7e1d3d3fc \
+    --hash=sha256:8dc4487097571f7311188c3eca2a3e86cd1f1db4c37c7a017bcc3fd38486cbfe \
+    --hash=sha256:90986cc9aab9d7d1d8f38bcbf65d3f7ac83bdd90c35765db7d691b4829698cba \
+    --hash=sha256:9352e6cdb510a7b1a5d3ccaccec730e82e50cf3484a3af7bdaab19e23b9589ff \
+    --hash=sha256:935b1cfad9b908b0fa845010f4271df4c2f04e1cd26e3f18acd61a45f93c9e36 \
+    --hash=sha256:9b659d77f8726fa5e7038967dda6b68d53cf34472c094cfa5b845454713b90d5 \
+    --hash=sha256:9bd3d1557c3fe1a095068210708a03e3e4795973392af6f4047060e70abd9a6c \
+    --hash=sha256:9bf452ff4d4981f25a18e9476e002bcc9263e7928024aa4d7148e25f7be3f929 \
+    --hash=sha256:9d7fb25b4442fae0cb2590272d06ab4f6caa526ee36a994edb81e946b874813e \
+    --hash=sha256:9db1ba1c1e6a84245a9dd866265b56b8a1e9461549cc72ed296d8cbfbd32961b \
+    --hash=sha256:9eb0b0e602064527a045ea28c4f174ed69383587e29cebe28947e3b84106eb2a \
+    --hash=sha256:9fd7f32e2f0fb334e7ecc5adb5cf0458785bd3a9d9d86f950e1715f101cebce5 \
+    --hash=sha256:a378e12ccc06d76efde115caf4073b7e5ff3cc18291d1341f9e65fb882e3f754 \
+    --hash=sha256:a4161eee7799863aee237c35c90427861f7b994416dd81ae829f560b0a81bdcd \
+    --hash=sha256:a9b4cf3685a135666d27d0d7a73fece74e2fad01d9b508fded89e843512f0e90 \
+    --hash=sha256:aa1120c653b76d8eafa50423b5eba06b5c9737f8692c74fa3afe03e84b8978ea \
+    --hash=sha256:b07c03f0da7e5279170df7745ddc732d526c8a198208936ec1a95c11ed2b2d5f \
+    --hash=sha256:b13b59e66f107cca1ba708dd5307179870ca1b15b19fcee7ccf722e5308d9212 \
+    --hash=sha256:b542ffc0a5c531eedc40419f291f1bd659aa8d4223408a5b51c88a2796083fd3 \
+    --hash=sha256:b5c696ae7cd7166b3657261adb855b461ff31f07823fdbae9de8bf80adfccc21 \
+    --hash=sha256:b68614fba0570349833b7dd999ff0aed4e5cc8d9eb6e3a7d4527be33c65e33d3 \
+    --hash=sha256:b8dd6c71d20c28d2d0eb0c51e7cccf3584afde3b1364f6629596186c9025bd54 \
+    --hash=sha256:b9b0c1f2aa7b0026b4bd50718100e8b04175e4f36e160aa852502377b5e572e7 \
+    --hash=sha256:c522420d78db2431887d45b518e304d86e27b9ad0b30f24e3806a6ad5d8bdbfc \
+    --hash=sha256:ccfd880988f8438d1c91c77d7edc58e70f4d2012e999167bc154c64c6f06ea6b \
+    --hash=sha256:cdb6cc6e1127d15879c47a8b3270716243da82d3e7feab1f5946872c75b3d60f \
+    --hash=sha256:cf66fb38703e61a486b01b56d43bb1f50698fbe99b6bd90feba10f24fab60b3b \
+    --hash=sha256:d13d07efbf655f9ae7a2352b630c52727b359005b21ba08a507585c9ac8c0896 \
+    --hash=sha256:d242f3c4ccf55b056e6cf901720dccde58f1df117898f2bbf3bcd6e38ec7c248 \
+    --hash=sha256:d24b38a825bcca41bb956de50eb98451ef291304a8607fad99e619043d3e79b9 \
+    --hash=sha256:d3c247d457ae9079974c7ce3c665396754a6d2baff7eaa51332212a8a5a3f13b \
+    --hash=sha256:d886baa46b2532135e7320067e6a44edb09ba5883a6096b0f9c044533984b8a8 \
+    --hash=sha256:e05a94a0442de86818a30281c6cc2cb9cc7aa148386fd3541c4d4774b73cb3a9 \
+    --hash=sha256:e1b99ad34613d5f8477fa5cf99bc4eaeaf27965588007c102370cd9a78fe9de5 \
+    --hash=sha256:e2eb7ea0ac3911a7aac9d8aaa36d40f216d99455b3274cd3fac38181bcd910cf \
+    --hash=sha256:e497ee34e8a3342bbde51b27c22d8db05a651df3361dd3daef5b3ab0d66f3e04 \
+    --hash=sha256:f11e09f10210a91c169e39c7a5a1f9090eaa73ad75555fafad5023c3053c47ba \
+    --hash=sha256:f466049b8e1ec0854287bbe9a074316826fe0e08dcf707245f98b1ae49e92650 \
+    --hash=sha256:f80361592c13d7226b4379c8941529b63fe1a9d0e05d2de8f3306b70e522b53f \
+    --hash=sha256:ffdd2f4950daf7815490f23087963e3420175b9609520b7ff5df64d351159c22
+    # via cachecontrol
+packageurl-python==0.17.6 \
+    --hash=sha256:1252ce3a102372ca6f86eb968e16f9014c4ba511c5c37d95a7f023e2ca6e5c25 \
+    --hash=sha256:31a85c2717bc41dd818f3c62908685ff9eebcb68588213745b14a6ee9e7df7c9
+    # via cyclonedx-python-lib
+packaging==26.3 \
+    --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \
+    --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c
+    # via
+    #   pip-audit
+    #   pip-requirements-parser
+pip==26.2.1 \
+    --hash=sha256:71138adf1f4ca900cdb7d289c21b7494329f2332b6d85f0e1c42108c0384ed3e \
+    --hash=sha256:f6ad667e89a1fe78046c8f13232b247200f5258d7828f3f7883d660878e0813f
+    # via pip-api
+pip-api==0.0.34 \
+    --hash=sha256:8b2d7d7c37f2447373aa2cf8b1f60a2f2b27a84e1e9e0294a3f6ef10eb3ba6bb \
+    --hash=sha256:9b75e958f14c5a2614bae415f2adf7eeb54d50a2cfbe7e24fd4826471bac3625
+    # via pip-audit
+pip-audit==2.10.1 \
+    --hash=sha256:1eb4565d19ebe5d48996f4b770b4d2b32887e12cb12cfa637f1a064011b55ffc \
+    --hash=sha256:99ef3f600a317c1945f1e89e227ef26e1c2d618429b8bd3fa6f4f7c440c4611a
+    # via -r .github/requirements/pip-audit.in
+pip-requirements-parser==32.0.1 \
+    --hash=sha256:4659bc2a667783e7a15d190f6fccf8b2486685b6dba4c19c3876314769c57526 \
+    --hash=sha256:b4fa3a7a0be38243123cf9d1f3518da10c51bdb165a2b2985566247f9155a7d3
+    # via pip-audit
+platformdirs==4.11.5 \
+    --hash=sha256:89f8d42695853b89c7170bd49bc3dc593f98a71e695ede88e06a3b247bc4563b \
+    --hash=sha256:e8b31f4f8bcbbedef91a6b57a706255e4f148d2a4e01648382a0a47342539173
+    # via pip-audit
+py-serializable==2.1.0 \
+    --hash=sha256:9d5db56154a867a9b897c0163b33a793c804c80cee984116d02d49e4578fc103 \
+    --hash=sha256:b56d5d686b5a03ba4f4db5e769dc32336e142fc3bd4d68a8c25579ebb0a67304
+    # via cyclonedx-python-lib
+pygments==2.21.0 \
+    --hash=sha256:2363c69b61c4a97c838da3b130dcd6468f4848992b21a82f2a63ec34377137d9 \
+    --hash=sha256:610ca751c9bc2492b38eb9a38a7fbc93edbbb2d7182edaf34e66ae493dee5c8c
+    # via rich
+pyparsing==3.3.2 \
+    --hash=sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d \
+    --hash=sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc
+    # via pip-requirements-parser
+requests==2.34.2 \
+    --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \
+    --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed
+    # via
+    #   cachecontrol
+    #   pip-audit
+rich==15.0.0 \
+    --hash=sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb \
+    --hash=sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36
+    # via pip-audit
+sortedcontainers==2.4.0 \
+    --hash=sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88 \
+    --hash=sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0
+    # via cyclonedx-python-lib
+tomli==2.4.1 \
+    --hash=sha256:01f520d4f53ef97964a240a035ec2a869fe1a37dde002b57ebc4417a27ccd853 \
+    --hash=sha256:0d85819802132122da43cb86656f8d1f8c6587d54ae7dcaf30e90533028b49fe \
+    --hash=sha256:136443dbd7e1dee43c68ac2694fde36b2849865fa258d39bf822c10e8068eac5 \
+    --hash=sha256:1d8591993e228b0c930c4bb0db464bdad97b3289fb981255d6c9a41aedc84b2d \
+    --hash=sha256:2190f2e9dd7508d2a90ded5ed369255980a1bcdd58e52f7fe24b8162bf9fedbd \
+    --hash=sha256:2c1c351919aca02858f740c6d33adea0c5deea37f9ecca1cc1ef9e884a619d26 \
+    --hash=sha256:36d2bd2ad5fb9eaddba5226aa02c8ec3fa4f192631e347b3ed28186d43be6b54 \
+    --hash=sha256:3d48a93ee1c9b79c04bb38772ee1b64dcf18ff43085896ea460ca8dec96f35f6 \
+    --hash=sha256:47149d5bd38761ac8be13a84864bf0b7b70bc051806bc3669ab1cbc56216b23c \
+    --hash=sha256:4ab97e64ccda8756376892c53a72bd1f964e519c77236368527f758fbc36a53a \
+    --hash=sha256:4b605484e43cdc43f0954ddae319fb75f04cc10dd80d830540060ee7cd0243cd \
+    --hash=sha256:504aa796fe0569bb43171066009ead363de03675276d2d121ac1a4572397870f \
+    --hash=sha256:51529d40e3ca50046d7606fa99ce3956a617f9b36380da3b7f0dd3dd28e68cb5 \
+    --hash=sha256:52c8ef851d9a240f11a88c003eacb03c31fc1c9c4ec64a99a0f922b93874fda9 \
+    --hash=sha256:559db847dc486944896521f68d8190be1c9e719fced785720d2216fe7022b662 \
+    --hash=sha256:5a881ab208c0baf688221f8cecc5401bd291d67e38a1ac884d6736cbcd8247e9 \
+    --hash=sha256:5cb41aa38891e073ee49d55fbc7839cfdb2bc0e600add13874d048c94aadddd1 \
+    --hash=sha256:5e262d41726bc187e69af7825504c933b6794dc3fbd5945e41a79bb14c31f585 \
+    --hash=sha256:5ee18d9ebdb417e384b58fe414e8d6af9f4e7a0ae761519fb50f721de398dd4e \
+    --hash=sha256:7008df2e7655c495dd12d2a4ad038ff878d4ca4b81fccaf82b714e07eae4402c \
+    --hash=sha256:734e20b57ba95624ecf1841e72b53f6e186355e216e5412de414e3c51e5e3c41 \
+    --hash=sha256:7c7e1a961a0b2f2472c1ac5b69affa0ae1132c39adcb67aba98568702b9cc23f \
+    --hash=sha256:7f86fd587c4ed9dd76f318225e7d9b29cfc5a9d43de44e5754db8d1128487085 \
+    --hash=sha256:7f94b27a62cfad8496c8d2513e1a222dd446f095fca8987fceef261225538a15 \
+    --hash=sha256:88dceee75c2c63af144e456745e10101eb67361050196b0b6af5d717254dddf7 \
+    --hash=sha256:8a650c2dbafa08d42e51ba0b62740dae4ecb9338eefa093aa5c78ceb546fcd5c \
+    --hash=sha256:8d65a2fbf9d2f8352685bc1364177ee3923d6baf5e7f43ea4959d7d8bc326a36 \
+    --hash=sha256:96481a5786729fd470164b47cdb3e0e58062a496f455ee41b4403be77cb5a076 \
+    --hash=sha256:a120733b01c45e9a0c34aeef92bf0cf1d56cfe81ed9d47d562f9ed591a9828ac \
+    --hash=sha256:b1d22e6e9387bf4739fbe23bfa80e93f6b0373a7f1b96c6227c32bef95a4d7a8 \
+    --hash=sha256:b8c198f8c1805dc42708689ed6864951fd2494f924149d3e4bce7710f8eb5232 \
+    --hash=sha256:c2541745709bad0264b7d4705ad453b76ccd191e64aa6f0fc66b69a293a45ece \
+    --hash=sha256:c742f741d58a28940ce01d58f0ab2ea3ced8b12402f162f4d534dfe18ba1cd6a \
+    --hash=sha256:c7f2c7f2b9ca6bdeef8f0fa897f8e05085923eb091721675170254cbc5b02897 \
+    --hash=sha256:d312ef37c91508b0ab2cee7da26ec0b3ed2f03ce12bd87a588d771ae15dcf82d \
+    --hash=sha256:d4d8fe59808a54658fcc0160ecfb1b30f9089906c50b23bcb4c69eddc19ec2b4 \
+    --hash=sha256:da25dc3563bff5965356133435b757a795a17b17d01dbc0f42fb32447ddfd917 \
+    --hash=sha256:eab21f45c7f66c13f2a9e0e1535309cee140182a9cdae1e041d02e47291e8396 \
+    --hash=sha256:eb0dc4e38e6a1fd579e5d50369aa2e10acfc9cace504579b2faabb478e76941a \
+    --hash=sha256:ec9bfaf3ad2df51ace80688143a6a4ebc09a248f6ff781a9945e51937008fcbc \
+    --hash=sha256:ede3e6487c5ef5d28634ba3f31f989030ad6af71edfb0055cbbd14189ff240ba \
+    --hash=sha256:f3c6818a1a86dd6dca7ddcaaf76947d5ba31aecc28cb1b67009a5877c9a64f3f \
+    --hash=sha256:f758f1b9299d059cc3f6546ae2af89670cb1c4d48ea29c3cacc4fe7de3058257 \
+    --hash=sha256:f8f0fc26ec2cc2b965b7a3b87cd19c5c6b8c5e5f436b984e85f486d652285c30 \
+    --hash=sha256:fd0409a3653af6c147209d267a0e4243f0ae46b011aa978b1080359fddc9b6cf \
+    --hash=sha256:ff18e6a727ee0ab0388507b89d1bc6a22b138d1e2fa56d1ad494586d61d2eae9 \
+    --hash=sha256:ff2983983d34813c1aeb0fa89091e76c3a22889ee83ab27c5eeb45100560c049
+    # via pip-audit
+tomli-w==1.2.0 \
+    --hash=sha256:188306098d013b691fcadc011abd66727d3c414c571bb01b1a174ba8c983cf90 \
+    --hash=sha256:2dd14fac5a47c27be9cd4c976af5a12d87fb1f0b4512f81d69cce3b35ae25021
+    # via pip-audit
+typing-extensions==4.16.0 \
+    --hash=sha256:481caa481374e813c1b176ada14e97f1f67a4539ce9cfeb3f350d78d6370c2e8 \
+    --hash=sha256:dc983d19a509c94dba722ee6abd33940f7c05a89e243c47e907eb4db6f1a43e5
+    # via cyclonedx-python-lib
+urllib3==2.7.0 \
+    --hash=sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c \
+    --hash=sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897
+    # via requests

.github/requirements/pytest-tool.in

@@ -0,0 +1 @@
+pytest==9.1.1

.github/requirements/pytest-tool.txt

@@ -0,0 +1,32 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/pytest-tool.in --generate-hashes --python-version 3.11 --python-platform linux --constraint requirements-ci.txt -o .github/requirements/pytest-tool.txt
+iniconfig==2.3.0 \
+    --hash=sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730 \
+    --hash=sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12
+    # via
+    #   -c requirements-ci.txt
+    #   pytest
+packaging==26.3 \
+    --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \
+    --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c
+    # via
+    #   -c requirements-ci.txt
+    #   pytest
+pluggy==1.6.0 \
+    --hash=sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3 \
+    --hash=sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746
+    # via
+    #   -c requirements-ci.txt
+    #   pytest
+pygments==2.20.0 \
+    --hash=sha256:6757cd03768053ff99f3039c1a36d6c0aa0b263438fcab17520b30a303a82b5f \
+    --hash=sha256:81a9e26dd42fd28a23a2d169d86d7ac03b46e2f8b59ed4698fb4785f946d0176
+    # via
+    #   -c requirements-ci.txt
+    #   pytest
+pytest==9.1.1 \
+    --hash=sha256:1088fbde8f2b49d95a549a195707afa7a76a3ce9bcadc26b6d71f0ffda5fe313 \
+    --hash=sha256:37a86b45efb9a47a61a36449063e8e18d0cab3161329fc099eb21783169c4f0c
+    # via
+    #   -c requirements-ci.txt
+    #   -r .github/requirements/pytest-tool.in

.github/requirements/security-scan-tools.in

@@ -0,0 +1,3 @@
+bandit==1.9.4
+semgrep==1.175.0
+jq==1.12.0

.github/requirements/twine.in

@@ -0,0 +1 @@
+twine==7.0.0

.github/requirements/twine.txt

@@ -0,0 +1,470 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/twine.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/twine.txt
+backports-tarfile==1.2.0 \
+    --hash=sha256:77e284d754527b01fb1e6fa8a1afe577858ebe4e9dad8919e34c862cb399bc34 \
+    --hash=sha256:d75e02c268746e1b8144c278978b6e98e85de6ad16f8e4b0844a154557eca991
+    # via jaraco-context
+certifi==2026.7.22 \
+    --hash=sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775 \
+    --hash=sha256:741e2c3b351ddf169a738da9f2c048608ff7f2c5cc02f1ebc6b118bb090d5d55
+    # via requests
+cffi==2.1.1 \
+    --hash=sha256:046bfc24911b37851ee1b51aab8bffe713d89c68c6a057b09484ce9fd5f69b4e \
+    --hash=sha256:06c72bb76605a4b0cd0aad6930b69d4baf7dd5d806cfc409b824191099700e66 \
+    --hash=sha256:0beceaabe56af686895136a2de78db54ecd8e4046b236b8fd6d6cb61389e9bf2 \
+    --hash=sha256:154852545011f779917b11c78db2358d095da62a9a172b78ad0a583ee5adc0d0 \
+    --hash=sha256:194cffa889098ced9976c3fc6340305e43f6303657d298da55366907c05c22d6 \
+    --hash=sha256:19ee6127ee34de7d83ce3d371ebc5ed91addbdcc39f9ab15ce4eb35a4e534971 \
+    --hash=sha256:1a18a57b58cfb21fc28d72e876acf10eaed67a1ed96226f92af4df681d571c4c \
+    --hash=sha256:1aa5645c30469b09530c4ebca77ebf8f17618293c58f8549cb1a543a50236e7d \
+    --hash=sha256:1dea0e4d7d4f11f619fe8c1d76caf49e24405b4b5743c0e3be16a500ecd930c9 \
+    --hash=sha256:208f941bb9d18e768138677f0a6d2ce01f590df56043dda1df1535ac57c88517 \
+    --hash=sha256:210019b6c7cf07f081b4c54635c8cf744377001350e29cc0f81c4377b4797735 \
+    --hash=sha256:246fa40ce8645a614ff682e0b70f37134e460eaf93a775e0cbe3cca585a67a80 \
+    --hash=sha256:25792eac27877609e7bb06d42ff88278a6624fff2ba9bbb523c09616b117e80f \
+    --hash=sha256:27350daa11d4f10c540e6e89dada4c54feb7256ad03e9a4dc075ebad7ba360d1 \
+    --hash=sha256:28907ab9bfb6aa13184cfc17c6b8e1023c5ab6fd7076d8c20a35e59fe04f8f29 \
+    --hash=sha256:2ae64be792b8966f2c69538199728b290e34726562896df1e5dc8ffd8d8188e8 \
+    --hash=sha256:31348097ff5bbe827ccc41795d4dd099d9f0625e7def00ee653c137a490c2a6c \
+    --hash=sha256:3143d81e29e1e20a9ce10901ec369012947876596f75a222235965f2b7ae832e \
+    --hash=sha256:3222ba5d678f80a030e6afbcc33dc1ae5cb45facabb61cee2c7016b8432fde48 \
+    --hash=sha256:3311ed60d36f83378794e1009ac6258bafbf81f7888b4caa7b35a521e3f95813 \
+    --hash=sha256:334644fbac4eff73d985a17a91226df55d0f394160c4cfb880e084c8f7161cac \
+    --hash=sha256:34e261f78cb6ceaaa36f42f2613f4380d94d9c759a9c73c769ee6e0247364632 \
+    --hash=sha256:363e05fa78e15116c3c32c210ee36884fd6b9afa6d440e47112c3bd511d64cb6 \
+    --hash=sha256:398aff33cee2767e3e781d2554c54bd0dff386bb437581e0d8011fde1a942ec1 \
+    --hash=sha256:3d22a20b1fb1632cc72c22f95f7b0d2961c3e1c235f245ba4c606c4771035659 \
+    --hash=sha256:42a494cee34437f05546455144f2b5d9ac09b1face62bcfce597d2e521066688 \
+    --hash=sha256:42e2f76b9455f5a9a844f770bf3e200ed3da0e15f5df3db9c31fe80b04b3d004 \
+    --hash=sha256:42f6930c31dc7f50732c9ae793c2786c7b6b044195967bbdde40bb9be81c4cc0 \
+    --hash=sha256:456a61fa52d579ebf9df2e9552ead5129855dbaff6c1e5a9b1bc408809bdc062 \
+    --hash=sha256:471cee653ae88de62096552e6d24ccb4a5adb8c8c9f10b5054d0122c15bf2779 \
+    --hash=sha256:49cbc70e6542d4ccccb936558d1064a8012541e78f821f955cff24e357776c94 \
+    --hash=sha256:4a7c934f7360e8cd64fe9efadcbd10c7c6364f531e432b9a4bf5ccbc9e0e8b50 \
+    --hash=sha256:4be96343e422f2dfcd12ab5c9f5aebe03f82f737c6bffeca6830b3875cb44aab \
+    --hash=sha256:4f42141fc14250de6dde5ee7ea4432be017252d91f19c5ad043c084cea629cac \
+    --hash=sha256:507a24c282e0f42f8ed737cf048572cbf580468da5555764a8331735e9c736b6 \
+    --hash=sha256:51b31d1c98274844cfd7838ce00bfc27c7423a4dc00fc0772fc3331c2cc90676 \
+    --hash=sha256:58acb8ab8e295e6c5ea12f888cbb13cf21511ef2a3303a23f4325c29d17fe5c1 \
+    --hash=sha256:5a59cc1c4442bc3d5c703bf720b51138d0bfc173618807c9ee2490a7541dd3d9 \
+    --hash=sha256:5bb4e7ea95dcd6a014a6fef62e62467d67d8e582326443f3d68e71d6320a9fcf \
+    --hash=sha256:5c58fe613dc5e5336357eff555824a314d8e43282600435c8d1cb6a7a2fedd13 \
+    --hash=sha256:5e7cecbaadb83884793e05828cee59b210b24583b9c7425d0ba6a754fe22eb4e \
+    --hash=sha256:616f097f2fe415bc92a247f02e11f634e1f9e9a83d327e3c915c15089c87869e \
+    --hash=sha256:63bbfd5ded17c4840ac07cd8f1c21ba9d9708141f840b324f422f41b207e3973 \
+    --hash=sha256:64faea20f4e2613363a1a9b9c7dd73058f3ecd00133a511e72ad7c511658f527 \
+    --hash=sha256:661c298b4821edebead0c91edd2b00374d67ad7c5a1f7a91d4442633b79d6a72 \
+    --hash=sha256:68e62fe11f30d5ca8289242866f0a5291402d8529ca2178ab8afc5c9694ae890 \
+    --hash=sha256:6a8dddef476fab96d066d578fc88526767b836ab5ab21754e1d5bf3879c31c7c \
+    --hash=sha256:6e192623c49c94421616a5778fba35cf0d5a8d000650c1967ef4448ee5cdd990 \
+    --hash=sha256:7225e4514edb64eb6740324353e0da0711954fd8d7da4576755b1c6e09b697cd \
+    --hash=sha256:75f80557d1389eddbd0de2681f6a390a0c5338c31ddaa821381c203fc3fd50d9 \
+    --hash=sha256:770de9db11e84213beec501cfcaa013b019820ca881e03344dea5844f7876d94 \
+    --hash=sha256:7750c6449dff7864bb9bb27ddfb0267756189201a3afc911d82b3caacd70dfc3 \
+    --hash=sha256:7bde5e4cc5c10140859842b9d383af292b22639a4dffb725314baf45968cef80 \
+    --hash=sha256:7ce713ace7c0e4520535b42b77eaa742c16dab813978064913e5a3cf82973b41 \
+    --hash=sha256:7da0c5eff80f0197f3b3d1232ec5a682a9325f4ae9016a78f5f5ca35f9ced1f5 \
+    --hash=sha256:7dbb61fe3a7699468030f71bbe5f8a0e326a151daa91beb11a6fc1f980c55e1c \
+    --hash=sha256:811bd1e21d32de12efca32393a0ab3f5133b54fce9bd44b8bd77ab07da14bf6a \
+    --hash=sha256:8ef53b2de9bcb9197d31854256575d59dbac0cba72ac627bb291ef5eceb74be4 \
+    --hash=sha256:937c0052c05a31ca1daf18de3158eed4dbfcb9cc107adbea227728d647be701e \
+    --hash=sha256:9d2055050ea716bd38b7f7f1579c275386646b4894c155a3e2f3cd62ed41b7c6 \
+    --hash=sha256:9f8d177621de5cb38ee3e731eda45d421db093ec0739f46a5594babda7987a98 \
+    --hash=sha256:a2d7755bef5a12ed488f4ef1f1b69ee9191d7396083b755a5d2295f6edb4768b \
+    --hash=sha256:a48d62ab9d6f4f98c983223a547af44be6ca3691074c31cecced6facd3ba2dc1 \
+    --hash=sha256:a4f00aa42f75d6e4595e8866e748cc1705adc0cddfeb2ca86d0d03993d63ba03 \
+    --hash=sha256:a6e721d4b0e45d5b65e87534470e67b18dcd092c83f68fba09f152b9cbc061af \
+    --hash=sha256:a730a083190634c65cca36ba5f489531576ebd79bcd5c8e172130f6453127231 \
+    --hash=sha256:a931079504ecc49efed7744c476a5c343a92fabf66dec2db95edb1b2fdc770e2 \
+    --hash=sha256:aa9511c62d14da7aacc9b4bf51f3f697a621e83b2d6919008243c3aad168eea3 \
+    --hash=sha256:ab36d55f9ed2d067327667c2fea18dda018eb628dd6347aa01dda6cf1f5d3836 \
+    --hash=sha256:ad2c86c495b899d862ea0f4b42891b8713a3bd45dd4105c7fd51c2a72f39f3a5 \
+    --hash=sha256:aeae0e330c9f6acd681f647d46cefd30c29f93e3392882e792e82080c9691399 \
+    --hash=sha256:b0431303acaea1089ad4b3e9ce4e6518193def1118d4073ca848635ee4ea2e96 \
+    --hash=sha256:b5bdfd1c873d4e093aabc0ca84c4ca6dbc4f752afb5c86f146d9742580c9da2e \
+    --hash=sha256:baed1e86cc735622097354b9d1281406caf42ff42a886d29faa8e8d1630333be \
+    --hash=sha256:c1453022f490d2459a11819d83ad1d586e9ff65a12ac3e705ffebd46d3685dcf \
+    --hash=sha256:c26608d2222fb1e94487e4a387d85f13eb55d5ed725cb25a0c589ac4ee60e7bc \
+    --hash=sha256:c7659f22557c5a0bc4855cd635f55edec690cc008a40768527762cb9fb263455 \
+    --hash=sha256:c8c69575568085ba0b1b10c0249d779a214aea6f6522e949a0fc9fb0fcb449d0 \
+    --hash=sha256:c8d2c9fd1f2d16f780d15127abb050d13d1a76c03a4bd87d7e4980e45e511e12 \
+    --hash=sha256:ca82be1a1d406ecfe1d25dc16cb33488e5a16bf4438c9fb590484ea29d92478b \
+    --hash=sha256:cc572dace3f60ef98d7b12ff411d20f5362feb31a0439eab0085bbfd349982d7 \
+    --hash=sha256:d18e5ac0f2f03f4f518d3e23db0f0cad7faa1da8620e9c09461d443bbf6e6692 \
+    --hash=sha256:d28630f5854ab07ab1fd4aba756de52326c82e6be15d414b12793f1975048b54 \
+    --hash=sha256:d9c275eaacd24aa73f94ffd6de08fc3f932424d8b6c376f4bed7cde376fe7bc3 \
+    --hash=sha256:da0e573f9f97159390c89d9f1a9e41908b66d408cc5b58d08cf3847d844c531b \
+    --hash=sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be \
+    --hash=sha256:dddad92b554513a31f272570678ba307fb9f618f05e3d4a5eacafff9eae03e1d \
+    --hash=sha256:df423d40ee8654634421812bc3b196da3f9bd7d32929da813f8394c4348a5358 \
+    --hash=sha256:df913725b79db7bcf03448f36b7bf8815363417d5b58deecf9305e3e30f0f21a \
+    --hash=sha256:e0bcb7e0f677f543555d2adff3bf19c05f66cdb4796e5ff602442ab2fe3c4ef7 \
+    --hash=sha256:e2d65b31f36619cda3999b78b2aa9632e76b78448e7a56fc4240824200e7c4fc \
+    --hash=sha256:e6e8cff14d6fb0be70a09c0bdc58096f501952d04624ebf867e0e56da2df8960 \
+    --hash=sha256:f16c709686a78c727bbbf059f92b0bf41c6fc60deec706d2dc19f529175a6125 \
+    --hash=sha256:f24fb43132a4c6b4cb4eb029492919b2db645be6808d738f244fd146c03c32cb \
+    --hash=sha256:f53e442b08449d42821fa4a4fba000095af9f62742a500f978a9f557ec44339a \
+    --hash=sha256:f5cfbc5fe74540d335175b656c725d74d90e3730c626d92575eea35029d9afaa \
+    --hash=sha256:f81b3b8f3d4e343550fa4baa0e479bba9f2d29ce9c2e9b51d1ce1718d7442fcf \
+    --hash=sha256:f8ec5e643a9a937f64e1999eb9f75d072263751912dc5cd06d3c85f8f44be7c3 \
+    --hash=sha256:fb92203a88b3d3053034db775110081c49d28be6551923805e039924093761e4 \
+    --hash=sha256:fcd22650c908d7b7da162bbfaab594a1227a15d1643a98c68b122ac642fa2264
+    # via cryptography
+charset-normalizer==3.5.1 \
+    --hash=sha256:00668ebb0609751758682eb0b5857e7c35b9f00e84dfdef062e103244ec94d45 \
+    --hash=sha256:012a22b88a77ca2e59b98ac5889b0deb604147666032f45e6d6e217634d2550d \
+    --hash=sha256:01e93745f7f219b703b60ba7afead36cfc4242782be5af484673fc500df12da5 \
+    --hash=sha256:04368edf83514385ffc3e1cfd4546e595f4f1272dd23ba437a93a9cc3741d47b \
+    --hash=sha256:0722590aabf9dc6a6c0343d523c05458fa2b5047dbe6302fd526bb570600753f \
+    --hash=sha256:07ffd07412fc5d5e84cd8952acf9ff7e4ed7a708e69d1bada19d8ba91711353f \
+    --hash=sha256:09a7bba9f739468c8e78c36a75c33768e53cb1959fc638f510454c14683f00d5 \
+    --hash=sha256:0b2b1b3fa5670c127b246df1d0c059defd41f689a868a3b9d79df9b1cac42d22 \
+    --hash=sha256:0c6dfb5ca6723eeed15aa8e564a014d69fcb8812f94eef11fe3631e0508199f5 \
+    --hash=sha256:0d929fc574b4d6fd9e7c0f5c2ede8716a41911923aa7fa5fce38e0818aa4a1ac \
+    --hash=sha256:13e3afe97712e8887cd516e960c63f0b93122971e5b5e4b2622fe7701771e838 \
+    --hash=sha256:15f024313246a4ed976c60f440bb8d257815513a681d212ff74fd46f7d715a90 \
+    --hash=sha256:195ce897c6153c0700078142cf8efe3e6454ca4cf4357499e4078dfd83396626 \
+    --hash=sha256:19a3dd5aa73cef1c99687c4fc57db016a9c17104ae1185da88ba566a5d3bebe4 \
+    --hash=sha256:1d1c7a53a6c2103925cdd6d7229f8c567379f211c869793df679f2e9f738c369 \
+    --hash=sha256:1f5883d77fd409a261abb5dc8ccbe335720d798b1de4abb3b1d47ccbbc76b53b \
+    --hash=sha256:21b82d8082f6f5e7f456ef0bd16323d08de1266efbfeb476e64b2a91d1471a4e \
+    --hash=sha256:252d099029bcbea642f2a06c4ed5046bdf8b5a8150b64afa5e027e88b106e5ee \
+    --hash=sha256:256dd4d85d9e4dc595e2bc983c980e73f62ddeb3165c58b4c3dfe78c5c8548c1 \
+    --hash=sha256:26422d45fd13551cf564c58932f7d72b4f58b93b0fcf18c35ba6be12b46bb102 \
+    --hash=sha256:2679de311c7946dde5d3b6f44941844133ff5c7cb86099c0061ab1e8901c20a8 \
+    --hash=sha256:29880d17a8eb0b5cfdfd8944b468322928059aa35f1f5fa8ff22b149ec0b42f8 \
+    --hash=sha256:2bced4061f000f7187254a02ad3433ae17eaf991747ceea2f478422590a5bba9 \
+    --hash=sha256:2e9cf9253119d8e5d111f05d71626786fd3d6193817316eab1ca088cdb8593cf \
+    --hash=sha256:2f06b7eae9dbe77fe1d644ca244dad508de8d302870a43f3c559b521270938a0 \
+    --hash=sha256:2f293479cce755c75f1697e87c409b7ae4c555c7dfecb6e988ad13abba943031 \
+    --hash=sha256:329fc3ccb63ad22d867d84c2adea759a64079a37ba4a343433b02c7a2816871e \
+    --hash=sha256:343fb4f2821043bd87095f7b08a1a181febc8e36ac64212143bbfd0a0e1bc235 \
+    --hash=sha256:3588e376b3ea2eea84976f67273d679f229e24c66dce7b82ae45aef04ff6e072 \
+    --hash=sha256:35aea775dc2bd5f54cd84a1cd2696cc3207c479cb9cf0bd346f0d343e4300ddb \
+    --hash=sha256:35fe081843b35aad20ffeccec3eeffbe637b15d14f3fb22cc1b59cd8ec17e93c \
+    --hash=sha256:36047af20e17097c3bb9476c2b7655f2f7aa51322c0ba58c07695bedf755a950 \
+    --hash=sha256:3617ac3cfd8b9888f145ad89dd6e692285834b0201c6074a5eeaad3fd4d668c2 \
+    --hash=sha256:366ec70f5547c640d3ce1985722490f23faf4eb5216a7eeba78277490e78dacb \
+    --hash=sha256:394fea06235c8543390050ed5f529187074b029fb027213f6c46ac11ab5d950e \
+    --hash=sha256:3d27167433c0d5f18dc850f07d0b3816221984fecdc405d6c157a6f0b8f8e9e6 \
+    --hash=sha256:3e5e1224c0a6a90e05843e07adfec669edebec17801c67072f51e59561d63c0b \
+    --hash=sha256:41876ee62a3dddf48ff1121ad8f0798032aa03f2fd35f21f34a4cab14f18d8d2 \
+    --hash=sha256:433c5a81eade63b47e522303bad236f59dba55ea6951746f5558355eeed8c75d \
+    --hash=sha256:4582c27e8c889d64811987b5967fbd3ae0c823fe1fd933b543d55ac20bb475fa \
+    --hash=sha256:485a0d363cafefcd2538a73c7c838daa2035f09b2c9f9b5e3133f80c6aeb84c2 \
+    --hash=sha256:494b70049a4d69aec6e8137c13af4cf8db8c9f9820a1392ac293b0dd2987a818 \
+    --hash=sha256:496846868fea80e479324862fa877f02411f2fd0f83b79ccee2607aa68b2a032 \
+    --hash=sha256:4abdc5f9ad448c1ecbfae2974b820535d6bc6e7eef63babbab3d81cf46968c71 \
+    --hash=sha256:4b599739b93b2cbeded49645ae3c8d1405c29ddfbceac1545c87a3f9580a9e96 \
+    --hash=sha256:4bea7f8ebe90bbd7f0e4a2de42ca6924ba23e3e76418c408ff82f1d46fabd687 \
+    --hash=sha256:4c4fb141a727957c93edfe5c32a26ceb6b5f6461d67146e2d39f51e16170bea8 \
+    --hash=sha256:4c9548dc78002099910abaebc0a72ac58b7d30931869e0351c09b507dff4ece3 \
+    --hash=sha256:4d26f14f041e83dd8edfd61f4cd4fa7285d31798b5bf1f28e70c367ba6c41d61 \
+    --hash=sha256:4f298bdadb8f0b9e5672877f647d1be9373ef5320c9e2f049795e26cad28b6a9 \
+    --hash=sha256:52ec005752a56ae79547a05c0139ca2501a0c866390b6115008456b9f0e7cde1 \
+    --hash=sha256:55261ac0d2941c42f196dd576f543d87a8ee03cd6f5e30dfb4d807b2e3b9121a \
+    --hash=sha256:56490c595a28b1bb27dfc583e816152a9767721ef58b2c03b13f954d2f707420 \
+    --hash=sha256:58d3e12c88e0950bca850ae1f7c256055c097639c2edb9eb123af9807d8b15e4 \
+    --hash=sha256:58d4aa13a59c969dbfdf9e6a9560e242cbfd9e8a8f50c2747714df1a423adf65 \
+    --hash=sha256:59171c6e45bf07d0d5cab3b0bf81d945035530f6873398b3b531c31184d46663 \
+    --hash=sha256:5b6d1386bf0096d26d3a863dc0a487a5b4eb9aa93cf5ba69683d29dde6b9d60f \
+    --hash=sha256:5c0ea61a470e070686aa30892fed79e297d2c8d0ab46b8bcdf027d38c51da591 \
+    --hash=sha256:5c84bec0ab5ae0c64bfe73a7d2adcb5ce73b467523fc27fd6a28ab2aa6cbe35a \
+    --hash=sha256:5ca0555312ae2fe82715cada7fac375530c2f3349e1eaa1bcb33d0283ac79a18 \
+    --hash=sha256:5d8531a6569d025f68e2321e7638fb7978f23db58e5f69f56913837aae03816e \
+    --hash=sha256:5e2d0e146dcb57034f8b97dc58d2d512cb90aba253960ce449f695fec6a82c6f \
+    --hash=sha256:5fc45d653ea8c9a20479167e11d4a0f8cb2fa3470737ab6f9c827532313187b7 \
+    --hash=sha256:6117b84ea48435e5356dc737f5121485c30920ba43375fa7b434fd753df0eac3 \
+    --hash=sha256:6199d5606e2bbf2b096cf64d03f8b6790c91081d5ac866b8e7bb6422738cc60c \
+    --hash=sha256:62b55f6722735a6c472f88361cde6640608773d9443cebdbb51abf436a1fcdd3 \
+    --hash=sha256:687c9ca3035544b113bea2055e180af96fb63c0c476e22a9180f51925186e7b7 \
+    --hash=sha256:6b7430cf5728e68f6c462254009a6ef4086e1bea43cf2f57aa9c55fb4f50ff96 \
+    --hash=sha256:6ba32c4d2abf1d2fe7cf27d280f4cca5664233b0f885549c7761719eb977f486 \
+    --hash=sha256:6c9cdde8becb25a7fde49924511aa2644d6f8081cc8df8e9452724303348d8e3 \
+    --hash=sha256:6df0ec430f9a831772c23ca5a224cba36517a58a84bb32c32bb59a9fa67c47f6 \
+    --hash=sha256:6e2912d4babbc65196ac13c2f53468dc57fb8b9c25ef913e8c59ddf7c6dc0e1b \
+    --hash=sha256:6e5e4d73d588ca5ed09df1b7dcd1b203d1df3c542e3f50d126c947d432b10731 \
+    --hash=sha256:70055ff39b97c99e7ae40ea3e393fb62aa2e44dbd9b29f8d14f42fb0025c3959 \
+    --hash=sha256:706bfd38730a5ac7a365793269a00f4e988178cec121391f4248d84ad8c972e9 \
+    --hash=sha256:7235dc28fc6dd9d832ac7c7bce95367dedb85929f17368a0c2bee1e080b9acbf \
+    --hash=sha256:774d157f112367ff4abd29019f38f023c24e00e56edc7829c20e358a5a913ad8 \
+    --hash=sha256:77efcff2b23071c349402ac1066667a3d011f62398d81408c9b88ad991747c9e \
+    --hash=sha256:789b8982559ae28dad2356519f841655756cdcd96616410590ae0b17454ee64f \
+    --hash=sha256:7ac76cf9afd34929d76eb7fcb63be476a4853d8a96f0dcf2d0db68a0cbdf9885 \
+    --hash=sha256:7c0c10730342b0c9b35dd1d619beb8214e520bd96a1f870f452680b238aab3e0 \
+    --hash=sha256:823f82903d189af463d7df250ef1f7f696f3cee08cc8d91deb565e8d425f6506 \
+    --hash=sha256:838648accb3a7fd9803fd45c87bce8509648eb0c11bc34e216141300977244f2 \
+    --hash=sha256:854066be00447fa8de2ccbbe893e2ffc4b123ef16d897af794c1e18bd4a714b0 \
+    --hash=sha256:85d5855daafc240cc045c026d7a15fd198a09b0fc8ff6f5ecbb5297b509cb11e \
+    --hash=sha256:85de3134b5379856e323ba37c19c9256d39425f7b76a63af52b09fb4664c2e8f \
+    --hash=sha256:87e4f41d375c0b9be2fb5251aee4b8a689169e134535aed81bf085c3b647451e \
+    --hash=sha256:88ca277405c2d3b71c4e1c2ee0e7966e807bcba86a69d11e19ba199d18ae4491 \
+    --hash=sha256:88e85ab89cb822c1e635f51d6d32e488f94e002e70e2f492bdb8b945543f345a \
+    --hash=sha256:8ac8c94b6539074e0f40899301273ac8402b9b3e01c7b7ba269ff30340aaaf20 \
+    --hash=sha256:8fe532b3c966d1fb794e0698e4589d0444017ae77fc0b31edea13c0e35bcc449 \
+    --hash=sha256:9085f87b0e38a2b92b8923059b4e8789fe40d9279712d15dcc670048d77079af \
+    --hash=sha256:90b7481fb62fbe172c558bc6fd1c4c98d82004a54a7551f20e11ac9bf0b8708c \
+    --hash=sha256:92caef967d287a407085d61176fce4012b1dd62daed4eb6d5ceb26d3d2538712 \
+    --hash=sha256:9362dd90aa7dab48c0054a21187791ccf05473f7dba5d92b8033ae62164675e7 \
+    --hash=sha256:94d78ecec2605a8d0398b0f365d5f12a63248438516f5dac536a5eff7337df4a \
+    --hash=sha256:94fbf1c0c6cc0d3d5e50f9a9313a8cdca90dd696d34b381cd1704f8c9e939f20 \
+    --hash=sha256:950f23cb393f85543777b0433f082cddd25b51ab398eac7971146495679efe5f \
+    --hash=sha256:96eefc178f8636b9c760c5829345307fd81cfae9ab1e80997dbddeb0f54ee9a3 \
+    --hash=sha256:96fef3e886d6a9874b14f27fc193fbdc69d5d8035783d86aa4e1cea594e695f9 \
+    --hash=sha256:977cdbd483a9cff38179bea4fd754289a6f2195c7abd414aba85410b3e66cc5e \
+    --hash=sha256:978eab16f55b4ab2c2a745be9a0a840bf8f09a7f227d9c76eb30214d078865a5 \
+    --hash=sha256:994e883d17c559cdfd38c84003c8b27d25424a1077272a17e7cd27bfe0bf57b2 \
+    --hash=sha256:9ac4444d8d4fd4c4bd08bf451ed3167aa9e7ec6cdb41b648794f1d1103652e36 \
+    --hash=sha256:9b5db6052055d34d41230fb78d7c439c23dc536a9896f6cb039e8dd92cfc1263 \
+    --hash=sha256:9d9a0dc7cbe9bec24c3f767c9122c41fe5a1bc43f47cd099d00d393e09769de4 \
+    --hash=sha256:9dbdd9205662134957cf0c324f639bdc5031c0ca056e2369e238db75187c0f11 \
+    --hash=sha256:9eea3ab2597a5e65fe65296e2d6a84570845a6b55532d90333d740d48bbc850a \
+    --hash=sha256:a2028475ba855475b8b4d3cfeb4994269c967aea8b9892dfba907f4263a863a3 \
+    --hash=sha256:a3a370082ce34d0612f421e15fe011c53bb1feff21a26d06ad4fb244dab5a375 \
+    --hash=sha256:a545775cfe815855ea32d7c27731d79da358ef2055b4a25830231b1622dd18aa \
+    --hash=sha256:a5cbd90ecf0fc62e64726917ad083b73001f0563657a87ec3c0b504e277dc90d \
+    --hash=sha256:a6d095662e73e74f0a49988e0593373e243e3a52e27bfeea0a859e88acf4a0f5 \
+    --hash=sha256:a6dac12ff6b846103483683f60c5f8fee205121adc58ffd87e90a90a3af69e99 \
+    --hash=sha256:a951ad59cad9145664a730d3036b40b844e74d2d3683da40111463cd3a83845d \
+    --hash=sha256:aa1099b956fb795e686d073568f6dc002a0bb89765ea6d5b055dd7d9bf1b116c \
+    --hash=sha256:aa2bb0b37202dca27175591f761108b5d34096ade1191ffe4808bdf6b1571488 \
+    --hash=sha256:aae2ee51122d3ae968a3837d97dc24a0aeebb0dea23694422cd172bd30017cd6 \
+    --hash=sha256:ab743e9bc90c1f73552ec33e10e3331315acd2c397b36065b591b0181de533cc \
+    --hash=sha256:ac00177c4831ffa650f8609e4bdddd5fe09c03b1c0c47acece7e6ea20421598b \
+    --hash=sha256:ac13b004224fb341e1e25a1ed5e19d32f57cdb2a403e01f003b46f051a550f6f \
+    --hash=sha256:acaf604462bf330b0d07e7a07c1d6e4adac79e5fb13e9c5140590542cafacc00 \
+    --hash=sha256:ae31a1a1db2ee6cc2942fccaf695c934bc7f3db9f2133a3fef1f367cf1a4ab10 \
+    --hash=sha256:ae4a097991662cd4fff0ddc74e0fe7874f82e00042fa0ea00855645ed0c79598 \
+    --hash=sha256:aea996a6aba25260827c9ea511d1addfde2da9eb686ac961838509086188b7e6 \
+    --hash=sha256:b39b69b347e5e47a3b5b8cfc005c68c1ba347474e3960236c4944a8ecd174962 \
+    --hash=sha256:b54e7e13267d49ffbfe68e25b3cbd774dab38fa37238f71265e91b36146eb21c \
+    --hash=sha256:b9af956078716df40d985fb0dfeb2c2120c5ca92ba4ff4b388acfd01cdc14d08 \
+    --hash=sha256:ba2f37ee79e6338845261a3c5b1784e5d1acdff2c0785b284f1b633033d136ab \
+    --hash=sha256:ba501e667c17d8411f98e67a022d9604ef179aff0e459b7e292c796837c13573 \
+    --hash=sha256:baf3775a2635e5a11fbd5e4e64ee69c7e86875d224a5c72aca4c141064589a90 \
+    --hash=sha256:bb57753e36e4855b8ca375069482250a6246372331a3e4f3407eaebb007443f5 \
+    --hash=sha256:bd6c173f04743d483881bffa1478d5a4624475b8cd1d2194956a75548e191c18 \
+    --hash=sha256:be47f99644b208bff7766314013f9acf57b056b04191d570d68ad14022cf5b1d \
+    --hash=sha256:c010f5581d9c612804cc59fcf7b524b707fbcb72828551237ab545bb5c7034af \
+    --hash=sha256:c1dcc36dcb96abc02236e182d17e0f71430152a6c2c7447421da2d2dc144edea \
+    --hash=sha256:c428c6c31eb5f4277d7f8eccaf767fbd548ddd5ce3c8b4f4cbbfab3d96b5904c \
+    --hash=sha256:c658c50ac0c98cd755a2dd50b7977d3bca7df401dcc47fbdfa87db53ef7d4e8b \
+    --hash=sha256:c71fb0d56c920c269cd3e2e3fe7c610e3f1fdb21a6ce60efa6430ff63676cea6 \
+    --hash=sha256:c7b742bf31c88566b4bb6335a7f393bb322e580b6bb98df7bd0c25e6e3519ce8 \
+    --hash=sha256:cc0329df4caaceb950d2f580b5ac716a377f7059624a0bafaeaf8a218c6ed774 \
+    --hash=sha256:cc5d36d96478aa9c60654bd932525bf32964c62a7281eafdf16d85003a8d6004 \
+    --hash=sha256:ce854f5f478050ade5a238731c4ca985a7d3b3cb53ff600a9b5c3b689b5f0a7a \
+    --hash=sha256:ced3fdd71aaa83ce593746c2edb42b7a59cb4c19c8b5c407781c72e493aae55a \
+    --hash=sha256:cee5dd7c6fb5dd52a0fe2a740f9bc6e3593f5f8b1788bde49de02086f30182b2 \
+    --hash=sha256:cfa1c0cc3a8f9f53f1243a5a99ac36fd003880199383b37672e86ddda9cb07e2 \
+    --hash=sha256:d1ee1e296209fdce05b81b663250eefa02213a2da7b41bf26f7829b8ba3545aa \
+    --hash=sha256:d59b75732e9b6f27388e10c14b0259cc5f2e48c78627d185e6a177b58ad3cffe \
+    --hash=sha256:d63600d620ad0064c3a748b950ac5ea38a80190e5498532efefa4b7b3f1da1f3 \
+    --hash=sha256:dd732602a7009217f658d5863d12d79d373a4de0eebc111094bcdd3bb8e0a6cc \
+    --hash=sha256:e06efa066f7dbadbc84ebc126a97c452a6451dfcf589d89d788484949e1cf795 \
+    --hash=sha256:e199fb99720074809a7720f1c0b4d919eea8b87e88713e0f8f602f7bef543d9d \
+    --hash=sha256:e4b018dc5a0eee4676e38fe84a47a427816c590b93b55d9025274ec4d6ffc2dc \
+    --hash=sha256:e6621fb2a4988d6e53eedc455e5903e2679f3967b8acb3d639f1b63c14a2e893 \
+    --hash=sha256:e71c909f353863b2b89c83de2ebed71ea6d0df8a6ef65a128193c5e650766bef \
+    --hash=sha256:e90251c0c7bdd54a100a0dce3c07b7e637278c93af29dbf78ebb89a58c4bac7d \
+    --hash=sha256:e9fbdce1e47394b09bc9f26ab117dfc8d6491977a11d86f592bb42c779db2fda \
+    --hash=sha256:eb12fb2ba69ffa05f8695f61c69e591dc4b4a12ac3757ac8af8adb259bf56d17 \
+    --hash=sha256:eda059b6bc8bc0812d626fd91a7ce01bf583df0a61296eff390fd94141a34e30 \
+    --hash=sha256:f03ac127268b43ef4fe9e6ab6794a6794b49485a0cc0c1db79876d2f33f75bc7 \
+    --hash=sha256:f298e218441525d3794428b4c8b8fb8662c6d3ea79925d4807ee6b9a96a3bca5 \
+    --hash=sha256:f5542f9b941279d82d41eb0aa9f98eba36fe4df5c7086c651df7944935b37182 \
+    --hash=sha256:f6f7deae3feb4edfa2efaf7c574fe88cbf055038a6abdb40188e4fff66d5699f \
+    --hash=sha256:f9b1e28d0e8dbfa858abdba91d6b547beaf2df1a59bec6da6faae7b96a4991a9 \
+    --hash=sha256:f9f8405c2c758532c74fed975dbee57be1f31a6e865c031870c79a6ed3212ada \
+    --hash=sha256:fa48b1b63d639f9483e0633e092f5851e2348c352f1f9bb6c8182f87884ef876 \
+    --hash=sha256:fb78f6e7fcd8ad785d28cd577168bc1aaee827b25bb8755638f694794ea98f0a \
+    --hash=sha256:fbc597639158fd7c14d55e808718848319540f51b0e6746e3eefa59723a4a348 \
+    --hash=sha256:fce8cbd4997efeb450bd298b54f755dcdff18d496f7a5ddbb4867c6d7c88fdc3 \
+    --hash=sha256:fd0350afdc3aabd5576f60ea109228bd5538139713c7b094c5cd27c73a98bc6f \
+    --hash=sha256:fd0a274c0e5f9a21565cd9d3dd749b61f96b7aa1e20a93aa1ba4029518f2e5c0 \
+    --hash=sha256:fdb8a068947befafba9952162645dc2fecaeb400e64584829ed5e9b2fbe21a7f
+    # via requests
+cryptography==50.0.1 \
+    --hash=sha256:01f41478cf33fc605a6a089cd56d28b45c6c0b45a1928b61797f2621a04bac71 \
+    --hash=sha256:05ba322c4da95b262a212c345af888ef2c37c88c0509756ea00a0e6d68850f23 \
+    --hash=sha256:16c5ecd954b3330ebfb6605eca4fd952da8bef376551d5cc264534e3770a9ee6 \
+    --hash=sha256:2a93d05e34d5f67fba6f891fe85d929999baa7195e853923ea6d7576c9e68c5e \
+    --hash=sha256:2b34d76a652ea2b6faf777c35df230c5637842cd904e04f16230c3f9f03e4361 \
+    --hash=sha256:2ebbfb0f1fed745e91796e3e1080a1440423fdae8ece1b995a1d80883a409054 \
+    --hash=sha256:30a125032e5642a21ff816e021152bd4e7e94f03eff3f4b7fca41cd22bc3110f \
+    --hash=sha256:330fbb252391c596f1ae42c5754449dc924e6ad012dca8efe0d703f9f2d12ec6 \
+    --hash=sha256:359e62deae718bce96170e223fdcb6357e4fbd3bb7a3a75f4430763532560e49 \
+    --hash=sha256:407fe2b6db00939c05c0e945e9914238f2f0a430974839429dafc82b1ee6bee5 \
+    --hash=sha256:42be3bb70596b3abe4ac097b75be223e8b3ab614a0e5de068e3dcc54d71d6149 \
+    --hash=sha256:4c4188f7c0cf655be5c06342b817ed0f9595b69ffa2b12026e5353eed29dea88 \
+    --hash=sha256:51593d180cf6d179bde5c5d065bed81386b1f381656ae7d042b7ffc87a9895ad \
+    --hash=sha256:51afcfceb15597cf2635068e4ac9a56b2abde622edde17f37d85fd7b5306497a \
+    --hash=sha256:53e279950892dc102c6b4e52af03ae5ea92fac572a1ddab78ca73a997f62b69f \
+    --hash=sha256:55d16b1ef3ee0958d893a977b19777887e546c9954ea81b200c3301a864013f2 \
+    --hash=sha256:5dd9bda1c12b4162f6ff568eeb5e0ff956c28d14406e875cfe8a63a2d414ff20 \
+    --hash=sha256:5fe002589592ed749ce77fe0695fcbd3500dd61d7d6db5858a7544c612fa8e45 \
+    --hash=sha256:5fe939deeb161024a6be98229c953b6591fef1f41214497a78fe793a244c017f \
+    --hash=sha256:693c99b49bd37d0d096e4334c10232c77248c415b98d35236094cdf96d57258b \
+    --hash=sha256:76de83fbd91ac49c0feaaa983d0748fd7a53176afac5fb3bf7478d244f0eb527 \
+    --hash=sha256:79bf008d1f9af6071c797ad133e39915dfee7614f18f18f4db9072eb715064a3 \
+    --hash=sha256:804728ce710890870f3aaa344b2e161172d258d768ac139d02cfd9092d0d94e6 \
+    --hash=sha256:8921d58f426793c5f1b47f0b59575780de9a095214958d0eb37d909593db8367 \
+    --hash=sha256:8df2de9102026855887e4587084f6eabd80ed0f345b8ad8a7ac27ab9bf4723e0 \
+    --hash=sha256:9cb3cb952cf5a8abd50c782a98a89d71699715e802fe349704b47f2425b42a94 \
+    --hash=sha256:9dde0a357190eb3b1da1bb9ab750e9c85cba82ca5977aa0836cbb94e92611239 \
+    --hash=sha256:9ebcdd5519be9b652a46f507817a74591774fc3d6923ac364e4dfa64e36b291b \
+    --hash=sha256:a0b1a59e3a089064a0ec309e9428c8e3ae4e161419d20ac33600767e83fc658a \
+    --hash=sha256:a255449073358275b64b67d3f595f268bbef70e72b6edb65e0c70c735bf739c9 \
+    --hash=sha256:a8f40ea47330e71b594a7e246898f93177c259490c63183dbaf9e571d71ed9a5 \
+    --hash=sha256:ac02b07824d4d1001bd4367599f839c19cb171924c796e52c23508ac14c2c0cc \
+    --hash=sha256:aed8db4f6d71c51efb89530e12d9464e7bf2923d46c3205dc794a2a93f8c0648 \
+    --hash=sha256:b8f852c65863251b9e3a1b8c150ce21e59b522dbb6a7d4bc80e680d38388e986 \
+    --hash=sha256:be224a65493ec5b74a158ff22a5522ce4a5ca1e543c647a3a4730d4a09e5f959 \
+    --hash=sha256:ca83d00d9e69cd5eb63f2e69c3a5a59e0cecae5ae14c6ae0b35830fe3b37bad0 \
+    --hash=sha256:cbf74a81765ee67413503ca6e26dcc4f6f5a519822436cc0a1b97aab6c1b8a17 \
+    --hash=sha256:d63ae8f6481fec907ac0f588eee8a90aefde112c633131fe540e5711ddbb5a4e \
+    --hash=sha256:e22dfed744bd4002e909464cb23d2f0b05c6f3113a79ef2e9864a53db737c733 \
+    --hash=sha256:e2ca8fd1b6b4b82a1c4cb02841d0837e3c12336c2e24b520ab8ab3b969733d8f \
+    --hash=sha256:e74591e283fe6eb956416c929eb58262a719fe0311fd9054c62c3350ed8760d8 \
+    --hash=sha256:f74455bb086a85d5e81246412602aaa97ed095e504cd40dd261ef50be42205bf \
+    --hash=sha256:fb4b9672d389c738b175c4166e78310f8a70358886aacd9173ee03a85ffdc671 \
+    --hash=sha256:fc3ed7ebd2a8c96f5b166de0ab9b624996bef3b07bbeb19364dfb78222c22c80 \
+    --hash=sha256:fd3718b960d0b5dd213cdf03f3bcb7000e69dda0de8b956061947ff6bcff5558 \
+    --hash=sha256:ff838d62ec1bfce4f9ba7fa16f4a7b554cd8d0c299e6be37502161a660c84eef
+    # via secretstorage
+docutils==0.23 \
+    --hash=sha256:25d013af9bf23bc1c7b2b093dff4208166c53a94786c9e447808335ef1185fea \
+    --hash=sha256:746f5060322511280a1e50eb76846ed6bf2342984b2ac04dc42caa1a8d78799e
+    # via readme-renderer
+id==1.6.1 \
+    --hash=sha256:d0732d624fb46fd4e7bc4e5152f00214450953b9e772c182c1c22964def1a069 \
+    --hash=sha256:f5ec41ed2629a508f5d0988eda142e190c9c6da971100612c4de9ad9f9b237ca
+    # via twine
+idna==3.19 \
+    --hash=sha256:5e0811a4383b21dc5838069f801c4fb62113b7447663d2530d2bd6e77b49bf15 \
+    --hash=sha256:815e7be7a7806d54abb586dc943addc79e8b2ee16915059658cbeff4b1b43bf4
+    # via requests
+importlib-metadata==9.0.1 \
+    --hash=sha256:ab830580bc0ef3db61ce8fae716389e5462b67e033018bab6d8f80ef17172f99 \
+    --hash=sha256:bba5600596a7e21f3eef53281cf28d6a5195634d2f2b78ff9501a3272c6eaab0
+    # via keyring
+jaraco-classes==3.4.0 \
+    --hash=sha256:47a024b51d0239c0dd8c8540c6c7f484be3b8fcf0b2d85c13825780d3b3f3acd \
+    --hash=sha256:f662826b6bed8cace05e7ff873ce0f9283b5c924470fe664fff1c2f00f581790
+    # via keyring
+jaraco-context==6.1.2 \
+    --hash=sha256:bf8150b79a2d5d91ae48629d8b427a8f7ba0e1097dd6202a9059f29a36379535 \
+    --hash=sha256:f1a6c9d391e661cc5b8d39861ff077a7dc24dc23833ccee564b234b81c82dfe3
+    # via keyring
+jaraco-functools==4.6.0 \
+    --hash=sha256:880c577ec9720b3a052d5bc611fb9f2269b3d87902ef42440df443b88e443280 \
+    --hash=sha256:99e3dc0060c5cbe8fcd1cdb36258e2a65ca40f1566b2033b12abb1bb44dd3c30
+    # via keyring
+jeepney==0.9.0 \
+    --hash=sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683 \
+    --hash=sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732
+    # via
+    #   keyring
+    #   secretstorage
+keyring==25.7.0 \
+    --hash=sha256:be4a0b195f149690c166e850609a477c532ddbfbaed96a404d4e43f8d5e2689f \
+    --hash=sha256:fe01bd85eb3f8fb3dd0405defdeac9a5b4f6f0439edbb3149577f244a2e8245b
+    # via twine
+markdown-it-py==4.2.0 \
+    --hash=sha256:04a21681d6fbb623de53f6f364d352309d4094dd4194040a10fd51833e418d49 \
+    --hash=sha256:9f7ebbcd14fe59494226453aed97c1070d83f8d24b6fc3a3bcf9a38092641c4a
+    # via rich
+mdurl==0.1.2 \
+    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+    # via markdown-it-py
+more-itertools==11.1.0 \
+    --hash=sha256:48e8f4d9e7e5878571ecf6f2b4e57634f93cd474cc8cfbd2376f2d11b396e30d \
+    --hash=sha256:4b65538ae22f6fed0ce4874efd317463a7489796a0939fa66824dd542125a192
+    # via
+    #   jaraco-classes
+    #   jaraco-functools
+nh3==0.3.7 \
+    --hash=sha256:157ec1eb7a62f3d9a7badb8d82d89aa810e3e24e097eedfa481a25d0c8a99877 \
+    --hash=sha256:15f5fbf090f5c88d61c820e1fc1fceecb6520cca9fe85649c06b57ef9dc9ff62 \
+    --hash=sha256:18f4278ecd157d43cb35acd5aae9f35cfa79f546b4922bd86536adc0f6312102 \
+    --hash=sha256:19f288c938ec6eef1f5d2c6cab47838e71fef8097e1c1233802be5a6230ba086 \
+    --hash=sha256:4968fe8d2db97c6f047659bf46a449fd8ec377f44ebf3e0a1b96c0d3a333ae32 \
+    --hash=sha256:5ffdfcb9a686ffb12765376bcfb6b5b55728516d3c0ee317d29982381ded3df8 \
+    --hash=sha256:614dac4a4c36ad084e78447d16fe898dedd762e354a7ab9cda2984e82f67883d \
+    --hash=sha256:618e3059caf41ccdf5dcccb3fa9df4cf6e4efe23d1382a8bbfca272a8a4f8bfc \
+    --hash=sha256:6698a822132beedab80f131c08d8d0ac5a178ddeb488d02ca4b67716ecfac7af \
+    --hash=sha256:6c3aa50eb26e9228238271db9f983cbc3b006dfbfeca2d4dc34c33ddc6ac5ea5 \
+    --hash=sha256:6e4280115d44c3b278eef712a86748c1a723105cd79feec46952383117ab4e59 \
+    --hash=sha256:70f5ac8626e899a4bab0ef74ca2f5bd602f49c7b739e6e5026b4afc6d63dac42 \
+    --hash=sha256:71860d01c16f4d8c72e334e0674beb2b0899dbd0bf760de18932ef4390303848 \
+    --hash=sha256:808def0c8c07843e6e50dc84f532457bfa2cfd17417b219a5d9e7c773709331a \
+    --hash=sha256:874b7d67a067bd29a59223f6270fc30da4edd8e6d87fd219fc93bcbaa662c946 \
+    --hash=sha256:91a4dab4e94d9fc54b9f67b1adfb23e81fab7ab43f33c3b8c97be9aa38f789ba \
+    --hash=sha256:94fd6e59553fbb9ffd8ba71bbd5a54e3126ba01799a097ae30d5341d750bc6ac \
+    --hash=sha256:9b7279d43323a25225df23576af6594a16693f61431170848b8b2ac21ad4f174 \
+    --hash=sha256:bc42bb1193c1e28a1e74c2cabaca178e118a7103e8832699fef8a2b3e2496493 \
+    --hash=sha256:be53a4825585f701955cb9baf49f478f56eb81e20294329fe4bc689dd5dd81fa \
+    --hash=sha256:d56e76bd3cadb09b6b0cef364850811663734b348a25f5f587a2819c495367bd \
+    --hash=sha256:de2b2aab32ea303405debefdcfc58043d3e635fa3f67b9eb140d2b0e0c0d2563 \
+    --hash=sha256:e8fd1ab205258b29254f72db377d99e2c96aa7653ef3b015ccab0420b094b506 \
+    --hash=sha256:eae64328e46a25785535afcb6885b6f182ecaf5ee8c88f8c075422db8aacc65b \
+    --hash=sha256:f04b7d333b27f13ca439da3cf1c75c2fba34f104969f6ce4ac8e7079699c2f4a \
+    --hash=sha256:f266d3f1b3647449923a8e406524632220dd5d8b647078dfe45b885d33d10479 \
+    --hash=sha256:fd4a70efb45d5372174f718878eb7a35c12677626a63b2f103b23b833457dcac
+    # via readme-renderer
+packaging==26.3 \
+    --hash=sha256:94edc256424af38762eb31306eed28beb9f0efc50a8837492c9d6fd6004aed79 \
+    --hash=sha256:d7193f7c8e4e93f444fde0262bf90af30e16fa0ad0ad44cb553c87339b23cd1c
+    # via twine
+pycparser==3.0 \
+    --hash=sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29 \
+    --hash=sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992
+    # via cffi
+pygments==2.21.0 \
+    --hash=sha256:2363c69b61c4a97c838da3b130dcd6468f4848992b21a82f2a63ec34377137d9 \
+    --hash=sha256:610ca751c9bc2492b38eb9a38a7fbc93edbbb2d7182edaf34e66ae493dee5c8c
+    # via
+    #   readme-renderer
+    #   rich
+readme-renderer==46.0 \
+    --hash=sha256:af3e964914f6310a33ff67b72a4bdd940bed8d7c3bdecd2d14f40edf284bfe90 \
+    --hash=sha256:d0dae1f74bb273b534770cb4cccb6bb78735540afdb03c2146f4e19dcd412560
+    # via twine
+requests==2.34.2 \
+    --hash=sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0 \
+    --hash=sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed
+    # via
+    #   requests-toolbelt
+    #   twine
+requests-toolbelt==1.0.0 \
+    --hash=sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6 \
+    --hash=sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06
+    # via twine
+rfc3986==2.0.0 \
+    --hash=sha256:50b1502b60e289cb37883f3dfd34532b8873c7de9f49bb546641ce9cbd256ebd \
+    --hash=sha256:97aacf9dbd4bfd829baad6e6309fa6573aaf1be3f6fa735c8ab05e46cecb261c
+    # via twine
+rich==15.0.0 \
+    --hash=sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb \
+    --hash=sha256:edd07a4824c6b40189fb7ac9bc4c52536e9780fbbfbddf6f1e2502c31b068c36
+    # via twine
+secretstorage==3.5.0 \
+    --hash=sha256:0ce65888c0725fcb2c5bc0fdb8e5438eece02c523557ea40ce0703c266248137 \
+    --hash=sha256:f04b8e4689cbce351744d5537bf6b1329c6fc68f91fa666f60a380edddcd11be
+    # via keyring
+twine==7.0.0 \
+    --hash=sha256:85cdb29c518efef867360ae4acd4b0dfd61c8654a22fca08e6f8539f05022177 \
+    --hash=sha256:b854164df26db268af05f49aa5c0344b10e27a494343ff05b1e0bad3b135f5a7
+    # via -r .github/requirements/twine.in
+urllib3==2.7.0 \
+    --hash=sha256:231e0ec3b63ceb14667c67be60f2f2c40a518cb38b03af60abc813da26505f4c \
+    --hash=sha256:9fb4c81ebbb1ce9531cce37674bbc6f1360472bc18ca9a553ede278ef7276897
+    # via
+    #   id
+    #   requests
+    #   twine
+zipp==4.1.0 \
+    --hash=sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f \
+    --hash=sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602
+    # via importlib-metadata

.github/requirements/uv-tool.in

@@ -0,0 +1 @@
+uv==0.12.1

.github/requirements/uv-tool.txt

@@ -0,0 +1,23 @@
+# This file was autogenerated by uv via the following command:
+#    uv pip compile .github/requirements/uv-tool.in --generate-hashes --python-version 3.11 --python-platform linux -o .github/requirements/uv-tool.txt
+uv==0.12.1 \
+    --hash=sha256:04290ea4001dca31ac8a8324113a4930dccad69ce35dbf6eaae307d54880890d \
+    --hash=sha256:153ec0959a15397514438aefc1d7cd04235f335dd6bb53ea0f9e6e82c5a49f03 \
+    --hash=sha256:173ee216f17d89fc39f65339d311a53584fc7de4918d27c0f3c7edafabc6b54d \
+    --hash=sha256:1de49d9b04438f1ad2f41a1441dbbe19e230b94fca56d632818cfaed69e03bfc \
+    --hash=sha256:1e8fd95fe98768e29436ad57f9ef7b68dc294b7b9862ef63396af8b15ab85e6c \
+    --hash=sha256:27211df9b277f440dea438a4e525ba40250fb721ad39b8927eefc2d91f9aea15 \
+    --hash=sha256:29399e1e73b67ed24abe82bc971aa4eb8419c4de804784290f39cf681f0b51ce \
+    --hash=sha256:2e9b0b86e180abc5968b979c6e25203b32e85969abb5083ee1e8b88a5aa98a76 \
+    --hash=sha256:3bd5db002adc763aa8d277f5b44f8d6e3fd82d20f2e51225b0bbdae1badc7259 \
+    --hash=sha256:41b8fc2335f682312a1ca39a7b4abfd6af800992065c663582ca3e4d51cf9258 \
+    --hash=sha256:5bd04849dd5346517cc4e57b4b3aa0b01c67c423878260c04f5893a038fe25b6 \
+    --hash=sha256:6f7e72543264d2420ebb2ddc84696a751af2d6c5910046b7666589118f47292b \
+    --hash=sha256:71f86410264c69a3e8acd18171897dd8ab1a13350cf40f718e4def5db2b724be \
+    --hash=sha256:76d87de420213ca92fa403e87023c4c7c6956c6726c6b96d91c42cfe620173a3 \
+    --hash=sha256:9331dda0dc4990512c232f86e1d3a7b83c13f459777fcc2bd46030911b40eaaa \
+    --hash=sha256:b255ac23958e45f39f9c7a4cd65890df5ef46f539a3b14de03bd296bbba9cb60 \
+    --hash=sha256:bd02f2da212e6a983115dc64a6fc94e9256c2d60e056d6b669de0a6025aaec05 \
+    --hash=sha256:e35e0030480a8c3bf8ecd87ae4a6f6a224009e15e96a6fbb3634ac11ab75d582 \
+    --hash=sha256:ead7ad064f291a5df358c3ffa8ffab347a32bd5a75a6a068ca22254c2539a829
+    # via -r .github/requirements/uv-tool.in

.github/scripts/filter_checkov_skipped.py

@@ -0,0 +1,76 @@
+"""Drop checkov-suppressed results from its SARIF output before upload.
+
+checkov's SARIF exporter includes every evaluated check as an ordinary
+result, including ones it internally marked SKIPPED via an inline
+`# checkov:skip=` comment or a `checkov.io/skipN` resource annotation - it
+never uses SARIF's `suppressions` field, and never drops them. checkov's
+JSON output *does* correctly record which checks were skipped, so this
+cross-references the two: any SARIF result whose (check_id, file) pair
+appears in the JSON's skipped_checks is removed before GitHub ever sees it.
+
+Without this, every already-suppressed finding reopens as a brand new code
+scanning alert on every run, forever (see #6035/#6036, #6112-6115,
+#6128-6131 for the pattern this was chasing before this script existed).
+
+Usage: filter_checkov_skipped.py <json_path> <sarif_in_path> <sarif_out_path>
+"""
+
+import json
+import sys
+
+
+def path_suffix(path: str, segments: int = 2) -> str:
+    """Last N path segments, normalized to forward slashes, lowercased.
+
+    checkov's JSON file_path and SARIF artifactLocation.uri are relative to
+    different roots (the scanned directory vs. a temp helm-render dir), so
+    they can't be compared directly - but the last couple of segments
+    (e.g. "templates/service.yaml") are stable across both and specific
+    enough in practice to avoid cross-file collisions.
+    """
+    normalized = path.replace("\\", "/").strip("/")
+    return "/".join(normalized.split("/")[-segments:]).lower()
+
+
+def main() -> None:
+    json_path, sarif_in_path, sarif_out_path = sys.argv[1:4]
+
+    with open(json_path, encoding="utf-8") as f:
+        checkov_json = json.load(f)
+    if isinstance(checkov_json, dict):
+        checkov_json = [checkov_json]
+
+    skipped = set()
+    for block in checkov_json:
+        for check in block.get("results", {}).get("skipped_checks", []):
+            skipped.add((check["check_id"], path_suffix(check["file_path"])))
+
+    with open(sarif_in_path, encoding="utf-8") as f:
+        sarif = json.load(f)
+
+    removed = 0
+    for run in sarif.get("runs", []):
+        kept = []
+        for result in run.get("results", []):
+            rule_id = result.get("ruleId")
+            locations = result.get("locations") or [{}]
+            uri = (
+                locations[0]
+                .get("physicalLocation", {})
+                .get("artifactLocation", {})
+                .get("uri", "")
+            )
+            if (rule_id, path_suffix(uri)) in skipped:
+                removed += 1
+                continue
+            kept.append(result)
+        run["results"] = kept
+
+    with open(sarif_out_path, "w", encoding="utf-8") as f:
+        json.dump(sarif, f)
+
+    print(f"Removed {removed} checkov-suppressed result(s) from the SARIF before upload.")
+
+
+if __name__ == "__main__":
+    main()

.github/workflows/benchmark.yml

@@ -28,11 +28,37 @@ jobs:
 
           BENCHMARK_REAL_LIBS: "1"
         run: |
-          python -m pip install --upgrade pip
-          pip install -e .
-          pip install -r benchmarks/requirements.txt
-          python -m spacy download en_core_web_sm
-          pip install rdflib neo4j faiss-cpu torch pyarrow pdfplumber python-pptx openpyxl lxml python-docx beautifulsoup4 chardet langdetect
+          pip install -r .github/requirements/bootstrap.txt --require-hashes
+          # --no-deps + a hash-pinned install of the same base dependency set
+          # (rather than a bare `pip install -e .`) so every fetched package
+          # is hash-verified (Scorecard Pinned-Dependencies); the local
+          # editable install itself has nothing to hash.
+          #
+          # --no-deps only skips *runtime* dependency resolution - `-e .`
+          # still does a PEP 517 build, which by default creates an isolated
+          # build env and fetches [build-system] requires (setuptools,
+          # wheel) completely outside any hash checking. Install
+          # pep517-build.txt (pins that exact build-system.requires) first
+          # and pass --no-build-isolation so pip reuses those hash-verified
+          # copies instead of fetching its own.
+          pip install -r .github/requirements/pep517-build.txt --require-hashes
+          pip install --no-deps --no-build-isolation -e .
+          pip install -r .github/requirements/base-deps.txt --require-hashes
+          # NOTE: benchmarks/ does not currently exist in this repo (neither
+          # requirements.txt nor benchmarks_runner.py below), so this job
+          # already fails on any real invocation - pre-existing, unrelated to
+          # this pinning change. The `pip install -r benchmarks/requirements.txt`
+          # step that used to be here is dropped rather than fixed: there's
+          # nothing to hash-pin without knowing what that file should
+          # contain, and an unpinned install here would just re-trip
+          # Scorecard's Pinned-Dependencies check for no real benefit, since
+          # the job can't run to completion regardless.
+          #
+          # `python -m spacy download en_core_web_sm` fetches an unpinned,
+          # unhashed wheel from spacy-models' GitHub releases - replaced with
+          # a hash-pinned direct-URL install of the same 3.8.0 model (matches
+          # the spacy==3.8.15 pinned in base-deps.txt) via benchmark-extra.txt.
+          pip install -r .github/requirements/benchmark-extra.txt --require-hashes
 
       - name: Execute Benchmarks (Real Mode)
         env:

.github/workflows/ci.yml

@@ -33,21 +33,58 @@ jobs:
       - name: Install Explorer frontend dependencies
         working-directory: explorer
         run: npm ci
+      - name: Install Playwright Chromium
+        working-directory: explorer
+        run: npx playwright install --with-deps chromium
       - name: Test Explorer frontend
         working-directory: explorer
         run: |
           npm run test:graph-store
           npm run test:graph-workspace
           npm run test:plugin-registry
+          npm run test:deterministic-e2e
       - name: Build Explorer frontend
         working-directory: explorer
         run: npm run build
+      - name: Install Explorer backend test dependencies
+        run: |
+          # Run the deterministic backend path before the all-extras CI
+          # environment is installed.  The Explorer extra supplies the
+          # production API dependencies without importing optional vector
+          # providers such as Pinecone during test collection.
+          #
+          # --no-deps + a separate hash-pinned install (rather than the old
+          # `pip install -e ".[explorer]" pytest==9.1.1`) so every fetched
+          # package is hash-verified (Scorecard Pinned-Dependencies); the
+          # local editable install itself has nothing to hash.
+          # .github/requirements/explorer-extra-py311.txt is
+          # `uv pip compile pyproject.toml --extra explorer --python-version 3.11 --constraint requirements-ci.txt --generate-hashes`
+          # - regenerate it the same way if pyproject.toml's base/explorer
+          # deps change. Resolved specifically for this job's python 3.11
+          # (see the Dockerfile's explorer-extra-py313.txt for why this
+          # can't be shared with python 3.13: audioread needs extra
+          # standard-aifc/standard-sunau hashes only on 3.13+).
+          #
+          # --no-deps only skips *runtime* dependency resolution - `-e .`
+          # still does a PEP 517 build, which by default creates an isolated
+          # build env and fetches [build-system] requires (setuptools,
+          # wheel) completely outside any hash checking. Install
+          # pep517-build.txt (pins that exact build-system.requires) first
+          # and pass --no-build-isolation so pip reuses those hash-verified
+          # copies instead of fetching its own.
+          pip install -r .github/requirements/pep517-build.txt --require-hashes
+          pip install --no-deps --no-build-isolation -e .
+          pip install -r .github/requirements/explorer-extra-py311.txt --require-hashes
+          pip install -r .github/requirements/pytest-tool.txt --require-hashes
+      - name: Test deterministic Explorer backend path
+        run: |
+          pytest -q tests/explorer/test_explorer_deterministic_rendering_e2e.py
       - name: Install pinned Python dependencies
         run: |
-          pip install -r requirements-ci.txt
+          pip install -r requirements-ci.txt --require-hashes
       - name: Verify requirements-ci.txt is up to date
         run: |
-          pip install uv==0.12.1
+          pip install -r .github/requirements/uv-tool.txt --require-hashes
           # Re-resolve with the committed file as a constraint: upstream package
           # releases must NOT fail CI (deps only change when pyproject.toml
           # changes intentionally). Compare only version lines (pkg==ver),
@@ -58,10 +95,10 @@ jobs:
           diff \
             <(grep -E '^[a-zA-Z0-9._-]+==' requirements-ci.txt | sed 's/ \\$//') \
             <(grep -E '^[a-zA-Z0-9._-]+==' /tmp/requirements-ci-check.txt)
-      - run: pip install build
-      # wheel is build-time only (not in requirements-ci.txt) — install the
-      # same pinned version [build-system] declares so --no-isolation works.
-      - run: pip install wheel==0.48.0
+      # build is a dev-time dependency; wheel is build-time only (neither is
+      # in requirements-ci.txt) — install the same pinned versions
+      # [build-system] declares so --no-isolation works below.
+      - run: pip install -r .github/requirements/build-tools.txt --require-hashes
       - name: Build package (no isolation — pinned deps)
         run: python -m build --no-isolation
       - name: Verify Explorer frontend is packaged

.github/workflows/codeql.yml

@@ -10,13 +10,15 @@ on:
 
 permissions:
   contents: read
-  security-events: write
-  actions: read
 
 jobs:
   analyze:
     name: Analyze Python
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      security-events: write  # for github/codeql-action/upload-sarif below
+      actions: read           # for github/codeql-action/init's CodeQL bundle cache lookup
 
     steps:
       - name: Checkout repository
@@ -32,7 +34,7 @@ jobs:
       # meaningful state carried over from a failed attempt.
       - name: Initialize CodeQL (attempt 1)
         id: codeql-init-1
-        uses: github/codeql-action/init@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/init@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
         continue-on-error: true
         with:
           languages: python
@@ -42,7 +44,7 @@ jobs:
       - name: Initialize CodeQL (attempt 2)
         id: codeql-init-2
         if: steps.codeql-init-1.outcome == 'failure'
-        uses: github/codeql-action/init@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/init@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
         continue-on-error: true
         with:
           languages: python
@@ -52,17 +54,17 @@ jobs:
       - name: Initialize CodeQL (attempt 3)
         id: codeql-init-3
         if: steps.codeql-init-2.outcome == 'failure'
-        uses: github/codeql-action/init@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/init@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
         with:
           languages: python
           queries: security-and-quality
           config-file: .github/codeql/codeql-config.yml
 
       - name: Autobuild
-        uses: github/codeql-action/autobuild@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/autobuild@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/analyze@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
         with:
           category: "/language:python"
           upload: false
@@ -72,7 +74,7 @@ jobs:
         # Uploads results only when Default Setup is not active.
         # If Default Setup is still enabled, this step skips gracefully
         # instead of failing the workflow with HTTP 409.
-        uses: github/codeql-action/upload-sarif@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+        uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
         with:
           sarif_file: ${{ steps.codeql.outputs.sarif-output }}
           category: "/language:python"

.github/workflows/container-scan.yml

@@ -0,0 +1,75 @@
+name: Container Security Scan
+
+on:
+  push:
+    branches: [main]
+    # Mirrors .dockerignore's opt-in list exactly - anything not listed there
+    # can't reach the build context, so it can't change the built image.
+    paths:
+      - 'Dockerfile'
+      - '.dockerignore'
+      - 'pyproject.toml'
+      - 'README.md'
+      - 'LICENSE'
+      - 'MANIFEST.in'
+      - '.github/requirements/explorer-extra-py313.txt'
+      - '.github/requirements/pep517-build.txt'
+      - 'semantica/**'
+      - 'integrations/**'
+      - 'explorer/**'
+      - '.github/workflows/container-scan.yml'
+  schedule:
+    - cron: '30 2 * * 1'  # weekly, catches new CVEs published against the base image between pushes
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  scan:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      security-events: write  # for github/codeql-action/upload-sarif below
+    steps:
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+
+      - name: Build image
+        run: docker build -t semantica:scan .
+
+      # Run Trivy as a digest-pinned image rather than the aquasecurity/trivy-action
+      # marketplace wrapper: the aquasecurity GitHub org has an IP allow list on its
+      # API that 403s verify-action-pins.sh's live tag->SHA check from Actions-runner
+      # IPs, and this repo already treats Trivy's action pin as a known past target
+      # for tag-repointing (see the LiteLLM/Trivy 2026 incident note above). Pulling
+      # by sha256 digest from Docker Hub is immutable and verifiable independently of
+      # GitHub's API, so it sidesteps both problems at once instead of carving a skip
+      # exception into the pin verifier for an org already flagged as higher-risk.
+      #
+      # Report-only for now: this is Trivy's first run against this image, so we
+      # don't yet know the CRITICAL/HIGH baseline. Findings still land in the
+      # Security tab either way. Once triaged, add `--exit-code 1` (like
+      # Safety/Bandit-HIGH in security-scan.yml) to make it a hard gate.
+      - name: Scan image for vulnerabilities (Trivy)
+        run: |
+          docker run --rm \
+            -v /var/run/docker.sock:/var/run/docker.sock \
+            -v "$PWD:/output" \
+            aquasec/trivy@sha256:62b1e65e8869bc4b4c6aa4fa2b21595256c7c2f6018a9d9ad61caf87187c1969 \
+            image --format sarif --output /output/trivy-results.sarif \
+            --severity CRITICAL,HIGH --ignore-unfixed semantica:scan
+
+      - name: Upload Trivy SARIF
+        if: always()
+        uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
+        with:
+          sarif_file: trivy-results.sarif
+          category: trivy-container
+
+      - name: Generate SBOM (Syft)
+        if: always()
+        uses: anchore/sbom-action@3ad7283483fc7af8ff2b4ea19663c2d5ca935e26 # v0.24.2
+        with:
+          image: semantica:scan
+          format: spdx-json
+          output-file: semantica-sbom.spdx.json

.github/workflows/defender-for-devops.yml

@@ -28,12 +28,14 @@ on:
 
 permissions:
   contents: read
-  security-events: write
 
 jobs:
   MSDO:
     # currently only windows-latest is supported
     runs-on: windows-latest
+    permissions:
+      contents: read
+      security-events: write  # for github/codeql-action/upload-sarif below
 
     steps:
     - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
@@ -57,7 +59,7 @@ jobs:
         # avoiding the guardian.cmd/checkov exit-code bug in the MSDO wrapper.
         tools: eslint,templateanalyzer,terrascan
     - name: Upload results to Security tab
-      uses: github/codeql-action/upload-sarif@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+      uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
       with:
         sarif_file: ${{ steps.msdo.outputs.sarifFile }}
 
@@ -66,7 +68,7 @@ jobs:
         python-version: "3.12"
 
     - name: Install Checkov
-      run: python -m pip install checkov==3.3.1
+      run: pip install -r .github/requirements/checkov.txt --require-hashes
 
     - name: Run Checkov
       shell: pwsh
@@ -74,15 +76,31 @@ jobs:
         PYTHONUTF8: "1"
       run: |
         New-Item -ItemType Directory -Force reports | Out-Null
-        checkov --directory . --framework kubernetes helm dockerfile github_actions secrets bicep arm --soft-fail --output sarif --output-file-path reports/checkov.sarif
-        if (-not (Test-Path reports/checkov.sarif)) {
+        checkov --directory . --framework kubernetes helm dockerfile github_actions secrets bicep arm --soft-fail --output sarif --output json --output-file-path reports
+        if (-not (Test-Path reports/results_sarif.sarif)) {
           $sarif = Get-ChildItem -Path reports -Recurse -Filter *.sarif | Select-Object -First 1
           if ($null -eq $sarif) { throw "Checkov did not produce a SARIF file" }
-          Copy-Item $sarif.FullName reports/checkov.sarif
+          Copy-Item $sarif.FullName reports/results_sarif.sarif
+        }
+        if (-not (Test-Path reports/results_json.json)) {
+          $json = Get-ChildItem -Path reports -Recurse -Filter *.json | Select-Object -First 1
+          if ($null -eq $json) { throw "Checkov did not produce a JSON file" }
+          Copy-Item $json.FullName reports/results_json.json
         }
 
+    # checkov's SARIF exporter includes checks it internally marked SKIPPED
+    # (via the inline `# checkov:skip=` comments / `checkov.io/skipN`
+    # annotations already on the Helm chart) as ordinary un-suppressed
+    # results - it never uses SARIF's own `suppressions` field, so GitHub
+    # opens a fresh alert for the same already-suppressed finding on every
+    # single run (see #6035/#6036, #6112-6115, #6128-6131). checkov's JSON
+    # output does correctly record the skip, so cross-reference it here
+    # instead of re-dismissing the same alerts by hand forever.
+    - name: Filter checkov's own suppressed checks out of the SARIF
+      run: python .github/scripts/filter_checkov_skipped.py reports/results_json.json reports/results_sarif.sarif reports/checkov.sarif
+
     - name: Upload Checkov results to Security tab
-      uses: github/codeql-action/upload-sarif@ff2f1c621b7f889edc0d3c761ac2e6a3f8cdb0dd # v4
+      uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
       if: always()
       with:
         sarif_file: reports/checkov.sarif

.github/workflows/docs.yml

@@ -65,4 +65,4 @@ jobs:
 
       - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@cd2ce8fcbc39b97be8ca5fce6e763baed58fa128 # v5
+        uses: actions/deploy-pages@368f82528645a54fb793d4d04e342629a3f51346 # v5

.github/workflows/install-matrix.yml

@@ -0,0 +1,59 @@
+name: Install Matrix
+
+permissions:
+  contents: read
+
+on:
+  schedule:
+    - cron: '0 6 * * 1'  # weekly, catches upstream dependency breakage between releases
+  workflow_run:
+    # The Release workflow publishes the GitHub release *before* it uploads to
+    # PyPI (see release.yml), so triggering on `release: published` would race
+    # the PyPI upload and could pass by silently installing the prior version.
+    # workflow_run fires only after the whole Release workflow - including the
+    # PyPI publish step - has finished.
+    workflows: ['Release']
+    types: [completed]
+  workflow_dispatch:
+
+jobs:
+  verify-install:
+    if: github.event_name != 'workflow_run' || github.event.workflow_run.conclusion == 'success'
+    name: pip install semantica (${{ matrix.os }}, py${{ matrix.python-version }})
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ['3.9', '3.10', '3.11', '3.12']
+    steps:
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+
+      - name: Pin expected version for release-triggered runs
+        id: expected-version
+        if: github.event_name == 'workflow_run'
+        shell: bash
+        env:
+          EXPECTED_TAG: ${{ github.event.workflow_run.head_branch }}
+        run: |
+          expected="${EXPECTED_TAG#v}"
+          if [ -z "$expected" ]; then
+            echo "::error::Could not determine a release tag from the triggering workflow run (head_branch was empty)."
+            exit 1
+          fi
+          echo "constraint===$expected" >> "$GITHUB_OUTPUT"
+
+      - id: setup-semantica
+        uses: ./.github/actions/setup-semantica
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          version: ${{ steps.expected-version.outputs.constraint }}
+
+      - name: Smoke test import
+        shell: bash
+        run: |
+          python -c "
+          import semantica
+          print('semantica', semantica.__version__, 'installed and importable')
+          "

.github/workflows/release.yml

@@ -16,7 +16,7 @@ jobs:
       cancel-in-progress: false
     permissions:
       contents: write      # for the GitHub Release
-      id-token: write      # for PyPI Trusted Publishing (OIDC) and attestation signing
+      id-token: write      # for PyPI Trusted Publishing (OIDC), attestation signing, and Sigstore
       attestations: write  # for SLSA build provenance
     # If you add another job to this workflow, give it its own explicit
     # `permissions:` block rather than relying on the workflow-level default
@@ -39,11 +39,11 @@ jobs:
       # Install the pinned dependency set (with hashes) so the sdist/wheel
       # build runs against the same versions CI tests against.
       - name: Install pinned build dependencies
-        run: pip install -r requirements-ci.txt
-      - run: pip install build
-      # wheel is build-time only (not in requirements-ci.txt) — install the
-      # same pinned version [build-system] declares so --no-isolation works.
-      - run: pip install wheel==0.48.0
+        run: pip install -r requirements-ci.txt --require-hashes
+      # build is a dev-time dependency; wheel is build-time only (neither is
+      # in requirements-ci.txt) — install the same pinned versions
+      # [build-system] declares so --no-isolation works below.
+      - run: pip install -r .github/requirements/build-tools.txt --require-hashes
       - name: Build package (no isolation — pinned deps)
         run: python -m build --no-isolation
       - name: Verify Explorer frontend is packaged
@@ -63,11 +63,29 @@ jobs:
 
           print("Explorer frontend is packaged")
           PY
+      - name: Verify PyPI long-description will render
+        run: |
+          pip install -r .github/requirements/twine.txt --require-hashes
+          twine check dist/*
       - name: Attest build provenance
         uses: actions/attest-build-provenance@4d101475d8b20a2381f78447822ac1eab6504dd8 # v4
         with:
           subject-path: 'dist/*'
-      - uses: softprops/action-gh-release@3d0d9888cb7fd7b750713d6e236d1fcb99157228 # v3
+      # attest-build-provenance publishes to the GH attestations API only, which
+      # OpenSSF Scorecard's Signed-Releases check does not inspect - it looks for
+      # signature files attached as release assets. Sign here too so
+      # `dist/*.sigstore.json` bundles ship alongside the wheel/sdist on the
+      # GitHub Release itself.
+      - name: Sign artifacts with Sigstore
+        uses: sigstore/gh-action-sigstore-python@790bc6befb9d733738f18d8f895854b453640ec9 # v3.5.0
         with:
-          files: dist/*
+          inputs: |
+            dist/*.whl
+            dist/*.tar.gz
+      - uses: softprops/action-gh-release@efb35369e0ad2afab669f228072c1b0d510eae64 # v3.0.3
+        with:
+          files: |
+            dist/*.whl
+            dist/*.tar.gz
+            dist/*.sigstore.json
       - uses: pypa/gh-action-pypi-publish@dc37677b2e1c63e2034f94d8a5b11f265b73ba33 # release/v1

.github/workflows/scorecard.yml

@@ -0,0 +1,45 @@
+name: Scorecard supply-chain security
+
+permissions: read-all
+
+on:
+  branch_protection_rule:
+  schedule:
+    - cron: '30 1 * * 6'  # weekly
+  push:
+    branches: [main]
+
+jobs:
+  analysis:
+    name: Scorecard analysis
+    runs-on: ubuntu-latest
+    permissions:
+      security-events: write  # to upload SARIF results
+      id-token: write         # to publish results and get a badge
+      contents: read
+      actions: read           # to detect GitHub Actions workflows
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+        with:
+          persist-credentials: false
+
+      - name: Run analysis
+        uses: ossf/scorecard-action@2d1146689b8cda280b9bc96326124645441f03bc # v2.4.4
+        with:
+          results_file: results.sarif
+          results_format: sarif
+          publish_results: true
+
+      - name: Upload artifact
+        uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
+        with:
+          name: SARIF file
+          path: results.sarif
+          retention-days: 5
+
+      - name: Upload to code-scanning
+        uses: github/codeql-action/upload-sarif@cdf488f595d80d6e07e03d4674febd5ab45fa938 # v4
+        with:
+          sarif_file: results.sarif

.github/workflows/security-scan.yml

@@ -3,6 +3,7 @@ name: Security Scan
 on:
   schedule:
     - cron: '30 1 * * 1,4'  # Mon/Thu 7 AM IST
+  workflow_dispatch:
   push:
     branches: [main]
     paths-ignore:
@@ -44,46 +45,101 @@ jobs:
         
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          # Install the pinned dependency set FIRST so Safety scans Semantica's
-          # exact CI/release dependency tree (requirements-ci.txt is generated
-          # from pyproject.toml extras, so this covers the project's real deps).
-          pip install -r requirements-ci.txt
-          # Tooling AFTER the pinned set: installing safety/bandit/semgrep/jq
-          # first lets the pinned requirements overwrite their transitive deps
-          # (e.g. rich), which breaks the safety CLI at runtime.
-          pip install safety bandit semgrep jq
+          pip install -r .github/requirements/bootstrap.txt --require-hashes
+          # Install the pinned dependency set FIRST so pip-audit scans
+          # Semantica's exact CI/release dependency tree (requirements-ci.txt
+          # is generated from pyproject.toml extras, so this covers the
+          # project's real deps).
+          pip install -r requirements-ci.txt --require-hashes
+          # Tooling AFTER the pinned set: installing it first would let the
+          # pinned requirements overwrite the tooling's own transitive deps.
+          pip install -r .github/requirements/pip-audit.txt --require-hashes
+          pip install -r .github/requirements/security-scan-tools.txt --require-hashes
 
-      - name: Run Safety Check (Package Vulnerabilities)
+      - name: Run pip-audit (Package Vulnerabilities)
+        continue-on-error: true
         run: |
-          # NOTE: Safety 3.x repurposed --output to select a console format
-          # (json/text/screen/...), not a file path. Writing JSON to a file
-          # now requires --save-json; the previous `--output safety-report.json`
-          # usage was silently invalid and never produced a report.
-          safety check --save-json safety-report.json || true
+          # Keep publishing reports and the PR comment even when the audit
+          # gate fails. The final gate below preserves the failure status.
+          echo 'AUDIT_SCAN_STATUS=failed' >> "$GITHUB_ENV"
 
-          # Guard 1: fail loudly if Safety exited before writing a report at all
-          # (network error, API auth failure, tool crash). Without this check a
-          # missing or empty file causes jq to fall back to "0", making a broken
+          # Same dependency tree Safety used to scan, and the same tool and
+          # invocation already proven reliable in security.yml.
+          pip-audit -r requirements-ci.txt --format=json --output=pip-audit-report.json || true
+
+          # Guard 1: fail loudly if pip-audit exited before writing a report
+          # at all (network error, tool crash). Without this check a missing
+          # or empty file causes jq to fall back to "0", making a broken
           # scanner indistinguishable from a clean scan.
-          if [ ! -s safety-report.json ]; then
-            echo "::error::Safety scan produced no report (safety-report.json is missing or empty). Treating as failure — check for network errors, API auth failures, or Safety crashes in the logs above."
+          if [ ! -s pip-audit-report.json ]; then
+            echo "::error::pip-audit produced no report (pip-audit-report.json is missing or empty). Treating as failure — check for network errors or pip-audit crashes in the logs above."
+            exit 1
+          fi
+
+          # Guard 2: fail closed when the report doesn't have the shape the
+          # checks below assume: a non-empty dependencies array, each entry
+          # either carrying an array-valued vulns field or being a dependency
+          # pip-audit couldn't resolve/audit, which it reports as
+          # {"name": ..., "skip_reason": ...} with no vulns field at all
+          # (see pip_audit._format.json.JsonFormat._format_dep). That's a
+          # normal, documented report shape, not a malformed one — treating
+          # it as invalid would fail the whole job over a single unauditable
+          # package, the same kind of scan-unrelated CI break this migration
+          # away from Safety was meant to fix.
+          if ! jq -e '
+            (.dependencies | type == "array" and length > 0)
+            and all(.dependencies[]; type == "object" and ((.vulns | type == "array") or (.skip_reason | type == "string")))
+          ' pip-audit-report.json >/dev/null 2>&1; then
+            echo "::error::pip-audit report has an invalid dependency structure. Expected a non-empty dependencies array where every entry has either a vulns array or a skip_reason. Treating as failure."
             exit 1
           fi
 
           echo "Checking for package vulnerabilities..."
 
-          # No || echo "0" fallback: if jq fails (malformed JSON, missing key,
-          # vulnerabilities:null) VULNS will be empty or "null" so guard 2 below
-          # catches it rather than silently treating the broken report as zero.
-          VULNS=$(jq '.vulnerabilities | length' safety-report.json 2>/dev/null)
+          # Guard 2 above already confirmed pip-audit-report.json is valid
+          # JSON with a well-shaped dependencies array, so this count is
+          # always a plain non-negative integer.
+          SKIPPED=$(jq '[.dependencies[] | select(has("skip_reason"))] | length' pip-audit-report.json)
+          if [ "$SKIPPED" -gt 0 ]; then
+            echo "⚠️ pip-audit could not audit $SKIPPED dependencies (see pip-audit-report.json for skip_reason):"
+            jq -r '.dependencies[] | select(has("skip_reason")) | "  - \(.name): \(.skip_reason)"' pip-audit-report.json
+          fi
 
-          # Guard 2: ensure VULNS is a non-negative integer before the -gt
+          # Vulnerability IDs reviewed and accepted as non-actionable for this
+          # project. Empty for now: pip-audit's OSV-backed database doesn't
+          # currently carry either of the findings Safety used to flag here
+          # (cuda-toolkit CVE-2025-33228, torchvision CVE-2026-65918), so
+          # there's nothing to exclude. Left in place so a future finding can
+          # be added the same way without restructuring this step - see git
+          # history on this file for the reasoning behind past entries.
+          IGNORED_VULN_IDS=""
+
+          # Exported so the "Comment PR with Security Results" step below can
+          # apply the same exclusion list to the raw report - it reads
+          # pip-audit-report.json independently in JS, so without this the PR
+          # comment would show an accepted finding as live even though this
+          # gate correctly treats it as non-actionable.
+          echo "IGNORED_VULN_IDS=$IGNORED_VULN_IDS" >> "$GITHUB_ENV"
+
+          # No []? / || echo "0" fallback: if jq fails (malformed JSON) VULNS
+          # will be empty or "null" so Guard 3 below catches it rather than
+          # silently treating the broken report as zero.
+          # `.vulns // []` guards against skipped dependencies, which carry
+          # no vulns field at all (see the skip_reason handling above) -
+          # without the fallback, iterating `null[]` raises inside jq and
+          # this whole computation silently evaluates to empty.
+          VULNS=$(jq --arg ignored "$IGNORED_VULN_IDS" '
+            ($ignored | split(",") | map(select(length > 0))) as $ignore_list
+            | [.dependencies[] | (.vulns // [])[] | select(.id as $id | ($ignore_list | index($id)) | not)]
+            | length
+          ' pip-audit-report.json 2>/dev/null)
+
+          # Guard 3: ensure VULNS is a non-negative integer before the -gt
           # comparison. "null" (missing/null key) or "" (jq parse failure) would
           # cause bash's -gt to throw an arithmetic error and fall through to the
           # success branch — the same silent-pass bug as a missing file.
           if ! [[ "$VULNS" =~ ^[0-9]+$ ]]; then
-            echo "::error::Safety report exists but 'vulnerabilities' is missing or non-numeric (got: '${VULNS}'). The report may be malformed or Safety may have written an error-only JSON. Treating as failure."
+            echo "::error::pip-audit report exists but dependency vulnerabilities are missing or non-numeric (got: '${VULNS}'). The report may be malformed or contain an error-only JSON response. Treating as failure."
             exit 1
           fi
 
@@ -92,12 +148,18 @@ jobs:
             echo "CI will fail to prevent merging of vulnerable dependencies"
             echo ""
             echo "Vulnerability details:"
-            jq -r '.vulnerabilities[] | "- \(.package_name)==\(.analyzed_version): \(.vulnerability_id) (\(.CVE // "no CVE assigned"))"' safety-report.json || true
+            jq --arg ignored "$IGNORED_VULN_IDS" -r '
+              ($ignored | split(",") | map(select(length > 0))) as $ignore_list
+              | .dependencies[] as $dependency
+              | ($dependency.vulns // [])[] | select(.id as $id | ($ignore_list | index($id)) | not)
+              | "- \($dependency.name)==\($dependency.version): \(.id)"
+            ' pip-audit-report.json || true
             exit 1
           else
-            echo "✅ No security vulnerabilities found"
+            echo "✅ No actionable security vulnerabilities found${IGNORED_VULN_IDS:+ (ignored: $IGNORED_VULN_IDS)}"
+            echo 'AUDIT_SCAN_STATUS=passed' >> "$GITHUB_ENV"
           fi
-        
+
       - name: Run Bandit (Code Security Linter)
         run: |
           bandit -r semantica/ -f json -o bandit-report.json || true
@@ -135,17 +197,18 @@ jobs:
           fi
         
       - name: Upload Security Reports
+        if: always()
         uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7
         with:
           name: security-reports
           retention-days: 14
           path: |
-            safety-report.json
+            pip-audit-report.json
             bandit-report.json
             semgrep-report.json
         
       - name: Comment PR with Security Results
-        if: github.event_name == 'pull_request'
+        if: always() && github.event_name == 'pull_request'
         uses: actions/github-script@3a2844b7e9c422d3c10d287c895573f7108da1b3 # v9
         with:
           script: |
@@ -168,6 +231,12 @@ jobs:
               }
 
               const items = parse(data);
+              if (items === null) {
+                return [
+                  '### ' + title,
+                  '⚠️ Invalid report structure in ' + reportPath + ' — check the job logs.',
+                ].join('\n');
+              }
               if (items.length === 0) {
                 return [`### ${title}`, `✅ No findings.`].join('\n');
               }
@@ -184,14 +253,64 @@ jobs:
               return lines.join('\n');
             }
 
-            const safetySection = renderSection(
-              'Safety — dependency vulnerabilities',
-              'safety-report.json',
-              (data) => (data.vulnerabilities || []).map(
-                (v) => `- \`${v.package_name}==${v.analyzed_version}\`: ${v.vulnerability_id}` +
-                  (v.CVE ? ` (${v.CVE})` : '') + ` — ${v.advisory || 'no advisory text'}`
-              )
-            );
+            // Mirrors the shell step's own IGNORED_VULN_IDS (passed through
+            // $GITHUB_ENV) so an accepted, non-actionable CVE that the CI
+            // gate already excluded doesn't reappear here as a live finding -
+            // this reads the same raw, unfiltered pip-audit-report.json.
+            const ignoredVulnIds = (process.env.IGNORED_VULN_IDS || '')
+              .split(',')
+              .map((id) => id.trim())
+              .filter(Boolean);
+
+            // A dependency pip-audit couldn't resolve/audit is reported as
+            // {"name": ..., "skip_reason": ...} with no vulns field at all
+            // (see pip_audit._format.json.JsonFormat._format_dep) - that's a
+            // normal report shape, not a malformed one, so it must not be
+            // treated as an invalid dependency below.
+            const isSkipped = (dependency) => typeof dependency.skip_reason === 'string';
+
+            let skippedDeps = [];
+            try {
+              const auditData = JSON.parse(fs.readFileSync('pip-audit-report.json', 'utf8'));
+              skippedDeps = (auditData.dependencies || []).filter(
+                (dependency) => dependency && typeof dependency === 'object' && isSkipped(dependency)
+              );
+            } catch (e) {
+              // Unreadable/unparseable report - renderSection's own
+              // report-missing branch below surfaces this.
+            }
+
+            const pipAuditSection = renderSection(
+              'pip-audit — dependency vulnerabilities',
+              'pip-audit-report.json',
+              (data) => {
+                if (
+                  !Array.isArray(data.dependencies) ||
+                  data.dependencies.length === 0 ||
+                  data.dependencies.some(
+                    (dependency) =>
+                      !dependency ||
+                      typeof dependency !== 'object' ||
+                      (!Array.isArray(dependency.vulns) && !isSkipped(dependency))
+                  )
+                ) {
+                  return null;
+                }
+
+                return data.dependencies.flatMap((dependency) =>
+                  (dependency.vulns || [])
+                    .filter((vulnerability) => !ignoredVulnIds.includes(vulnerability.id))
+                    .map(
+                      (vulnerability) => `- \`${dependency.name}==${dependency.version}\`: ${vulnerability.id}` +
+                        (vulnerability.fix_versions?.length ? ` (fixed by ${vulnerability.fix_versions.join(', ')})` : '')
+                    )
+                );
+              }
+            ) + (ignoredVulnIds.length
+              ? `\n\n_Excluded as accepted, non-actionable findings: ${ignoredVulnIds.join(', ')} — see the workflow file's inline comments for why._`
+              : '') + (skippedDeps.length
+              ? `\n\n_Could not be audited: ${skippedDeps.map((d) => `\`${d.name}\` (${d.skip_reason})`).join(', ')}_`
+              : '');
 
             const banditSection = renderSection(
               'Bandit — HIGH-severity code issues',
@@ -212,7 +331,7 @@ jobs:
             const comment = [
               '# 🔒 Security Scan Results',
               '',
-              safetySection,
+              pipAuditSection,
               '',
               banditSection,
               '',
@@ -222,7 +341,7 @@ jobs:
               '',
               '*This security scan runs automatically on source-code PRs and bi-weekly (skipped for doc/markdown-only changes).*',
               '',
-              '📊 **Security Policy**: CI fails on Safety vulnerabilities and Bandit HIGH-severity findings. Semgrep findings above are informational and do not block merge.',
+              '📊 **Security Policy**: CI fails on pip-audit vulnerabilities and Bandit HIGH-severity findings. Semgrep findings above are informational and do not block merge.',
             ].join('\n');
 
             try {
@@ -237,3 +356,11 @@ jobs:
               console.log('⚠️ Could not post security comment:', error.message);
               console.log('📋 Security scan results saved to artifacts');
             }
+
+      - name: Enforce Audit Gate
+        if: always()
+        run: |
+          if [ "${AUDIT_SCAN_STATUS:-failed}" != "passed" ]; then
+            echo "::error::pip-audit scan failed. See the pip-audit output and uploaded reports above."
+            exit 1
+          fi

.github/workflows/security.yml

@@ -1,42 +0,0 @@
-name: Security
-
-on:
-  schedule:
-    - cron: '0 0 * * 1'
-  workflow_dispatch:
-  pull_request:
-    branches: [main]
-    paths:
-      - 'pyproject.toml'
-      - 'requirements-ci.txt'
-      - '.github/workflows/security.yml'
-
-permissions:
-  contents: read
-
-jobs:
-  audit:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
-      - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7
-        with:
-          python-version: '3.11'
-      # Upgrade first: actions/setup-python's baked-in setuptools has been
-      # behind known-vulnerable floors before (e.g. PYSEC-2026-3447 /
-      # setuptools 75.1.0), so don't trust the preinstalled one.
-      - run: python -m pip install --upgrade pip setuptools
-      # Audit the pinned dependency set (requirements-ci.txt is compiled from
-      # pyproject.toml with --extra all — the same coverage as the [all]
-      # extra, minus the Linux-only gpu set — so this keeps scan parity with
-      # CI/release builds without a time-dependent resolution). This is the
-      # fix for PYSEC-2024-38 (#869): the bare-env job never had fastapi or
-      # python-multipart installed to look at.
-      - run: pip install -r requirements-ci.txt
-      # PR runs gate on findings, since they're scoped to actual
-      # pyproject.toml changes under review. The schedule/workflow_dispatch
-      # runs stay non-blocking until a full pass over pre-existing findings
-      # across the whole [all] tree has been done.
-      - run: pip install pip-audit
-      - run: pip-audit -r requirements-ci.txt
-        continue-on-error: ${{ github.event_name != 'pull_request' }}

CHANGELOG.md

@@ -9,6 +9,37 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+
+- **Salesforce ingestor** (#1240) by @Sameer6305
+  - New `SalesforceConnector` / `SalesforceData` / `SalesforceIngestor` (`semantica.ingest`, lazy export), following the same Connector + Data + Ingestor pattern already used for Snowflake/Databricks/SAP
+  - Auth covers both landscapes Salesforce actually uses: username + password + security token (SOAP login), session_id + instance_url (reusing an existing session), and username + consumer_key + private key (JWT Bearer); production and sandbox are selected via `domain`, and credentials can come from environment variables. Credential material is never intentionally written to logs, exceptions, or `repr()`
+  - `ingest_sobject()`, `ingest_query()`, `list_sobjects()`, `get_sobject_schema()`, `export_as_documents()` against standard sObjects, custom objects (`__c`), custom metadata (`__mdt`), platform events (`__e`), namespaced objects, and relationship-field traversal (e.g. `Owner.Name`); pagination follows `nextRecordsUrl`/`query_more()` and stops once a caller's `limit` is satisfied
+  - New `pip install semantica[db-salesforce]` extra (`simple-salesforce>=1.12.0`)
+  - New `tests/test_salesforce_ingestor.py`
+  - Docs: `docs/integrations/salesforce.md`
+
+- **`ErasureCoordinator` completes the erasure workflow `purge_node()` only starts — the graph node was removed while the same content survived verbatim in `AgentMemory` and as an embedding** (closes #1018) by @pravit-amp
+  - New `semantica/context/erasure.py`, exporting `ErasureCoordinator` and `ErasureReceipt` from `semantica.context`. `purge_node()`/`purge_edge()` (#957) are graph-scope by design and their changelog entry documents this gap explicitly; the changelog also names GDPR Article 17 as the motivation, and an Article 17 erasure that removes the node while the content stays retrievable by similarity search is not an erasure — it is worse than not offering one, because `purge_node()` returns `True` and writes a tombstone attesting the content is gone
+  - The coordinator **composes** the existing public APIs — nothing in `context_graph.py` or `agent_memory.py` changes behaviorally, and `ContextGraph` keeps its documented graph-scope contract rather than acquiring references to `AgentMemory`/`vector_store` that would invert the dependency
+  - `erase_entity(entity_id, reason=..., at=..., vector_ids=...)` returns an `ErasureReceipt`; `erase_entities([...])` returns one receipt per entity, in order, so one entity's failure does not stop the rest
+  - **Honest partial reporting is the point.** Each store reports one of five statuses — `erased`, `not_found`, `not_configured` (store never bound; normal), `unsupported` (store cannot delete at all; retrying will not help), `failed` — and `receipt.complete` is `False` when any store reports `unsupported`/`failed`, with `receipt.incomplete_stores` naming them. A receipt reading `graph: erased, memory: 14 erased, vectors: unsupported on faiss` is actionable; a bare `True` is a compliance liability
+  - **Erasure runs outward-in: vectors → memory → graph.** The graph tombstone is the durable attestation that an erasure happened, so writing it first would let a crash mid-cascade leave a record claiming more than occurred. Erasing the graph last means a partial failure leaves the node present and the receipt incomplete — recoverable and honest; the reverse is neither
+  - **Partial failure is a result, not an exception**: a store that raises is recorded as `failed` (with the exception type) and the remaining legs still run, rather than aborting into a half-erased state with no record of which half
+  - **The memory sweep cannot be silently truncated.** `find_by_entity(entity_id, limit=10)` returned `results[:limit]`, so the obvious hand-rolled cascade erases the first ten items and reports success — an erasure check computed from a page already truncated by the very `limit` it was called with. The coordinator sweeps in pages until dry (deleting as it goes, so the next page is the remainder) rather than passing one large number that is only correct until someone exceeds it, then **re-queries once after the sweep** and reports `failed` with the residual count if anything survived. It also stops rather than spinning if `batch_delete` reports no progress on a non-empty page. Note `find_by_entity` returns items keyed `memory_id`, not `id`
+  - **`unsupported` vector backends are detected by probing, not by calling and catching.** `faiss_store.py`, `milvus_store.py` and `weaviate_store.py` expose no delete at all (FAISS cannot remove from a flat index without a rebuild), while the `VectorStore` facade declares `delete_vectors()` for *every* backend and only raises `NotImplementedError` once called — so probing the facade alone cannot tell a deletable backend from a delete-less one, and the coordinator looks at the backend it wraps. Probing also keeps a missing method distinguishable from an `AttributeError` raised *inside* a working one, which is exactly where guessing wrong produces a false clean bill of health. `NotImplementedError` at call time is still caught and reported as `unsupported`; a store returning `False` is reported as `failed`
+  - Backends are reached under either supported name — `delete_vectors(ids)` (pinecone/qdrant) or `delete(ids)` (pgvector/sqlite-vec) — and the receipt records which was used
+  - `vector_store` defaults to `memory.vector_store` when a memory is supplied, stays overridable for deployments binding a store the memory does not own, and accepts `False` to disable the vector leg. Vectors owned by memory items are removed by the memory leg's own `delete_memory()` cascade; the explicit vector leg covers entity-keyed embeddings written by something other than `AgentMemory`
+  - The receipt's `erased_at` is normalized through `ContextGraph`'s own temporal normalizer, so the receipt and the tombstone written by the same erasure cannot disagree about when it happened; an unparseable `at` is rejected before any store is touched rather than half way through the cascade
+  - `purge_node()`'s docstring now points at the coordinator, so callers reading the graph-scope caveat find the thing that completes the workflow
+  - New `tests/context/test_erasure_coordinator.py`: 48 tests against **real** `ContextGraph`/`AgentMemory` instances rather than mocks — the bug lives in the interaction between them, so mocking it away would test nothing. Covers the 25-items-on-one-entity regression that fails against a naive single `find_by_entity()` call, all three vector-backend shapes (`delete_vectors`/`delete`/neither) plus the facade-over-delete-less-backend shape, residual/no-progress/no-identifier memory failures, partial failure continuing the cascade, idempotency, receipt serialization, and `at` normalization
+  - Full `tests/context/` suite: 738 passed
+  - **Fixed during review** (Qodo): `erase_entity()` resolved `erased_at` up front but passed the caller's original `at` down to `purge_node()`, so on the default `at=None` path the coordinator and the graph each took their own `now()` and the receipt attested to a different instant than the tombstone it points at — breaking the one invariant this module states most loudly. The resolved timestamp is now passed to the graph. The existing test passed only because it supplied an explicit `at`, which hides the drift; a regression test now covers the `at=None` path that callers actually use
+  - **Fixed during review** (Qodo): the vectors leg treated any return value other than the literal `False` as success, but no in-repo backend returns a bool — Qdrant returns `{"status": <UpdateStatus>}` and Pinecone `{"deleted": True}`, so every dict was read as a success and the backend's own account of the delete was discarded. Delete results are now interpreted by shape (bool, dict with explicit failure markers, `None` for a void method, anything else at face value) and the backend payload is recorded in the receipt as `backend_result`, stringified so the receipt stays JSON-serializable as the audit record it is meant to be. Bool markers are matched by identity so a `0` count is not read as `False`, and string markers match as substrings so an enum rendering as `"UpdateStatus.FAILED"` is not read as a success
+  - **Fixed during review** (Qodo): the constructor's "at least one store" guard used `not vector_store`, rejecting a valid store whose `__bool__`/`__len__` makes an empty instance falsey, and reporting `vector_store=None` in the error when an object had been passed; it now distinguishes `None` (absent) from `False` (deliberately disabled) from any other value (provided), and echoes what it actually received
+  - **Fixed during review** (Qodo): `at` annotations accepted only `str`/`datetime` while the shared `ContextGraph` normalizer they delegate to also takes epoch seconds; widened to `int`/`float` with the docstrings updated, so the coordinator no longer advertises less than the graph API it wraps
+  - **Known limitation, unchanged by this PR**: erasure still cannot be *completed* on FAISS/Milvus/Weaviate — `delete_vectors()` is declared on the `VectorStore` facade (`vector_store.py:786`) but not implemented across the backend set, under at least three different names. That is worth its own issue; the coordinator ships reporting `unsupported` and starts reporting `erased` for those backends once it is fixed, with no API change here
+
 ## [0.6.7] - 2026-08-28
 
 ### Added
@@ -128,6 +159,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   - **Also fixed, on the JSON-LD paths**: the first fix covered the Turtle, N-Triples and RDF/XML serializers, and left both JSON-LD writers interpolating the entity's own text into `f"semantica:entity/{text}"` and the endpoints into `f"semantica:rel/{source}_{target}"`. Three consequences, all live in 0.6.5: an entity whose text contained a space produced an invalid IRI, and a JSON-LD parser dropped that node in full rather than reporting it, so the entity disappeared from the export; every relationship carrying `source`/`target` rather than `source_id`/`target_id` minted the identical `semantica:rel/_`, collapsing all of them onto one node whose types and endpoints merged; and the JSON-LD `@id` disagreed with the Turtle IRI for the same entity, so the two serializations of one knowledge graph were two different graphs. Both JSON-LD writers now use `mint_entity_iri`/`mint_relationship_iri`, and `JSONExporter.export_entities`/`export_relationships` declare the `semantica` prefix their `@context` was already writing `semantica:entities` against — without it a processor reads that as an IRI in the scheme `semantica`, which is the original #1101 defect on a third path
   - `tests/export/test_jsonld_iri_minting.py` parses each export with a real JSON-LD processor and asserts the entity survives, the relationships stay distinct, no term expands into the `semantica` scheme, and the JSON-LD `@id` equals the Turtle IRI
   - 236 export and ontology tests pass
+- **`semantica.evals` runner gains per-metric objectives** (#1091)
+  - `evaluate()` now accepts `config={"<evaluator>": {"objective": {"direction": "maximize"|"minimize", "threshold": X}}}` to override the evaluator's default pass verdict with a threshold; `{"objective": {"expect": bool}}` expresses a Boolean expectation
+  - `minimize` requires a `threshold` — omitting it or setting it to `None` raises `ValueError`; `maximize` without a threshold is a no-op (the evaluator's own verdict stands); `expect` cannot be combined with `direction`/`threshold`; invalid config raises `ValueError` before any evaluator runs
+  - Error metrics are never affected by objectives (error wins over fail)
+  - Backward compatible: no `objective` key → existing behavior unchanged
+  - New tests in `tests/evals/test_runner.py::TestObjective`
+- **`semantica.evals` is now a fully implemented evaluation module** (was a "Coming Soon" stub in the package layout)
+  - `evaluate(cases, evaluators, config=None, target_fn=None)` runner with per-case `pass`/`fail`/`error` status and an aggregate `pass_rate`, using a registry of named evaluators (`list_evaluators()`)
+  - 10 built-in evaluators: `exact_match`, `regex_match`, `numeric_range`, `temporal_range`, `length_range`, `keyword_check`, `levenshtein` (edit-distance similarity), `rouge` (in-house token F1, no new dependencies), `llm_as_judge` (lazy: caller-supplied `judge_fn`), and `decision_scores` (composite over `semantica.context.Decision`)
+  - `decision_scores` validates field-level (expected outcome, confidence bounds, non-empty maker/reasoning/scenario) and governance-level (provenance record presence; opt-in `PolicyEngine.check_compliance`) checks, coercing dict inputs via `Decision(**actual)` and never crashing on malformed input; an interface slot for causal-chain/embedding checks is reserved and raises `NotImplementedError` (V2)
+  - `__version__` is `0.1.0`, and the module ships a usage guide at `semantica/evals/usage.md` with worked import/run/interpret examples
+  - `semantica.evals` is reachable through the root package lazy module proxy (`semantica.evals`)
+  - 99 unit tests in `tests/evals/` covering every evaluator, registry errors, runner aggregation, decision coercion, and per-metric objectives; `python -m pytest tests/evals -q` → 99 passed
 
 - **First-class CrewAI integration** (#988, closes #962) by @Shindevrp
   - New `pip install semantica[crewai]` extra (`crewai>=0.80.0`) — crewai core provides `BaseTool`/`BaseKnowledgeSource`, so `crewai-tools` is intentionally not included, and the extra is intentionally **not** part of the `all` bundle: crewai hard-requires `chromadb~=1.1.0`, which is affected by the unpatched pre-auth code-injection CVE-2026-45829 (see `integrations/crewai/README.md`)
@@ -213,6 +257,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
+- **RETE engine matched every fact against every rule — `AlphaNode._matches()` and `BetaNode._can_join()` were placeholder stubs that always returned `True`** (closes #300)
+  - `semantica/reasoning/rete_engine.py` shipped a Rete network whose per-condition alpha test and cross-condition beta join were both `return True` stubs, so `match_patterns()` fired every rule for every fact regardless of predicate, arity, or shared-variable consistency
+  - New module-level `unify_condition()` reuses the regex-based approach from `Reasoner._match_pattern()`: a condition pattern like `Person(?x)` / `Parent(?x, ?y)` is compiled against a fact's `predicate(arg, ...)` string, `?var` becomes a named capture group, and a variable seen twice within one condition (e.g. `Loves(?x, ?x)`) becomes a backreference, so it only unifies when both positions hold the same value. Returns the bindings dict or `None`
+  - Reworked propagation to carry partial-match **tokens** instead of bare facts: a new `Token` dataclass bundles the accumulated `facts` with the consistent `bindings`. `AlphaNode` emits a single-fact token per match; `BetaNode.join()` merges a left token with a right token, concatenating their facts in condition order and returning the merged token only when shared variables agree (conflicting values → `None`, no join). Terminal activations carry the full fact list and accumulated bindings through to the emitted match
+  - This fixes a P1 chained-join defect: rules with three or more conditions (e.g. `Person(?x)`, `Parent(?x, ?y)`, `Located(?y, ?z)`) previously lost bindings and accumulated wrong facts at the third join, and a conflicting third condition could spuriously fire. Beta nodes now keep both `left_tokens` and `right_tokens` memories and join each new token against every token on the opposite side, so deep chains stay binding-consistent and third-level conflicts are correctly suppressed
+  - Fixed an adjacent network-topology bug surfaced by the above: newly created beta nodes were never appended to their input nodes' `children`, so tokens could not propagate; propagation was reworked to support chained joins and to thread bindings end-to-end
+  - Reconciled with the rule-actions/provenance layer (#1096) merged after this fix was opened: `execute_matches()` still dedupes and fires `Rule.actions`/legacy `handler` through a bound `Reasoner` via `_make_activation_key`, now sourced from the Token model's own `bindings` instead of the interim `_bindings_for_rule()` regex re-extraction, which is removed as redundant
+  - New `tests/reasoning/test_rete_engine.py`: `unify_condition` unit cases (single/multi variable, literal args, predicate mismatch, repeated-variable equality), alpha match/reject, beta consistent-join vs conflict-reject, end-to-end rules (single-condition fires only the matching fact; multi-condition join fires only on consistent bindings), and a `TestThreeConditionChain` suite (valid three-condition match, third-level conflict suppression, insertion-order independence, `Match.facts` complete and in condition order, multiple left tokens joining one right fact, parity against `Reasoner._match_rule()`, and `reset()` clearing all token memory)
+
 - **KG provenance tests asserted on generated ID strings instead of stored records, and `kg_provenance.py` was missed by the `utcnow` sweep** (closes #946) by @pravit-amp
   - The KG workflow and integration suites checked that a tracker call returned an ID matching a prefix (`assert cent_id.startswith("centrality_")`) without ever reading the record back, so an ID generator that returned a well-formed string and wrote nothing would have passed. Worse, some of those calls named tracker methods that do not exist anywhere in `semantica/` (`track_layer_analysis`, `track_centrality_score`), so the assertions were satisfied with no real interaction behind them
   - Those tests now read provenance back through `get_provenance()` and assert on algorithm metadata, and call the methods that actually persist records. Verified by mutation rather than by a green run alone: neutering the manager's storage write (`self.storage.store(...)` → no-op) fails 10 tests

CITATION.cff

@@ -0,0 +1,20 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+title: "Semantica: Graph-Native Infrastructure for Context and Accountable AI Systems"
+type: software
+authors:
+  - name: "Semantica"
+repository-code: "https://github.com/semantica-agi/semantica"
+url: "https://getsemantica.ai"
+license: MIT
+version: 0.6.7
+date-released: 2026-08-28
+keywords:
+  - knowledge-graph
+  - context-graph
+  - ai-agents
+  - llm
+  - decision-intelligence
+  - provenance
+  - explainability
+  - graph-rag

Dockerfile

@@ -1,5 +1,5 @@
 # syntax=docker/dockerfile:1
-FROM node:26-alpine AS frontend-builder
+FROM node:26-alpine@sha256:2d984a15c9b54fd0aeb608b8e0d0d83529eb34d2966db27a1fb4f1edc3d298a3 AS frontend-builder
 
 WORKDIR /app
 COPY explorer/package*.json ./explorer/
@@ -9,7 +9,18 @@ RUN npm ci
 COPY explorer/ ./
 RUN mkdir -p /app/semantica && npm run build
 
-FROM python:3.13-slim AS runtime
+# CVE-2026-14456 (OpenSSL QUIC-server DoS, flagged against this base image's
+# openssl/libssl3t64/openssl-provider-legacy): the Debian fix
+# (3.5.7-1~deb13u2) is only in trixie-proposed-updates as of this writing,
+# not yet promoted to trixie-security, so there's no package to pin here
+# today. Deliberately NOT running `apt-get upgrade` to chase it - that
+# breaks build reproducibility (terrascan AC_DOCKER_0052) and still
+# wouldn't reach a proposed-updates-only package. Once Debian ships the fix
+# and rebuilds this tag, the docker Dependabot ecosystem in
+# .github/dependabot.yml opens a PR bumping the digest pin above. Also: this
+# image only serves plain HTTP via uvicorn and never opens a QUIC listener,
+# so the bug isn't reachable here regardless.
+FROM python:3.13-slim@sha256:7ce4b6dfe35e55397b7cda544f8a13f191b7ae28dc5aad71fe664dbc9bc2623f AS runtime
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
@@ -22,12 +33,35 @@ WORKDIR /app
 RUN groupadd --system semantica \
     && useradd --system --gid semantica --home-dir /app --shell /usr/sbin/nologin semantica
 
-COPY pyproject.toml README.md LICENSE MANIFEST.in ./
+COPY pyproject.toml README.md LICENSE MANIFEST.in \
+     .github/requirements/explorer-extra-py313.txt .github/requirements/pep517-build.txt ./
 COPY semantica/ ./semantica/
 COPY integrations/ ./integrations/
 COPY --from=frontend-builder /app/semantica/static ./semantica/static
 
-RUN pip install --no-cache-dir ".[explorer]" \
+# explorer-extra-py313.txt is `uv pip compile pyproject.toml --extra explorer
+# --python-version 3.13 --constraint requirements-ci.txt --generate-hashes`
+# (see ci.yml's explorer-extra-py311.txt for the CI counterpart, resolved
+# for CI's python 3.11 instead - the two aren't interchangeable: audioread
+# (via librosa) needs standard-aifc/standard-sunau only on python>=3.13,
+# since aifc/sunau left stdlib there, so a 3.11-resolved lockfile is
+# missing hashes pip needs on this image's actual 3.13 interpreter and
+# --require-hashes fails outright rather than silently under-pinning).
+# Every fetched package is hash-verified (Scorecard Pinned-Dependencies)
+# and pinned to the same versions CI audited, e.g. msgpack==1.2.1 and
+# setuptools==84.0.0 (which also replaces the base image's vulnerable
+# 70.3.0, CVE-2025-47273 - nothing else in the tree pulls a newer copy).
+# --no-deps on the local package itself: it's our own source tree, not a
+# fetch, so there's nothing to hash-pin there - but `pip install .` still
+# does a PEP 517 build, which by default creates an *isolated* build env
+# and fetches [build-system] requires (setuptools, wheel) completely
+# outside any hash checking. pep517-build.txt pins that exact
+# build-system.requires; installing it first and passing
+# --no-build-isolation makes pip reuse those hash-verified copies instead
+# of fetching its own.
+RUN pip install --no-cache-dir -r explorer-extra-py313.txt -r pep517-build.txt --require-hashes \
+    && pip install --no-cache-dir --no-deps --no-build-isolation . \
+    && rm -f explorer-extra-py313.txt pep517-build.txt \
     && chown -R semantica:semantica /app
 
 USER semantica

GROWTH.md

@@ -0,0 +1,131 @@
+# Growth & Distribution Playbook
+
+North star: **10,000 developers who actually use Semantica in real projects**, not a raw PyPI download number. Downloads are a lagging indicator of distribution, not a target to optimize directly.
+
+```
+GitHub stars → Website visitors → PyPI installs → Weekly active users → Production deployments → Enterprise customers
+```
+The last two matter far more than the download count.
+
+## Guardrails — do not do this
+
+- No fake/looping CI jobs that repeatedly `pip install semantica` purely to inflate the graph. It's detectable, it produces zero real users, and it damages credibility with anyone doing diligence (investors, enterprise buyers, security reviewers).
+- No package-splitting purely to multiply install counts — only split into `semantica-*` packages when there's a real architectural reason.
+- No meaningless Docker pulls or notebook launches with no real content behind them.
+- Every item below should get someone from "installed it" to "used it for something real." If a channel can't do that, it's not worth building.
+
+## 30-day priority sprint
+
+Ordered by leverage-to-effort ratio; do these first.
+
+| # | Initiative | Target |
+| - | ---------- | ------ |
+| 1 | ✅ GitHub Actions example + reusable `setup-semantica` composite action + install-matrix badge | done |
+| 2 | Google Colab notebooks | 10 |
+| 3 | Docker images (RAG, Graph, Agent, API) | 4-5 |
+| 4 | Hugging Face Spaces demos | 3-4 |
+| 5 | LangChain integration + example | 1 |
+| 6 | LlamaIndex integration + example | 1 |
+| 7 | Vector/graph DB integrations (Qdrant, Weaviate, Neo4j) | 3 |
+| 8 | MCP server + example | 1 (already have `mcp/` — package as a distributable example) |
+| 9 | Production-quality starter repos (FastAPI, Streamlit, Gradio) | 3 |
+| 10 | `awesome-rag` / `awesome-llm` / `awesome-knowledge-graph` list submissions | 3+ PRs |
+
+Push everything through: GitHub → Discord (`sV34vps5hH`) → X (`@BuildSemantica`) → GitHub Discussions → Reddit → Hacker News → relevant newsletters.
+
+## Full channel checklist
+
+### CI/CD (highest-intent distribution — installs tied to real pipelines)
+
+- [x] GitHub Actions example in `examples/ci/github-actions.yml`
+- [x] Reusable composite GitHub Action — [`.github/actions/setup-semantica`](.github/actions/setup-semantica/action.yml), modeled on `actions/setup-python`; usable by any repo as `uses: semantica-agi/semantica/.github/actions/setup-semantica@main`
+- [x] "pip install" status badge in the README, backed by [`.github/workflows/install-matrix.yml`](.github/workflows/install-matrix.yml) — verifies the *published* package installs cleanly on Ubuntu/macOS/Windows across Python 3.9-3.12, weekly + on every release
+- [x] GitLab CI template — `examples/ci/gitlab-ci.yml`
+- [x] CircleCI template — `examples/ci/circleci-config.yml`
+- [ ] Jenkins, Azure DevOps, Bitbucket Pipelines, Buildkite, Travis CI equivalents
+
+### Release pipeline hardening (already had Trusted Publishing/OIDC + SLSA attestation — this rounds it out to match top-tier OSS release practice)
+
+- [x] `twine check` gate in `.github/workflows/release.yml` before publish — catches a broken PyPI long-description render before it goes live instead of after (a malformed README on the live PyPI page is a silent conversion killer)
+- [x] `CITATION.cff` (see Academic & research below)
+- [x] OpenSSF Scorecard (see Discoverability below)
+- [ ] Considered and deliberately skipped: Release Drafter / auto-generated changelogs — this repo hand-curates `CHANGELOG.md` with far more detail (PR numbers, contributors, phase-1 limitations) than a bot would produce. Don't introduce this without checking with maintainers first.
+- [ ] Renovate / Dependabot config templates that auto-bump the `semantica` version in downstream repos — real recurring CI runs on real adopters
+- [ ] Nightly scheduled workflow template that tests a downstream project against `semantica@latest`
+
+### Containers & dev environments
+
+- [ ] Official Docker images: RAG, Graph, Agent, API, `+Postgres`, `+Neo4j`, `+Qdrant`
+- [ ] `docker-compose` examples (repo already has `docker-compose.dev.yml` / `docker-compose.yml` as a base)
+- [ ] `.devcontainer/devcontainer.json` for one-click "Reopen in Container"
+- [ ] GitHub Codespaces-ready config
+- [ ] Gitpod config
+- [ ] "Use this template" GitHub repo button so new projects start with `semantica` in `requirements.txt`
+
+### Notebooks & hosted demos
+
+- [ ] 10-20 Google Colab notebooks (Graph RAG, agent memory, entity resolution, semantic search, document intelligence)
+- [ ] Kaggle Notebooks/Kernels
+- [ ] Binder / mybinder.org config for instant repo launch
+- [ ] SageMaker Studio Lab / Databricks Community Edition / Paperspace Gradient examples
+- [ ] Hugging Face Spaces (Streamlit/Gradio) demos with `semantica` in `requirements.txt`
+- [ ] Public hosted playground (source on GitHub, install visible)
+
+### Framework & data-store integrations
+
+- [x] LangChain integration — `integrations/langchain/` (`SemanticaRetriever`, `SemanticaVectorStore`, `SemanticaKGTool`/`SemanticaDecisionTool`), `pip install semantica[langchain]`, shipped in 0.6.7
+- [ ] LlamaIndex integration + example
+- [ ] LangGraph example
+- [ ] Neo4j integration/example (docs already list it as a supported graph store — turn into a runnable example repo)
+- [ ] Vector DB examples: Qdrant, Weaviate, Milvus, Pinecone, Chroma, FAISS, pgvector, OpenSearch/Elasticsearch (FAISS/Pinecone/Weaviate/Qdrant/Milvus/PgVector already supported per `docs/community-projects.md` — package each as a standalone example)
+- [ ] LLM provider quickstarts: OpenAI, Anthropic, Gemini, Groq, Ollama, HuggingFace, DeepSeek, LiteLLM (already-supported providers per docs — each gets its own copy-paste quickstart)
+- [ ] CrewAI / Agno integration examples (already documented under `docs/integrations/`) — promote as standalone repos, not just docs pages
+
+### Package managers & installers
+
+- [ ] conda-forge feedstock
+- [ ] Homebrew formula for the CLI
+- [ ] Nix/nixpkgs packaging
+- [ ] Chocolatey / Scoop (Windows)
+- [ ] Document `uv add semantica` and `poetry add semantica` explicitly alongside `pip install`
+
+### Downstream packages & CLI
+
+- [ ] Genuinely useful `semantica-*` packages only where warranted (e.g. `semantica-rag`, `semantica-connectors`) — each pulls `semantica` as a real dependency
+- [ ] Make sure `semantica init / ingest / index / query / serve` CLI flows are the default onboarding path in every tutorial
+- [ ] VS Code extension wrapping the CLI (scaffold + run commands from the command palette)
+- [ ] JetBrains plugin equivalent
+
+### Templates & starters
+
+- [ ] Cookiecutter templates: `cookiecutter-semantic-rag`, `cookiecutter-ai-agent`, `cookiecutter-enterprise-rag`
+- [ ] Starter repos: FastAPI, Streamlit, Gradio, Next.js frontend + Semantica backend
+- [ ] Cloud deploy templates: AWS, GCP, Azure, Modal, Railway, Render, Fly.io (repo already has `deploy/azure`, `deploy/gcp`, `deploy/fly`, `deploy/railway`, `deploy/render`, `deploy/kubernetes`, `deploy/helm` — link these prominently from the README/quickstart, they're already-built distribution surface)
+- [ ] Terraform / Pulumi / Helm modules published to their respective registries
+
+### Discoverability & curation
+
+- [ ] Submit to `awesome-rag`, `awesome-llm`, `awesome-knowledge-graph`, `awesome-python`
+- [ ] Pitch newsletters with engaged Python/AI audiences (Python Weekly, Import AI, TLDR AI, etc.)
+- [x] PyPI trove classifiers/keywords and `project.urls` (Homepage/Docs/Repository/Changelog/Bug Tracker) — already complete in `pyproject.toml`
+- [ ] Get listed on Papers With Code for any retrieval/graph-RAG benchmark work
+- [x] [OpenSSF Scorecard](https://scorecard.dev/viewer/?uri=github.com/semantica-agi/semantica) badge + weekly workflow (`.github/workflows/scorecard.yml`) — a concrete trust signal security/procurement teams check before greenlighting adoption, which gates real (non-CI-bot) install growth at enterprises
+
+### Academic & research
+
+- [x] `CITATION.cff` at repo root — enables GitHub's native "Cite this repository" button, feeds Google Scholar/academic tooling; complements `docs/citation.md` (still needs a real Zenodo DOI to replace the `XXXXXXX` placeholder in both places once one is minted)
+- [ ] arXiv paper if there's real architectural novelty to describe
+- [ ] Zenodo DOI for citability (`docs/citation.md` already exists — make sure it points to a real DOI)
+- [ ] Workshop/tutorial sessions at PyData/ODSC-style events with hands-on install steps
+- [ ] University course material / bootcamp adoption outreach
+
+### Content
+
+- [ ] Reproducible benchmark repos (Graph RAG vs vector RAG, retrieval@k, enterprise-scale retrieval) with `pip install semantica && python benchmark.py`
+- [ ] 20-30 real-world example applications (RAG, enterprise document intelligence, financial entity graphs, code knowledge graphs, research discovery, agent memory)
+- [ ] Blog/tutorial posts on Dev.to, Medium, personal blogs — always with runnable code, not just prose
+- [ ] Contribute integrations/PRs to other projects building RAG/agents/knowledge graphs — "I implemented Semantica support" beats "please use Semantica"
+
+## Tracking
+
+Don't just watch the raw PyPI number — use download analytics (e.g. PePy) to separate CI/bot traffic from real installs, and track the funnel above end-to-end where possible (stars → site visits → installs → weekly actives).

README.md

@@ -18,15 +18,15 @@
 
 > Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.
 
-**Decision Intelligence  ·  Context Management  ·  Deterministic Reasoning  ·  Ontology Management  ·  Knowledge Modeling  ·  End-to-End Traceability**
+**Context Management  ·  Knowledge Modeling  ·  Deterministic Reasoning  ·  Ontology Management  ·  Decision Intelligence  ·  End-to-End Traceability**
 
-**Open Source  ·  Self-Hostable  ·  Auditable  ·  Governed  ·  Zero Vendor Lock-In**
+**Open Source  ·  Governed  ·  Zero Vendor Lock-In**
 
 **Polyglot Graph Storage  ·  RDF & LPG Support  ·  W3C Standards  ·  Interoperable**
 
 #### Built for High-Stakes, Regulated Domains
 
-[![GitHub Stars](https://img.shields.io/github/stars/semantica-agi/semantica?style=flat-square&color=FFD700&logo=github&logoColor=white&label=Stars)](https://github.com/semantica-agi/semantica) [![GitHub Forks](https://img.shields.io/github/forks/semantica-agi/semantica?style=flat-square&color=6E40C9&logo=github&logoColor=white&label=Forks)](https://github.com/semantica-agi/semantica/network/members) [![Contributors](https://img.shields.io/github/contributors/semantica-agi/semantica?style=flat-square&color=2EA043&logo=github&logoColor=white)](https://github.com/semantica-agi/semantica/graphs/contributors) [![PyPI](https://img.shields.io/pypi/v/semantica.svg?style=flat-square&color=0066CC&logo=pypi&logoColor=white)](https://pypi.org/project/semantica/) [![Total Downloads](https://static.pepy.tech/badge/semantica?style=flat-square)](https://pepy.tech/project/semantica) [![Python 3.8+](https://img.shields.io/badge/python-3.8+-3776AB?style=flat-square&logo=python&logoColor=white)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/actions/workflow/status/semantica-agi/semantica/ci.yml?style=flat-square&label=CI)](https://github.com/semantica-agi/semantica/actions) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/semantica-agi/semantica)
+[![GitHub Stars](https://img.shields.io/github/stars/semantica-agi/semantica?style=flat-square&color=FFD700&logo=github&logoColor=white&label=Stars)](https://github.com/semantica-agi/semantica) [![GitHub Forks](https://img.shields.io/github/forks/semantica-agi/semantica?style=flat-square&color=6E40C9&logo=github&logoColor=white&label=Forks)](https://github.com/semantica-agi/semantica/network/members) [![Contributors](https://img.shields.io/github/contributors/semantica-agi/semantica?style=flat-square&color=2EA043&logo=github&logoColor=white)](https://github.com/semantica-agi/semantica/graphs/contributors) [![PyPI](https://img.shields.io/pypi/v/semantica.svg?style=flat-square&color=0066CC&logo=pypi&logoColor=white)](https://pypi.org/project/semantica/) [![Total Downloads](https://static.pepy.tech/badge/semantica?style=flat-square)](https://pepy.tech/project/semantica) [![Python 3.8+](https://img.shields.io/badge/python-3.8+-3776AB?style=flat-square&logo=python&logoColor=white)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/actions/workflow/status/semantica-agi/semantica/ci.yml?style=flat-square&label=CI)](https://github.com/semantica-agi/semantica/actions) [![Install Matrix](https://img.shields.io/github/actions/workflow/status/semantica-agi/semantica/install-matrix.yml?style=flat-square&label=pip%20install)](https://github.com/semantica-agi/semantica/actions/workflows/install-matrix.yml) [![OpenSSF Scorecard](https://api.scorecard.dev/projects/github.com/semantica-agi/semantica/badge?style=flat-square)](https://scorecard.dev/viewer/?uri=github.com/semantica-agi/semantica) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/semantica-agi/semantica)
 
 [![Website](https://img.shields.io/badge/Website-getsemantica.ai-000000?style=flat-square&logo=googlechrome&logoColor=white)](https://getsemantica.ai/) [![Docs](https://img.shields.io/badge/Docs-docs.getsemantica.ai-0099FF?style=flat-square&logo=readthedocs&logoColor=white)](https://docs.getsemantica.ai/) [![Discord](https://img.shields.io/badge/Discord-Join%20Community-5865F2?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/sV34vps5hH) [![Twitter/X](https://img.shields.io/badge/Follow-%40BuildSemantica-000000?style=flat-square&logo=x&logoColor=white)](https://x.com/BuildSemantica) [![YouTube](https://img.shields.io/badge/YouTube-Watch%20Demos-FF0000?style=flat-square&logo=youtube&logoColor=white)](https://www.youtube.com/watch?v=QfnNZg4-dZA) [![Changelog](https://img.shields.io/badge/Changelog-View-6E40C9?style=flat-square&logo=keepachangelog&logoColor=white)](CHANGELOG.md)
 
@@ -56,20 +56,18 @@ pip install semantica
 
 ---
 
-Most AI agents act without a trail. They store embeddings, not meaning: context that can't be explained, decisions that can't be audited. In lending, that gap is a compliance exposure, not an inconvenience: an underwriting agent's approval has to survive a regulator's "why" months later.
-
-Semantica sits underneath your LLM, vector store, and agent framework as a deterministic infrastructure layer: no LLM required for graph construction, reasoning, or provenance.
+Most AI agents run on embeddings, not meaning: similarity scores with no structure, no relationships, and no way to explain why a result came back. Semantica is the semantic/context layer underneath your LLM, vector store, and agent framework: a deterministic infrastructure layer (no LLM required for graph construction, reasoning, or provenance) that turns fragmented enterprise data into a structured, queryable Context Graph and knowledge graph, governed by ontologies and controlled vocabularies (OWL, SHACL, SKOS) so the meaning of your data is explicit, not just its embedding. Decision provenance and audit trails fall out of that structure as a property, not the product itself; in domains a regulator can question, that same structure just happens to double as a straight answer to "why."
 
 > ⚠️ **System-level explainability, not foundation-model explainability.** Semantica does not expose or reconstruct what happens *inside* the LLM — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. Semantica explains what's *outside* the model: the context and data fed in, the decision produced, its provenance, relevant relationships, applied policies, and the full execution trail.
 
 **Who it's for:**
 
-- **AI/ML platform teams** shipping agents that make consequential decisions and need structured, queryable context built from fragmented raw data, not just a vector index
-- **Data platform teams on Databricks or Snowflake** who need to turn tables already sitting in Unity Catalog or a Snowflake warehouse into a governed, lineage-tracked knowledge graph, without exporting that data to a third-party SaaS first
-- **Compliance, risk, and audit teams** who need a straight answer to "why did the AI do that?" in a format a regulator will actually accept
-- **Regulated enterprises** (finance, healthcare, legal, government, defense) that can't ship a black box, and can't send their data to someone else's SaaS to get one
+- **AI/ML platform teams** shipping agents that make consequential decisions and need structured, queryable context, not just a vector index
+- **Data platform teams on Databricks or Snowflake** turning tables already in Unity Catalog or a warehouse into a governed, lineage-tracked knowledge graph, without exporting to a third-party SaaS
+- **Compliance, risk, and audit teams** who need a straight answer to "why did the AI do that?" in a format a regulator accepts
+- **Regulated enterprises** (finance, healthcare, legal, government, defense) that can't ship a black box or send their data to someone else's SaaS to get one
 - **Platform and infra engineers** who want the KG, reasoning, and provenance stack self-hosted and swappable, not locked to one vendor's backend
-- **Data and knowledge engineers** building a KG from messy, multi-source data: entities and relationships get extracted, conflicting or contradictory facts are flagged instead of silently overwritten, and duplicates are merged before they turn into noise
+- **Data and knowledge engineers** building a KG from messy, multi-source data, where conflicting facts get flagged and duplicates get merged, not silently overwritten
 
 **[Quick Start](#quick-start)**  ·  **[Architecture](#architecture)**  ·  **[What You Get](#what-semantica-gives-you)**  ·  **[Why Semantica](#why-semantica)**  ·  **[Decision Intelligence](#decision-intelligence)**  ·  **[Context Graphs](#context-graphs)**  ·  **[Recipe: Audit Trail](#recipe-audit-trail-for-a-regulated-decision)**  ·  **[Module Reference](#module-reference)**  ·  **[Integrations](#integrations)**  ·  **[CLI](#cli)**  ·  **[Performance](#performance)**  ·  **[Install](#installation)**
 
@@ -83,7 +81,7 @@ Semantica sits underneath your LLM, vector store, and agent framework as a deter
 - **Full Auditability:** W3C PROV-O provenance on every fact, with audit trails exportable to JSON, CSV, or RDF
 - **Deterministic Reasoning:** Forward chaining, Rete network, Datalog, and SPARQL with fully explainable paths, not black boxes
 - **Knowledge Pipeline:** Multi-source ingestion, entity-aware chunking, NER/relation/event extraction, and knowledge graph construction, with semantic deduplication and provenance-preserving merges throughout
-- **Enterprise Data Platforms:** Native connectors for Databricks (Unity Catalog + Delta Lake, PAT/OAuth M2M auth, catalog/schema/table/lineage introspection) and Snowflake (warehouse/database/schema, key-pair and OAuth auth), so tables already living in your lakehouse or warehouse become graph nodes with provenance, not another export/import hop
+- **Enterprise Data Platforms:** Native connectors for Databricks (Unity Catalog + Delta Lake, PAT/OAuth M2M auth, catalog/schema/table/lineage introspection), Snowflake (warehouse/database/schema, key-pair and OAuth auth), and SAP OData (Business Partners, Sales Orders, OAuth2/Basic auth), so data already living in your lakehouse or warehouse becomes graph nodes with provenance, not another export/import hop
 - **Graph Analytics:** Centrality, community detection, link prediction, and shortest-path queries over the graph you just built
 - **Polyglot Graph Storage:** Native RDF (embedded Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J via SPARQL) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune via Cypher), plus vector stores, all swappable without touching your code
 - **Visualization:** Explore any graph, ontology, or timeline in an interactive browser workbench
@@ -141,10 +139,6 @@ compliant = graph.check_decision_rules({"category": "vendor_selection"})  # poli
 
 ```bash
 semantica doctor
-# Python 3.11.9         pass
-# semantica 0.6.7       pass
-# faiss vector store    pass
-# Config file           pass    ~/.semantica/config.yaml
 ```
 
 **Running in a script or CI?** Progress bars are written only when stdout is an interactive terminal (or a Jupyter notebook), so piping and redirecting stay clean by default. Override with `SEMANTICA_DISABLE_PROGRESS=1` to silence progress everywhere, or `SEMANTICA_FORCE_PROGRESS=1` to keep it when stdout is redirected. `SEMANTICA_DISABLE_PROGRESS` takes precedence.
@@ -169,7 +163,7 @@ Sources → Ingest → Parse → Normalize → Split → Extract → Conflict De
    → Vector Store + Polyglot Graph Store (RDF & LPG) → Export / Visualize / REST · MCP · CLI
 ```
 
-- **Ingest:** files, web, databases, enterprise data platforms (Databricks, Snowflake), cloud (Google Drive, Elasticsearch), streams (Kafka, Kinesis), Git, email, MCP
+- **Ingest:** files, web, databases, enterprise data platforms (Databricks, Snowflake, SAP), cloud (Google Drive, Elasticsearch), streams (Kafka, Kinesis), Git, email, MCP
 - **Parse → Normalize → Split:** document parsing, text/entity/date normalization, GraphRAG-native entity-aware chunking
 - **Extract → Conflict Detection → Deduplication:** NER, relations, events, triplets; conflicting facts flagged and resolved before they merge
 - **Knowledge Graph:** `GraphBuilder` constructs the graph; bi-temporal facts and full graph analytics (centrality, communities, link prediction) run on top of it
@@ -279,7 +273,7 @@ retrieved = ctx.retrieve("who approved the Acme contract?")
 
 ## Recipe: Audit Trail for a Regulated Decision
 
-The flagship pattern: record a causally-linked decision chain, attach provenance to every entity, and export a regulator-ready audit trail.
+One pattern built on the same Context Graph: record a causally-linked decision chain, attach provenance to every entity, and export a regulator-ready audit trail.
 
 ```python
 from semantica.context import ContextGraph
@@ -322,7 +316,7 @@ Every module below is independently importable, with working code samples verifi
 
 | Module | What it does |
 | --- | --- |
-| [`semantica.ingest`](#semanticaingest-multi-source-ingestion) | Files, web, databases, APIs, streams, email, Git, Parquet, Databricks, Snowflake, MCP |
+| [`semantica.ingest`](#semanticaingest-multi-source-ingestion) | Files, web, databases, APIs, streams, email, Git, Parquet, Databricks, Snowflake, SAP, MCP |
 | [`semantica.semantic_extract`](#semanticasemantic_extract-ner-relations-events-triplets) | NER, relation extraction, event detection, triplet generation |
 | [`semantica.kg`](#semanticakg-knowledge-graph-construction--analysis) | Graph construction, centrality, communities, link prediction |
 | [`semantica.reasoning`](#semanticareasoning-forward-chaining-rete-datalog-sparql) | Forward chaining, Rete, Datalog, SPARQL, fully explainable |
@@ -351,7 +345,7 @@ Expand any module below for its runnable example.
 <summary><b><code>semantica.ingest</code></b>: Multi-Source Ingestion</summary>
 <a id="semanticaingest-multi-source-ingestion"></a>
 
-Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, or MCP servers, all through a unified interface.
+Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, SAP, or MCP servers, all through a unified interface.
 
 ```python
 from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, DBIngestor
@@ -402,7 +396,7 @@ orders = snowflake.ingest_table("ORDERS", limit=10_000)
 
 > **Security Note:** Never hardcode credentials (`token`, `password`, `private_key`) in production code; pass them via environment variables (e.g., `DATABRICKS_TOKEN`, `SNOWFLAKE_PASSWORD`) or a secrets manager.
 
-**Supported sources:** Local files (PDF, DOCX, PPTX, HTML, TXT, CSV, JSON, YAML, Excel, XML) · Web pages · RSS/Atom feeds · REST APIs · Databases (PostgreSQL, MySQL, SQLite, Oracle, SQL Server) · Parquet datasets · Databricks (Unity Catalog + Delta Lake) · Snowflake · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources · Apache Arrow/Feather/IPC (`ArrowIngestor`)
+**Supported sources:** Local files (PDF, DOCX, PPTX, HTML, TXT, CSV, JSON, YAML, Excel, XML) · Web pages · RSS/Atom feeds · REST APIs · Databases (PostgreSQL, MySQL, SQLite, Oracle, SQL Server) · Parquet datasets · Databricks (Unity Catalog + Delta Lake) · Snowflake · SAP (OData v2/v4) · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources · Apache Arrow/Feather/IPC (`ArrowIngestor`)
 
 DuckDB, Elasticsearch, Google Drive, HuggingFace, MongoDB, and Pandas ingestion also ship (`DuckDBIngestor`, `ElasticIngestor`, `GDriveIngestor`, `HuggingFaceIngestor`, `MongoIngestor`, `PandasIngestor`) but aren't re-exported from the top-level `semantica.ingest` namespace yet — import them directly: `from semantica.ingest.duckdb_ingestor import DuckDBIngestor`.
 
@@ -1030,7 +1024,7 @@ team = Team(agents=[researcher, analyst], mode="coordinate")
 
 ## More Recipes
 
-The flagship audit-trail recipe is [above](#recipe-audit-trail-for-a-regulated-decision). Here are three more common patterns.
+The audit-trail recipe is [above](#recipe-audit-trail-for-a-regulated-decision). Here are three more common patterns.
 
 <details>
 <summary><b>End-to-End GraphRAG Pipeline</b></summary>
@@ -1147,7 +1141,7 @@ if report.valid:
 | **Vector Store** | FAISS · Pinecone · Weaviate · Qdrant · Milvus · PgVector · hybrid + filtered search |
 | **Graph Databases (LPG)** | Neo4j · FalkorDB · Apache AGE · AWS Neptune |
 | **Triple Stores (RDF)** | Oxigraph (embedded) · Blazegraph · Apache Jena · Eclipse RDF4J · unified `TripletStore` interface · SPARQL query & bulk load |
-| **Enterprise Data Platforms** | Databricks (`DatabricksIngestor`: Unity Catalog + Delta Lake, PAT/OAuth M2M, table/query ingestion, catalog/schema/table/lineage introspection) · Snowflake (`SnowflakeIngestor`: warehouse/database/schema, password/key-pair/OAuth auth) |
+| **Enterprise Data Platforms** | Databricks (`DatabricksIngestor`: Unity Catalog + Delta Lake, PAT/OAuth M2M, table/query ingestion, catalog/schema/table/lineage introspection) · Snowflake (`SnowflakeIngestor`: warehouse/database/schema, password/key-pair/OAuth auth) · SAP (`SAPIngestor`: OData v2/v4, OAuth2/Basic auth, Business Partners/Sales Orders) |
 | **LLM Providers** | **All already supported today:** OpenAI (GPT-4o, o1, o3) · Anthropic (Claude) · Google Gemini · Mistral · Meta Llama · Groq · Cohere · Azure OpenAI · AWS Bedrock · Ollama · DeepSeek · Perplexity · Together AI · Fireworks AI · Replicate · HuggingFace · via `semantica.llms` and LiteLLM |
 
 ---
@@ -1519,6 +1513,7 @@ pip install semantica[vectorstore-qdrant]   # Qdrant vector store
 pip install semantica[vectorstore-pinecone] # Pinecone vector store
 pip install semantica[db-snowflake]         # Snowflake
 pip install semantica[db-databricks]        # Databricks (SDK + SQL connector)
+pip install semantica[ingest-sap]           # SAP OData
 pip install semantica[ingest-parquet]       # Parquet / PyArrow
 pip install semantica[ingest-arrow]        # Apache Arrow, Feather, IPC
 pip install semantica[viz]                  # HTML interactive visualization
@@ -1534,6 +1529,20 @@ git clone https://github.com/semantica-agi/semantica.git
 cd semantica && pip install -e ".[dev]" && pytest tests/
 ```
 
+### CI & Deployment
+
+Wiring `semantica` into your own CI is a two-minute job. On GitHub Actions, use the reusable composite action:
+
+```yaml
+- uses: semantica-agi/semantica/.github/actions/setup-semantica@main
+  with:
+    python-version: '3.11'
+```
+
+Copy-paste starting templates for GitHub Actions, GitLab CI, and CircleCI live in [examples/ci/](examples/ci/). The published package itself is verified installable across Ubuntu/macOS/Windows and Python 3.9-3.12 every week by the [Install Matrix workflow](.github/workflows/install-matrix.yml).
+
+Ready-made deployment configs for AWS, GCP, Azure, Fly.io, Railway, Render, Kubernetes, and Helm are in [deploy/](deploy/).
+
 ---
 
 ## Enterprise

SECURITY.md

@@ -153,7 +153,7 @@ that attack chain.
 - **Risk**: a PR merges without its security/CI checks passing.
   **Control**: merges require the `build`, `Analyze Python` (CodeQL), and `security-scan` checks to pass, in strict mode (checks must be re-run against the latest `main`).
 - **Risk**: a compromised scanner job reaches secrets or write access.
-  **Control**: scanning jobs (`CodeQL`, `security-scan.yml`, `security.yml`, `defender-for-devops.yml`) run with read-only, least-privilege permissions (typically `contents: read` + `security-events: write` only) and never share a job, environment, or secret scope with the publish job.
+  **Control**: scanning jobs (`CodeQL`, `security-scan.yml`, `defender-for-devops.yml`) run with read-only, least-privilege permissions (typically `contents: read` + `security-events: write` only) and never share a job, environment, or secret scope with the publish job.
 - **Risk**: secrets are committed accidentally.
   **Control**: GitHub secret scanning and push protection are both enabled at the repository level, rejecting pushes that contain recognizable credential patterns before they land in history.
 
@@ -164,8 +164,7 @@ Every scan below runs continuously in CI, not just at release time:
 - **CodeQL** (`security-and-quality` query pack) — Python source: injection, unsafe deserialization, and other code-level vulnerability classes. Runs in `codeql.yml` on every push/PR to `main` and weekly.
 - **Bandit** — Python-specific security anti-patterns (hardcoded secrets, unsafe `eval`/`pickle`, weak crypto, etc.); CI fails on any HIGH-severity finding. Runs in `security-scan.yml` on every push/PR to `main` and twice weekly.
 - **Semgrep** (`p/security` ruleset) — cross-language static-analysis security patterns. Runs in `security-scan.yml` on every push/PR to `main` and twice weekly.
-- **Safety** — known CVEs in Semantica's own installed dependencies, including optional LLM-provider extras such as LiteLLM; CI fails on any match. Runs in `security-scan.yml` on every push/PR to `main` and twice weekly.
-- **pip-audit** — independent, PyPA-maintained vulnerability database cross-check against installed dependencies (Safety and pip-audit use different advisory sources, so both run). Runs in `security.yml` weekly.
+- **pip-audit** — PyPA-maintained, OSV-backed vulnerability database cross-check against Semantica's pinned dependency tree, including optional LLM-provider extras such as LiteLLM; CI fails on any match. Runs in `security-scan.yml` on every push/PR to `main` and twice weekly, and can be triggered on demand via `workflow_dispatch`.
 - **Microsoft Defender for DevOps** (`eslint`, `templateanalyzer`, `terrascan`) — JavaScript/TypeScript lint-security rules and infrastructure-as-code misconfigurations. Runs in `defender-for-devops.yml` on every push/PR to `main` and weekly.
 - **Checkov** — Kubernetes, Helm, Dockerfile, GitHub Actions, and secrets-pattern IaC scanning; results upload to the same Security tab as CodeQL. Runs in `defender-for-devops.yml` on every push/PR to `main` and weekly.
 - **GitGuardian** — secret-detection check on every pull request, installed as a GitHub App integration (not a repo-local workflow). Runs on every PR.

cookbook/advanced/09_Semantic_Layer_Construction.ipynb

@@ -1,222 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/semantica-agi/semantica/blob/main/cookbook/advanced/09_Semantic_Layer_Construction.ipynb)\n",
-    "\n",
-    "# Semantic Layer Construction\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "Build an enterprise semantic layer: construct knowledge graph, generate ontology, create semantic layer, export RDF, and store in triplet store.\n",
-    "\n",
-    "\n",
-    "**Documentation**: [API Reference](https://semantica.readthedocs.io/concepts/)\n",
-    "\n",
-    "## Installation\n",
-    "\n",
-    "Install Semantica from PyPI:\n",
-    "\n",
-    "```bash\n",
-    "pip install semantica\n",
-    "# Or with all optional dependencies:\n",
-    "pip install semantica[all]\n",
-    "```\n",
-    "\n",
-    "## Workflow: Build KG → Generate Ontology → Create Semantic Layer → Export RDF \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -qU semantica\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from semantica.kg import GraphBuilder\n",
-    "from semantica.ontology import OntologyGenerator\n",
-    "from semantica.export import RDFExporter\n",
-    "from semantica.triplet_store import TripletStore\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 1: Build Knowledge Graph\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "builder = GraphBuilder()\n",
-    "\n",
-    "entities = [\n",
-    "    {\"id\": \"e1\", \"type\": \"Person\", \"name\": \"Alice\", \"properties\": {\"age\": 30, \"role\": \"Engineer\"}},\n",
-    "    {\"id\": \"e2\", \"type\": \"Person\", \"name\": \"Bob\", \"properties\": {\"age\": 35, \"role\": \"Manager\"}},\n",
-    "    {\"id\": \"e3\", \"type\": \"Organization\", \"name\": \"Tech Corp\", \"properties\": {\"founded\": 2010}},\n",
-    "    {\"id\": \"e4\", \"type\": \"Project\", \"name\": \"Project Alpha\", \"properties\": {\"status\": \"active\"}},\n",
-    "]\n",
-    "\n",
-    "relationships = [\n",
-    "    {\"source\": \"e1\", \"target\": \"e2\", \"type\": \"reports_to\"},\n",
-    "    {\"source\": \"e1\", \"target\": \"e3\", \"type\": \"works_for\"},\n",
-    "    {\"source\": \"e2\", \"target\": \"e3\", \"type\": \"works_for\"},\n",
-    "    {\"source\": \"e1\", \"target\": \"e4\", \"type\": \"works_on\"},\n",
-    "]\n",
-    "\n",
-    "knowledge_graph = builder.build(entities, relationships)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2: Generate Ontology\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generator = OntologyGenerator()\n",
-    "ontology = generator.generate_from_graph(knowledge_graph)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3: Create Semantic Layer\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_mappings(kg, ontology):\n",
-    "    mappings = {\n",
-    "        \"entity_type_mappings\": {},\n",
-    "        \"relationship_type_mappings\": {},\n",
-    "        \"property_mappings\": {}\n",
-    "    }\n",
-    "    \n",
-    "    entity_types = set(e.get(\"type\") for e in entities)\n",
-    "    ontology_classes = ontology.get(\"classes\", [])\n",
-    "    \n",
-    "    for entity_type in entity_types:\n",
-    "        matching_class = next((cls for cls in ontology_classes if cls.get(\"name\") == entity_type), None)\n",
-    "        if matching_class:\n",
-    "            mappings[\"entity_type_mappings\"][entity_type] = matching_class.get(\"uri\", entity_type)\n",
-    "    \n",
-    "    relationship_types = set(r.get(\"type\") for r in relationships)\n",
-    "    ontology_properties = ontology.get(\"properties\", [])\n",
-    "    \n",
-    "    for rel_type in relationship_types:\n",
-    "        matching_prop = next((prop for prop in ontology_properties if prop.get(\"name\") == rel_type), None)\n",
-    "        if matching_prop:\n",
-    "            mappings[\"relationship_type_mappings\"][rel_type] = matching_prop.get(\"uri\", rel_type)\n",
-    "    \n",
-    "    return mappings\n",
-    "\n",
-    "mappings = create_mappings(knowledge_graph, ontology)\n",
-    "\n",
-    "semantic_layer = {\n",
-    "    \"graph\": knowledge_graph,\n",
-    "    \"ontology\": ontology,\n",
-    "    \"mappings\": mappings,\n",
-    "    \"metadata\": {\n",
-    "        \"version\": \"1.0\",\n",
-    "        \"created_at\": \"2024-01-01\",\n",
-    "        \"description\": \"Enterprise semantic layer\"\n",
-    "    }\n",
-    "}\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 4: Export RDF\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "exporter = RDFExporter()\n",
-    "# Export Knowledge Graph\n",
-    "exporter.export(knowledge_graph, \"knowledge_graph.ttl\", format=\"turtle\")\n",
-    "print(\"Exported knowledge graph to knowledge_graph.ttl\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "Enterprise semantic layer construction:\n",
-    "- Knowledge Graph Built\n",
-    "- Ontology Generated\n",
-    "- Semantic Layer Created with Mappings\n",
-    "- RDF Export Completed\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

cookbook/advanced/13_Manual_Ontology_Snowflake_Mapping.ipynb

@@ -1,435 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.0"
-  }
- },
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-0",
-   "metadata": {},
-   "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/semantica-agi/semantica/blob/main/cookbook/advanced/13_Manual_Ontology_Snowflake_Mapping.ipynb)\n",
-    "\n",
-    "# Manual Ontology + Snowflake Mapping\n",
-    "\n",
-    "This notebook answers a specific workflow:\n",
-    "\n",
-    "> *\"I want to design the ontology myself — not have AI infer it from my tables — and then map Snowflake data to it explicitly.\"*\n",
-    "\n",
-    "### What this notebook demonstrates\n",
-    "\n",
-    "| Step | What happens | Who controls it |\n",
-    "|---|---|---|\n",
-    "| 1 | Design ontology classes and properties | **You** (Python dict) |\n",
-    "| 2 | Model n-ary facts with reification | **You** (`AssociativeClassBuilder`) |\n",
-    "| 3 | Pull rows from Snowflake | Semantica `SnowflakeIngestor` |\n",
-    "| 4 | Map columns → ontology-aligned graph | **You** (explicit transform) |\n",
-    "| 5 | Validate + export OWL / SHACL | Semantica `OntologyEngine` |\n",
-    "| 6 | Load to triplet store and query | Semantica `TripletStore` |\n",
-    "\n",
-    "### What this notebook does NOT do\n",
-    "\n",
-    "- No LLM-driven ontology generation\n",
-    "- No schema introspection or table-to-class inference\n",
-    "- No \"suggest ontology from my data\"\n",
-    "\n",
-    "### Standards coverage\n",
-    "\n",
-    "| Feature | Status |\n",
-    "|---|---|\n",
-    "| OWL 2 (Turtle / RDF-XML) | Supported |\n",
-    "| SHACL 1.1 shapes | Supported |\n",
-    "| SPARQL 1.1 | Supported |\n",
-    "| Reification / n-ary facts | Supported via `AssociativeClassBuilder` |\n",
-    "| SPARQL 1.2 (reifier annotation, `LATERAL`) | Planned |\n",
-    "| SHACL 1.2 (`sh:severity` extensions, SHACL-AF) | Planned |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -qU semantica"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from typing import Any, Dict, List\n",
-    "\n",
-    "from semantica.ingest import SnowflakeIngestor\n",
-    "from semantica.kg.methods import build_kg\n",
-    "from semantica.ontology import AssociativeClassBuilder, OntologyEngine\n",
-    "from semantica.triplet_store import TripletStore"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-3",
-   "metadata": {},
-   "source": [
-    "## Step 1: Hand-Design the Ontology in Python\n",
-    "\n",
-    "You define every class and property explicitly. Nothing is read from Snowflake at this stage.\n",
-    "\n",
-    "**Design decisions that belong to you:**\n",
-    "- Which classes exist and what they mean\n",
-    "- Which properties are datatype vs. object properties\n",
-    "- Domain, range, and cardinality constraints\n",
-    "- Which properties are required (later enforced by SHACL)\n",
-    "\n",
-    "This dict versions with your code. It does not change when your database schema changes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-4",
-   "metadata": {},
-   "outputs": [],
-   "source": "BASE_URI = \"https://example.com/hr/\"\n\n# Your ontology — designed by you, not inferred by Semantica.\nontology: Dict[str, Any] = {\n    \"name\": \"EmploymentDomainOntology\",\n    \"uri\": f\"{BASE_URI}EmploymentDomainOntology\",\n    \"namespace\": {\"base_uri\": BASE_URI},\n\n    # You decide the class taxonomy\n    \"classes\": [\n        {\"name\": \"Person\",          \"uri\": f\"{BASE_URI}Person\"},\n        {\"name\": \"Organization\",    \"uri\": f\"{BASE_URI}Organization\"},\n        {\"name\": \"Role\",            \"uri\": f\"{BASE_URI}Role\"},\n        # EmploymentEvent is a reification node.\n        # It connects Person + Organization + Role and carries salary/date context.\n        {\"name\": \"EmploymentEvent\", \"uri\": f\"{BASE_URI}EmploymentEvent\"},\n    ],\n\n    # Each property carries a full URI so TripletStore stores it as hr:<name>\n    # rather than the default urn:property:<name>.\n    # This ensures SPARQL queries using PREFIX hr: match what is actually stored.\n    \"properties\": [\n        # Datatype properties\n        {\"name\": \"name\",      \"uri\": f\"{BASE_URI}name\",      \"type\": \"datatype\", \"domain\": \"Person\",          \"range\": \"string\",  \"required\": True},\n        {\"name\": \"legalName\", \"uri\": f\"{BASE_URI}legalName\", \"type\": \"datatype\", \"domain\": \"Organization\",    \"range\": \"string\",  \"required\": True},\n        {\"name\": \"title\",     \"uri\": f\"{BASE_URI}title\",     \"type\": \"datatype\", \"domain\": \"Role\",            \"range\": \"string\",  \"required\": True},\n        {\"name\": \"startDate\", \"uri\": f\"{BASE_URI}startDate\", \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"date\"},\n        {\"name\": \"endDate\",   \"uri\": f\"{BASE_URI}endDate\",   \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"date\"},\n        {\"name\": \"salary\",    \"uri\": f\"{BASE_URI}salary\",    \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"decimal\"},\n\n        # Object properties — reification spokes (required)\n        {\"name\": \"employee\",  \"uri\": f\"{BASE_URI}employee\",  \"type\": \"object\",   \"domain\": \"EmploymentEvent\", \"range\": \"Person\",       \"required\": True},\n        {\"name\": \"employer\",  \"uri\": f\"{BASE_URI}employer\",  \"type\": \"object\",   \"domain\": \"EmploymentEvent\", \"range\": \"Organization\", \"required\": True},\n        {\"name\": \"role\",      \"uri\": f\"{BASE_URI}role\",      \"type\": \"object\",   \"domain\": \"EmploymentEvent\", \"range\": \"Role\",         \"required\": True},\n\n        # Shortcut edges — direct person→org / person→role without traversing the event node\n        {\"name\": \"worksFor\",  \"uri\": f\"{BASE_URI}worksFor\",  \"type\": \"object\",   \"domain\": \"Person\",          \"range\": \"Organization\"},\n        {\"name\": \"hasRole\",   \"uri\": f\"{BASE_URI}hasRole\",   \"type\": \"object\",   \"domain\": \"Person\",          \"range\": \"Role\"},\n    ],\n}\n\nontology"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-5",
-   "metadata": {},
-   "source": [
-    "## Step 2: Reification — Modeling N-Ary Facts\n",
-    "\n",
-    "**The problem with binary triples:**\n",
-    "A simple triple `(Alice, worksFor, Acme)` cannot carry extra context such as salary, start date, or role.\n",
-    "Standard RDF reification and OWL n-ary patterns solve this by introducing an intermediate node.\n",
-    "\n",
-    "Semantica's `AssociativeClassBuilder` is the Pythonic API for this pattern:\n",
-    "\n",
-    "```\n",
-    "EmploymentEvent\n",
-    "    ├── employee  → Person          (required)\n",
-    "    ├── employer  → Organization    (required)\n",
-    "    ├── role      → Role            (required)\n",
-    "    ├── startDate → xsd:date\n",
-    "    ├── endDate   → xsd:date\n",
-    "    └── salary    → xsd:decimal\n",
-    "```\n",
-    "\n",
-    "**On SPARQL 1.1 vs. SPARQL 1.2:**\n",
-    "- **SPARQL 1.1 (current):** traverse the event node explicitly — `?event hr:employee ?person ; hr:salary ?salary`\n",
-    "- **SPARQL 1.2 (planned):** the draft reifier annotation syntax allows attaching context to triples directly, without a separate intermediate node. Semantica will adopt this once the spec is ratified.\n",
-    "\n",
-    "**On SHACL 1.1 vs. SHACL 1.2:**\n",
-    "- **SHACL 1.1 (current):** `sh:NodeShape` + `sh:PropertyShape` constraints are exported for all `required` properties and enforced at load time.\n",
-    "- **SHACL 1.2 (planned):** `sh:severity` profile extensions and SHACL-AF rules are on the roadmap."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-6",
-   "metadata": {},
-   "outputs": [],
-   "source": "assoc_builder = AssociativeClassBuilder()\n\nemployment_assoc = assoc_builder.create_associative_class(\n    name=\"EmploymentEvent\",\n    connects=[\"Person\", \"Organization\", \"Role\"],\n    temporal=True,  # adds startDate / endDate handling\n    properties={\n        \"startDate\": \"xsd:date\",\n        \"endDate\":   \"xsd:date\",\n        \"salary\":    \"xsd:decimal\",\n    },\n)\n\nvalidation_result = assoc_builder.validate_associative_class(employment_assoc)\n\n# AssociativeClass is a dataclass — use attribute access, not .get()\nprint(\"AssociativeClass structure:\")\nprint(f\"  name:       {employment_assoc.name}\")\nprint(f\"  connects:   {employment_assoc.connects}\")\nprint(f\"  temporal:   {employment_assoc.temporal}\")\nprint(f\"  properties: {list(employment_assoc.properties.keys())}\")\nprint(f\"\\nValidation passed: {validation_result}\")"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-7",
-   "metadata": {},
-   "source": [
-    "## Step 3: Ingest Snowflake Rows (Extraction Only)\n",
-    "\n",
-    "`SnowflakeIngestor` retrieves rows — nothing more. It does **not**:\n",
-    "- Inspect your table schema\n",
-    "- Suggest classes or properties\n",
-    "- Infer relationships from column names\n",
-    "\n",
-    "Set `USE_LIVE_SNOWFLAKE=true` plus the env vars below to connect to a real warehouse.\n",
-    "Otherwise the stub data is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fetch_rows_from_snowflake() -> List[Dict[str, Any]]:\n",
-    "    if os.getenv(\"USE_LIVE_SNOWFLAKE\", \"false\").lower() != \"true\":\n",
-    "        return [\n",
-    "            {\n",
-    "                \"EMPLOYEE_ID\":   \"E100\",\n",
-    "                \"EMPLOYEE_NAME\": \"Alice Johnson\",\n",
-    "                \"ORG_ID\":        \"O10\",\n",
-    "                \"ORG_NAME\":      \"Acme Corp\",\n",
-    "                \"ROLE_ID\":       \"R7\",\n",
-    "                \"ROLE_TITLE\":    \"Senior Engineer\",\n",
-    "                \"START_DATE\":    \"2025-01-15\",\n",
-    "                \"END_DATE\":      None,\n",
-    "                \"SALARY\":        160000,\n",
-    "            },\n",
-    "            {\n",
-    "                \"EMPLOYEE_ID\":   \"E101\",\n",
-    "                \"EMPLOYEE_NAME\": \"Bob Singh\",\n",
-    "                \"ORG_ID\":        \"O10\",\n",
-    "                \"ORG_NAME\":      \"Acme Corp\",\n",
-    "                \"ROLE_ID\":       \"R9\",\n",
-    "                \"ROLE_TITLE\":    \"Data Architect\",\n",
-    "                \"START_DATE\":    \"2024-09-01\",\n",
-    "                \"END_DATE\":      None,\n",
-    "                \"SALARY\":        185000,\n",
-    "            },\n",
-    "        ]\n",
-    "\n",
-    "    ingestor = SnowflakeIngestor(\n",
-    "        account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n",
-    "        user=os.getenv(\"SNOWFLAKE_USER\"),\n",
-    "        password=os.getenv(\"SNOWFLAKE_PASSWORD\"),\n",
-    "        warehouse=os.getenv(\"SNOWFLAKE_WAREHOUSE\"),\n",
-    "        database=os.getenv(\"SNOWFLAKE_DATABASE\"),\n",
-    "        schema=os.getenv(\"SNOWFLAKE_SCHEMA\", \"PUBLIC\"),\n",
-    "    )\n",
-    "    query = (\n",
-    "        \"SELECT EMPLOYEE_ID, EMPLOYEE_NAME, \"\n",
-    "        \"ORG_ID, ORG_NAME, ROLE_ID, ROLE_TITLE, \"\n",
-    "        \"START_DATE, END_DATE, SALARY \"\n",
-    "        \"FROM HR_EMPLOYMENT_FACT\"\n",
-    "    )\n",
-    "    data = ingestor.ingest_query(query)\n",
-    "    ingestor.close()\n",
-    "    return data.data\n",
-    "\n",
-    "\n",
-    "rows = fetch_rows_from_snowflake()\n",
-    "rows[:2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-9",
-   "metadata": {},
-   "source": [
-    "## Step 4: Map Rows to Ontology Concepts Explicitly\n",
-    "\n",
-    "This is the semantic transformation layer — the part that makes your ontology real.\n",
-    "\n",
-    "Semantica does not guess which column becomes which entity or property.\n",
-    "Every assignment is code you write and own:\n",
-    "\n",
-    "- **Stable node IDs** — deterministic, collision-safe, derived from business keys\n",
-    "- **Class assignment** — matches what you declared in Step 1\n",
-    "- **Property routing** — each column value goes to the correct ontology property\n",
-    "- **Reification wiring** — `EmploymentEvent` is linked to its three participants\n",
-    "\n",
-    "When your Snowflake schema changes, only this function needs updating. The ontology stays stable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-10",
-   "metadata": {},
-   "outputs": [],
-   "source": "def map_rows_to_kg(rows: List[Dict[str, Any]]) -> Dict[str, Any]:\n    entities: Dict[str, Dict[str, Any]] = {}\n    relationships: List[Dict[str, Any]] = []\n\n    for row in rows:\n        # Stable, deterministic node IDs derived from business keys\n        person_id = f\"person:{row['EMPLOYEE_ID']}\"\n        org_id    = f\"org:{row['ORG_ID']}\"\n        role_id   = f\"role:{row['ROLE_ID']}\"\n        # Event ID includes all three participants + start date so that\n        # a re-hired employee gets a distinct event node, not an overwrite.\n        event_id  = f\"employment:{row['EMPLOYEE_ID']}:{row['ORG_ID']}:{row['START_DATE']}\"\n\n        # Entities — \"type\" must match a class name from Step 1\n        entities[person_id] = {\n            \"id\": person_id,\n            \"type\": \"Person\",\n            \"properties\": {\"name\": row[\"EMPLOYEE_NAME\"]},\n        }\n        entities[org_id] = {\n            \"id\": org_id,\n            \"type\": \"Organization\",\n            \"properties\": {\"legalName\": row[\"ORG_NAME\"]},\n        }\n        entities[role_id] = {\n            \"id\": role_id,\n            \"type\": \"Role\",\n            \"properties\": {\"title\": row[\"ROLE_TITLE\"]},\n        }\n\n        # Reification node — filter out None values so TripletStore does not\n        # stringify None as the literal \"None\" for open-ended employment.\n        event_props = {\n            \"startDate\": row[\"START_DATE\"],\n            \"endDate\":   row[\"END_DATE\"],\n            \"salary\":    row[\"SALARY\"],\n        }\n        entities[event_id] = {\n            \"id\": event_id,\n            \"type\": \"EmploymentEvent\",\n            \"properties\": {k: v for k, v in event_props.items() if v is not None},\n        }\n\n        # Full URIs for relationship types so TripletStore stores hr:<type>\n        # instead of the default urn:property:<type>, keeping SPARQL consistent.\n        relationships.extend([\n            # Shortcut edges — fast SPARQL when context is not needed\n            {\"source\": person_id, \"target\": org_id,    \"type\": f\"{BASE_URI}worksFor\"},\n            {\"source\": person_id, \"target\": role_id,   \"type\": f\"{BASE_URI}hasRole\"},\n            # Reification spokes — full context via the event node\n            {\"source\": event_id,  \"target\": person_id, \"type\": f\"{BASE_URI}employee\"},\n            {\"source\": event_id,  \"target\": org_id,    \"type\": f\"{BASE_URI}employer\"},\n            {\"source\": event_id,  \"target\": role_id,   \"type\": f\"{BASE_URI}role\"},\n        ])\n\n    return build_kg([{\"entities\": list(entities.values()), \"relationships\": relationships}])\n\n\nkg = map_rows_to_kg(rows)\nprint(f\"Entities built:      {len(kg.get('entities', []))}\")\nprint(f\"Relationships built: {len(kg.get('relationships', []))}\")\n\nsample = next((e for e in kg[\"entities\"] if e[\"type\"] == \"EmploymentEvent\"), None)\nprint(f\"\\nSample EmploymentEvent node: {sample}\")"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-11",
-   "metadata": {},
-   "source": [
-    "## Step 5: Validate Ontology and Export OWL + SHACL\n",
-    "\n",
-    "`OntologyEngine` validates your ontology dict and serialises it to standards-compliant files.\n",
-    "\n",
-    "**Output files:**\n",
-    "- `employment_manual_ontology.ttl` — OWL 2 Turtle\n",
-    "- `employment_manual_shapes.ttl` — SHACL 1.1 node and property shapes\n",
-    "\n",
-    "**Standards status:**\n",
-    "\n",
-    "| Standard | Semantica support |\n",
-    "|---|---|\n",
-    "| SPARQL 1.1 | Full |\n",
-    "| SHACL 1.1 (`sh:NodeShape`, `sh:PropertyShape`, `sh:minCount`, `sh:datatype`, `sh:class`) | Full |\n",
-    "| SPARQL 1.2 (reifier annotation syntax, `LATERAL`) | Tracked — not yet implemented |\n",
-    "| SHACL 1.2 (`sh:severity` profiles, SHACL-AF extensions) | Tracked — not yet implemented |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-12",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "engine = OntologyEngine(base_uri=BASE_URI)\n",
-    "\n",
-    "validation = engine.validate(ontology)\n",
-    "owl_ttl    = engine.to_owl(ontology,   format=\"turtle\")\n",
-    "shacl_ttl  = engine.to_shacl(ontology, format=\"turtle\")\n",
-    "\n",
-    "engine.export_owl(ontology,   \"employment_manual_ontology.ttl\", format=\"turtle\")\n",
-    "engine.export_shacl(ontology, \"employment_manual_shapes.ttl\",   format=\"turtle\")\n",
-    "\n",
-    "print(f\"Ontology valid:      {validation.valid}\")\n",
-    "print(f\"Ontology consistent: {validation.consistent}\")\n",
-    "print(f\"OWL output:          {len(owl_ttl):,} chars → employment_manual_ontology.ttl\")\n",
-    "print(f\"SHACL output:        {len(shacl_ttl):,} chars → employment_manual_shapes.ttl\")\n",
-    "\n",
-    "print(\"\\n--- SHACL shapes (first 20 lines) ---\")\n",
-    "print(\"\\n\".join(shacl_ttl.splitlines()[:20]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-13",
-   "metadata": {},
-   "source": [
-    "## Best-Practice Architecture\n",
-    "\n",
-    "```\n",
-    "┌──────────────────────────────────┐\n",
-    "│  Ontology as code (Python dict)  │  ← versioned alongside your application\n",
-    "│  + AssociativeClass for n-ary    │\n",
-    "└───────────────┬──────────────────┘\n",
-    "                │ validate + export\n",
-    "                ▼\n",
-    "┌───────────────────────────────────┐\n",
-    "│  OWL 2 Turtle  │  SHACL 1.1      │  ← standards-compliant artifacts\n",
-    "└───────────────┬───────────────────┘\n",
-    "                │\n",
-    "                ▼\n",
-    "┌──────────────────────────────────┐\n",
-    "│  Snowflake — raw data access     │  ← no schema introspection\n",
-    "└───────────────┬──────────────────┘\n",
-    "                │ explicit mapping layer\n",
-    "                ▼\n",
-    "┌──────────────────────────────────┐\n",
-    "│  Ontology-aligned KG             │  ← types, IDs, edges match Step 1\n",
-    "└───────────────┬──────────────────┘\n",
-    "                │ optional\n",
-    "                ▼\n",
-    "┌──────────────────────────────────┐\n",
-    "│  Triplet store + SPARQL 1.1      │\n",
-    "└──────────────────────────────────┘\n",
-    "```\n",
-    "\n",
-    "**Why this split matters:**\n",
-    "If Semantica inferred the ontology from your Snowflake schema, every schema migration would risk silently changing your semantic model.\n",
-    "With this pattern, schema changes only touch the mapping function in Step 4 — the ontology remains stable and under your control."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-14",
-   "metadata": {},
-   "source": [
-    "## SPARQL Query Patterns\n",
-    "\n",
-    "Two query styles are available because we wrote both shortcut edges and reification spokes.\n",
-    "\n",
-    "### Simple lookup — shortcut edge (no context needed)\n",
-    "\n",
-    "```sparql\n",
-    "PREFIX hr: <https://example.com/hr/>\n",
-    "\n",
-    "SELECT ?personName ?orgName\n",
-    "WHERE {\n",
-    "    ?person  a hr:Person ;\n",
-    "             hr:name     ?personName ;\n",
-    "             hr:worksFor ?org .\n",
-    "    ?org     hr:legalName ?orgName .\n",
-    "}\n",
-    "```\n",
-    "\n",
-    "### Contextual lookup — via reification node (salary, dates, role)\n",
-    "\n",
-    "```sparql\n",
-    "PREFIX hr: <https://example.com/hr/>\n",
-    "\n",
-    "SELECT ?personName ?roleTitle ?salary ?startDate\n",
-    "WHERE {\n",
-    "    ?event   a            hr:EmploymentEvent ;\n",
-    "             hr:employee  ?person ;\n",
-    "             hr:role      ?role ;\n",
-    "             hr:salary    ?salary ;\n",
-    "             hr:startDate ?startDate .\n",
-    "    ?person  hr:name   ?personName .\n",
-    "    ?role    hr:title  ?roleTitle .\n",
-    "}\n",
-    "ORDER BY DESC(?salary)\n",
-    "```\n",
-    "\n",
-    "### Future: SPARQL 1.2 reifier syntax\n",
-    "\n",
-    "The SPARQL 1.2 draft introduces annotation syntax that lets you attach context directly to triples, without a separate intermediate node.\n",
-    "Once the spec is ratified Semantica will adopt it, and the contextual query above may be expressible more concisely."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-15",
-   "metadata": {},
-   "source": [
-    "## Step 6 (Optional): Load to Triplet Store and Run SPARQL\n",
-    "\n",
-    "Set `STORE_TO_TRIPLET=true` to load the KG into a live triplet store and run the contextual reification query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if os.getenv(\"STORE_TO_TRIPLET\", \"false\").lower() == \"true\":\n",
-    "    store = TripletStore(\n",
-    "        backend=os.getenv(\"TRIPLET_BACKEND\", \"blazegraph\"),\n",
-    "        endpoint=os.getenv(\"TRIPLET_ENDPOINT\", \"http://localhost:9999/blazegraph\"),\n",
-    "        namespace=os.getenv(\"TRIPLET_NAMESPACE\", \"kb\"),\n",
-    "    )\n",
-    "    store_result = store.store(knowledge_graph=kg, ontology=ontology)\n",
-    "    print(\"Store result:\", store_result)\n",
-    "\n",
-    "    # Contextual reification query — person + role + salary via EmploymentEvent\n",
-    "    query = \"\"\"\n",
-    "        PREFIX hr: <https://example.com/hr/>\n",
-    "\n",
-    "        SELECT ?personName ?roleTitle ?salary ?startDate\n",
-    "        WHERE {\n",
-    "            ?event   a            hr:EmploymentEvent ;\n",
-    "                     hr:employee  ?person ;\n",
-    "                     hr:role      ?role ;\n",
-    "                     hr:salary    ?salary ;\n",
-    "                     hr:startDate ?startDate .\n",
-    "            ?person  hr:name   ?personName .\n",
-    "            ?role    hr:title  ?roleTitle .\n",
-    "        }\n",
-    "        ORDER BY DESC(?salary)\n",
-    "        LIMIT 10\n",
-    "    \"\"\"\n",
-    "    result = store.execute_query(query)\n",
-    "    print(result)\n",
-    "else:\n",
-    "    print(\"Skipping triplet-store load/query (set STORE_TO_TRIPLET=true to enable)\")"
-   ]
-  }
- ]
-}

cookbook/introduction/07_Building_Knowledge_Graphs.ipynb

@@ -10,15 +10,16 @@
     "\n",
     "## Overview\n",
     "\n",
-    "This notebook demonstrates how to build knowledge graphs from entities and relationships using Semantica's graph building modules. You'll learn to use `GraphBuilder` and `EntityResolver`.\n",
+    "This notebook demonstrates how to build knowledge graphs from extracted entities and relationships using Semantica's graph building modules. You'll learn to use `GraphBuilder` and `EntityResolver`.\n",
     "\n",
     "**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/kg/)\n",
     "\n",
     "### Learning Objectives\n",
     "\n",
-    "- Use `GraphBuilder` to construct knowledge graphs\n",
-    "- Use `EntityResolver` to resolve entity conflicts\n",
-    "**Note**: For deduplication, use the `semantica.deduplication` module.\n",
+    "- Extract entity mentions and relations, and map them into graph records\n",
+    "- Use `GraphBuilder` to construct a graph whose edges come from the actual extracted relations\n",
+    "- Use `EntityResolver` to merge duplicate mentions and remap relationship endpoints\n",
+    "- Use the `semantica.deduplication` module and report the complete deduplicated entity set\n",
     "\n",
     "## Installation\n",
     "\n",
@@ -32,120 +33,217 @@
     "\n",
     "---\n",
     "\n",
-    "## Step 1: Build Knowledge Graph\n",
+    "## Step 1: Extract Entities and Relations\n",
     "\n",
-    "Construct a knowledge graph from entities and relationships.\n"
+    "Extract entity mentions and relations from text. The sample text mentions `Apple Inc.` in two separate sentences, so we can later show how duplicate mentions are resolved into one canonical entity.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "!pip install semantica\n"
-   ]
+    "%pip install semantica\n",
+    "\n",
+    "# spaCy models are distributed separately from the spaCy library. This lesson\n",
+    "# relies on the English model to recognize standalone places such as Cupertino.\n",
+    "import sys\n",
+    "import subprocess\n",
+    "import spacy\n",
+    "\n",
+    "try:\n",
+    "    spacy.load(\"en_core_web_sm\")\n",
+    "except OSError:\n",
+    "    subprocess.check_call([sys.executable, \"-m\", \"spacy\", \"download\", \"en_core_web_sm\"])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from semantica.kg import GraphBuilder\n",
     "from semantica.semantic_extract import NERExtractor, RelationExtractor\n",
     "\n",
-    "builder = GraphBuilder()\n",
+    "text = (\n",
+    "    \"Apple Inc. is headquartered in Cupertino, California. \"\n",
+    "    \"Tim Cook is the CEO of Apple Inc. \"\n",
+    "    \"The company is a technology company.\"\n",
+    ")\n",
+    "\n",
     "ner_extractor = NERExtractor()\n",
     "relation_extractor = RelationExtractor()\n",
     "\n",
-    "text = \"Apple Inc. is a technology company. Tim Cook is the CEO of Apple Inc. Apple Inc. is headquartered in Cupertino, California.\"\n",
+    "mentions = ner_extractor.extract(text)\n",
+    "relations = relation_extractor.extract(text, mentions)\n",
     "\n",
-    "entities_list = ner_extractor.extract(text)\n",
-    "relationships_list = relation_extractor.extract(text, entities_list)\n",
+    "print(\"Entity mentions:\")\n",
+    "for mention in mentions:\n",
+    "    print(f\"  {mention.text!r:<13} {mention.label:<7} span=[{mention.start_char}:{mention.end_char}]\")\n",
     "\n",
-    "entities = []\n",
-    "for i, entity in enumerate(entities_list[:5], 1):\n",
-    "    entities.append({\n",
-    "        \"id\": f\"e{i}\",\n",
-    "        \"type\": entity.label,\n",
-    "        \"name\": entity.text,\n",
-    "        \"properties\": {}\n",
-    "    })\n",
-    "\n",
-    "relationships = []\n",
-    "for i, rel in enumerate(relationships_list[:3], 1):\n",
-    "    relationships.append({\n",
-    "        \"source\": f\"e{1}\",\n",
-    "        \"target\": f\"e{i+1}\",\n",
-    "        \"type\": rel.predicate,\n",
-    "        \"properties\": {}\n",
-    "    })\n",
-    "\n",
-    "knowledge_graph = builder.build(entities, relationships)\n",
-    "\n",
-    "print(f\"Built knowledge graph with {len(knowledge_graph.get('entities', []))} entities\")\n",
-    "print(f\"Relationships: {len(knowledge_graph.get('relationships', []))}\")"
-   ]
+    "print(\"\\nExtracted relations:\")\n",
+    "for rel in relations:\n",
+    "    print(f\"  {rel.subject.text!r} --{rel.predicate}--> {rel.object.text!r}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2: Entity Resolution\n",
+    "## Step 2: Build the Knowledge Graph\n",
     "\n",
-    "Resolve entity conflicts and duplicates.\n"
+    "Give every mention a graph ID, then translate each relation's `subject` and `object` into those IDs. Building edges from the actual relation endpoints — rather than guessing endpoints from list positions — is what keeps the graph faithful to the text.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "from semantica.kg import GraphBuilder\n",
+    "\n",
+    "entities = []\n",
+    "span_to_id = {}\n",
+    "for i, mention in enumerate(mentions, 1):\n",
+    "    graph_id = f\"e{i}\"\n",
+    "    span_to_id[(mention.start_char, mention.end_char)] = graph_id\n",
+    "    entities.append({\n",
+    "        \"id\": graph_id,\n",
+    "        \"type\": mention.label,\n",
+    "        \"name\": mention.text,\n",
+    "        \"properties\": {},\n",
+    "    })\n",
+    "\n",
+    "relationships = []\n",
+    "for rel in relations:\n",
+    "    source_id = span_to_id.get((rel.subject.start_char, rel.subject.end_char))\n",
+    "    target_id = span_to_id.get((rel.object.start_char, rel.object.end_char))\n",
+    "    if source_id is None or target_id is None:\n",
+    "        print(f\"Skipping relation with unmapped endpoint: \"\n",
+    "              f\"{rel.subject.text!r} --{rel.predicate}--> {rel.object.text!r}\")\n",
+    "        continue\n",
+    "    relationships.append({\n",
+    "        \"source\": source_id,\n",
+    "        \"target\": target_id,\n",
+    "        \"type\": rel.predicate,\n",
+    "        \"properties\": {},\n",
+    "    })\n",
+    "\n",
+    "builder = GraphBuilder()\n",
+    "knowledge_graph = builder.build({\"entities\": entities, \"relationships\": relationships})\n",
+    "\n",
+    "id_to_name = {entity[\"id\"]: entity[\"name\"] for entity in entities}\n",
+    "\n",
+    "print(f\"Graph entities ({len(knowledge_graph['entities'])}):\")\n",
+    "for entity in knowledge_graph[\"entities\"]:\n",
+    "    print(f\"  {entity['id']}: {entity['name']} ({entity['type']})\")\n",
+    "\n",
+    "print(f\"\\nGraph relationships ({len(knowledge_graph['relationships'])}):\")\n",
+    "for relationship in knowledge_graph[\"relationships\"]:\n",
+    "    print(f\"  {id_to_name[relationship['source']]} \"\n",
+    "          f\"--{relationship['type']}--> {id_to_name[relationship['target']]}\")\n",
+    "\n",
+    "edges = {\n",
+    "    (id_to_name[r[\"source\"]], r[\"type\"], id_to_name[r[\"target\"]])\n",
+    "    for r in knowledge_graph[\"relationships\"]\n",
+    "}\n",
+    "assert (\"Apple Inc.\", \"located_in\", \"Cupertino\") in edges\n",
+    "assert (\"Tim Cook\", \"works_for\", \"Apple Inc.\") in edges"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Entity Resolution\n",
+    "\n",
+    "The graph currently contains two nodes for the same organization. `EntityResolver` merges duplicate mentions into one canonical entity and records which source IDs were merged (`merged_from`), so relationship endpoints can be remapped onto the canonical entity.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from semantica.kg import EntityResolver\n",
     "\n",
     "entity_resolver = EntityResolver()\n",
-    "\n",
     "resolved_entities = entity_resolver.resolve_entities(entities)\n",
     "\n",
-    "print(f\"Original entities: {len(entities)}\")\n",
-    "print(f\"Resolved entities: {len(resolved_entities)}\")"
-   ]
+    "canonical_id = {}\n",
+    "for entity in resolved_entities:\n",
+    "    for source_id in entity.get(\"merged_from\", [entity[\"id\"]]):\n",
+    "        canonical_id[source_id] = entity[\"id\"]\n",
+    "    if entity.get(\"merged_from\"):\n",
+    "        print(f\"Merged {entity['merged_from']} -> {entity['id']}: {entity['name']}\")\n",
+    "\n",
+    "print(f\"\\nMentions in: {len(entities)}, resolved entities out: {len(resolved_entities)}\")\n",
+    "\n",
+    "resolved_names = {entity[\"id\"]: entity[\"name\"] for entity in resolved_entities}\n",
+    "print(\"\\nRelationships remapped onto canonical entities:\")\n",
+    "for relationship in relationships:\n",
+    "    source = canonical_id[relationship[\"source\"]]\n",
+    "    target = canonical_id[relationship[\"target\"]]\n",
+    "    print(f\"  {resolved_names[source]} --{relationship['type']}--> {resolved_names[target]}\")\n",
+    "\n",
+    "canonical_entities = {(entity[\"name\"], entity[\"type\"]) for entity in resolved_entities}\n",
+    "assert canonical_entities == {\n",
+    "    (\"Apple Inc.\", \"ORG\"),\n",
+    "    (\"Tim Cook\", \"PERSON\"),\n",
+    "    (\"Cupertino\", \"GPE\"),\n",
+    "    (\"California\", \"GPE\"),\n",
+    "}\n",
+    "assert len(resolved_entities) == 4"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3: Deduplication\n",
+    "## Step 4: Deduplication\n",
     "\n",
-    "Remove duplicate entities from the graph.\n"
+    "The `semantica.deduplication` module gives finer control over the same problem. Note that `merge_duplicates` returns one `MergeOperation` per duplicate *group* — the complete deduplicated collection is those merged entities plus every entity that was not part of any group.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from semantica.deduplication import DuplicateDetector, EntityMerger, MergeStrategy\n",
     "\n",
-    "# Detect duplicates\n",
     "detector = DuplicateDetector(similarity_threshold=0.8)\n",
-    "duplicate_groups = detector.detect_duplicate_groups(knowledge_graph.get('entities', []))\n",
+    "duplicate_groups = detector.detect_duplicate_groups(entities)\n",
+    "print(f\"Duplicate groups: {len(duplicate_groups)}\")\n",
+    "for group in duplicate_groups:\n",
+    "    print(f\"  {[entity['name'] for entity in group.entities]} \"\n",
+    "          f\"(confidence={group.confidence:.2f})\")\n",
     "\n",
-    "# Merge duplicates\n",
     "merger = EntityMerger()\n",
     "merge_operations = merger.merge_duplicates(\n",
-    "    knowledge_graph.get('entities', []),\n",
-    "    strategy=MergeStrategy.KEEP_MOST_COMPLETE\n",
+    "    entities, strategy=MergeStrategy.KEEP_MOST_COMPLETE\n",
     ")\n",
     "\n",
-    "deduplicated_entities = [op.merged_entity for op in merge_operations]\n",
+    "merged_source_ids = {\n",
+    "    entity[\"id\"] for op in merge_operations for entity in op.source_entities\n",
+    "}\n",
+    "untouched_entities = [e for e in entities if e[\"id\"] not in merged_source_ids]\n",
+    "deduplicated_entities = untouched_entities + [\n",
+    "    op.merged_entity for op in merge_operations\n",
+    "]\n",
     "\n",
-    "print(f\"Original entities: {len(knowledge_graph.get('entities', []))}\")\n",
-    "print(f\"Deduplicated entities: {len(deduplicated_entities)}\")\n"
-   ]
+    "print(f\"\\nMerge operations: {len(merge_operations)}\")\n",
+    "print(f\"Deduplicated entities ({len(deduplicated_entities)}):\")\n",
+    "for entity in deduplicated_entities:\n",
+    "    print(f\"  {entity['id']}: {entity['name']} ({entity['type']})\")\n",
+    "\n",
+    "assert len(merge_operations) == 1\n",
+    "assert len(deduplicated_entities) == 4"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -155,9 +253,10 @@
     "\n",
     "You've learned how to build knowledge graphs:\n",
     "\n",
-    "- **GraphBuilder**: Construct knowledge graphs from entities and relationships\n",
-    "- **EntityResolver**: Resolve entity conflicts and duplicates\n",
-    "- **Deduplication**: Use `semantica.deduplication` module for removing duplicate entities\n",
+    "- **Extraction to graph**: map each mention to a graph ID and build edges from the actual `Relation.subject` / `Relation.object` endpoints\n",
+    "- **GraphBuilder**: construct knowledge graphs from explicit `{\"entities\": ..., \"relationships\": ...}` input\n",
+    "- **EntityResolver**: merge duplicate mentions into canonical entities and remap relationship endpoints\n",
+    "- **Deduplication**: combine `MergeOperation` results with untouched entities to get the complete deduplicated set\n",
     "\n",
     "Next: Learn how to analyze graphs in the Graph_Analytics notebook.\n"
    ]

cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb

@@ -10,7 +10,7 @@
     "\n",
     "## Overview\n",
     "\n",
-    "This notebook walks you through creating your first knowledge graph from a simple document. You'll learn the complete end-to-end workflow from ingesting a file to visualizing the resulting knowledge graph.\n",
+    "This notebook walks you through creating your first knowledge graph from a simple document. You'll learn the complete end-to-end workflow from ingesting a file to visualizing the resulting knowledge graph — and every step consumes the real output of the step before it.\n",
     "\n",
     "> [!TIP]\n",
     "> This is the perfect starting point if you are new to Semantica. No prior knowledge of knowledge graphs is required!\n",
@@ -19,10 +19,10 @@
     "\n",
     "### 🎯 Learning Objectives\n",
     "\n",
-    "- **Understand the Workflow**: Learn the `File → Parse → Extract → Graph` pipeline\n",
+    "- **Understand the Workflow**: Learn the `File → Parse → Extract → Graph → Visualize` pipeline\n",
     "- **Ingest Data**: Load documents using `FileIngestor`\n",
     "- **Parse Content**: Extract text using `DocumentParser`\n",
-    "- **Extract Knowledge**: Identify entities using `NERExtractor`\n",
+    "- **Extract Knowledge**: Identify entities and relations using `NERExtractor` and `RelationExtractor`\n",
     "- **Build Graph**: Construct a graph using `GraphBuilder`\n",
     "- **Visualize**: See your graph come to life with `KGVisualizer`\n",
     "\n",
@@ -40,71 +40,76 @@
     "\n",
     "## 🔄 Simple End-to-End Workflow\n",
     "\n",
-    "The complete workflow consists of four main steps:\n",
+    "The complete workflow consists of five main steps:\n",
     "\n",
     "1. **📥 Ingest** - Load data from files or other sources\n",
     "2. **📄 Parse** - Extract and structure content from documents\n",
     "3. **⛏️ Extract** - Identify entities and relationships\n",
     "4. **🕸️ Build Graph** - Construct the knowledge graph\n",
+    "5. **📊 Visualize** - Render and analyze the graph\n",
     "\n",
-    "Each step is demonstrated in the code cells below.\n",
+    "Each step is demonstrated in the code cells below, and each cell can be rerun on its own: the sample file is only removed by the optional cleanup cell at the very end.\n",
     "\n",
     "> [!TIP]\n",
     "> **Alternative: Using Semantica Framework**\n",
-    "> \n",
+    ">\n",
     "> For a simpler, high-level approach, you can use the `Semantica` framework class which orchestrates all these steps:\n",
-    "> \n",
+    ">\n",
     "> ```python\n",
     "> from semantica.core import Semantica\n",
-    "> \n",
+    ">\n",
     "> framework = Semantica()\n",
     "> framework.initialize()\n",
-    "> \n",
+    ">\n",
     "> result = framework.build_knowledge_base(\n",
     ">     sources=[\"sample_document.txt\"],\n",
     ">     embeddings=True,\n",
     ">     graph=True\n",
     "> )\n",
-    "> \n",
+    ">\n",
     "> framework.shutdown()\n",
     "> ```\n",
-    "> \n",
+    ">\n",
     "> This notebook shows the step-by-step approach for learning. See [Core Module Usage Guide](../../../semantica/core/core_usage.md) for more details.\n",
     "\n",
     "---\n",
     "\n",
     "## 📂 Step 1: Ingest a File\n",
     "\n",
-    "In this step, we'll use `FileIngestor` to load a document. The ingestor supports various file formats including PDF, DOCX, TXT, and more.\n"
+    "In this step, we'll use `FileIngestor` to load a document. The ingestor supports various file formats including PDF, DOCX, TXT, and more. Writing the sample file is idempotent, so this cell can be rerun at any time.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "!pip install semantica"
-   ]
+    "%pip install semantica\n",
+    "\n",
+    "# spaCy models are distributed separately from the spaCy library. This lesson\n",
+    "# relies on the English model to recognize standalone places such as Cupertino.\n",
+    "import sys\n",
+    "import subprocess\n",
+    "import spacy\n",
+    "\n",
+    "try:\n",
+    "    spacy.load(\"en_core_web_sm\")\n",
+    "except OSError:\n",
+    "    subprocess.check_call([sys.executable, \"-m\", \"spacy\", \"download\", \"en_core_web_sm\"])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from semantica.ingest import FileIngestor\n",
     "from pathlib import Path\n",
     "\n",
-    "# Initialize the ingestor\n",
-    "ingestor = FileIngestor()\n",
+    "from semantica.ingest import FileIngestor\n",
     "\n",
-    "# Create a sample document for demonstration\n",
-    "sample_text = \"\"\"\n",
-    "Apple Inc. is a technology company founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in 1976.\n",
-    "The company is headquartered in Cupertino, California.\n",
-    "Tim Cook is the current CEO of Apple Inc.\n",
-    "Apple designs and manufactures consumer electronics, software, and online services.\n",
+    "sample_text = \"\"\"Apple Inc. is headquartered in Cupertino, California.\n",
+    "In 1976, Steve Jobs founded Apple Inc.\n",
+    "Tim Cook is the CEO of Apple Inc.\n",
     "\"\"\"\n",
     "\n",
     "sample_file = Path(\"sample_document.txt\")\n",
@@ -113,12 +118,14 @@
     "print(f\"File: {sample_file}\")\n",
     "print(f\"Content length: {len(sample_text)} characters\")\n",
     "\n",
-    "# Ingest the file\n",
+    "ingestor = FileIngestor()\n",
     "file_object = ingestor.ingest_file(sample_file, read_content=True)\n",
     "print(f\"  File name: {file_object.name}\")\n",
     "print(f\"  File type: {file_object.file_type}\")\n",
-    "print(f\"  Content available: {file_object.content is not None}\")\n"
-   ]
+    "print(f\"  Content available: {file_object.content is not None}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -126,64 +133,58 @@
    "source": [
     "## 📄 Step 2: Parse the Document\n",
     "\n",
-    "After ingesting the file, we need to parse it to extract the text content. The `DocumentParser` handles various file formats and extracts structured content.\n"
+    "After ingesting the file, we need to parse it to extract the text content. `DocumentParser.parse_document()` returns the extracted text under the `\"text\"` key.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from semantica.parse import DocumentParser\n",
     "\n",
     "parser = DocumentParser()\n",
-    "# Parse the document to extract text\n",
     "parsed_document = parser.parse_document(str(sample_file))\n",
-    "parsed_content = parsed_document.get(\"content\", \"\")\n",
-    "print(f\"  Parsed content length: {len(parsed_content) if parsed_content else 0} characters\")\n",
-    "print(f\"  Preview: {parsed_content[:200] if parsed_content else 'N/A'}...\")"
-   ]
+    "\n",
+    "parsed_content = parsed_document.get(\"text\", \"\")\n",
+    "assert parsed_content.strip(), \"Parsing produced no text — check the input file\"\n",
+    "\n",
+    "print(f\"Parsed content length: {len(parsed_content)} characters\")\n",
+    "print(f\"Preview: {parsed_content[:120]}...\")"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ⛏️ Step 3: Extract Entities\n",
+    "## ⛏️ Step 3: Extract Entities and Relations\n",
     "\n",
-    "Now we'll extract entities from the parsed text using Named Entity Recognition (NER). This identifies people, organizations, locations, dates, and other entities in the text.\n",
-    "\n",
-    "> [!NOTE]\n",
-    "> In a real scenario, you would use `NERExtractor` with an LLM or model backend. Here we simulate the output for demonstration purposes.\n"
+    "Now we'll extract entities and relations from the parsed text. `NERExtractor` identifies people, organizations, locations and dates; `RelationExtractor` finds relations between those mentions. Both operate on the *parsed content from Step 2* — not on a copy of the raw string.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from semantica.semantic_extract import NamedEntityRecognizer, NERExtractor\n",
+    "from semantica.semantic_extract import NERExtractor, RelationExtractor\n",
     "\n",
-    "ner = NamedEntityRecognizer()\n",
-    "extractor = NERExtractor()\n",
+    "ner_extractor = NERExtractor()\n",
+    "relation_extractor = RelationExtractor()\n",
     "\n",
-    "print(f\"\\nText: {parsed_content[:100]}...\")\n",
+    "mentions = ner_extractor.extract(parsed_content)\n",
+    "relations = relation_extractor.extract(parsed_content, mentions)\n",
     "\n",
-    "# Simulated extraction results\n",
-    "expected_entities = [\n",
-    "    {\"text\": \"Apple Inc.\", \"type\": \"Organization\", \"start\": 0, \"end\": 10},\n",
-    "    {\"text\": \"Steve Jobs\", \"type\": \"Person\", \"start\": 50, \"end\": 60},\n",
-    "    {\"text\": \"Steve Wozniak\", \"type\": \"Person\", \"start\": 62, \"end\": 75},\n",
-    "    {\"text\": \"Ronald Wayne\", \"type\": \"Person\", \"start\": 81, \"end\": 93},\n",
-    "    {\"text\": \"1976\", \"type\": \"Date\", \"start\": 97, \"end\": 101},\n",
-    "    {\"text\": \"Cupertino, California\", \"type\": \"Location\", \"start\": 130, \"end\": 151},\n",
-    "    {\"text\": \"Tim Cook\", \"type\": \"Person\", \"start\": 153, \"end\": 161},\n",
-    "]\n",
+    "print(\"Entity mentions:\")\n",
+    "for mention in mentions:\n",
+    "    print(f\"  {mention.text!r:<13} {mention.label:<7} span=[{mention.start_char}:{mention.end_char}]\")\n",
     "\n",
-    "for entity in expected_entities:\n",
-    "    print(f\"  - {entity['text']} ({entity['type']})\")\n"
-   ]
+    "print(\"\\nExtracted relations:\")\n",
+    "for rel in relations:\n",
+    "    print(f\"  {rel.subject.text!r} --{rel.predicate}--> {rel.object.text!r}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -191,58 +192,68 @@
    "source": [
     "## 🕸️ Step 4: Build the Knowledge Graph\n",
     "\n",
-    "Using the extracted entities and relationships, we'll construct a knowledge graph. The graph represents entities as nodes and relationships as edges.\n"
+    "Using the extracted entities and relations, we construct a knowledge graph with `GraphBuilder`. Every mention gets a graph ID, and each edge is built from the actual `Relation.subject` / `Relation.object` endpoints.\n",
+    "\n",
+    "> [!NOTE]\n",
+    "> The graph will contain one node per *mention*, so `Apple Inc.` appears three times. Merging duplicate mentions into one canonical entity is covered in [07_Building_Knowledge_Graphs.ipynb](./07_Building_Knowledge_Graphs.ipynb).\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from semantica.kg import GraphBuilder\n",
-    "import networkx as nx\n",
+    "\n",
+    "entities = []\n",
+    "span_to_id = {}\n",
+    "for i, mention in enumerate(mentions, 1):\n",
+    "    graph_id = f\"e{i}\"\n",
+    "    span_to_id[(mention.start_char, mention.end_char)] = graph_id\n",
+    "    entities.append({\n",
+    "        \"id\": graph_id,\n",
+    "        \"type\": mention.label,\n",
+    "        \"name\": mention.text,\n",
+    "        \"properties\": {},\n",
+    "    })\n",
+    "\n",
+    "relationships = []\n",
+    "for rel in relations:\n",
+    "    source_id = span_to_id.get((rel.subject.start_char, rel.subject.end_char))\n",
+    "    target_id = span_to_id.get((rel.object.start_char, rel.object.end_char))\n",
+    "    if source_id is None or target_id is None:\n",
+    "        print(f\"Skipping relation with unmapped endpoint: \"\n",
+    "              f\"{rel.subject.text!r} --{rel.predicate}--> {rel.object.text!r}\")\n",
+    "        continue\n",
+    "    relationships.append({\n",
+    "        \"source\": source_id,\n",
+    "        \"target\": target_id,\n",
+    "        \"type\": rel.predicate,\n",
+    "        \"properties\": {},\n",
+    "    })\n",
     "\n",
     "builder = GraphBuilder()\n",
+    "knowledge_graph = builder.build({\"entities\": entities, \"relationships\": relationships})\n",
     "\n",
-    "# Prepare data for graph construction\n",
-    "entities_data = [\n",
-    "    {\"id\": f\"entity_{i}\", \"name\": entity[\"text\"], \"type\": entity[\"type\"]}\n",
-    "    for i, entity in enumerate(expected_entities)\n",
-    "]\n",
+    "id_to_name = {entity[\"id\"]: entity[\"name\"] for entity in entities}\n",
     "\n",
-    "relationships_data = [\n",
-    "    {\"source\": \"entity_0\", \"target\": \"entity_1\", \"type\": \"founded_by\"},\n",
-    "    {\"source\": \"entity_0\", \"target\": \"entity_2\", \"type\": \"founded_by\"},\n",
-    "    {\"source\": \"entity_0\", \"target\": \"entity_3\", \"type\": \"founded_by\"},\n",
-    "    {\"source\": \"entity_0\", \"target\": \"entity_4\", \"type\": \"founded_in\"},\n",
-    "    {\"source\": \"entity_0\", \"target\": \"entity_5\", \"type\": \"located_in\"},\n",
-    "    {\"source\": \"entity_6\", \"target\": \"entity_0\", \"type\": \"ceo_of\"},\n",
-    "]\n",
+    "print(f\"Nodes (entities): {len(knowledge_graph['entities'])}\")\n",
+    "for entity in knowledge_graph[\"entities\"]:\n",
+    "    print(f\"  {entity['id']}: {entity['name']} ({entity['type']})\")\n",
     "\n",
-    "# Build the graph using NetworkX\n",
-    "kg = nx.DiGraph()\n",
+    "print(f\"\\nEdges (relationships): {len(knowledge_graph['relationships'])}\")\n",
+    "for relationship in knowledge_graph[\"relationships\"]:\n",
+    "    print(f\"  {id_to_name[relationship['source']]} \"\n",
+    "          f\"--{relationship['type']}--> {id_to_name[relationship['target']]}\")\n",
     "\n",
-    "for entity in entities_data:\n",
-    "    kg.add_node(entity[\"id\"], name=entity[\"name\"], type=entity[\"type\"])\n",
-    "\n",
-    "for rel in relationships_data:\n",
-    "    source_name = entities_data[int(rel[\"source\"].split(\"_\")[1])][\"name\"]\n",
-    "    target_name = entities_data[int(rel[\"target\"].split(\"_\")[1])][\"name\"]\n",
-    "    kg.add_edge(rel[\"source\"], rel[\"target\"], type=rel[\"type\"])\n",
-    "\n",
-    "print(f\"  Nodes (entities): {len(kg.nodes)}\")\n",
-    "print(f\"  Edges (relationships): {len(kg.edges)}\")\n",
-    "\n",
-    "for node_id in kg.nodes():\n",
-    "    node_data = kg.nodes[node_id]\n",
-    "    print(f\"  Node: {node_data['name']} ({node_data['type']})\")\n",
-    "\n",
-    "for source, target, data in kg.edges(data=True):\n",
-    "    source_name = kg.nodes[source]['name']\n",
-    "    target_name = kg.nodes[target]['name']\n",
-    "    print(f\"  {source_name} --[{data['type']}]--> {target_name}\")\n"
-   ]
+    "edges = {\n",
+    "    (id_to_name[r[\"source\"]], r[\"type\"], id_to_name[r[\"target\"]])\n",
+    "    for r in knowledge_graph[\"relationships\"]\n",
+    "}\n",
+    "assert (\"Apple Inc.\", \"located_in\", \"Cupertino\") in edges\n",
+    "assert (\"Tim Cook\", \"works_for\", \"Apple Inc.\") in edges"
+   ],
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "markdown",
@@ -250,49 +261,81 @@
    "source": [
     "## 📊 Step 5: Visualize and Analyze\n",
     "\n",
-    "Finally, we'll visualize the knowledge graph and analyze its structure. This helps you understand the relationships and entities in your data.\n"
+    "Finally, we render the knowledge graph with `KGVisualizer` and look at its structure. `visualize_network()` accepts the `GraphBuilder` result directly and can save an interactive HTML file.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "from semantica.visualization import KGVisualizer\n",
     "\n",
     "visualizer = KGVisualizer()\n",
-    "\n",
-    "print(f\"  Total entities: {len(kg.nodes)}\")\n",
-    "print(f\"  Total relationships: {len(kg.edges)}\")\n",
+    "fig = visualizer.visualize_network(\n",
+    "    knowledge_graph, output=\"html\", file_path=\"knowledge_graph.html\"\n",
+    ")\n",
+    "print(\"Saved interactive visualization to knowledge_graph.html\")\n",
     "\n",
     "entity_types = {}\n",
-    "for node_id in kg.nodes():\n",
-    "    entity_type = kg.nodes[node_id]['type']\n",
-    "    entity_types[entity_type] = entity_types.get(entity_type, 0) + 1\n",
+    "for entity in knowledge_graph[\"entities\"]:\n",
+    "    entity_types[entity[\"type\"]] = entity_types.get(entity[\"type\"], 0) + 1\n",
     "\n",
-    "for etype, count in entity_types.items():\n",
-    "    print(f\"  - {etype}: {count}\")\n",
+    "print(\"\\nEntities by type:\")\n",
+    "for entity_type, count in sorted(entity_types.items()):\n",
+    "    print(f\"  - {entity_type}: {count}\")\n",
     "\n",
-    "rel_types = {}\n",
-    "for _, _, data in kg.edges(data=True):\n",
-    "    rel_type = data.get('type', 'unknown')\n",
-    "    rel_types[rel_type] = rel_types.get(rel_type, 0) + 1\n",
+    "relationship_types = {}\n",
+    "for relationship in knowledge_graph[\"relationships\"]:\n",
+    "    relationship_types[relationship[\"type\"]] = (\n",
+    "        relationship_types.get(relationship[\"type\"], 0) + 1\n",
+    "    )\n",
     "\n",
-    "for rtype, count in rel_types.items():\n",
-    "    print(f\"  - {rtype}: {count}\")\n",
+    "print(\"\\nRelationships by type:\")\n",
+    "for relationship_type, count in sorted(relationship_types.items()):\n",
+    "    print(f\"  - {relationship_type}: {count}\")\n",
     "\n",
-    "# Cleanup\n",
-    "if sample_file.exists():\n",
-    "    sample_file.unlink()\n"
+    "fig"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧹 Optional: Clean Up\n",
+    "\n",
+    "Run this cell only when you are done with the notebook. Earlier cells read `sample_document.txt`, so they stay rerunnable until you delete it here.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "for path in [sample_file, Path(\"knowledge_graph.html\")]:\n",
+    "    if path.exists():\n",
+    "        path.unlink()\n",
+    "        print(f\"Removed {path}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You've built your first knowledge graph, end to end:\n",
+    "\n",
+    "- **FileIngestor** loaded the sample document\n",
+    "- **DocumentParser** returned its text under the `\"text\"` key\n",
+    "- **NERExtractor** / **RelationExtractor** produced real mentions and relations from that text\n",
+    "- **GraphBuilder** turned them into a graph whose edges come from the actual relation endpoints\n",
+    "- **KGVisualizer** rendered the result as an interactive network\n",
+    "\n",
+    "Next: merge duplicate mentions with `EntityResolver` in [07_Building_Knowledge_Graphs.ipynb](./07_Building_Knowledge_Graphs.ipynb), or explore graph metrics in the Graph Analytics notebook.\n"
+   ]
   }
  ],
  "metadata": {

cookbook/introduction/14_Ontology.ipynb

@@ -497,7 +497,8 @@
     "**Next Steps**:\n",
     "*   Try customizing the `NamespaceManager` to use your organization's URL.\n",
     "*   Explore `OntologyEvaluator` for deeper quality metrics.\n",
-    "*   Feed the generated ontology into the **Knowledge Graph** module to start reasoning over your data!"
+    "*   Feed the generated ontology into the **Knowledge Graph** module to start reasoning over your data!\n",
+    "*   Put the graph, ontology, and explicit mappings together in [Semantic Layer Basics](./26_Semantic_Layer_Basics.ipynb)."
    ]
   }
  ],

cookbook/introduction/26_Semantic_Layer_Basics.ipynb

@@ -0,0 +1,418 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/semantica-agi/semantica/blob/main/cookbook/introduction/26_Semantic_Layer_Basics.ipynb)\n",
+    "\n",
+    "# Semantic Layer Basics: Putting the Knowledge Graph, Ontology, and Mappings Together\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This lesson connects three things you have already met — a knowledge graph, an ontology, and RDF export — into one minimal *semantic layer*: a knowledge graph whose types, relationships, and properties are **explicitly mapped** to ontology terms, so the resulting RDF can be queried with SPARQL against a shared vocabulary.\n",
+    "\n",
+    "**Documentation**: [API Reference](https://semantica.readthedocs.io/concepts/)\n",
+    "\n",
+    "### 🎯 Learning Objectives\n",
+    "\n",
+    "- Build a small knowledge graph with `GraphBuilder`\n",
+    "- Generate a starter ontology from the graph with `OntologyGenerator`\n",
+    "- Write **explicit** entity-type, relationship-type, and property mappings to ontology terms\n",
+    "- Produce ontology-aligned RDF and store it with `TripletStore`\n",
+    "- Answer a business question with one small SPARQL query\n",
+    "\n",
+    "### 📚 Prerequisites\n",
+    "\n",
+    "- [07_Building_Knowledge_Graphs.ipynb](./07_Building_Knowledge_Graphs.ipynb) — graphs from entities and relationships\n",
+    "- [14_Ontology.ipynb](./14_Ontology.ipynb) — ontology generation\n",
+    "- [20_Triplet_Store.ipynb](./20_Triplet_Store.ipynb) — triplet store backends\n",
+    "\n",
+    "> [!NOTE]\n",
+    "> **Teaching mappings vs. governed mappings.** The mappings in this lesson are a demo: they live in a Python dict and are derived from a generated ontology. A production semantic layer uses governed identifiers, hand-designed ontologies, explicit source mappings, validation (SHACL), provenance, and versioning — that workflow is covered in [Advanced: Manual Ontology + Snowflake Mapping](../advanced/13_Manual_Ontology_Snowflake_Mapping.ipynb).\n",
+    "\n",
+    "## Installation\n",
+    "\n",
+    "The triplet-store step uses the embedded Oxigraph backend, so install with that extra. Pin at least 0.6.7: earlier releases could generate ontology classes with no URI (#1103), which silently breaks the mappings below instead of failing loudly.\n",
+    "\n",
+    "```bash\n",
+    "pip install \"semantica[tripletstore-oxigraph]>=0.6.7\"\n",
+    "```\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Step 1: Build a Knowledge Graph\n",
+    "\n",
+    "Start from a small, explicit set of entities and relationships — two people, an organization, and a project.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "!pip install \"semantica[tripletstore-oxigraph]>=0.6.7\"\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "from semantica.kg import GraphBuilder\n",
+    "\n",
+    "entities = [\n",
+    "    {\"id\": \"e1\", \"type\": \"Person\", \"name\": \"Alice\", \"properties\": {\"age\": 30, \"role\": \"Engineer\"}},\n",
+    "    {\"id\": \"e2\", \"type\": \"Person\", \"name\": \"Bob\", \"properties\": {\"age\": 35, \"role\": \"Manager\"}},\n",
+    "    {\"id\": \"e3\", \"type\": \"Organization\", \"name\": \"Tech Corp\", \"properties\": {\"founded\": 2010}},\n",
+    "    {\"id\": \"e4\", \"type\": \"Project\", \"name\": \"Project Alpha\", \"properties\": {\"status\": \"active\"}},\n",
+    "]\n",
+    "\n",
+    "relationships = [\n",
+    "    {\"source\": \"e1\", \"target\": \"e2\", \"type\": \"reports_to\", \"properties\": {}},\n",
+    "    {\"source\": \"e1\", \"target\": \"e3\", \"type\": \"works_for\", \"properties\": {}},\n",
+    "    {\"source\": \"e2\", \"target\": \"e3\", \"type\": \"works_for\", \"properties\": {}},\n",
+    "    {\"source\": \"e1\", \"target\": \"e4\", \"type\": \"works_on\", \"properties\": {}},\n",
+    "]\n",
+    "\n",
+    "builder = GraphBuilder()\n",
+    "knowledge_graph = builder.build({\"entities\": entities, \"relationships\": relationships})\n",
+    "\n",
+    "id_to_name = {entity[\"id\"]: entity[\"name\"] for entity in entities}\n",
+    "\n",
+    "print(f\"Entities ({len(knowledge_graph['entities'])}):\")\n",
+    "for entity in knowledge_graph[\"entities\"]:\n",
+    "    print(f\"  {entity['id']}: {entity['name']} ({entity['type']}) {entity['properties']}\")\n",
+    "\n",
+    "print(f\"\\nRelationships ({len(knowledge_graph['relationships'])}):\")\n",
+    "for relationship in knowledge_graph[\"relationships\"]:\n",
+    "    print(f\"  {id_to_name[relationship['source']]} \"\n",
+    "          f\"--{relationship['type']}--> {id_to_name[relationship['target']]}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2: Generate a Starter Ontology\n",
+    "\n",
+    "`OntologyGenerator` infers OWL classes and properties from graph records. Because `GraphBuilder` keeps business attributes inside each entity's `properties` dictionary while ontology inference reads record fields, we first create a flat **inference view**. The knowledge graph itself remains unchanged. Two settings matter here:\n",
+    "\n",
+    "- `base_uri` puts every generated term in *your* namespace\n",
+    "- `min_occurrences=1` includes classes that occur only once (the default of 2 would drop `Organization` and `Project` from this tiny demo graph)\n",
+    "\n",
+    "Note that the generator normalizes names: the relationship type `works_for` becomes the ontology property `worksFor`. That is exactly why the next step maps terms **explicitly** instead of matching names.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "from semantica.ontology import OntologyGenerator\n",
+    "\n",
+    "BASE_URI = \"https://example.org/company/\"\n",
+    "\n",
+    "# Adapt the property-graph representation to the record shape consumed by\n",
+    "# OntologyGenerator, so age/role/founded/status become declared properties.\n",
+    "ontology_input = {\n",
+    "    \"entities\": [\n",
+    "        {\n",
+    "            **{key: value for key, value in entity.items() if key != \"properties\"},\n",
+    "            **entity.get(\"properties\", {}),\n",
+    "        }\n",
+    "        for entity in knowledge_graph[\"entities\"]\n",
+    "    ],\n",
+    "    \"relationships\": knowledge_graph[\"relationships\"],\n",
+    "}\n",
+    "\n",
+    "generator = OntologyGenerator(base_uri=BASE_URI, min_occurrences=1)\n",
+    "ontology = generator.generate_from_graph(ontology_input)\n",
+    "\n",
+    "# OntologyGenerator calls datatype properties `data`; TripletStore's public\n",
+    "# ontology contract calls them `datatype`. Normalize that boundary explicitly.\n",
+    "store_ontology = {\n",
+    "    **ontology,\n",
+    "    \"properties\": [\n",
+    "        {**prop, \"type\": \"datatype\" if prop[\"type\"] == \"data\" else prop[\"type\"]}\n",
+    "        for prop in ontology[\"properties\"]\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "print(\"Classes:\")\n",
+    "for ontology_class in ontology[\"classes\"]:\n",
+    "    print(f\"  {ontology_class['name']:<14} {ontology_class['uri']}\")\n",
+    "\n",
+    "print(\"\\nProperties:\")\n",
+    "for prop in ontology[\"properties\"]:\n",
+    "    print(f\"  {prop['name']:<14} {prop['type']:<7} {prop['uri']}  \"\n",
+    "          f\"(domain={prop['domain']}, range={prop['range']})\")\n",
+    "\n",
+    "assert len(ontology[\"classes\"]) == 3"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Map the Graph to Ontology Terms\n",
+    "\n",
+    "The heart of a semantic layer is the mapping contract: which source type, relationship, and property corresponds to which ontology term.\n",
+    "\n",
+    "- **Entity types** and **relationship types**: each generated class/property records the source name it was inferred from (`metadata[\"inferred_from\"]`), so the mapping is read off the ontology itself — no fragile name matching between `works_for` and `worksFor`.\n",
+    "- **Properties**: the flat inference view makes `name`, `age`, `role`, `founded`, and `status` real generated datatype properties. Every mapping therefore points to a term declared in the ontology — no URI is invented only at mapping time.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "entity_type_mappings = {\n",
+    "    ontology_class[\"metadata\"][\"inferred_from\"]: ontology_class[\"uri\"]\n",
+    "    for ontology_class in ontology[\"classes\"]\n",
+    "}\n",
+    "\n",
+    "relationship_type_mappings = {\n",
+    "    prop[\"metadata\"][\"inferred_from\"]: prop[\"uri\"]\n",
+    "    for prop in ontology[\"properties\"]\n",
+    "    if prop[\"type\"] == \"object\"\n",
+    "}\n",
+    "\n",
+    "datatype_property_uris = {\n",
+    "    prop[\"metadata\"][\"inferred_from\"]: prop[\"uri\"]\n",
+    "    for prop in ontology[\"properties\"]\n",
+    "    if prop[\"type\"] != \"object\"\n",
+    "}\n",
+    "\n",
+    "property_mappings = datatype_property_uris\n",
+    "\n",
+    "semantic_layer = {\n",
+    "    \"graph\": knowledge_graph,\n",
+    "    \"ontology\": ontology,\n",
+    "    \"mappings\": {\n",
+    "        \"entity_type_mappings\": entity_type_mappings,\n",
+    "        \"relationship_type_mappings\": relationship_type_mappings,\n",
+    "        \"property_mappings\": property_mappings,\n",
+    "    },\n",
+    "}\n",
+    "\n",
+    "for mapping_name, mapping in semantic_layer[\"mappings\"].items():\n",
+    "    print(f\"{mapping_name}:\")\n",
+    "    for source, target in mapping.items():\n",
+    "        print(f\"  {source:<12} -> {target}\")\n",
+    "\n",
+    "# Every type and relationship in the graph must have an ontology term\n",
+    "assert set(entity_type_mappings) == {entity[\"type\"] for entity in entities}\n",
+    "assert set(relationship_type_mappings) == {rel[\"type\"] for rel in relationships}\n",
+    "assert set(property_mappings) == {\"name\", \"age\", \"role\", \"founded\", \"status\"}\n",
+    "assert set(property_mappings.values()) <= {prop[\"uri\"] for prop in ontology[\"properties\"]}"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4: Apply the Mappings\n",
+    "\n",
+    "Applying the semantic layer means rewriting the graph so every type, relationship, and property key is an ontology term. This *aligned* graph — not the original one — is what gets exported and stored.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "aligned_graph = {\n",
+    "    \"entities\": [\n",
+    "        {\n",
+    "            **entity,\n",
+    "            \"type\": entity_type_mappings[entity[\"type\"]],\n",
+    "            \"properties\": {\n",
+    "                property_mappings[\"name\"]: entity[\"name\"],\n",
+    "                **{\n",
+    "                    property_mappings[key]: value\n",
+    "                    for key, value in entity[\"properties\"].items()\n",
+    "                },\n",
+    "            },\n",
+    "        }\n",
+    "        for entity in knowledge_graph[\"entities\"]\n",
+    "    ],\n",
+    "    \"relationships\": [\n",
+    "        {**rel, \"type\": relationship_type_mappings[rel[\"type\"]]}\n",
+    "        for rel in knowledge_graph[\"relationships\"]\n",
+    "    ],\n",
+    "}\n",
+    "\n",
+    "print(\"Aligned entity sample:\")\n",
+    "sample = aligned_graph[\"entities\"][0]\n",
+    "print(f\"  id:   {sample['id']}\")\n",
+    "print(f\"  type: {sample['type']}\")\n",
+    "for key, value in sample[\"properties\"].items():\n",
+    "    print(f\"  {key} = {value}\")\n",
+    "\n",
+    "print(\"\\nAligned relationship sample:\")\n",
+    "print(f\"  {aligned_graph['relationships'][0]['type']}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5: Store and Export Complete Ontology-Aligned RDF\n",
+    "\n",
+    "`TripletStore.store()` materializes both the ontology declarations and the aligned instance graph. We then read those triples through the store's public API and serialize that complete RDF graph as Turtle. This avoids the compact `RDFExporter` entity projection, which does not include arbitrary entries from an entity's `properties` dictionary.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "from rdflib import Graph, Literal, URIRef\n",
+    "from rdflib.namespace import OWL, RDF\n",
+    "from semantica.triplet_store import TripletStore\n",
+    "\n",
+    "store = TripletStore(backend=\"oxigraph\")\n",
+    "result = store.store(aligned_graph, store_ontology)\n",
+    "print(f\"Stored triples: {result['processed']} (failed: {result['failed']})\")\n",
+    "\n",
+    "rdf_graph = Graph()\n",
+    "for triplet in store.get_triplets():\n",
+    "    datatype = triplet.metadata.get(\"datatype\")\n",
+    "    if datatype:\n",
+    "        object_term = Literal(triplet.object, datatype=URIRef(datatype))\n",
+    "    elif triplet.object.startswith((\"http://\", \"https://\", \"urn:\")):\n",
+    "        object_term = URIRef(triplet.object)\n",
+    "    else:\n",
+    "        object_term = Literal(triplet.object)\n",
+    "    rdf_graph.add((URIRef(triplet.subject), URIRef(triplet.predicate), object_term))\n",
+    "\n",
+    "rdf_graph.serialize(destination=\"semantic_layer.ttl\", format=\"turtle\")\n",
+    "turtle = open(\"semantic_layer.ttl\", encoding=\"utf-8\").read()\n",
+    "print(turtle[:600])\n",
+    "\n",
+    "# The exported RDF contains declarations plus mapped instance facts.\n",
+    "declared_datatype_properties = {\n",
+    "    str(subject) for subject in rdf_graph.subjects(RDF.type, OWL.DatatypeProperty)\n",
+    "}\n",
+    "assert result[\"failed\"] == 0\n",
+    "assert set(property_mappings.values()) <= declared_datatype_properties\n",
+    "assert (\n",
+    "    URIRef(BASE_URI + \"e1\"),\n",
+    "    URIRef(property_mappings[\"role\"]),\n",
+    "    Literal(\"Engineer\"),\n",
+    ") in rdf_graph\n",
+    "assert (\n",
+    "    URIRef(BASE_URI + \"e1\"),\n",
+    "    URIRef(relationship_type_mappings[\"works_for\"]),\n",
+    "    URIRef(BASE_URI + \"e3\"),\n",
+    ") in rdf_graph\n",
+    "print(\"... exported semantic_layer.ttl\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 6: Query the Semantic Layer\n",
+    "\n",
+    "The embedded Oxigraph backend runs in memory, so there is nothing to start beyond installing the `tripletstore-oxigraph` extra. The organization is constrained by its mapped `name` predicate; the query therefore means *Tech Corp*, rather than accidentally matching employees of every organization.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "query = f\"\"\"\n",
+    "SELECT ?name ?role WHERE {{\n",
+    "    ?person <{BASE_URI}worksFor> ?org .\n",
+    "    ?org <{BASE_URI}name> \"Tech Corp\" .\n",
+    "    ?person <{BASE_URI}name> ?name .\n",
+    "    ?person <{BASE_URI}role> ?role .\n",
+    "}}\n",
+    "ORDER BY ?name\n",
+    "\"\"\"\n",
+    "query_result = store.execute_query(query)\n",
+    "\n",
+    "print(\"\\nWho works for Tech Corp, and in which role?\")\n",
+    "for binding in query_result.bindings:\n",
+    "    print(f\"  {binding['name']['value']} — {binding['role']['value']}\")\n",
+    "\n",
+    "assert [(row[\"name\"][\"value\"], row[\"role\"][\"value\"]) for row in query_result.bindings] == [\n",
+    "    (\"Alice\", \"Engineer\"),\n",
+    "    (\"Bob\", \"Manager\"),\n",
+    "]"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🧹 Optional: Clean Up\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "ttl_file = Path(\"semantic_layer.ttl\")\n",
+    "if ttl_file.exists():\n",
+    "    ttl_file.unlink()\n",
+    "    print(f\"Removed {ttl_file}\")"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "A minimal semantic layer is a composition, and you have now built each part:\n",
+    "\n",
+    "1. **Knowledge graph** — `GraphBuilder` from explicit entities and relationships\n",
+    "2. **Ontology** — `OntologyGenerator` with your `base_uri`\n",
+    "3. **Explicit mappings** — entity types, relationship types, and properties, each tied to an ontology term\n",
+    "4. **Ontology-aligned RDF** — the mappings applied to the graph, materialized with `TripletStore`, and serialized to Turtle from the store's own triples\n",
+    "5. **Queryable store** — `TripletStore` (embedded Oxigraph) answering a SPARQL question over the shared vocabulary\n",
+    "\n",
+    "### Where to go next\n",
+    "\n",
+    "The production version of this workflow — hand-designed governed ontologies, explicit source-to-ontology mappings from a warehouse, n-ary modeling, SHACL validation, provenance, and versioning — is covered in [Advanced: Manual Ontology + Snowflake Mapping](../advanced/13_Manual_Ontology_Snowflake_Mapping.ipynb).\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/choose-your-module.md

@@ -226,7 +226,7 @@ Pick your goal to see the minimum imports and a working skeleton.
   </Tab>
 
   <Tab title="MCP — Claude / Cursor">
-    Use Semantica from Claude Desktop, Cursor, VS Code, or any MCP-aware tool — no Python code required after setup. 12 tools available instantly.
+    Use Semantica from Claude Desktop, Cursor, VS Code, or any MCP-aware tool — no Python code required after setup. 15 tools available instantly.
 
     **Step 1 — Install:**
     ```bash

docs/cli-setup.md

@@ -53,7 +53,7 @@ python -c "import semantica; print(semantica.__version__)"
 - **semantica-server** — Starts the REST API server. Binds to `0.0.0.0:8000`. Use this when another service or application needs programmatic access to Semantica over HTTP.
 - **semantica-worker** — Background task processor. Run alongside `semantica-server` when you need async pipeline execution outside the request cycle. Start the server first, then start one or more workers pointing at the same backend.
 - **semantica-explorer** — Launches the browser dashboard. Requires `pip install semantica[explorer]`. Use this to explore a saved knowledge graph interactively. See [Explorer Setup](explorer-setup).
-- **semantica-mcp** — Runs the MCP server over stdio. Configure it in your MCP client's settings file to expose all 12 tools and 3 resources to Claude Desktop, Cursor, Windsurf, or any MCP-aware client. See [MCP Server](reference/mcp_server).
+- **semantica-mcp** — Runs the MCP server over stdio. Configure it in your MCP client's settings file to expose all 15 tools and 3 resources to Claude Desktop, Cursor, Windsurf, or any MCP-aware client. See [MCP Server](reference/mcp_server).
 
 
 ## Usage Examples
@@ -229,6 +229,6 @@ Install the [Microsoft Visual C++ Redistributable](https://aka.ms/vs/17/release/
 ## Next Steps
 
 - [Explorer Setup](explorer-setup) — Build a graph, save it, and launch the browser dashboard.
-- [MCP Server](reference/mcp_server) — All 12 tools and 3 resources exposed over the MCP protocol.
+- [MCP Server](reference/mcp_server) — All 15 tools and 3 resources exposed over the MCP protocol.
 - [Installation](installation) — Virtual environments, optional extras, and platform-specific notes.
 - [Quickstart](quickstart) — End-to-end pipeline walkthrough with working code.

docs/cookbook.md

@@ -36,6 +36,7 @@ Essential guides to master the Semantica framework.
 - **[Graph Store](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/09_Graph_Store.ipynb)** — Persisting knowledge graphs in Neo4j or FalkorDB. Topics: Neo4j, Cypher, Persistence · *Intermediate*
 - **[Ontology](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/14_Ontology.ipynb)** — Defining domain schemas and ontologies to structure your data. Topics: OWL, RDF, Schema Design · *Intermediate*
 - **[Seed Data](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/25_Seed_Data.ipynb)** — Bootstrapping a knowledge graph from trusted CSV, JSON, database, and API sources before extraction runs. Topics: SeedDataManager, Foundation Graphs · *Intermediate*
+- **[Semantic Layer Basics](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/26_Semantic_Layer_Basics.ipynb)** — Capstone tutorial that combines a knowledge graph, generated ontology, explicit mappings, ontology-aligned RDF, and a SPARQL query. Topics: Semantic Layer, Ontology Mapping, Oxigraph, SPARQL · *Intermediate*
 
 
 ## Advanced Concepts

docs/docs.json

@@ -106,7 +106,8 @@
               "integrations/langchain",
               "integrations/docling",
               "integrations/snowflake",
-              "integrations/databricks"
+              "integrations/databricks",
+              "integrations/salesforce"
             ]
           },
           {

docs/faq.md

@@ -16,7 +16,7 @@ icon: "circle-question"
 | Python version? | 3.8+ (3.11+ recommended) |
 | API key required? | Optional: pattern extraction works with no keys |
 | Works with LangChain / LlamaIndex? | Yes: Semantica is a layer on top, not a replacement |
-| Production-ready? | Yes: 1,000+ tests, v0.5.0 ships with 12 security fixes |
+| Production-ready? | Yes: 1,000+ tests, security fixes shipped in every release (see [CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md)) |
 | Latest version? | **v0.6.7** (August 2026) |
 | Local LLMs? | Yes: Ollama via LiteLLM, HuggingFaceLLM for air-gapped |
 
@@ -70,9 +70,9 @@ Yes: MIT licensed, no vendor lock-in, no paywalled features. Some capabilities r
 
 <Accordion title="What's the latest version?" icon="star">
 
-**v0.5.0**: released May 2026.
+**v0.6.7**: released August 2026.
 
-Highlights: Ontology Hub, Distance Intelligence, Parquet/XML ingestion, 12 security fixes, Graph Explorer redesign, NER gateway fix.
+Highlights: first-class LangChain integration, SAP OData ingestor, human-editable Markdown round-trip persistence for `ContextGraph`, a structured Action layer for the reasoning engine, and a public `run_shacl_validation` entry point. The 0.6.x line also added first-class CrewAI support and the Semantica RDF vocabulary with deterministic IRIs. See the [CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md) for the full history.
 
 ```bash
 pip install --upgrade semantica
@@ -269,13 +269,13 @@ Groq, OpenAI, Anthropic, Google Gemini, Ollama (fully local), DeepSeek, Novita A
 
 <Accordion title="Is Semantica production-ready?" icon="shield-check">
 
-Yes. v0.5.0 ships with:
+Yes. Every release ships with:
 
 - 1,000+ passing tests across Python 3.8–3.12
 - `PipelineValidator` and `FailureHandler` with exponential backoff and configurable retry policies
 - W3C PROV-O provenance tracking across all modules
 - Change management with SHA-256 checksums and full audit trails
-- 12 security vulnerability fixes: eval injection, pickle deserialization, SQL injection, XXE, SSRF, ReDoS, path traversal, and more
+- Ongoing security hardening: eval injection, pickle deserialization, SQL injection, XXE, SSRF, ReDoS, and path traversal fixes have all landed across recent releases (see the [CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md) security sections)
 
 </Accordion>
 

docs/getting-started.md

@@ -183,7 +183,7 @@ icon: "rocket"
     }
     ```
 
-    12 tools available instantly: extract entities, query graph, record decisions, run reasoning, export results.
+    15 tools available instantly: extract entities, query graph, record decisions, run reasoning, export results.
 
     **Next:** [MCP Server reference →](reference/mcp_server)
   </Tab>

docs/guides/llm-integrations.md

@@ -28,6 +28,7 @@ The `semantica.llms` module provides a unified interface for connecting to Large
 ## When To Use / When Not To Use
 
 **Use LLM integrations for:**
+
 - Text generation, summarization, and question-answering tasks
 - Complex reasoning that requires natural language understanding
 - Structured data extraction from unstructured text
@@ -35,6 +36,7 @@ The `semantica.llms` module provides a unified interface for connecting to Large
 - Tasks where context, ambiguity, or domain knowledge matter
 
 **Deterministic tools may be better for:**
+
 - Pattern matching that regular expressions can handle
 - Simple rule-based classification with clear criteria
 - Mathematical calculations or statistical analysis
@@ -42,6 +44,7 @@ The `semantica.llms` module provides a unified interface for connecting to Large
 - Data transformations with known logic
 
 **A full LLM may be unnecessary for:**
+
 - Simple keyword search or exact string matching
 - Deterministic workflows with predefined decision trees
 - High-frequency, low-latency operations where inference overhead matters
@@ -59,7 +62,7 @@ Four factors drive provider selection, each optimized for different use cases:
 
 **Accuracy** matters most in high-stakes decisions: clinical contraindication checks, credit committee reasoning, and legal document analysis. Frontier models like Claude or GPT-4 available through `LiteLLM` provide the strongest reasoning capabilities.
 
-**Data residency** constraints eliminate cloud providers for classified or HIPAA-regulated workloads. `HuggingFaceLLM` with local model paths enables fully air-gapped deployments without network calls.
+**Data residency** constraints eliminate cloud providers for classified or HIPAA-regulated workloads. `HuggingFaceLLM` with local model paths, or `Ollama` pointed at a local server, both enable fully air-gapped deployments without network calls.
 
 **Cost at scale** favors high-throughput providers like Novita AI for bulk extraction pipelines processing thousands of documents per hour where per-token costs accumulate quickly.
 
@@ -143,6 +146,131 @@ risk_data = oai.generate_structured(
 
 The default model `gpt-3.5-turbo` is fine for classification and light extraction. Switch to `gpt-4o` for complex multi-step regulatory reasoning or document understanding.
 
+## Anthropic — Complex Reasoning and Structured Extraction
+
+**Anthropic** provides the Claude model family, built with an emphasis on careful, instruction-following behavior and strong performance on multi-step reasoning, long-document analysis, and code-related tasks. Claude models tend to be more cautious about ambiguous instructions than other providers. That matters when the cost of a confidently wrong answer is high.
+
+The `Anthropic` provider wraps the Claude API. Reach for it when the task involves reasoning through several dependent steps (not just single-turn extraction), when you're processing long source documents that need to stay in context, or when you need schema-validated structured output rather than best-effort JSON.
+
+Install with `pip install "semantica[llm-anthropic]"` (or just `pip install anthropic`) before using this provider.
+
+```python
+from semantica.llms import Anthropic
+
+claude = Anthropic(model="claude-sonnet-4-6", api_key="YOUR_ANTHROPIC_KEY")
+# api_key falls back to the ANTHROPIC_API_KEY environment variable
+
+# is_available() only confirms a client was constructed from some key.
+# It does not validate the key or check network reachability - an
+# invalid or expired key still passes this check and fails at generate().
+if not claude.is_available():
+    raise RuntimeError("Anthropic provider not configured - set ANTHROPIC_API_KEY")
+
+# Plain generation - multi-step reasoning over a contract clause
+verdict = claude.generate(
+    "A vendor contract has a 30-day termination-for-convenience clause "
+    "but a 90-day data-return obligation that survives termination. "
+    "If the customer terminates on day 1, when must vendor-held data "
+    "be returned? Answer with the date basis only.",
+    temperature=0.1,
+)
+print(verdict)
+# "Day 120 from termination notice. The 90-day return period runs from
+#  the termination date (day 30), not from the notice date."
+
+# Structured, schema-validated output
+from pydantic import BaseModel
+
+class ContractRisk(BaseModel):
+    clause: str
+    risk_level: str
+    days_to_deadline: int
+
+risk = claude.generate_typed(
+    "Extract the termination clause risk from: vendor contract, "
+    "30-day termination for convenience, 90-day post-termination "
+    "data return obligation.",
+    schema=ContractRisk,
+)
+print(risk.risk_level, risk.days_to_deadline)
+# "medium" 90
+```
+
+Model selection follows the same tier structure as the other providers: a Haiku model for high-volume classification where cost matters more than depth, a Sonnet model as the default for most extraction and reasoning tasks, an Opus model when a task genuinely needs the deepest reasoning available and latency/cost are secondary. Check Anthropic's docs for the current model identifiers, since they're versioned and change over time.
+
+## Gemini — Long Context and Multimodal Input
+
+**Gemini** is Google's model family, with a context window large enough to hold entire codebases or long regulatory filings in a single call, and native support for image and document input alongside text. Reach for it when a task needs to reference a large amount of source material at once, or when the input isn't plain text.
+
+The `Gemini` provider tries the newer `google-genai` SDK first and falls back to the older `google-generativeai` package if that's what's installed. Install with `pip install "semantica[llm-gemini]"` (or `pip install google-genai`) before using this provider.
+
+```python
+from semantica.llms import Gemini
+
+gemini = Gemini(model="gemini-pro", api_key="YOUR_GEMINI_KEY")
+# api_key falls back to the GEMINI_API_KEY environment variable
+
+if not gemini.is_available():
+    raise RuntimeError("Gemini provider not configured - set GEMINI_API_KEY")
+
+response = gemini.generate(
+    "Summarize the key obligations in a standard NDA in three bullet points."
+)
+print(response)
+
+data = gemini.generate_structured(
+    "Extract the party names and effective date from: "
+    "This Agreement is entered into between Acme Corp and Globex LLC, "
+    "effective January 1, 2026."
+)
+print(data)
+```
+
+## Ollama — Local, Air-Gapped Inference
+
+**Ollama** runs models entirely on your own machine, with no API key and no outbound network call. It's the right choice for air-gapped environments, offline development, or any workload where the source data can't leave the local network.
+
+Unlike the other providers here, `Ollama` takes a `base_url` instead of an `api_key`. It talks to a local Ollama server over HTTP. Start the server with `ollama serve` and pull a model with `ollama pull llama2` before using this provider. Install the Python client with `pip install "semantica[llm-ollama]"` (or `pip install ollama`).
+
+```python
+from semantica.llms import Ollama
+
+llm = Ollama(model="llama2", base_url="http://localhost:11434")
+
+if not llm.is_available():
+    raise RuntimeError("Ollama provider not configured - is 'ollama serve' running?")
+
+response = llm.generate("Explain the difference between a hash map and a tree map.")
+print(response)
+```
+
+`is_available()` for Ollama does a real connectivity check (it calls the server's `list()` endpoint), unlike the API-key-based providers above, so a `False` here usually means the server isn't running rather than a missing credential.
+
+## DeepSeek — Budget Reasoning at Scale
+
+**DeepSeek** exposes an OpenAI-compatible API at a fraction of the cost of the larger US providers, with reasoning quality that holds up well for extraction and classification work. It's a reasonable default when you're processing a large volume of documents and don't need the deepest reasoning tier.
+
+Install with `pip install "semantica[llm-deepseek]"` (or `pip install openai`, since DeepSeek is accessed through the OpenAI client pointed at a different base URL).
+
+```python
+from semantica.llms import DeepSeek
+
+llm = DeepSeek(model="deepseek-chat", api_key="YOUR_DEEPSEEK_KEY")
+# api_key falls back to the DEEPSEEK_API_KEY environment variable
+
+if not llm.is_available():
+    raise RuntimeError("DeepSeek provider not configured - set DEEPSEEK_API_KEY")
+
+response = llm.generate("List three risks of using a floating IP in a Kubernetes ingress.")
+print(response)
+
+data = llm.generate_structured(
+    "Extract the CVE ID and affected product from: "
+    "CVE-2024-3400 affects PAN-OS GlobalProtect gateways."
+)
+print(data)
+```
+
 ## LiteLLM — One Interface, 100+ Providers
 
 **LiteLLM** is a universal adapter that provides a single interface to over 100 different LLM providers, including Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex AI, and local Ollama instances. It acts as a translation layer, converting your unified API calls into provider-specific requests, enabling easy switching between providers without code changes.
@@ -306,30 +434,32 @@ for t in triplets:
 
 ## Novita AI — Cost-Efficient Bulk Extraction
 
-Novita AI exposes an OpenAI-compatible API and is available as a built-in provider for the extraction layer. It is accessed differently from the `semantica.llms` classes — through `create_provider` from `semantica.semantic_extract.providers` — making it the right choice for high-volume NER pipelines where per-call cost matters.
+**Novita AI** exposes an OpenAI-compatible API at low per-call cost, making it a reasonable choice for high-volume NER pipelines where cost matters more than getting the single best answer.
+
+Install with `pip install "semantica[llm-novita]"` (or `pip install openai`, since Novita is accessed through the OpenAI client pointed at a different base URL).
+
+```python
+from semantica.llms import Novita
+
+llm = Novita(model="deepseek/deepseek-v3.2", api_key="YOUR_NOVITA_KEY")
+# api_key falls back to the NOVITA_API_KEY environment variable
+
+if not llm.is_available():
+    raise RuntimeError("Novita provider not configured - set NOVITA_API_KEY")
+
+response = llm.generate("Summarize the Basel III leverage ratio requirement.")
+
+data = llm.generate_structured(
+    "Extract drug names and dosages from: "
+    "Patient received warfarin 5mg daily, aspirin 75mg daily, metformin 500mg twice daily."
+)
+```
+
+Novita is also reachable as a provider name string for the NER interface, without going through the `Novita` class directly:
 
 ```python
-from semantica.semantic_extract.providers import create_provider
 from semantica.semantic_extract import NamedEntityRecognizer
 
-# create_provider pools instances — same key reuses the same object
-provider = create_provider(
-    "novita",
-    api_key="YOUR_NOVITA_KEY",       # or set NOVITA_API_KEY env var
-    model="deepseek/deepseek-v3.2",  # default model
-)
-
-if provider.is_available():
-    # Plain generation
-    response = provider.generate("Summarise the Basel III leverage ratio requirement.")
-
-    # Structured extraction — returns parsed dict
-    data = provider.generate_structured(
-        "Extract drug names and dosages from: "
-        "Patient received warfarin 5mg daily, aspirin 75mg daily, metformin 500mg twice daily."
-    )
-
-# Use Novita through the NER interface — provider name as string
 ner = NamedEntityRecognizer(
     methods=["llm"],
     provider="novita",
@@ -339,11 +469,9 @@ entities = ner.extract_entities(
     "CVE-2024-3400 is exploited by UNC3886 targeting PAN-OS GlobalProtect."
 )
 for e in entities:
-    print("{} ({}) — conf={:.2f}".format(e.text, e.label, e.confidence))
+    print("{} ({}) conf={:.2f}".format(e.text, e.label, e.confidence))
 ```
 
-Novita requires the `openai` Python client under the hood — install with `pip install "semantica[llm-openai]"` or `pip install openai`.
-
 ## Domain Examples
 
 <Tabs>

docs/guides/mcp-server.md

@@ -11,7 +11,7 @@ MCP stands for the Model Context Protocol. It is an open standard that allows ex
 The Semantica MCP server exposes your knowledge graph as 12 callable tools. By connecting it, any compatible AI client can traverse the graph live, record decisions, run analytics, and export results during a conversation — without you having to write custom tool wrappers.
 
 <Info>
-  The Semantica MCP server exposes 12 tools and 3 read-only resources. All tools accept and return JSON. No configuration beyond an optional environment variable for graph persistence is required.
+  The Semantica MCP server exposes 15 tools and 3 read-only resources. All tools accept and return JSON. No configuration beyond an optional environment variable for graph persistence is required.
 </Info>
 
 ## Architecture & Communication
@@ -132,7 +132,7 @@ docker run --rm -i \
   ghcr.io/semantica-agi/semantica-mcp:latest
 ```
 
-## What the Agent Can Do: The 12 Tools
+## What the Agent Can Do: The 15 Tools
 
 Once connected, the LLM can call any of these tools during a conversation. The agent chains them automatically — you do not orchestrate the sequence, you just describe what you want.
 
@@ -140,6 +140,8 @@ Once connected, the LLM can call any of these tools during a conversation. The a
 
 **Knowledge graph manipulation** — `add_entity` adds a node, `add_relationship` adds a directed edge. After extraction, the agent calls these to persist what it found into the live graph.
 
+**Live graph queries and edits** — `query_graph` reads the graph without exporting it: fetch one node, walk its neighbours up to five hops, or keyword-search nodes. `update_node` merges properties onto an existing node (for example marking a task node `done`), and `delete_node` archives a node it no longer tracks. When `SEMANTICA_KG_PATH` is set, `update_node` and `delete_node` write their changes back to that file so they survive a restart.
+
 **Decision intelligence** — `record_decision` writes a decision as a provenance node with confidence score, reasoning, and decision maker identity. `query_decisions` retrieves past decisions by query or category. `find_precedents` finds the most similar past decisions by semantic similarity. `get_causal_chain` traces decision causality upstream or downstream.
 
 **Reasoning** — `run_reasoning` applies forward-chaining IF/THEN rules over a set of facts and returns derived conclusions.

docs/index.md

@@ -369,7 +369,7 @@ Semantica was designed for domains where every decision must be explainable and
 | `semantica.reasoning` | Forward chaining, Rete, deductive, abductive, SPARQL, Datalog |
 | `semantica.ontology` | SHACL, SKOS, alignments, diff/migration, auto-generation, OWL/RDF |
 | `semantica.explorer` | FastAPI Knowledge Explorer, Ontology Hub, Distance Intelligence, SHACL Studio |
-| `semantica.mcp_server` | MCP stdio server: 12 tools for Claude Desktop, VS Code, Cursor, Windsurf, Cline |
+| `semantica.mcp_server` | MCP stdio server: 15 tools for Claude Desktop, VS Code, Cursor, Windsurf, Cline |
 | `semantica.vector_store` | FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector |
 | `semantica.graph_store` | Neo4j, FalkorDB, Apache AGE, Amazon Neptune |
 | `semantica.triplet_store` | In-memory and persistent RDF triple store with SPARQL |
@@ -404,7 +404,7 @@ Semantica was designed for domains where every decision must be explainable and
 - 1,000+ passing tests with full regression coverage
 - `PipelineValidator` catches configuration errors at startup
 - `FailureHandler` with exponential backoff and dead-letter queues
-- 12 security vulnerabilities fixed in v0.5.0
+- Ongoing security hardening: fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
 
 **Modular by Design** — Import only what you need.
 - Use `NERExtractor` without a graph store

docs/integrations/salesforce.md

@@ -0,0 +1,376 @@
+---
+title: "Salesforce Integration"
+description: "Ingest CRM records from Salesforce sObjects and SOQL queries into Semantica's KG pipeline."
+icon: "cloud"
+---
+
+> Extract Accounts, Contacts, Opportunities, and custom objects from Salesforce into Semantica with username/password/security-token, JWT bearer, or session-based authentication.
+
+
+## Installation
+
+```bash
+# Install with Salesforce support
+pip install "semantica[db-salesforce]"
+
+# Or install the connector separately
+pip install simple-salesforce>=1.12.0
+```
+
+
+## Basic Usage
+
+```python
+from semantica.ingest import SalesforceIngestor
+import os
+
+ingestor = SalesforceIngestor(
+    username=os.getenv("SALESFORCE_USERNAME"),
+    password=os.getenv("SALESFORCE_PASSWORD"),
+    security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+    domain=os.getenv("SALESFORCE_DOMAIN", "login"),   # "test" for sandbox
+)
+
+data = ingestor.ingest_sobject("Account", fields=["Id", "Name", "Industry"], limit=1000)
+print(f"Retrieved {data.row_count} of {data.total_size} matching records")
+print(f"Columns: {data.columns}")
+```
+
+<Tip>
+Use environment variables (or a `.env` file with `python-dotenv`) to keep credentials out of source code. `SalesforceIngestor()` with no arguments reads from `SALESFORCE_*` environment variables automatically.
+</Tip>
+
+
+## Authentication Methods
+
+<Tabs>
+  <Tab title="Username / Password / Security Token">
+    ```python
+    import os
+    from semantica.ingest import SalesforceIngestor
+
+    ingestor = SalesforceIngestor(
+        username=os.getenv("SALESFORCE_USERNAME"),
+        password=os.getenv("SALESFORCE_PASSWORD"),
+        security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+        domain="login",   # production; use "test" for sandbox
+    )
+    ```
+    Set the required environment variables before running:
+    ```bash
+    export SALESFORCE_USERNAME="your-username@example.com"
+    export SALESFORCE_PASSWORD="your-password"
+    export SALESFORCE_SECURITY_TOKEN="your-security-token"
+    ```
+    The standard server-side flow. The security token is appended to the
+    password during Salesforce SOAP login. Generate or reset it under
+    **Settings → My Personal Information → Reset My Security Token**.
+  </Tab>
+  <Tab title="JWT Bearer (Recommended for CI/CD)">
+    ```python
+    import os
+    from semantica.ingest import SalesforceIngestor
+
+    ingestor = SalesforceIngestor(
+        username=os.getenv("SALESFORCE_USERNAME"),
+        consumer_key=os.getenv("SALESFORCE_CONSUMER_KEY"),
+        privatekey_file=os.getenv("SALESFORCE_PRIVATE_KEY_FILE"),
+        domain="login",   # or "test" for sandbox
+    )
+    ```
+    ```bash
+    export SALESFORCE_USERNAME="your-username@example.com"
+    export SALESFORCE_CONSUMER_KEY="your-connected-app-consumer-key"
+    export SALESFORCE_PRIVATE_KEY_FILE="/path/to/server.key"
+    ```
+    The JWT bearer flow authenticates with a signed token — no password
+    is transmitted. Ideal for server-to-server integrations and CI/CD
+    pipelines. Requires a Salesforce connected app configured with
+    **Use digital signatures** and the pre-authorised user listed under
+    **Manage → Profiles / Permission Sets**.
+
+    If you prefer to pass the key material as a string instead of a file
+    path, use `SALESFORCE_PRIVATE_KEY` (the PEM contents) in place of
+    `SALESFORCE_PRIVATE_KEY_FILE`.
+  </Tab>
+  <Tab title="Session ID + Instance URL">
+    ```python
+    ingestor = SalesforceIngestor(
+        session_id=os.getenv("SALESFORCE_SESSION_ID"),
+        instance_url=os.getenv("SALESFORCE_INSTANCE_URL"),
+    )
+    ```
+    Use this when your environment already manages the OAuth token
+    lifecycle (e.g. a connected app obtaining tokens via the web-server
+    or device flow). Pass the access token as `session_id` and the full
+    instance URL (e.g. `https://myorg.my.salesforce.com`) as
+    `instance_url`.
+  </Tab>
+  <Tab title="Sandbox">
+    ```python
+    import os
+    from semantica.ingest import SalesforceIngestor
+
+    ingestor = SalesforceIngestor(
+        username=os.getenv("SALESFORCE_USERNAME"),
+        password=os.getenv("SALESFORCE_PASSWORD"),
+        security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+        domain="test",   # routes to test.salesforce.com
+    )
+    ```
+    ```bash
+    export SALESFORCE_USERNAME="your-sandbox-username@example.com.sandbox"
+    export SALESFORCE_PASSWORD="your-password"
+    export SALESFORCE_SECURITY_TOKEN="your-security-token"
+    export SALESFORCE_DOMAIN="test"
+    ```
+    Replace `domain="login"` with `domain="test"` (or set
+    `SALESFORCE_DOMAIN=test` in your environment) to connect to a
+    developer or full sandbox.
+  </Tab>
+</Tabs>
+
+### Environment variables
+
+All constructor parameters have environment-variable fallbacks:
+
+| Variable | Parameter | Default |
+|---|---|---|
+| `SALESFORCE_USERNAME` | `username` | — |
+| `SALESFORCE_PASSWORD` | `password` | — |
+| `SALESFORCE_SECURITY_TOKEN` | `security_token` | — |
+| `SALESFORCE_DOMAIN` | `domain` | `"login"` |
+| `SALESFORCE_INSTANCE_URL` | `instance_url` | — |
+| `SALESFORCE_SESSION_ID` | `session_id` | — |
+| `SALESFORCE_CONSUMER_KEY` | `consumer_key` | — |
+| `SALESFORCE_PRIVATE_KEY_FILE` | `privatekey_file` | — |
+| `SALESFORCE_PRIVATE_KEY` | `privatekey` | — |
+| `SALESFORCE_API_VERSION` | `api_version` | library default (`59.0`) |
+
+
+## Object Ingestion
+
+### Ingest a standard object
+
+```python
+data = ingestor.ingest_sobject(
+    "Account",
+    fields=["Id", "Name", "Industry", "AnnualRevenue", "BillingCity"],
+    where="Type = 'Customer' AND AnnualRevenue > 1000000",
+    order_by="Name ASC",
+    limit=5000,
+)
+print(f"Retrieved {data.row_count} of {data.total_size} matching records")
+```
+
+<Note>
+`data.row_count` is the number of records in `data.data` (i.e. what was actually returned after any `limit`). `data.total_size` is Salesforce's `totalSize` — the number of records matching the query *before* the limit. Compare them to know whether you got all results.
+</Note>
+
+### Ingest a custom object
+
+Custom objects end with `__c` in their API name:
+
+```python
+data = ingestor.ingest_sobject(
+    "My_Custom_Object__c",
+    fields=["Id", "Name", "Custom_Field__c"],
+)
+```
+
+Relationship traversal fields (`Owner.Name`) are also supported:
+
+```python
+data = ingestor.ingest_sobject(
+    "Contact",
+    fields=["Id", "Name", "Email", "Account.Name", "Owner.Name"],
+    limit=10000,
+)
+```
+
+### Let Semantica choose the fields
+
+When `fields` is omitted, all selectable fields are fetched via `describe()`
+(one extra API call). Compound address and geolocation fields (`type=address`,
+`type=location`) are automatically excluded — select their components
+(`BillingStreet`, `BillingCity`, `Location__Latitude__s`, …) individually if
+you need them.
+
+```python
+data = ingestor.ingest_sobject("Opportunity")
+```
+
+
+## Raw SOQL Ingestion
+
+Pass any valid SOQL query verbatim — pagination is handled automatically:
+
+```python
+data = ingestor.ingest_query("""
+    SELECT Id, Name, StageName, Amount, CloseDate,
+           Account.Name, Owner.Name
+    FROM Opportunity
+    WHERE IsClosed = false
+    ORDER BY CloseDate ASC
+""")
+print(f"Open opportunities: {data.row_count}")
+```
+
+The query is passed to the Salesforce REST API unchanged. The caller is
+responsible for SOQL correctness and safety.
+
+<Warning>
+`ingest_query` does not validate or sanitise the SOQL string. Use
+`ingest_sobject` (which validates sObject names, field names, and WHERE/ORDER
+BY fragments) when building queries from application-controlled inputs.
+</Warning>
+
+
+## Document Export
+
+Convert ingested records to the Semantica document format for use with
+`GraphBuilder`:
+
+```python
+documents = ingestor.export_as_documents(
+    data,
+    id_field="Id",                            # default; Salesforce 18-char record Id
+    text_fields=["Name", "Description"],      # omit to join all string fields
+)
+
+print(f"Created {len(documents)} documents")
+# Each document:
+# {
+#   "id": "001xx000003GYk2AAG",
+#   "text": "Acme Corp Enterprise software company",
+#   "metadata": {
+#     "source": "salesforce",
+#     "sobject": "Account",
+#     "instance_url": "https://myorg.my.salesforce.com",
+#     "row_data": { ... full cleaned record ... }
+#   }
+# }
+```
+
+Feed the documents directly into `GraphBuilder`:
+
+```python
+from semantica.kg import GraphBuilder
+
+builder = GraphBuilder()
+kg = builder.build(documents)
+```
+
+
+## Object and Schema Discovery
+
+```python
+# List all accessible sObjects
+sobject_names = ingestor.list_sobjects()
+print(sobject_names[:10])   # ["Account", "Case", "Contact", ...]
+
+# Inspect fields for a specific sObject
+schema = ingestor.get_sobject_schema("Account")
+for field in schema["fields"]:
+    print(f"{field['name']}: {field['type']} (nillable={field['nillable']})")
+```
+
+
+## Context Manager
+
+Prefer the context manager for long-running jobs — it opens one connection on
+entry and closes it on exit, so every ingestion call inside the `with` block
+reuses the same authenticated session:
+
+```python
+with SalesforceIngestor(
+    username=os.getenv("SALESFORCE_USERNAME"),
+    password=os.getenv("SALESFORCE_PASSWORD"),
+    security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+) as sf:
+    accounts = sf.ingest_sobject("Account", limit=10000)
+    contacts = sf.ingest_sobject("Contact", limit=10000)
+    sobjects = sf.list_sobjects()
+```
+
+
+## Convenience Function
+
+Use `ingest_salesforce()` for one-liner ingestion:
+
+```python
+from semantica.ingest import ingest_salesforce
+
+# Fetch records
+data = ingest_salesforce(
+    method="sobject",
+    sobject_name="Account",
+    fields=["Id", "Name", "Industry"],
+    limit=500,
+)
+
+# Execute raw SOQL (credentials from environment variables)
+data = ingest_salesforce(
+    method="query",
+    soql="SELECT Id, Name FROM Contact WHERE IsActive = true",
+)
+
+# Ingest + export to documents in one step
+docs = ingest_salesforce(
+    method="documents",
+    sobject_name="Account",
+    text_fields=["Name", "Description"],
+    limit=1000,
+)
+
+# List accessible sObjects
+sobject_names = ingest_salesforce(method="list_sobjects")
+```
+
+Or use the unified `ingest()` dispatcher:
+
+```python
+from semantica.ingest import ingest
+
+result = ingest(
+    None,
+    source_type="salesforce",
+    method="sobject",
+    sobject_name="Account",
+    fields=["Id", "Name"],
+    limit=500,
+)
+data = result["data"]   # SalesforceData
+```
+
+
+## Troubleshooting
+
+```python
+import os
+from semantica.ingest import SalesforceConnector
+
+connector = SalesforceConnector(
+    username=os.getenv("SALESFORCE_USERNAME"),
+    password=os.getenv("SALESFORCE_PASSWORD"),
+    security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+)
+if not connector.test_connection():
+    print("Connection failed: check username, password, security token, and domain")
+```
+
+Common causes of authentication failures:
+
+- **Wrong domain**: production orgs use `domain="login"`; sandboxes use `domain="test"`.
+- **Stale security token**: reset it under **Settings → Reset My Security Token**. The new token is emailed to you.
+- **IP restriction**: your org's trusted IP ranges may block the originating IP. Check **Setup → Network Access**.
+- **API access disabled**: ensure the connected profile has the **API Enabled** permission.
+
+
+## See Also
+
+- [Ingest Module](../reference/ingest) — Full `SalesforceIngestor` API and all other ingestors.
+- [Snowflake Integration](snowflake) — Relational warehouse connector with a similar design.
+- [Databricks Integration](databricks) — Lakehouse connector.
+- [Installation](../installation) — All optional dependency extras.
+- [Knowledge Graph](../reference/kg) — Build a KG from ingested Salesforce data.

docs/modules.md

@@ -438,7 +438,7 @@ Exposes Semantica as an MCP stdio server for IDE and agent integrations.
 python -m semantica.mcp_server
 ```
 
-**Integrations:** Claude Desktop, VS Code, Cursor, Windsurf, Cline: 12 MCP tools exposed
+**Integrations:** Claude Desktop, VS Code, Cursor, Windsurf, Cline: 15 MCP tools exposed
 
 ### Seed
 

docs/quickstart.md

@@ -5,7 +5,7 @@ icon: "rocket"
 ---
 
 <Info>
-  **v0.5.0** — Ontology Hub, Distance Intelligence, Parquet & XML ingestion, 12 security fixes. <a href="https://github.com/semantica-agi/semantica/releases" style={{color:"#10B981",fontWeight:600,textDecoration:"none"}}>What's new →</a>
+  **v0.6.7** — first-class LangChain integration, SAP OData ingestor, human-editable Markdown persistence for `ContextGraph`, and a structured Action layer for the reasoning engine. <a href="https://github.com/semantica-agi/semantica/releases" style={{color:"#10B981",fontWeight:600,textDecoration:"none"}}>What's new →</a>
 </Info>
 
 This guide walks you through the end-to-end pipeline for building your first knowledge graph. Start here after installation. An LLM API key is optional: pattern-based extraction works out of the box.
@@ -35,7 +35,7 @@ Verify:
 
 ```bash
 python -c "import semantica; print(semantica.__version__)"
-# 0.5.0
+# 0.6.7
 ```
 
 
@@ -327,10 +327,11 @@ print(f"Relationships active in 2023: {result_2023['num_relationships']}")
 <Accordion title="Persistent graph store: Neo4j, FalkorDB, Apache AGE" icon="database">
 
 ```python
-from semantica.graph_store import Neo4jStore
+from semantica.graph_store import GraphStore
 from semantica.kg import GraphBuilder
 
-store = Neo4jStore(
+store = GraphStore(
+    backend="neo4j",
     uri="bolt://localhost:7687",
     user="neo4j",
     password="password",

docs/reference/context.md

@@ -25,6 +25,7 @@ icon: "brain"
 | `DecisionRecorder` | Record decisions with embeddings, causal chains, and metadata |
 | `PolicyEngine` | Policy management: `add_policy()`, `check_compliance()`, `get_applicable_policies()` |
 | `CausalChainAnalyzer` | Trace how decisions influenced each other: `get_causal_chain(decision_id)` |
+| `ErasureCoordinator` | Erase an entity across graph, memory, and vector store, returning an auditable `ErasureReceipt` |
 
 
 ## What You Get
@@ -634,6 +635,100 @@ queried together safely. Vector-store writes are deferred until the in-memory im
 commits; adapter synchronization remains best-effort and logs failures.
 
 
+## ErasureCoordinator
+
+`ContextGraph.purge_node()` is scoped to one graph: the node is removed and a
+tombstone is written, but the same content can still be live as an `AgentMemory`
+item and as an embedding in the vector store. `ErasureCoordinator` drives the
+cascade across every bound store and returns an `ErasureReceipt` recording what
+each one reported.
+
+```python
+from semantica.context import AgentMemory, ContextGraph, ErasureCoordinator
+
+coordinator = ErasureCoordinator(graph=graph, memory=memory)
+
+receipt = coordinator.erase_entity(
+    "customer-4471",
+    reason="GDPR Art. 17 request #882",
+)
+
+if not receipt.complete:
+    # These stores may still hold the entity; handle them out of band.
+    print(receipt.incomplete_stores)
+```
+
+<Warning>
+Check the receipt — the call returning is not proof the data is gone. FAISS,
+Milvus, and Weaviate expose no delete method, so erasure cannot be completed on
+those backends today; the receipt reports `unsupported` rather than a success it
+did not achieve.
+</Warning>
+
+### Constructor Parameters
+
+| Parameter | Type | Default | Description |
+| :--- | :--- | :--- | :--- |
+| `graph` | `ContextGraph` | `None` | Anything exposing `purge_node()` |
+| `memory` | `AgentMemory` | `None` | Anything exposing `find_by_entity()` and `batch_delete()` |
+| `vector_store` | `VectorStore` | `memory.vector_store` | Store holding entity-keyed embeddings; pass `False` to disable the leg |
+
+At least one store is required; a store that is not supplied reports
+`not_configured` rather than being silently skipped.
+
+### Methods
+
+| Method | Returns | Description |
+| :--- | :--- | :--- |
+| `erase_entity(entity_id, reason, at, vector_ids)` | `ErasureReceipt` | Erase one entity from every bound store |
+| `erase_entities(entity_ids, reason, at)` | `List[ErasureReceipt]` | One receipt per entity, in order; one failure does not stop the rest |
+
+### Store Statuses
+
+| Status | Meaning |
+| :--- | :--- |
+| `erased` | Reached, data removed. On the vectors leg this means the store accepted the delete for the ids given — backends offer no portable existence check, so it is not a count of embeddings that were really there |
+| `not_found` | Reached, held nothing for this entity |
+| `not_configured` | No such store was bound — normal, not a failure |
+| `unsupported` | The store cannot delete at all; retrying will not help |
+| `failed` | The store was reached and the deletion did not succeed |
+
+### ErasureReceipt
+
+| Member | Type | Description |
+| :--- | :--- | :--- |
+| `entity_id` | `str` | Entity the erasure was requested for |
+| `reason` | `Optional[str]` | Recorded in the receipt and the graph tombstone |
+| `erased_at` | `str` | ISO-8601; matches the tombstone's `purged_at` |
+| `stores` | `Dict[str, Dict]` | Per-store outcome keyed `vectors`, `memory`, `graph` |
+| `complete` | `bool` | `False` when any store reports `unsupported` or `failed` |
+| `incomplete_stores` | `List[str]` | Stores that may still hold the entity's data |
+| `to_dict()` | `Dict` | Serialized receipt, safe to persist as an audit record |
+
+```python
+receipt.to_dict()
+# {
+#   "entity_id": "customer-4471",
+#   "reason": "GDPR Art. 17 request #882",
+#   "erased_at": "2026-08-16T09:03:36.813220",
+#   "complete": False,
+#   "stores": {
+#     "vectors": {"status": "unsupported", "backend": "faiss",
+#                 "detail": "backend exposes no delete()/delete_vectors(); ..."},
+#     "memory":  {"status": "erased", "items": 14},
+#     "graph":   {"status": "erased", "nodes": 1, "edges": 3},
+#   },
+# }
+```
+
+Erasure runs outward-in — vectors, then memory, then the graph. The tombstone is
+the durable attestation that an erasure happened, so it is written last: a crash
+mid-cascade leaves the node present and the receipt incomplete, rather than a
+tombstone claiming more than actually happened. A store that raises is recorded
+as `failed` and the remaining stores are still erased. Erasing the same entity
+twice returns a receipt saying there was nothing left to do rather than raising.
+
+
 ## PolicyEngine
 
 `PolicyEngine` manages versioned policies stored in the knowledge graph. Policies are stored as nodes and can be linked to decisions:

docs/reference/mcp_server.md

@@ -6,7 +6,7 @@ icon: "plug"
 
 **`semantica.mcp_server`** exposes Semantica's knowledge graph, decision intelligence, semantic extraction, and reasoning capabilities as an [MCP (Model Context Protocol)](https://modelcontextprotocol.io) **server over stdio**:
 
-- 12 MCP tools exposed: extract entities, query graph, record decisions, run reasoning, export results
+- 15 MCP tools exposed: extract entities, query graph, record decisions, run reasoning, export results
 - No Python code required after launch: configure once, use from any MCP-aware client
 - Compatible with Claude Desktop, Windsurf, Cline, Continue, VS Code, Roo Code, Cursor
 
@@ -40,7 +40,7 @@ python -m semantica.mcp_server
 
 ## What You Get
 
-- **12 MCP Tools** — Extract entities, extract relations, record decisions, query decisions, find precedents, trace causal chains, add entities, add relationships, run analytics, summarise graph, run reasoning, export graph.
+- **15 MCP Tools** — Extract entities, extract relations, record decisions, query decisions, find precedents, trace causal chains, add entities, add relationships, run analytics, summarise graph, run reasoning, export graph, query the live graph, update nodes, archive nodes.
 - **3 Readable Resources** — Live graph JSON (`semantica://graph/summary`), decision list, and schema/version info: readable by any MCP client.
 - **Zero Infrastructure** — Runs over stdio: no server, no port, no Docker required. One config block to activate in any MCP client.
 - **Persistent Graphs** — Point `SEMANTICA_KG_PATH` at a saved graph file to reload it automatically on every server startup.
@@ -159,7 +159,7 @@ The MCP server is included in the base install: no extras required.
 
 ## Tools
 
-The MCP server exposes 12 tools that any connected AI assistant can call:
+The MCP server exposes 15 tools that any connected AI assistant can call:
 
 | Tool | Category | Description |
 | :---- | :-------- | :----------- |
@@ -173,6 +173,9 @@ The MCP server exposes 12 tools that any connected AI assistant can call:
 | `add_relationship` | Graph Operations | Add a directed edge between two nodes |
 | `get_graph_summary` | Graph Operations | Node count, decision count, graph status |
 | `get_graph_analytics` | Graph Operations | PageRank centrality and community detection |
+| `query_graph` | Graph Operations | Fetch a node, traverse its neighbours, or keyword-search nodes |
+| `update_node` | Graph Operations | Merge properties onto a node and persist to `SEMANTICA_KG_PATH` |
+| `delete_node` | Graph Operations | Soft-delete (archive) a node and persist to `SEMANTICA_KG_PATH` |
 | `run_reasoning` | Reasoning | Forward-chain IF/THEN rules over facts |
 | `export_graph` | Reasoning & Export | Serialise the graph (`turtle`/`ttl`: RDF Turtle aliases, `nt`, `xml`, `json-ld`, `json`) |
 
@@ -386,6 +389,51 @@ Takes no input parameters.
 
 </Accordion>
 
+<Accordion title="query_graph" icon="magnifying-glass">
+
+Read the live graph in one of three modes, set by `mode`:
+
+- `node` — return a single node by `node_id`.
+- `neighbors` (default) — traverse outward and inward from `node_id` up to `depth` hops (clamped to 1-5, default 1). Optional `relationship_types` filters edge types; optional `limit` caps results.
+- `search` — keyword match `query` against each node's id and content. Optional `node_type` restricts the scan; `limit` defaults to 50.
+
+**Input:**
+
+```json
+{ "mode": "neighbors", "node_id": "apple_inc", "depth": 2 }
+```
+
+</Accordion>
+
+<Accordion title="update_node" icon="pen">
+
+Merge a set of properties onto an existing node. The change is applied in memory and, when `SEMANTICA_KG_PATH` is set, written back to that file so it survives a restart. Returns `persisted: false` when no path is configured.
+
+**Input:**
+
+```json
+{
+  "node_id": "task_42",
+  "properties": { "status": "done", "note": "shipped in v0.6.7" }
+}
+```
+
+`node_id` and a non-empty `properties` object are required. Updating a missing node returns an error.
+
+</Accordion>
+
+<Accordion title="delete_node" icon="box-archive">
+
+Soft-delete a node: it stays in the graph for history but is marked `status: "archived"`. Persists to `SEMANTICA_KG_PATH` when configured.
+
+**Input:**
+
+```json
+{ "node_id": "task_42" }
+```
+
+</Accordion>
+
 </AccordionGroup>
 
 ### Reasoning

docs/superpowers/plans/2026-08-19-evals-objective.md

@@ -0,0 +1,338 @@
+# Objective Layer for semantica.evals Runner — Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Add per-metric objective support (direction + threshold, or Boolean expectation) to the `evaluate()` runner, overriding evaluator default pass verdicts, backward-compatible when no objective is configured.
+
+**Architecture:** The runner already iterates evaluators and computes per-case status. Objectives are read from `config["<name>"]["objective"]`, validated up front, and applied to each returned metric's `passed` field (and `details`) before aggregation. Error metrics always win over objectives.
+
+**Tech Stack:** Python 3.8+, stdlib only (typing, dataclasses). pytest for tests.
+
+## Global Constraints
+
+- Python >= 3.8: use `typing.Dict/List/Optional/Union`, never builtin generics or `|`.
+- Zero new dependencies.
+- Do not change the `EvalMetric` shape, the `evaluate()` signature, or the evaluator function signature.
+- Existing behavior with no `objective` configured must be byte-for-byte unchanged (all 62 existing tests keep passing).
+- Error metrics (`meta` contains `"error"`) always classify the case as `error`, regardless of objective.
+- Config errors are programmer errors: raise `ValueError` from `evaluate()` before any evaluator runs (fail-fast).
+- Tests go in `tests/evals/`, pytest class style, no new files outside the listed paths.
+
+---
+
+### Task 1: Objective parsing, validation, and re-decision in the runner
+
+**Files:**
+- Modify: `semantica/evals/runner.py`
+- Test: `tests/evals/test_runner.py`
+
+**Interfaces:**
+- Consumes: `EvalMetric` from `.types` (fields: `score`, `passed`, `meta`); `evaluate(cases, evaluators, config=None, target_fn=None)` existing signature.
+- Produces: private helpers `_parse_objective(name, eval_config) -> Optional[Dict]` (returns `None` when no objective configured, raises `ValueError` on invalid config) and `_apply_objective(metric, objective) -> bool` (returns the re-decided `passed`). Public `evaluate()` behavior extended as specified.
+
+- [ ] **Step 1: Write the failing tests**
+
+Append a new test class to `tests/evals/test_runner.py`:
+
+```python
+class TestObjective:
+    def test_maximize_with_threshold_pass(self):
+        # levenshtein similarity 1.0 for identical, objective demands >= 0.5
+        result = evaluate(
+            [("apple", "apple")],
+            evaluators=["levenshtein"],
+            config={"levenshtein": {"objective": {"direction": "maximize", "threshold": 0.5}}},
+        )
+        assert result.cases[0].status == "pass"
+        assert result.cases[0].metrics["levenshtein"].passed is True
+
+    def test_maximize_with_threshold_fail(self):
+        result = evaluate(
+            [("apple", "aple")],  # similarity < 1.0
+            evaluators=["levenshtein"],
+            config={"levenshtein": {"objective": {"direction": "maximize", "threshold": 0.99}}},
+        )
+        assert result.cases[0].status == "fail"
+        assert result.cases[0].metrics["levenshtein"].passed is False
+        assert "levenshtein" in result.cases[0].details
+
+    def test_minimize_with_threshold_pass(self):
+        # edit distance normalized ~0.2; objective: distance <= 0.5
+        result = evaluate(
+            [("night", "nacht")],
+            evaluators=["levenshtein"],
+            config={"levenshtein": {"objective": {"direction": "minimize", "threshold": 0.5}}},
+        )
+        assert result.cases[0].status == "pass"
+        assert result.cases[0].metrics["levenshtein"].passed is True
+
+    def test_minimize_with_threshold_fail(self):
+        result = evaluate(
+            [("night", "nacht")],
+            evaluators=["levenshtein"],
+            config={"levenshtein": {"objective": {"direction": "minimize", "threshold": 0.1}}},
+        )
+        assert result.cases[0].status == "fail"
+
+    def test_expect_true_on_boolean_metric(self):
+        result = evaluate(
+            [("ok", "ok")],
+            evaluators=["exact_match"],
+            config={"exact_match": {"objective": {"expect": True}}},
+        )
+        assert result.cases[0].status == "pass"
+
+    def test_expect_false_overrides_passing_metric(self):
+        # exact_match passes (score 1.0) but expectation is false -> fail
+        result = evaluate(
+            [("ok", "ok")],
+            evaluators=["exact_match"],
+            config={"exact_match": {"objective": {"expect": False}}},
+        )
+        assert result.cases[0].status == "fail"
+        assert result.cases[0].metrics["exact_match"].passed is False
+        assert "exact_match" in result.cases[0].details
+
+    def test_maximize_without_threshold_is_noop(self):
+        # identical behavior to no objective: evaluator's own verdict stands
+        result = evaluate(
+            [("ok", "no")],
+            evaluators=["exact_match"],
+            config={"exact_match": {"objective": {"direction": "maximize"}}},
+        )
+        assert result.cases[0].status == "fail"
+
+    def test_minimize_without_threshold_raises(self):
+        with pytest.raises(ValueError):
+            evaluate(
+                [("a", "b")],
+                evaluators=["levenshtein"],
+                config={"levenshtein": {"objective": {"direction": "minimize"}}},
+            )
+
+    def test_bad_direction_raises(self):
+        with pytest.raises(ValueError):
+            evaluate(
+                [("a", "b")],
+                evaluators=["levenshtein"],
+                config={"levenshtein": {"objective": {"direction": "sideways", "threshold": 0.5}}},
+            )
+
+    def test_expect_with_direction_raises(self):
+        with pytest.raises(ValueError):
+            evaluate(
+                [("a", "b")],
+                evaluators=["levenshtein"],
+                config={"levenshtein": {"objective": {"expect": True, "direction": "maximize"}}},
+            )
+
+    def test_error_metric_wins_over_objective(self):
+        result = evaluate(
+            [("[invalid", "x")],
+            evaluators=["regex_match"],
+            config={"regex_match": {"objective": {"direction": "maximize", "threshold": 0.0}}},
+        )
+        assert result.cases[0].status == "error"
+        assert result.errors == 1
+        assert result.failed == 0
+
+    def test_no_objective_unchanged(self):
+        result = evaluate([("ok", "no")], evaluators=["exact_match"])
+        assert result.cases[0].status == "fail"
+```
+
+- [ ] **Step 2: Run tests to verify they fail**
+
+Run: `python3 -m pytest tests/evals/test_runner.py -q`
+Expected: the new `TestObjective` tests fail (objective config ignored → `exact_match` passes under `expect:false` etc.); the pre-existing tests in the file still pass.
+
+- [ ] **Step 3: Implement objective parsing, validation, and re-decision**
+
+In `semantica/evals/runner.py`, add two helpers before `evaluate` and wire them into the evaluator loop.
+
+```python
+def _parse_objective(name, eval_config):
+    """Return the validated objective dict, or None when not configured.
+
+    Raises ValueError for invalid configurations (programmer error).
+    """
+    objective = (eval_config or {}).get("objective")
+    if objective is None:
+        return None
+    direction = objective.get("direction")
+    threshold = objective.get("threshold")
+    expect = objective.get("expect")
+
+    if expect is not None:
+        if direction is not None or threshold is not None:
+            raise ValueError(
+                f"objective for '{name}': 'expect' cannot be combined with "
+                "'direction' or 'threshold'"
+            )
+        return {"expect": bool(expect)}
+    if direction == "minimize":
+        if threshold is None:
+            raise ValueError(
+                f"objective for '{name}': 'minimize' requires a 'threshold'"
+            )
+        return {"direction": "minimize", "threshold": float(threshold)}
+    if direction == "maximize":
+        if threshold is None:
+            # no bar to re-decide against; treat as absent (evaluator default stands)
+            return None
+        return {"direction": "maximize", "threshold": float(threshold)}
+    raise ValueError(
+        f"objective for '{name}': 'direction' must be 'maximize' or 'minimize' "
+        f"(got {direction!r})"
+    )
+
+
+def _apply_objective(metric, objective):
+    """Return the objective-adjusted pass verdict for a non-error metric."""
+    if "expect" in objective:
+        return bool(metric.score) == objective["expect"]
+    if objective["direction"] == "minimize":
+        return metric.score <= objective["threshold"]
+    return metric.score >= objective["threshold"]
+```
+
+Then modify the evaluator loop in `evaluate()` so the parsed objective is computed once per case (outside the evaluator loop, since it only depends on merged config), and applied inside the loop:
+
+```python
+        objective_by_name = {
+            name: _parse_objective(name, merged.get(name) or {})
+            for name in evaluators
+        }
+        metrics: Dict[str, EvalMetric] = {}
+        details: Dict[str, Any] = {}
+        failed, errored = False, False
+        for name in evaluators:
+            eval_config = merged.get(name) or {}
+            try:
+                metric = get_evaluator(name)(actual, expected, config=eval_config)
+                objective = objective_by_name.get(name)
+                if objective is not None and "error" not in metric.meta:
+                    metric = EvalMetric(metric.score, _apply_objective(metric, objective), metric.meta)
+                metrics[name] = metric
+                if "error" in metric.meta:
+                    errored = True
+                    details[name] = metric.meta
+                elif not metric.passed:
+                    failed = True
+                    details[name] = metric.meta
+            except Exception as exc:  # noqa: BLE001
+                errored = True
+                metrics[name] = EvalMetric(0.0, False, {"error": str(exc)})
+                details[name] = {"error": str(exc)}
+```
+
+Note: `objective_by_name` is computed once per case (it depends only on merged config), so invalid config raises `ValueError` at the first case — satisfying the fail-fast requirement. `EvalMetric` is a frozen dataclass, so the re-verdict constructs a new instance preserving score/meta.
+
+- [ ] **Step 4: Run tests to verify they pass**
+
+Run: `python3 -m pytest tests/evals/test_runner.py -q`
+Expected: all `TestObjective` tests pass; pre-existing tests still pass.
+
+- [ ] **Step 5: Run the full evals suite**
+
+Run: `python3 -m pytest tests/evals -q`
+Expected: 62 existing + new tests all pass (no regressions).
+
+- [ ] **Step 6: Commit**
+
+```bash
+git add semantica/evals/runner.py tests/evals/test_runner.py
+git commit -m "feat(evals): add per-metric objective support to runner"
+```
+
+---
+
+### Task 2: Documentation — usage.md and CHANGELOG
+
+**Files:**
+- Modify: `semantica/evals/usage.md`
+- Modify: `CHANGELOG.md`
+
+**Interfaces:**
+- Consumes: the objective config surface implemented in Task 1 (exact keys: `objective.direction`, `objective.threshold`, `objective.expect`; validation rules).
+- Produces: docs only.
+
+- [ ] **Step 1: Add objective section to usage.md**
+
+Append a section after the existing "Run the runner over decision records" section:
+
+```markdown
+## Set per-evaluator objectives
+
+By default each evaluator decides its own pass/fail. To override that
+verdict at the run level, configure an **objective** per evaluator name:
+
+```python
+from semantica.evals import evaluate
+
+# Require a minimum similarity (default direction is maximize):
+evaluate(
+    [("apple", "aple")],
+    evaluators=["levenshtein"],
+    config={"levenshtein": {"objective": {"direction": "maximize", "threshold": 0.7}}},
+)
+
+# Lower is better — override the direction:
+evaluate(
+    [("night", "nacht")],
+    evaluators=["levenshtein"],
+    config={"levenshtein": {"objective": {"direction": "minimize", "threshold": 0.5}}},
+)
+
+# Boolean expectation on a 0/1 metric:
+evaluate(
+    [("ok", "ok")],
+    evaluators=["exact_match"],
+    config={"exact_match": {"objective": {"expect": False}}},
+)
+```
+
+Rules:
+
+- `maximize` + `threshold`: pass iff `score >= threshold`. `maximize` without
+  a threshold is a no-op (the evaluator's own verdict stands).
+- `minimize` + `threshold`: pass iff `score <= threshold`. `minimize`
+  **requires** a threshold — omitting it raises `ValueError`.
+- `expect` (`true`/`false`): pass iff `bool(score)` matches; cannot be
+  combined with `direction`/`threshold`.
+- A metric whose `meta` contains `"error"` is always an error, never affected
+  by an objective.
+- Invalid objective config raises `ValueError` before any evaluator runs.
+```
+
+- [ ] **Step 2: Add CHANGELOG entry**
+
+Under `## [Unreleased]` → `### Added`, insert a new bullet at the top (before the `semantica.evals` module entry), following existing style:
+
+```markdown
+- **`semantica.evals` runner gains per-metric objectives** (#1091)
+  - `evaluate()` now accepts `config={"<evaluator>": {"objective": {"direction": "maximize"|"minimize", "threshold": X}}}` to override the evaluator's default pass verdict with a threshold; `{"objective": {"expect": bool}}` expresses a Boolean expectation
+  - `minimize` requires a `threshold`; `maximize` without one is a no-op; `expect` cannot be combined with `direction`/`threshold`; invalid config raises `ValueError` before any evaluator runs
+  - Error metrics are never affected by objectives (error wins over fail)
+  - Backward compatible: no `objective` key → existing behavior unchanged
+  - New tests in `tests/evals/test_runner.py::TestObjective`
+```
+
+- [ ] **Step 3: Verify docs examples run**
+
+Run the three examples from Step 1 as a Python script (import `evaluate`, run each snippet) to confirm they don't raise unexpectedly. No test output assertion needed beyond "no exception" and sensible status values.
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add semantica/evals/usage.md CHANGELOG.md
+git commit -m "docs(evals): document per-metric objectives"
+```
+
+---
+
+## Self-Review Notes
+
+- **Spec coverage:** §3.1 (config surface) → Task 1 helpers + Task 2 docs; §3.2 (semantics: maximize/minimize/expect) → Task 1 `_apply_objective`; §3.3 (error wins) → Task 1 error branch + `test_error_metric_wins_over_objective`; §3.4 rules 1-3 (validation) → Task 1 `_parse_objective` + 4 validation tests; §3.4 rule 4 → error branch; §3.5 (aggregation unchanged, details on final verdict) → Task 1 loop + `test_expect_false_overrides_passing_metric` asserts `details`; §4 (fail-fast ValueError) → `_parse_objective` at case top; §5 (tests) → Task 1 test class; §6 (compat) → `test_no_objective_unchanged` + full-suite green.
+- **Type consistency:** `_parse_objective(name, eval_config) -> Optional[Dict]`, `_apply_objective(metric, objective) -> bool`; `EvalMetric(score, passed, meta)` positional construction preserved everywhere.
+- **Backward compat:** objective parsed to `None` for absent config → loop behavior identical to before.

docs/superpowers/specs/2026-08-19-evals-objective-design.md

@@ -0,0 +1,115 @@
+# Design: Objective layer for `semantica.evals` runner
+
+**Date:** 2026-08-19
+**Issue:** semantica-agi/semantica#1091 (assigned to pkupt)
+**Base:** PR #1090 (`semantica.evals` module)
+
+## 1. Problem
+
+`semantica.evals` runs named evaluators and aggregates per-case pass/fail, but the pass judgement is hard-coded inside each evaluator — a higher score always means "better". There is no way to express an evaluation objective at the run level:
+
+- apply a threshold the evaluator does not encode (e.g. "F1 must be ≥ 0.7");
+- reverse the direction (e.g. "lower edit distance is better");
+- express a Boolean expectation (e.g. "this metric should be `false`").
+
+This blocks the domain-specific benchmark harnesses `docs/community-projects.md` says `semantica.evals` supports. Palantir AIP Evals models exactly this: each metric has an **objective** (Boolean expected value, or numeric `maximize`/`minimize` direction with an optional threshold), and a test case passes when **all** its metrics meet their objectives.
+
+## 2. Scope
+
+In scope:
+
+- A per-metric objective configuration consumed by the `evaluate()` runner.
+- Runner-level pass/fail re-decision for numeric scores and Boolean metrics.
+- Backward-compatible behavior when no objective is configured.
+- Tests and docs.
+
+Out of scope:
+
+- Changing the evaluator signature or the `EvalMetric` shape.
+- Multi-iteration test cases (AIP Evals has them; Semantica's runner is single-iteration per case).
+- Objective-aware aggregation beyond per-case `pass`/`fail` (existing `pass_rate` semantics are kept).
+
+## 3. Design
+
+### 3.1 Configuration surface
+
+Objective is configured per evaluator inside the runner's `config`, under the evaluator name:
+
+```python
+config = {
+    "<evaluator_name>": {
+        "objective": {
+            "direction": "maximize" | "minimize",
+            "threshold": <float>,   # optional
+        }
+    }
+}
+```
+
+Boolean-form objective (shorthand): for metrics whose score is Boolean-like (0.0/1.0) or for semantic clarity, `{"objective": {"expect": true}}` / `{"objective": {"expect": false}}` is also supported.
+
+### 3.2 Evaluation semantics
+
+For each metric produced by an evaluator during a case run, if an objective exists for that evaluator name, the runner recomputes the metric's pass verdict:
+
+- **maximize**: pass iff `score >= threshold`. If no `threshold` is given, the objective is treated as absent (evaluator's own verdict stands) — see 3.4 rule 2.
+- **minimize**: pass iff `score <= threshold` (threshold required, see 3.4 rule 1).
+- **expect**: pass iff `bool(score)` equals `expect` (for Boolean-style metrics).
+
+When an objective is present, the runner **overrides** `metric.passed` with the objective verdict. When absent, `metric.passed` is used unchanged (existing behavior).
+
+The `objective` key is a **reserved runner-level key**: it is consumed by the runner and is passed through to the evaluator function inside `eval_config` (evaluators already ignore unknown config keys via `cfg.get(...)`, so this is harmless); evaluators must not rely on it. The runner re-decision happens on the metric the evaluator returns, so no evaluator change is required.
+
+### 3.3 Interaction with errors
+
+An `EvalMetric` whose `meta` contains `"error"` remains classified as an error regardless of objective (error wins over fail, per the existing contract). Objectives only affect non-error metrics.
+
+### 3.4 Ambiguity rules (explicit decisions)
+
+1. **`minimize` without `threshold`** is rejected at config-validation time with a clear error (`ValueError`), because "lowest is best" has no absolute pass bar without a threshold. (AIP Evals allows direction-only; we require threshold to keep pass/fail well-defined.) — *Chosen for determinism; revisit if a use case demands direction-only minimize.*
+2. **`maximize` without `threshold`** behaves like no objective (pass iff evaluator's own `passed`), because the evaluator's default is already "higher is better".
+3. **`expect` with a numeric `direction`/`threshold`** is a config error (`ValueError`): pick one form.
+4. **Objective on a metric that errors** → the error wins (3.3), objective ignored.
+
+### 3.5 Aggregation
+
+Unchanged:
+
+- Case `status`: `"error"` if any metric errored, else `"fail"` if any failed, else `"pass"`.
+- `pass_rate` = passed / total (1.0 on empty).
+- `metrics` dict holds the (possibly re-verdict'd) `EvalMetric`; the re-verdict is observable via `metric.passed`.
+- `details[name]` is populated when a metric ends up failed **after** objective re-decision (i.e. objective-failed metrics appear in `details`; metrics that pass under objective are not recorded there). This mirrors the existing "record failures in details" behavior applied to the final verdict.
+
+### 3.6 Files
+
+- `semantica/evals/runner.py` — add objective parsing/validation and re-decision inside the evaluator loop.
+- `tests/evals/test_runner.py` — new test class(es) for objective semantics.
+- `semantica/evals/usage.md` — document the objective config and examples.
+- `CHANGELOG.md` — `[Unreleased]` entry.
+
+No new dependencies; Python ≥ 3.8 (stdlib `typing`).
+
+## 4. Error handling
+
+- Invalid objective config (`direction` not in {maximize, minimize}, both `expect` and `direction`, `minimize` without threshold, non-numeric threshold) → `ValueError` raised at runner config parse, before any evaluator runs. Deterministic, fail-fast.
+- These are programmer errors, not per-case data errors — no per-case `error` status involved.
+
+## 5. Testing
+
+New tests in `tests/evals/test_runner.py`:
+
+1. maximize + threshold: score ≥ threshold → pass; below → fail.
+2. minimize + threshold: score ≤ threshold → pass; above → fail (e.g. levenshtein on a close pair).
+3. minimize without threshold → `ValueError`.
+4. expect=true / expect=false on a Boolean metric (exact_match) — pass/fail per expectation.
+5. no objective → existing behavior unchanged (evaluator's own verdict).
+6. objective + error metric → error wins (status=error, not fail).
+7. config error (bad direction) → `ValueError` raised by `evaluate()`.
+8. objective turns a passing metric into failing → `details` records it; case status becomes fail.
+9. backward-compat: all existing 62 tests keep passing.
+
+## 6. Compatibility
+
+- Public API (`evaluate`, `list_evaluators`, `get_evaluator`, types) unchanged in signature.
+- `EvalMetric` shape unchanged (score, passed, meta) — only `passed` may be recomputed by the runner.
+- Existing configs (no `objective` key) behave identically.

examples/ci/README.md

@@ -0,0 +1,36 @@
+# CI templates
+
+Copy-paste starting points for wiring `semantica` into your own project's CI. Each file is a
+complete, working config — rename it into your project (see the comment at the top of each file
+for the target path) and swap the smoke-test / test step for whatever your project does with
+Semantica. Each template installs `semantica` unconditionally and your own project's dependencies
+only if a `requirements.txt` is present; if your project uses `pyproject.toml`, Poetry, or Pipenv
+instead, adjust the marked install line (each file calls it out inline).
+
+| File | Target path in your repo |
+| ---- | ------------------------- |
+| [`github-actions.yml`](github-actions.yml) | `.github/workflows/semantica.yml` |
+| [`gitlab-ci.yml`](gitlab-ci.yml) | `.gitlab-ci.yml` |
+| [`circleci-config.yml`](circleci-config.yml) | `.circleci/config.yml` |
+
+If your own project is hosted on GitHub, you can skip the setup boilerplate entirely and use
+Semantica's reusable composite action instead:
+
+```yaml
+- uses: semantica-agi/semantica/.github/actions/setup-semantica@main
+  with:
+    python-version: '3.11'
+    # extras: 'explorer,all'   # optional
+    # version: '==0.6.7'       # optional, pin an exact release
+    # cache: 'pip'             # optional, only if your repo has a requirements.txt/pyproject.toml/etc.
+```
+
+`@main` always tracks this repo's default branch, which is convenient but — like any mutable
+ref — can change out from under you between runs. For production CI, pin it to a commit SHA
+instead (find one via `git rev-parse` against a tagged release, or the commit history for
+[`.github/actions/setup-semantica/`](../../.github/actions/setup-semantica/)) and update the pin
+deliberately when you want to pick up changes, the same way this repo's own workflows are pinned
+(see [`verify-action-pins.yml`](../../.github/workflows/verify-action-pins.yml)).
+
+It installs Python, installs `semantica`, and verifies the import (pip caching is opt-in via `cache: 'pip'`, since not every caller repo has a requirements file to key the cache on) — see
+[`.github/actions/setup-semantica/action.yml`](../../.github/actions/setup-semantica/action.yml).

examples/ci/circleci-config.yml

@@ -0,0 +1,40 @@
+# Drop this in as .circleci/config.yml in your own project.
+version: 2.1
+
+jobs:
+  test:
+    docker:
+      - image: cimg/python:3.11
+    steps:
+      - checkout
+      # A content-hashed cache key (e.g. `{{ checksum "requirements.txt" }}`)
+      # is more precise but breaks if that exact file doesn't exist in your
+      # project - swap in one matched to however you declare dependencies
+      # once you've adjusted the install step below.
+      - restore_cache:
+          keys:
+            - pip-cache-v1
+      - run:
+          name: Install dependencies
+          command: |
+            pip install --upgrade pip
+            pip install semantica
+            # Install your own project's dependencies however your project
+            # declares them - adjust this to match, e.g. `pip install -e .`
+            # for pyproject.toml / setup.cfg, or `poetry install`.
+            if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      - save_cache:
+          key: pip-cache-v1
+          paths:
+            - ~/.cache/pip
+      - run:
+          name: Smoke test
+          command: python -c "import semantica; print('semantica', semantica.__version__)"
+      - run:
+          name: Run tests
+          command: pytest
+
+workflows:
+  test:
+    jobs:
+      - test

examples/ci/github-actions.yml

@@ -0,0 +1,44 @@
+# Drop this in as .github/workflows/semantica.yml in your own project.
+#
+# Installs Semantica and runs a smoke import + your test suite. Swap the
+# smoke-test step for whatever your project actually does with Semantica
+# (build a context graph, run an ingest pipeline, etc.).
+#
+# Third-party actions below are pinned to a commit SHA rather than a mutable
+# tag - a moved tag can silently swap in different code. Update the pin (and
+# the trailing "# vX" comment) deliberately when you want a newer version;
+# see semantica-agi/semantica's own .github/workflows/verify-action-pins.yml
+# for one way to keep pins honest automatically.
+name: Semantica
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7
+
+      - uses: actions/setup-python@5fda3b95a4ea91299a34e894583c3862153e4b97 # v7
+        with:
+          python-version: '3.11'
+          cache: 'pip'
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install semantica
+          # Install your own project's dependencies however your project
+          # declares them - adjust this to match. Examples:
+          #   pip install -r requirements.txt
+          #   pip install -e .            # pyproject.toml / setup.cfg
+          #   pip install -e ".[dev]"
+          #   poetry install
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+      - name: Run tests
+        run: pytest

examples/ci/gitlab-ci.yml

@@ -0,0 +1,20 @@
+# Drop this in as .gitlab-ci.yml in your own project.
+semantica-test:
+  image: python:3.11-slim
+  cache:
+    paths:
+      - .cache/pip
+  variables:
+    PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"
+  script:
+    - pip install --upgrade pip
+    - pip install semantica
+    # Install your own project's dependencies however your project declares
+    # them - adjust this to match, e.g. `pip install -e .` for pyproject.toml
+    # / setup.cfg, or `poetry install`.
+    - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+    - python -c "import semantica; print('semantica', semantica.__version__)"
+    - pytest
+  rules:
+    - if: '$CI_PIPELINE_SOURCE == "merge_request_event"'
+    - if: '$CI_COMMIT_BRANCH == "main"'

examples/explorer_deterministic_rendering_example.py

@@ -0,0 +1,195 @@
+"""
+Deterministic Explorer Rendering E2E Example.
+
+Demonstrates building, serializing, and reloading a deterministic 4-node,
+3-edge knowledge graph baseline for visual inspection in Semantica Explorer (#1037).
+
+Graph topology:
+    Alice (Person, #63E6FF)       --WORKS_AT-->    Acme (Organization, #A78BFA)
+    Bob (Person, #63E6FF)         --KNOWS-->       Alice (Person, #63E6FF)
+    Acme (Organization, #A78BFA)  --LOCATED_IN-->  New York (Location, #34D399)
+
+Clean Checkout Prerequisites:
+    1. Python backend dependencies:
+       pip install -e ".[explorer]"
+    2. Frontend workspace dependencies:
+       cd explorer && npm install && cd ..
+
+Usage:
+    # 1. Generate the deterministic graph baseline:
+    python examples/explorer_deterministic_rendering_example.py
+
+    # 2. Launch Explorer with local dev authentication (Option A - Dev mode):
+    # Terminal 1 (Backend API):
+    SEMANTICA_ALLOW_ANONYMOUS=true python -m semantica.explorer --graph explorer_e2e_test_graph.json --port 8000 --no-browser
+    # Terminal 2 (Frontend UI):
+    cd explorer && npm run dev
+    # Open http://localhost:5173
+
+    # 2. Launch Explorer (Option B - Standalone CLI server):
+    SEMANTICA_ALLOW_ANONYMOUS=true python -m semantica.explorer --graph explorer_e2e_test_graph.json --port 8000
+    # Open http://localhost:8000
+
+    # Secure authentication alternative:
+    export SEMANTICA_API_KEY="your-secret-api-key"
+    python -m semantica.explorer --graph explorer_e2e_test_graph.json --port 8000
+    # Send HTTP header: X-API-Key: your-secret-api-key
+
+Verification Checklist:
+    - Exactly 4 nodes visible on canvas:
+        * Alice (Person, #63E6FF)
+        * Bob (Person, #63E6FF)
+        * Acme (Organization, #A78BFA)
+        * New York (Location, #34D399)
+    - Exactly 3 directed edges with canonical relationship labels:
+        * Alice -> Acme  (WORKS_AT)
+        * Bob -> Alice   (KNOWS)
+        * Acme -> New York (LOCATED_IN)
+    - Zoom behavior:
+        * Zoom in to Inspection tier (ratio <= 0.5): directional arrows and node labels scale clearly.
+        * Zoom out to Overview tier (ratio > 1.2): layout remains stable and non-colliding.
+    - Hover & Selection interactions:
+        * Hover over 'Alice': node halo triggers; incident edges (WORKS_AT, KNOWS) highlight in local context.
+        * Click an edge: Inspector panel confirms edgeType ('WORKS_AT', 'KNOWS', or 'LOCATED_IN').
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from semantica.context.context_graph import ContextGraph
+from semantica.explorer.session import GraphSession
+
+
+def build_deterministic_graph() -> ContextGraph:
+    """Build the exact 4-node, 3-edge graph specified in #1037."""
+    graph = ContextGraph(advanced_analytics=False)
+
+    # 1. Add exactly 4 nodes
+    graph.add_node(
+        "alice",
+        node_type="Person",
+        content="Alice",
+        color="#63E6FF",
+    )
+    graph.add_node(
+        "bob",
+        node_type="Person",
+        content="Bob",
+        color="#63E6FF",
+    )
+    graph.add_node(
+        "acme",
+        node_type="Organization",
+        content="Acme",
+        color="#A78BFA",
+    )
+    graph.add_node(
+        "new_york",
+        node_type="Location",
+        content="New York",
+        color="#34D399",
+    )
+
+    # 2. Add exactly 3 directed edges
+    graph.add_edge("alice", "acme", edge_type="WORKS_AT", weight=1.0)
+    graph.add_edge("bob", "alice", edge_type="KNOWS", weight=1.0)
+    graph.add_edge("acme", "new_york", edge_type="LOCATED_IN", weight=1.0)
+
+    return graph
+
+
+def main() -> None:
+    print("=" * 75)
+    print("Semantica Explorer Deterministic Graph Generator (#1037)")
+    print("=" * 75)
+
+    print("1. Building deterministic ContextGraph...")
+    graph = build_deterministic_graph()
+    print(
+        f"   ✓ Graph built with {len(graph.nodes)} nodes "
+        f"and {len(graph.edges)} edges."
+    )
+
+    output_path = Path("explorer_e2e_test_graph.json").resolve()
+    print(f"2. Persisting graph to '{output_path.name}'...")
+    graph.save_to_file(str(output_path))
+    print(f"   ✓ Graph saved to {output_path}")
+
+    # Verify JSON format
+    with open(output_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+    assert len(data.get("nodes", [])) == 4
+    assert len(data.get("edges", [])) == 3
+
+    print("3. Verifying reload via GraphSession.from_file()...")
+    session = GraphSession.from_file(str(output_path))
+    stats = session.get_stats()
+    nodes, total_nodes = session.get_nodes()
+    edges, total_edges = session.get_edges()
+
+    assert stats["node_count"] == 4
+    assert stats["edge_count"] == 3
+    assert total_nodes == 4
+    assert total_edges == 3
+
+    print(
+        f"   ✓ Graph reloaded successfully without mutation "
+        f"(nodes: {total_nodes}, edges: {total_edges}).\n"
+    )
+
+    print("=" * 75)
+    print("Clean Checkout Prerequisites:")
+    print("=" * 75)
+    print("    pip install -e '.[explorer]'")
+    print("    cd explorer && npm install && cd ..\n")
+
+    print("=" * 75)
+    print("Reproduction instructions to view in Semantica Explorer:")
+    print("=" * 75)
+    print("Option A (Frontend dev server + API backend — recommended for development):")
+    print(
+        f"    1. Backend:  SEMANTICA_ALLOW_ANONYMOUS=true python -m semantica.explorer "
+        f"--graph {output_path} --port 8000 --no-browser"
+    )
+    print("    2. Frontend: cd explorer && npm run dev")
+    print("    3. Open http://localhost:5173 to inspect the graph canvas.\n")
+
+    print("Option B (Standalone Explorer CLI server):")
+    print(
+        f"    SEMANTICA_ALLOW_ANONYMOUS=true python -m semantica.explorer "
+        f"--graph {output_path} --port 8000"
+    )
+    print("    Open http://localhost:8000\n")
+
+    print("Secure Authentication Alternative:")
+    print("    export SEMANTICA_API_KEY='your-secret-api-key'")
+    print(
+        f"    python -m semantica.explorer --graph {output_path} --port 8000"
+    )
+    print("    Send header: 'X-API-Key: your-secret-api-key'\n")
+
+    print("=" * 75)
+    print("Verification Checklist:")
+    print("=" * 75)
+    print("    1. Nodes (4 total):")
+    print("       - Alice (Person, #63E6FF)")
+    print("       - Bob (Person, #63E6FF)")
+    print("       - Acme (Organization, #A78BFA)")
+    print("       - New York (Location, #34D399)")
+    print("    2. Directed Edges & Canonical Labels (3 total):")
+    print("       - Alice -> Acme     [WORKS_AT]")
+    print("       - Bob   -> Alice    [KNOWS]")
+    print("       - Acme  -> New York [LOCATED_IN]")
+    print("    3. Zoom Interactions:")
+    print("       - Inspection tier (zoom in): directional arrows & labels remain legible.")
+    print("       - Overview tier (zoom out): nodes and edges maintain layout integrity.")
+    print("    4. Hover & Selection Interactions:")
+    print("       - Hover Alice: node halo triggers and incident edges (WORKS_AT, KNOWS) highlight.")
+    print("       - Click edge: Inspector panel displays edgeType label ('WORKS_AT', 'KNOWS', 'LOCATED_IN').")
+    print("=" * 75)
+
+
+if __name__ == "__main__":
+    main()

explorer/package-lock.json

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-      "license": "MIT"
+      "license": "MIT",
+      "peer": true
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     "node_modules/@types/hast": {
       "version": "3.0.5",
@@ -1642,7 +1642,6 @@
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       "license": "MIT",
-      "optional": true
+      "optional": true,
+      "peer": true
     },
     "node_modules/@types/unist": {
       "version": "3.0.3",
@@ -1725,7 +1724,6 @@
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-      "peer": true,
       "bin": {
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@@ -2070,9 +2067,9 @@
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+      "version": "2.11.20",
+      "resolved": "https://registry.npmjs.org/baseline-browser-mapping/-/baseline-browser-mapping-2.11.20.tgz",
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       "dev": true,
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@@ -2083,9 +2080,9 @@
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+      "version": "4.28.8",
+      "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz",
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@@ -2115,13 +2112,12 @@
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-        "electron-to-chromium": "^1.5.328",
-        "node-releases": "^2.0.36",
-        "update-browserslist-db": "^1.2.3"
+        "baseline-browser-mapping": "^2.11.12",
+        "caniuse-lite": "^1.0.30001809",
+        "electron-to-chromium": "^1.5.402",
+        "node-releases": "^2.0.53",
+        "update-browserslist-db": "^1.3.0"
       },
       "bin": {
         "browserslist": "cli.js"
@@ -2131,9 +2127,9 @@
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+      "version": "1.0.30001810",
+      "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001810.tgz",
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-      "license": "MIT"
+      "license": "MIT",
+      "peer": true
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@@ -2259,7 +2256,8 @@
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-      "license": "MIT"
+      "license": "MIT",
+      "peer": true
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@@ -2324,7 +2322,6 @@
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       "license": "(MPL-2.0 OR Apache-2.0)",
+      "peer": true,
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+      "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.420.tgz",
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+      "peer": true,
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       "dev": true,
-      "license": "MIT"
+      "license": "MIT",
+      "engines": {
+        "node": ">=18"
+      }
     },
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       "version": "4.1.1",
@@ -4414,7 +4415,6 @@
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@@ -5022,7 +5018,6 @@
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       "dev": true,
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-      "peer": true,
       "bin": {
         "tsc": "bin/tsc",
         "tsserver": "bin/tsserver"
@@ -5150,9 +5145,9 @@
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-      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.2.3.tgz",
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+      "version": "1.3.2",
+      "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.3.2.tgz",
+      "integrity": "sha512-UQ+MSxlhRm1bzjhU+DcuXfjFO1FzNtqhK5+9Yvlp90ItDLk5vT932A0rFu619nf7RVS+Y/VeaUW1jaRDqZ8VJw==",
       "dev": true,
       "funding": [
         {
@@ -5246,7 +5241,6 @@
       "resolved": "https://registry.npmjs.org/vis-data/-/vis-data-8.0.3.tgz",
       "integrity": "sha512-jhnb6rJNqkKR1Qmlay0VuDXY9ZlvAnYN1udsrP4U+krgZEq7C0yNSKdZqmnCe13mdnf9AdVcdDGFOzy2mpPoqw==",
       "license": "(Apache-2.0 OR MIT)",
-      "peer": true,
       "funding": {
         "type": "opencollective",
         "url": "https://opencollective.com/visjs"
@@ -5301,7 +5295,6 @@
       "integrity": "sha512-NTKlcQjlAK7MlQoyb6LgaqHc8sso/pVyUJYWMws3jg21uTJw/LddqIFPcPqP6PzpgbIcZyKI85sFE4HBrQDA8A==",
       "dev": true,
       "license": "MIT",
-      "peer": true,
       "dependencies": {
         "esbuild": "^0.25.0",
         "fdir": "^6.4.4",
@@ -5440,7 +5433,6 @@
       "integrity": "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==",
       "dev": true,
       "license": "MIT",
-      "peer": true,
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }

explorer/package.json

@@ -9,7 +9,8 @@
     "lint": "eslint .",
     "preview": "vite preview",
     "test:graph-store": "node --test tests/graphStore.multi-edge.test.mjs",
-    "test:graph-workspace": "node --import tsx --test tests/markdownContentViewer.test.ts tests/graphSceneState.display.test.ts tests/temporalLifecycle.test.ts",
+    "test:graph-workspace": "node --import tsx --test tests/markdownContentViewer.test.ts tests/graphSceneState.display.test.ts tests/temporalLifecycle.test.ts tests/deterministicExplorerRendering.test.ts tests/smallGraphLayout.test.ts tests/realtimeGraphAttributes.test.ts",
+    "test:deterministic-e2e": "node --import tsx --test tests/deterministicExplorerRendering.e2e.ts",
     "test:plugin-registry": "node --import tsx --test tests/pluginRegistry.temporal.test.mjs"
   },
   "dependencies": {

explorer/src/store/graphStore.ts

@@ -86,6 +86,7 @@ export interface EdgeAttributes {
   dominantEdgeType?: string;
   representativeWeight?: number;
   bundleKind?: "parallel" | "bidirectional" | "community";
+  isSmallGraph?: boolean;
   
  
   edgeType: string;

explorer/src/workspaces/GraphWorkspace/GraphWorkspace.tsx

@@ -21,7 +21,6 @@ import type Graph from "graphology";
 import { batchMergeEdges, batchMergeNodes, graph } from "../../store/graphStore";
 import { logEvent } from "../../store/registryStore";
 import type { EdgeAttributes, NodeAttributes } from "../../store/graphStore";
-import { curveGroupForPair } from "../../store/edgePairKeys.js";
 import { InspectorPanel, MetricChip, SurfaceCard } from "../../ui/primitives";
 import { lazy, Suspense } from "react";
 import { SigmaSceneAdapter } from "./SigmaSceneAdapter";
@@ -41,6 +40,9 @@ import {
 } from "./plugins";
 import { explorationEffectsShouldLoad, neighborhoodPanelShouldLoad, temporalOverlayShouldLoad } from "./pluginRegistryPredicates";
 import { shouldFetchTemporalBounds, shouldFetchTemporalSnapshot } from "./temporalLifecyclePredicates";
+import { createTemporalSnapshotGuards, type TemporalSnapshotResponse } from "./temporalSnapshotGuards";
+import { SMALL_GRAPH_MAX_NODES } from "./smallGraphLayout";
+import { buildRealtimeEdgeAttributes } from "./realtimeGraphAttributes";
 import type { LinkPrediction, PathResponse } from "./GraphInspectorPanel";
 import type { GraphSceneHandle, GraphSceneRuntime } from "./scene";
 import type {
@@ -1055,46 +1057,10 @@ function buildRealtimeNodeAttributes(payload: {
   };
 }
 
-function buildRealtimeEdgeAttributes(payload: {
-  id: string;
-  familyId?: string;
-  source_id: string;
-  target_id: string;
-  type?: string;
-  weight?: number;
-  properties?: Record<string, unknown>;
-}): EdgeAttributes {
-  const properties = payload.properties || {};
-  const isInferred = Boolean(properties.inferred);
-  const isBidirectional = graph.hasDirectedEdge(payload.target_id, payload.source_id);
-  const baseColor = isInferred ? GRAPH_THEME.palette.accent.path : GRAPH_THEME.palette.muted.edgeStructure;
-
-  return {
-    edgeId: payload.id,
-    familyId: payload.familyId || payload.id,
-    sourceId: payload.source_id,
-    targetId: payload.target_id,
-    weight: Number(payload.weight ?? 1),
-    edgeType: payload.type || "related_to",
-    properties,
-    size: 1,
-    baseSize: 1,
-    color: baseColor,
-    baseColor,
-    mutedColor: GRAPH_THEME.palette.muted.edgeOverview,
-    visualPriority: isInferred ? 0.95 : 0.5,
-    isBidirectional,
-    edgeFamily: isInferred ? "path" : isBidirectional ? "bidirectional" : "line",
-    curveGroup: isBidirectional ? curveGroupForPair(payload.source_id, payload.target_id) : null,
-    type: "line",
-    edgeVariant: isInferred ? "pathSignal" : isBidirectional ? "bidirectionalCurve" : "directional",
-    arrowVisibilityPolicy: isInferred ? "always" : "contextual",
-    relationshipStrength: isInferred ? 0.95 : 0.52,
-    isParallelPair: false,
-    parallelIndex: 0,
-    parallelCount: 1,
-    familySize: 1,
-  };
+function synchronizeRealtimeSmallGraphEdges(isSmallGraph: boolean): void {
+  graph.forEachEdge((edgeId) => {
+    graph.setEdgeAttribute(edgeId, "isSmallGraph", isSmallGraph);
+  });
 }
 
 function buildSelectedNodeState(
@@ -1354,6 +1320,7 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
   const lastExternalFocusTokenRef = useRef<number | undefined>(undefined);
   const pluginRuntimeRef = useRef<GraphSceneRuntime | null>(null);
   const appliedGraphSummarySignatureRef = useRef<string | null>(null);
+  const smallGraphModeRef = useRef(false);
   const pluginInteractionStateRef = useRef<GraphInteractionState>({
     hoveredNodeId: null,
     selectedNodeId: "",
@@ -1381,6 +1348,12 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
     }
 
     appliedGraphSummarySignatureRef.current = signature;
+    smallGraphModeRef.current = Boolean(
+      graphSummary.layoutReady
+      && !graphSummary.hasCoordinates
+      && graphSummary.nodeCount > 0
+      && graphSummary.nodeCount <= SMALL_GRAPH_MAX_NODES,
+    );
     setGraphReady(true);
     setGraphVersion((current) => current + 1);
     setIsLayoutRunning(!graphSummary.layoutReady);
@@ -1479,6 +1452,23 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
     summary?.edgeCount,
   ]);
 
+  // Guards the snapshot lifecycle: at most one in-flight request per scrubber
+  // position (identical-`at` polls are deduplicated, breaking the idle/play
+  // polling loop), applied snapshots are cached and re-applied on revisit, and
+  // a response applies only while the scrubber is still on its position
+  // (out-of-order responses cannot clobber the active-node count).
+  const temporalSnapshotGuardsRef = useRef<ReturnType<typeof createTemporalSnapshotGuards> | null>(null);
+  if (temporalSnapshotGuardsRef.current === null) {
+    temporalSnapshotGuardsRef.current = createTemporalSnapshotGuards();
+  }
+  const temporalSnapshotGuards = temporalSnapshotGuardsRef.current;
+
+  // A new graph summary means the graph data was replaced (reload/retry);
+  // snapshots cached against the previous graph are stale, so reset all state.
+  useEffect(() => {
+    temporalSnapshotGuards.reset();
+  }, [summary]);
+
   useEffect(() => {
     if (!canFetchTemporalSnapshot) {
       return;
@@ -1488,37 +1478,67 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
       return;
     }
 
+    const atMs = debouncedTime.getTime();
+    const { seq, cached } = temporalSnapshotGuards.begin(atMs);
+    if (seq === null) {
+      // An identical request is already in flight: one request per position.
+      return;
+    }
+
     let cancelled = false;
 
+    const applyData = (data: TemporalSnapshotResponse) => {
+      const nextActiveIds = new Set(data.active_node_ids);
+      requestAnimationFrame(() => {
+        if (cancelled) return;
+        if (!temporalSnapshotGuards.shouldApply(atMs, seq)) {
+          // The scrubber moved on (or this request was superseded): release the
+          // position so a return to it refetches instead of stalling.
+          temporalSnapshotGuards.finish(atMs, seq);
+          return;
+        }
+        const previous = prevActiveIdsRef.current;
+        previous.forEach((id) => {
+          if (!nextActiveIds.has(id) && graph.hasNode(id)) {
+            graph.setNodeAttribute(id, "hidden", true);
+          }
+        });
+        nextActiveIds.forEach((id) => {
+          if (graph.hasNode(id)) {
+            graph.setNodeAttribute(id, "hidden", false);
+          }
+        });
+        prevActiveIdsRef.current = nextActiveIds;
+        setActiveNodeCount(data.active_node_count);
+        setGraphVersion((current) => current + 1);
+        sceneRef.current?.getRuntime()?.requestRender();
+        temporalSnapshotGuards.apply(atMs, seq, data);
+      });
+    };
+
+    if (cached) {
+      // Returning to a position whose snapshot was already applied: re-apply
+      // the cached result without a network request.
+      applyData(cached);
+      return;
+    }
+
     const applySnapshot = async () => {
       try {
         const at = debouncedTime.toISOString();
         const response = await fetch(`/api/temporal/snapshot?at=${encodeURIComponent(at)}`);
-        if (!response.ok || cancelled) return;
-
-        const data: { active_node_ids: string[]; active_node_count: number } = await response.json();
+        if (!response.ok) {
+          // A failed request must be retryable if the scrubber returns.
+          if (!cancelled) temporalSnapshotGuards.finish(atMs, seq);
+          return;
+        }
         if (cancelled) return;
 
-        const nextActiveIds = new Set(data.active_node_ids);
-        requestAnimationFrame(() => {
-          if (cancelled) return;
-          const previous = prevActiveIdsRef.current;
-          previous.forEach((id) => {
-            if (!nextActiveIds.has(id) && graph.hasNode(id)) {
-              graph.setNodeAttribute(id, "hidden", true);
-            }
-          });
-          nextActiveIds.forEach((id) => {
-            if (graph.hasNode(id)) {
-              graph.setNodeAttribute(id, "hidden", false);
-            }
-          });
-          prevActiveIdsRef.current = nextActiveIds;
-          setActiveNodeCount(data.active_node_count);
-          setGraphVersion((current) => current + 1);
-          sceneRef.current?.getRuntime()?.requestRender();
-        });
+        const data: TemporalSnapshotResponse = await response.json();
+        if (cancelled) return;
+        applyData(data);
       } catch (fetchError) {
+        temporalSnapshotGuards.finish(atMs, seq);
         if (!cancelled) {
           console.error("[Temporal] Snapshot fetch failed", fetchError);
         }
@@ -1528,6 +1548,8 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
     applySnapshot();
     return () => {
       cancelled = true;
+      // A cancelled request must be retryable when its position is revisited.
+      temporalSnapshotGuards.finish(atMs, seq);
     };
   }, [
     canFetchTemporalSnapshot,
@@ -1843,18 +1865,26 @@ export function GraphWorkspace({ externalFocusNodeId, externalFocusToken }: Grap
               attributes: buildRealtimeNodeAttributes(payload),
             },
           ]);
+          if (graph.order > SMALL_GRAPH_MAX_NODES) {
+            smallGraphModeRef.current = false;
+          }
+          synchronizeRealtimeSmallGraphEdges(smallGraphModeRef.current);
           logEvent("add-node", `Added node ${payload.label ?? payload.id}${payload.nodeType ? ` (${payload.nodeType})` : ""} via realtime ws`, { nodeId: payload.id, nodeType: payload.nodeType });
           setGraphVersion((current) => current + 1);
           sceneRef.current?.getRuntime()?.requestRender();
         }
         if (eventType === "ADD_EDGE") {
+          const isSmallGraph = smallGraphModeRef.current;
           batchMergeEdges([
             {
               id: String(payload.id),
               familyId: payload.familyId ? String(payload.familyId) : String(payload.id),
               source: payload.source_id,
               target: payload.target_id,
-              attributes: buildRealtimeEdgeAttributes(payload),
+              attributes: buildRealtimeEdgeAttributes(payload, {
+                isBidirectional: graph.hasDirectedEdge(payload.target_id, payload.source_id),
+                isSmallGraph,
+              }),
             },
           ]);
           logEvent("add-edge", `Added edge ${payload.edgeType ?? payload.id} (${payload.source_id} → ${payload.target_id}) via realtime ws`, { edgeId: payload.id, edgeType: payload.edgeType, source: payload.source_id, target: payload.target_id });

explorer/src/workspaces/GraphWorkspace/graphSceneState.ts

@@ -1783,6 +1783,7 @@ export function resolveEdgeElementStyle(
   const isCommunityBundle = attrs.bundleKind === "community";
   const baseSize = Number(attrs.baseSize || attrs.size || 0.9);
   const visualPriority = Number(attrs.visualPriority ?? 0);
+  const isSmallGraphEdge = viewMode === "full" && attrs.isSmallGraph === true;
   const isFullBridgeEdge = viewMode === "full" && fullEdgeClass === "bridge";
   const isFullBackboneEdge = viewMode === "full" && fullEdgeClass === "backbone";
   const shouldCurveBridge = isFullBridgeEdge
@@ -1790,11 +1791,13 @@ export function resolveEdgeElementStyle(
   const visibilityPolicy = resolveEdgeVisibilityPolicy(theme, viewMode, zoomTier, isCommunityBundle);
   const isContextEdge = isContextEdgeState(state);
   const isNonCriticalEdge = isNonCriticalEdgeVariant(edgeVariant);
-  const belowPriorityThreshold = state === "default"
+  const belowPriorityThreshold = !isSmallGraphEdge && state === "default"
     && visualPriority < Math.max(tierConfig.edgePriorityThreshold, visibilityPolicy.defaultPriorityThreshold)
     && isNonCriticalEdge;
-  const hiddenByMutedState = (state === "muted" || state === "inactive") && visibilityPolicy.hideMuted;
-  const sampledOut = isNonCriticalEdge
+  const hiddenByMutedState = !isSmallGraphEdge
+    && (state === "muted" || state === "inactive")
+    && visibilityPolicy.hideMuted;
+  const sampledOut = !isSmallGraphEdge && isNonCriticalEdge
     && (
       (state === "default" && !isContextEdge && shouldSampleOutBackgroundEdge(visibilityPolicy.backgroundSampleRate, visualPriority, edgeId, sourceId, targetId))
       || (
@@ -1837,11 +1840,14 @@ export function resolveEdgeElementStyle(
       ? resolveEdgeCurvature(theme, state, edgeVariant, attrs, sourceId, targetId)
       : 0;
   const baseColor = resolveEdgeColor(theme, zoomTier, state, attrs, attrs.color, fullEdgeClass);
-  const lodAlpha = resolveEdgeLodAlpha(theme, viewMode, zoomTier, state, attrs, isCommunityBundle, fullEdgeClass);
+  const resolvedLodAlpha = resolveEdgeLodAlpha(theme, viewMode, zoomTier, state, attrs, isCommunityBundle, fullEdgeClass);
+  const lodAlpha = isSmallGraphEdge
+    ? Math.max(resolvedLodAlpha ?? 1, isContextEdge ? 0.62 : 0.46)
+    : resolvedLodAlpha;
   const color = lodAlpha === null ? baseColor : withAlpha(baseColor, lodAlpha);
   const rawSize = Math.max(
     baseSize * sizeMultiplier * (isCommunityBundle ? theme.grouped.style.edgeSizeScale : 1),
-    stateConfig.minSize,
+    isSmallGraphEdge ? Math.max(stateConfig.minSize, 0.9) : stateConfig.minSize,
   );
   
   const interactionMaxSize = (fullEdgeClass === "path" || state === "path")

explorer/src/workspaces/GraphWorkspace/realtimeGraphAttributes.ts

@@ -0,0 +1,50 @@
+import type { EdgeAttributes } from "../../store/graphStore";
+import { curveGroupForPair } from "../../store/edgePairKeys.js";
+import { GRAPH_THEME } from "./graphTheme";
+
+export type RealtimeEdgePayload = {
+  id: string;
+  familyId?: string;
+  source_id: string;
+  target_id: string;
+  type?: string;
+  weight?: number;
+  properties?: Record<string, unknown>;
+};
+
+export function buildRealtimeEdgeAttributes(
+  payload: RealtimeEdgePayload,
+  options: { isBidirectional: boolean; isSmallGraph: boolean },
+): EdgeAttributes {
+  const properties = payload.properties || {};
+  const isInferred = Boolean(properties.inferred);
+  const baseColor = isInferred ? GRAPH_THEME.palette.accent.path : GRAPH_THEME.palette.muted.edgeStructure;
+
+  return {
+    edgeId: payload.id,
+    familyId: payload.familyId || payload.id,
+    sourceId: payload.source_id,
+    targetId: payload.target_id,
+    weight: Number(payload.weight ?? 1),
+    edgeType: payload.type || "related_to",
+    properties,
+    size: 1,
+    baseSize: 1,
+    color: baseColor,
+    baseColor,
+    mutedColor: GRAPH_THEME.palette.muted.edgeOverview,
+    visualPriority: isInferred ? 0.95 : 0.5,
+    isBidirectional: options.isBidirectional,
+    edgeFamily: isInferred ? "path" : options.isBidirectional ? "bidirectional" : "line",
+    curveGroup: options.isBidirectional ? curveGroupForPair(payload.source_id, payload.target_id) : null,
+    type: "line",
+    edgeVariant: isInferred ? "pathSignal" : options.isBidirectional ? "bidirectionalCurve" : "directional",
+    arrowVisibilityPolicy: isInferred ? "always" : "contextual",
+    relationshipStrength: isInferred ? 0.95 : 0.52,
+    isParallelPair: false,
+    parallelIndex: 0,
+    parallelCount: 1,
+    familySize: 1,
+    isSmallGraph: options.isSmallGraph,
+  };
+}

explorer/src/workspaces/GraphWorkspace/smallGraphLayout.ts

@@ -0,0 +1,135 @@
+export const SMALL_GRAPH_MAX_NODES = 48;
+const PROVIDED_COORDINATE_COVERAGE = 0.92;
+const MAX_COMPONENT_RADIUS = 78;
+const COMPONENT_GAP = 48;
+
+type LayoutEdge = {
+  source: string;
+  target: string;
+};
+
+export function shouldUseSmallGraphLayout(nodeCount: number, coordinateCoverage: number): boolean {
+  return nodeCount > 0
+    && nodeCount <= SMALL_GRAPH_MAX_NODES
+    && coordinateCoverage < PROVIDED_COORDINATE_COVERAGE;
+}
+
+export function resolveGraphLayoutDecision(nodeCount: number, coordinateCoverage: number): {
+  useProvidedCoordinates: boolean;
+  useSmallGraphLayout: boolean;
+  layoutReady: boolean;
+} {
+  const useProvidedCoordinates = coordinateCoverage >= PROVIDED_COORDINATE_COVERAGE;
+  const useSmallGraphLayout = shouldUseSmallGraphLayout(nodeCount, coordinateCoverage);
+  return {
+    useProvidedCoordinates,
+    useSmallGraphLayout,
+    layoutReady: useProvidedCoordinates || useSmallGraphLayout,
+  };
+}
+
+export function resolveNodeLayoutPosition(
+  decision: ReturnType<typeof resolveGraphLayoutDecision>,
+  provided: { x: number | null; y: number | null },
+  seeded: { x: number; y: number } | undefined,
+): { x: number; y: number } {
+  if (decision.useProvidedCoordinates) {
+    return { x: provided.x ?? 0, y: provided.y ?? 0 };
+  }
+  if (decision.useSmallGraphLayout) {
+    return { x: seeded?.x ?? 0, y: seeded?.y ?? 0 };
+  }
+  return {
+    x: provided.x ?? seeded?.x ?? 0,
+    y: provided.y ?? seeded?.y ?? 0,
+  };
+}
+
+/**
+ * Produce a compact deterministic layout for small graphs.
+ *
+ * ForceAtlas2 is useful for large connected datasets, but it makes tiny graphs
+ * with several disconnected components look like scattered dots. This layout
+ * keeps each connected component together and packs components into a centered
+ * grid so instance relationships remain legible on first render.
+ */
+export function buildSmallGraphSeedPositions(
+  nodeIds: string[],
+  edges: LayoutEdge[],
+): Map<string, { x: number; y: number }> {
+  const ids = [...new Set(nodeIds)].sort((left, right) => left.localeCompare(right));
+  const adjacency = new Map(ids.map((id) => [id, new Set<string>()]));
+
+  edges.forEach(({ source, target }) => {
+    if (!adjacency.has(source) || !adjacency.has(target) || source === target) {
+      return;
+    }
+    adjacency.get(source)?.add(target);
+    adjacency.get(target)?.add(source);
+  });
+
+  const visited = new Set<string>();
+  const components: string[][] = [];
+  ids.forEach((start) => {
+    if (visited.has(start)) {
+      return;
+    }
+    const component: string[] = [];
+    const queue = [start];
+    visited.add(start);
+    while (queue.length > 0) {
+      const current = queue.shift();
+      if (!current) {
+        continue;
+      }
+      component.push(current);
+      [...(adjacency.get(current) ?? [])]
+        .sort((left, right) => left.localeCompare(right))
+        .forEach((neighbor) => {
+          if (!visited.has(neighbor)) {
+            visited.add(neighbor);
+            queue.push(neighbor);
+          }
+        });
+    }
+    component.sort((left, right) => {
+      const degreeDelta = (adjacency.get(right)?.size ?? 0) - (adjacency.get(left)?.size ?? 0);
+      return degreeDelta || left.localeCompare(right);
+    });
+    components.push(component);
+  });
+
+  components.sort((left, right) => right.length - left.length || left[0].localeCompare(right[0]));
+
+  const columns = Math.max(1, Math.ceil(Math.sqrt(components.length)));
+  const rows = Math.max(1, Math.ceil(components.length / columns));
+  // Adjacent cells must leave room for two maximum-radius components plus a
+  // readable gap. A smaller row height allows valid 12-node components to
+  // overlap vertically.
+  const cellWidth = MAX_COMPONENT_RADIUS * 2 + COMPONENT_GAP;
+  const cellHeight = MAX_COMPONENT_RADIUS * 2 + COMPONENT_GAP;
+  const positions = new Map<string, { x: number; y: number }>();
+
+  components.forEach((component, componentIndex) => {
+    const column = componentIndex % columns;
+    const row = Math.floor(componentIndex / columns);
+    const centerX = (column - (columns - 1) / 2) * cellWidth;
+    const centerY = (row - (rows - 1) / 2) * cellHeight;
+
+    if (component.length === 1) {
+      positions.set(component[0], { x: centerX, y: centerY });
+      return;
+    }
+
+    const radius = Math.min(MAX_COMPONENT_RADIUS, 30 + component.length * 9);
+    component.forEach((nodeId, nodeIndex) => {
+      const angle = -Math.PI / 2 + (nodeIndex * Math.PI * 2) / component.length;
+      positions.set(nodeId, {
+        x: centerX + Math.cos(angle) * radius,
+        y: centerY + Math.sin(angle) * radius,
+      });
+    });
+  });
+
+  return positions;
+}

explorer/src/workspaces/GraphWorkspace/temporalSnapshotGuards.ts

@@ -0,0 +1,113 @@
+/**
+ * Guards for the temporal snapshot fetch/apply lifecycle.
+ *
+ * The snapshot effect previously fetched /api/temporal/snapshot with no
+ * idempotency or ordering protection. Upstream churn (timeline recreation
+ * while bounds settle, play ticks resetting the playhead, drag events) could
+ * re-request the same `at` repeatedly, and responses could arrive after the
+ * scrubber had moved on.
+ *
+ * The guards enforce:
+ * - at most one in-flight request per scrubber position (identical `at`
+ *   values are deduplicated while a request is pending, breaking the
+ *   idle/play polling loop);
+ * - successful snapshots are cached per position and re-applied when the
+ *   scrubber returns (play wrap-around, back-scrubbing) without a refetch;
+ * - a response is applied only while the scrubber is still on its position,
+ *   so out-of-order responses cannot clobber a newer position's count;
+ * - failed, cancelled, or superseded requests release their position so it
+ *   can be fetched again on the next visit;
+ * - `reset()` drops all state when the underlying graph data is replaced
+ *   (reload/retry), because cached snapshots describe the previous graph.
+ *
+ * `createTemporalSnapshotGuards()` is stateful by design.
+ */
+
+export interface TemporalSnapshotResponse {
+  active_node_ids: string[];
+  active_node_count: number;
+}
+
+export interface TemporalSnapshotRequest {
+  /** null when the request was deduplicated because one is already in flight. */
+  seq: number | null;
+  /** The snapshot previously applied for this position, when revisiting it. */
+  cached: TemporalSnapshotResponse | null;
+}
+
+export interface TemporalSnapshotGuards {
+  /** Begin (or dedupe) a request for `atMs`; marks it as the current position. */
+  begin(atMs: number): TemporalSnapshotRequest;
+  /** True when the response for `atMs`/`seq` may be applied (scrubber still on `atMs`). */
+  shouldApply(atMs: number, seq: number): boolean;
+  /** Record a successful application and cache its snapshot for revisits. */
+  apply(atMs: number, seq: number, data: TemporalSnapshotResponse): void;
+  /** Release a position whose request failed, was cancelled, or was superseded. */
+  finish(atMs: number, seq: number): void;
+  /** Drop all state; call when the underlying graph data is replaced (reload). */
+  reset(): void;
+}
+
+interface SnapshotEntry {
+  seq: number;
+  /** null while the request is in flight (or before the first success). */
+  data: TemporalSnapshotResponse | null;
+}
+
+/** Upper bound on cached positions so long scrubbing sessions stay bounded. */
+const MAX_CACHED_POSITIONS = 256;
+
+export function createTemporalSnapshotGuards(): TemporalSnapshotGuards {
+  const entries = new Map<number, SnapshotEntry>();
+  let latestRequestSeq = 0;
+  let currentAtMs: number | null = null;
+
+  const evictOldest = () => {
+    while (entries.size > MAX_CACHED_POSITIONS) {
+      const oldestAtMs = entries.keys().next().value;
+      if (oldestAtMs === undefined) return;
+      entries.delete(oldestAtMs);
+    }
+  };
+
+  return {
+    begin(atMs) {
+      const existing = entries.get(atMs);
+      if (existing && existing.data === null) {
+        // Identical request already in flight: dedupe, but the scrubber is here now.
+        currentAtMs = atMs;
+        return { seq: null, cached: null };
+      }
+      latestRequestSeq += 1;
+      const seq = latestRequestSeq;
+      entries.set(atMs, { seq, data: existing?.data ?? null });
+      currentAtMs = atMs;
+      evictOldest();
+      return { seq, cached: existing?.data ?? null };
+    },
+
+    shouldApply(atMs, seq) {
+      return atMs === currentAtMs && entries.get(atMs)?.seq === seq;
+    },
+
+    apply(atMs, seq, data) {
+      const entry = entries.get(atMs);
+      if (entry && entry.seq === seq) {
+        entry.data = data;
+      }
+    },
+
+    finish(atMs, seq) {
+      const entry = entries.get(atMs);
+      if (entry && entry.seq === seq && entry.data === null) {
+        entries.delete(atMs);
+      }
+    },
+
+    reset() {
+      entries.clear();
+      latestRequestSeq = 0;
+      currentAtMs = null;
+    },
+  };
+}

explorer/src/workspaces/GraphWorkspace/useLoadGraph.ts

@@ -16,6 +16,11 @@ import {
 } from "./graphTheme";
 import { classifyEntityShape } from "./graphEntityShape";
 import { createGraphLoadProgress } from "./graphLoading";
+import {
+  buildSmallGraphSeedPositions,
+  resolveGraphLayoutDecision,
+  resolveNodeLayoutPosition,
+} from "./smallGraphLayout";
 import type { GraphLoadProgress, GraphLoadSummary } from "./types";
 
 const SEMANTIC_COLOR_FIELDS = [
@@ -318,6 +323,20 @@ interface EdgeListResponse {
 
 const PAGE_LIMIT = 1000;
 
+/** Surface the server's `detail` message (e.g. auth/setup guidance) on non-OK responses. */
+async function fetchErrorDetail(response: Response): Promise<string> {
+  try {
+    const body: unknown = await response.json();
+    const detail = (body as { detail?: unknown } | null)?.detail;
+    if (typeof detail === "string" && detail.trim()) {
+      return ` — ${detail.trim()}`;
+    }
+  } catch {
+    // Non-JSON or unreadable body: fall back to the status-only message.
+  }
+  return "";
+}
+
 async function fetchAllNodes(
   signal: AbortSignal,
   onProgress?: (progress: GraphLoadProgress) => void,
@@ -335,7 +354,7 @@ async function fetchAllNodes(
 
     const response = await fetch(url.toString(), { signal });
     if (!response.ok) {
-      throw new Error(`Fetch failed: ${response.status}`);
+      throw new Error(`Fetch failed: ${response.status}${await fetchErrorDetail(response)}`);
     }
 
     const data: NodeListResponse = await response.json();
@@ -390,7 +409,7 @@ async function fetchAllEdges(
 
     const response = await fetch(url.toString(), { signal });
     if (!response.ok) {
-      throw new Error(`Fetch failed: ${response.status}`);
+      throw new Error(`Fetch failed: ${response.status}${await fetchErrorDetail(response)}`);
     }
 
     const data: EdgeListResponse = await response.json();
@@ -539,10 +558,19 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
           : count;
       }, 0);
       const coordinateCoverage = fetchedNodes.length > 0 ? providedCoordinateCount / fetchedNodes.length : 0;
-      const useProvidedCoordinates = coordinateCoverage >= 0.92;
+      const {
+        useProvidedCoordinates,
+        useSmallGraphLayout,
+        layoutReady,
+      } = resolveGraphLayoutDecision(fetchedNodes.length, coordinateCoverage);
       const seededPositions = useProvidedCoordinates
         ? null
-        : buildClusterSeedPositions(
+        : useSmallGraphLayout
+          ? buildSmallGraphSeedPositions(
+              fetchedNodes.map((node) => node.id),
+              fetchedEdges,
+            )
+          : buildClusterSeedPositions(
             draftAttributes.map(({ id, attributes }) => ({
               id,
               semanticGroup: semanticKeyByNodeId.get(id) ?? structuralColorKey(id, attributes),
@@ -555,7 +583,9 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
         const colorIndex = hashString(semanticGroup) % GRAPH_THEME.palette.semantic.length;
         const baseColor = GRAPH_THEME.palette.semantic[colorIndex];
         const sizeRatio = nodePriorityById.get(id) ?? 0;
-        const dynamicSize = clamp(1.8, 1.8 + 8.8 * sizeRatio, 11.8);
+        const dynamicSize = useSmallGraphLayout
+          ? clamp(5.2, 5.2 + 6.6 * sizeRatio, 11.8)
+          : clamp(1.8, 1.8 + 8.8 * sizeRatio, 11.8);
         const hasTemporalBounds = Boolean(attributes.valid_from || attributes.valid_until);
         const provenanceCount = getProvenanceCount(attributes.properties ?? {});
         const properties = attributes.properties as Record<string, unknown>;
@@ -563,12 +593,11 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
         const providedX = readFiniteCoordinate(properties?.x);
         const providedY = readFiniteCoordinate(properties?.y);
         const seededPosition = seededPositions?.get(id);
-        const x = useProvidedCoordinates
-          ? providedX ?? 0
-          : providedX ?? seededPosition?.x ?? 0;
-        const y = useProvidedCoordinates
-          ? providedY ?? 0
-          : providedY ?? seededPosition?.y ?? 0;
+        const { x, y } = resolveNodeLayoutPosition(
+          { useProvidedCoordinates, useSmallGraphLayout, layoutReady },
+          { x: providedX, y: providedY },
+          seededPosition,
+        );
         return {
           id,
           attributes: {
@@ -589,6 +618,7 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
             borderSize: 0.72,
             entityShape,
             ...resolveNodeVariantMetadata(baseColor, sizeRatio, hasTemporalBounds, provenanceCount),
+            ...(useSmallGraphLayout ? { labelVisibilityPolicy: "always" as const } : {}),
           } as NodeAttributes,
         };
       });
@@ -645,6 +675,7 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
             parallelIndex,
             parallelCount,
             familySize: familyCounts.get(edge.familyId) ?? 1,
+            isSmallGraph: useSmallGraphLayout,
             ...resolveEdgeVariantMetadata(edge, sourcePriority, targetPriority, isBidirectional),
           } as EdgeAttributes,
         };
@@ -687,7 +718,7 @@ export function useLoadGraph(options: UseLoadGraphOptions = {}) {
         loadTimeMs: Math.round(performance.now() - startedAt),
         hasCoordinates: useProvidedCoordinates,
         layoutSource: useProvidedCoordinates ? "provided" : "runtime",
-        layoutReady: useProvidedCoordinates,
+        layoutReady,
       } satisfies GraphLoadSummary;
 
       onProgress?.(createGraphLoadProgress({

explorer/tests/deterministicExplorerRendering.e2e.ts

@@ -0,0 +1,103 @@
+import assert from "node:assert/strict";
+import { spawn, type ChildProcess } from "node:child_process";
+import { existsSync } from "node:fs";
+import { setTimeout as delay } from "node:timers/promises";
+import test from "node:test";
+import { chromium, type Page } from "playwright";
+
+const PORT = 4173;
+const BASE_URL = `http://127.0.0.1:${PORT}`;
+
+const nodes = [
+  { id: "alice", type: "Person", content: "Alice", properties: {} },
+  { id: "bob", type: "Person", content: "Bob", properties: {} },
+  { id: "acme", type: "Organization", content: "Acme", properties: {} },
+  { id: "new_york", type: "Location", content: "New York", properties: {} },
+];
+
+const edges = [
+  { id: "edge_alice_acme", familyId: "edge_alice_acme", source: "alice", target: "acme", type: "WORKS_AT", weight: 1, properties: {} },
+  { id: "edge_bob_alice", familyId: "edge_bob_alice", source: "bob", target: "alice", type: "KNOWS", weight: 1, properties: {} },
+  { id: "edge_acme_new_york", familyId: "edge_acme_new_york", source: "acme", target: "new_york", type: "LOCATED_IN", weight: 1, properties: {} },
+];
+
+let server: ChildProcess | undefined;
+
+async function startVite(): Promise<void> {
+  server = spawn("npm", ["run", "dev", "--", "--host", "127.0.0.1", "--port", String(PORT)], {
+    cwd: process.cwd(),
+    stdio: "ignore",
+  });
+
+  for (let attempt = 0; attempt < 50; attempt += 1) {
+    try {
+      const response = await fetch(BASE_URL);
+      if (response.ok) return;
+    } catch {
+      // Vite is still starting.
+    }
+    await delay(100);
+  }
+  throw new Error("Vite did not become ready");
+}
+
+async function installApiFixture(page: Page): Promise<void> {
+  await page.route("**/api/graph/**", async (route) => {
+    const pathname = new URL(route.request().url()).pathname;
+    if (pathname === "/api/graph/stats") {
+      await route.fulfill({ json: { node_count: 4, edge_count: 3 } });
+    } else if (pathname === "/api/graph/nodes") {
+      await route.fulfill({ json: { nodes, total: nodes.length, skip: 0, limit: 1000, next_cursor: null } });
+    } else if (pathname === "/api/graph/edges") {
+      await route.fulfill({ json: { edges, total: edges.length, skip: 0, limit: 1000, next_cursor: null } });
+    } else {
+      await route.continue();
+    }
+  });
+}
+
+test("real Explorer loading path hydrates and renders API edge labels", async (t) => {
+  await startVite();
+  t.after(async () => {
+    server?.kill();
+  });
+
+  const browser = await chromium.launch({
+    headless: true,
+    executablePath: process.env.CHROMIUM_PATH || (existsSync("/usr/bin/chromium") ? "/usr/bin/chromium" : undefined),
+  });
+  t.after(() => browser.close());
+  const page = await browser.newPage({ viewport: { width: 1440, height: 1000 } });
+
+  await page.addInitScript(() => {
+    const captured = (window as Window & { __capturedCanvasText?: string[] }).__capturedCanvasText = [];
+    const originalFillText = CanvasRenderingContext2D.prototype.fillText;
+    CanvasRenderingContext2D.prototype.fillText = function (text: string, ...args: [number, number, number?, number?]) {
+      captured.push(String(text));
+      return originalFillText.call(this, text, ...args);
+    };
+  });
+  await installApiFixture(page);
+  await page.goto(BASE_URL);
+  await page.getByRole("button", { name: /Open Semantica Explorer/ }).click();
+
+  await page.locator("canvas").nth(0).waitFor({ state: "attached" });
+  await page.waitForFunction(() => document.querySelectorAll("canvas").length >= 2);
+  await page.waitForFunction(() => {
+    const labels = (window as Window & { __capturedCanvasText?: string[] }).__capturedCanvasText ?? [];
+    return ["WORKS_AT", "KNOWS", "LOCATED_IN"].every((label) => labels.includes(label));
+  }, undefined, { timeout: 10_000 });
+
+  const capturedLabels = await page.evaluate(() => (window as Window & { __capturedCanvasText?: string[] }).__capturedCanvasText ?? []);
+  for (const label of ["WORKS_AT", "KNOWS", "LOCATED_IN"]) {
+    assert.ok(capturedLabels.includes(label), `Expected rendered edge label ${label}`);
+  }
+  assert.ok(capturedLabels.includes("Alice"));
+
+  await page.getByRole("button", { name: "Zoom In" }).click();
+  await page.waitForTimeout(250);
+  const labelsAfterZoom = await page.evaluate(() => (window as Window & { __capturedCanvasText?: string[] }).__capturedCanvasText ?? []);
+  for (const label of ["WORKS_AT", "KNOWS", "LOCATED_IN"]) {
+    assert.ok(labelsAfterZoom.includes(label), `Expected edge label ${label} after zoom`);
+  }
+});

explorer/tests/deterministicExplorerRendering.test.ts

@@ -0,0 +1,508 @@
+import test from "node:test";
+import assert from "node:assert/strict";
+
+import {
+  batchMergeEdges,
+  batchMergeNodes,
+  clearGraph,
+  graph,
+} from "../src/store/graphStore.ts";
+import {
+  buildStructuralDistanceSnapshot,
+  classifyFullGraphEdge,
+  resolveDisplayGraph,
+  resolveEdgeElementStyle,
+  resolveEdgeVisualState,
+  resolveNodeElementStyle,
+  resolveNodeVisualState,
+  shouldForceNodeLabel,
+} from "../src/workspaces/GraphWorkspace/graphSceneState.ts";
+import { GRAPH_THEME, type GraphZoomTier } from "../src/workspaces/GraphWorkspace/graphTheme.ts";
+
+test.beforeEach(() => {
+  clearGraph();
+});
+
+test.after(() => {
+  clearGraph();
+});
+
+/**
+ * Loads the canonical 4-node, 3-edge deterministic test graph (Semantica #1037).
+ *
+ * Graph structure:
+ *   Alice (Person)       --WORKS_AT-->    Acme (Organization)
+ *   Bob (Person)         --KNOWS-->       Alice (Person)
+ *   Acme (Organization)  --LOCATED_IN-->  New York (Location)
+ */
+function loadDeterministicTestGraph() {
+  batchMergeNodes([
+    {
+      id: "alice",
+      attributes: {
+        label: "Alice",
+        content: "Alice",
+        x: 0,
+        y: 0,
+        size: 8,
+        color: "#63E6FF",
+        baseColor: "#63E6FF",
+        nodeType: "Person",
+        semanticGroup: "Person",
+        properties: {},
+      },
+    },
+    {
+      id: "bob",
+      attributes: {
+        label: "Bob",
+        content: "Bob",
+        x: -50,
+        y: 0,
+        size: 8,
+        color: "#63E6FF",
+        baseColor: "#63E6FF",
+        nodeType: "Person",
+        semanticGroup: "Person",
+        properties: {},
+      },
+    },
+    {
+      id: "acme",
+      attributes: {
+        label: "Acme",
+        content: "Acme",
+        x: 50,
+        y: 0,
+        size: 8,
+        color: "#A78BFA",
+        baseColor: "#A78BFA",
+        nodeType: "Organization",
+        semanticGroup: "Organization",
+        properties: {},
+      },
+    },
+    {
+      id: "new_york",
+      attributes: {
+        label: "New York",
+        content: "New York",
+        x: 100,
+        y: 0,
+        size: 8,
+        color: "#34D399",
+        baseColor: "#34D399",
+        nodeType: "Location",
+        semanticGroup: "Location",
+        properties: {},
+      },
+    },
+  ]);
+
+  batchMergeEdges([
+    {
+      id: "edge_alice_acme",
+      source: "alice",
+      target: "acme",
+      attributes: {
+        edgeId: "edge_alice_acme",
+        edgeType: "WORKS_AT",
+        weight: 1.0,
+        visualPriority: 0.8,
+        baseSize: 0.8,
+        properties: {},
+      },
+    },
+    {
+      id: "edge_bob_alice",
+      source: "bob",
+      target: "alice",
+      attributes: {
+        edgeId: "edge_bob_alice",
+        edgeType: "KNOWS",
+        weight: 1.0,
+        visualPriority: 0.8,
+        baseSize: 0.8,
+        properties: {},
+      },
+    },
+    {
+      id: "edge_acme_new_york",
+      source: "acme",
+      target: "new_york",
+      attributes: {
+        edgeId: "edge_acme_new_york",
+        edgeType: "LOCATED_IN",
+        weight: 1.0,
+        visualPriority: 0.8,
+        baseSize: 0.8,
+        properties: {},
+      },
+    },
+  ]);
+}
+
+test("deterministic graph contains exactly 4 nodes and 3 edges in store", () => {
+  loadDeterministicTestGraph();
+
+  assert.equal(graph.order, 4, "Expected exactly 4 nodes");
+  assert.equal(graph.size, 3, "Expected exactly 3 edges");
+
+  // Verify node identities and labels
+  const alice = graph.getNodeAttributes("alice");
+  const bob = graph.getNodeAttributes("bob");
+  const acme = graph.getNodeAttributes("acme");
+  const newYork = graph.getNodeAttributes("new_york");
+
+  assert.equal(alice.label, "Alice");
+  assert.equal(alice.nodeType, "Person");
+  assert.equal(alice.color, "#63E6FF");
+
+  assert.equal(bob.label, "Bob");
+  assert.equal(bob.nodeType, "Person");
+  assert.equal(bob.color, "#63E6FF");
+
+  assert.equal(acme.label, "Acme");
+  assert.equal(acme.nodeType, "Organization");
+  assert.equal(acme.color, "#A78BFA");
+
+  assert.equal(newYork.label, "New York");
+  assert.equal(newYork.nodeType, "Location");
+  assert.equal(newYork.color, "#34D399");
+
+  // Verify edge connectivity and canonical edgeType labels
+  const edgeAliceAcme = graph.getEdgeAttributes("edge_alice_acme");
+  const edgeBobAlice = graph.getEdgeAttributes("edge_bob_alice");
+  const edgeAcmeNewYork = graph.getEdgeAttributes("edge_acme_new_york");
+
+  assert.equal(edgeAliceAcme.edgeType, "WORKS_AT");
+  assert.equal(graph.source("edge_alice_acme"), "alice");
+  assert.equal(graph.target("edge_alice_acme"), "acme");
+
+  assert.equal(edgeBobAlice.edgeType, "KNOWS");
+  assert.equal(graph.source("edge_bob_alice"), "bob");
+  assert.equal(graph.target("edge_bob_alice"), "alice");
+
+  assert.equal(edgeAcmeNewYork.edgeType, "LOCATED_IN");
+  assert.equal(graph.source("edge_acme_new_york"), "acme");
+  assert.equal(graph.target("edge_acme_new_york"), "new_york");
+});
+
+test("display graph resolution preserves all 4 nodes and 3 edges in full view", () => {
+  loadDeterministicTestGraph();
+
+  const { graph: displayGraph } = resolveDisplayGraph("", [], [], "full", { aggregationEnabled: false });
+
+  assert.equal(displayGraph.order, 4);
+  assert.equal(displayGraph.size, 3);
+  assert.ok(displayGraph.hasNode("alice"));
+  assert.ok(displayGraph.hasNode("bob"));
+  assert.ok(displayGraph.hasNode("acme"));
+  assert.ok(displayGraph.hasNode("new_york"));
+  assert.ok(displayGraph.hasEdge("edge_alice_acme"));
+  assert.ok(displayGraph.hasEdge("edge_bob_alice"));
+  assert.ok(displayGraph.hasEdge("edge_acme_new_york"));
+});
+
+test("structural distance calculation resolves correct hop counts across the 3-edge chain", () => {
+  loadDeterministicTestGraph();
+
+  // From Bob: Bob (0) -> Alice (1) -> Acme (2) -> New York (3)
+  const distances = buildStructuralDistanceSnapshot(graph, "bob", 3);
+
+  assert.equal(distances.bob, 0);
+  assert.equal(distances.alice, 1);
+  assert.equal(distances.acme, 2);
+  assert.equal(distances.new_york, 3);
+});
+
+test("edge rendering and canonical edge labels remain legible across zoom tiers and inspection modes", () => {
+  loadDeterministicTestGraph();
+
+  const canonicalEdges = [
+    { id: "edge_alice_acme", source: "alice", target: "acme", label: "WORKS_AT" },
+    { id: "edge_bob_alice", source: "bob", target: "alice", label: "KNOWS" },
+    { id: "edge_acme_new_york", source: "acme", target: "new_york", label: "LOCATED_IN" },
+  ];
+
+  // 1. Edge attributes preserve canonical edgeType labels in graph store:
+  for (const item of canonicalEdges) {
+    const attrs = graph.getEdgeAttributes(item.id);
+    assert.equal(attrs.edgeType, item.label, `Edge ${item.id} must have edgeType ${item.label}`);
+    assert.equal(graph.source(item.id), item.source);
+    assert.equal(graph.target(item.id), item.target);
+  }
+
+  // 2. In active context / neighbor state across all zoom tiers (overview, structure, inspection):
+  const allTiers: GraphZoomTier[] = ["overview", "structure", "inspection"];
+  for (const tier of allTiers) {
+    for (const item of canonicalEdges) {
+      const attrs = graph.getEdgeAttributes(item.id);
+      const contextStyle = resolveEdgeElementStyle(
+        GRAPH_THEME,
+        tier,
+        "neighbor",
+        attrs,
+        item.source,
+        item.target,
+        "full",
+        item.id,
+      );
+
+      assert.equal(
+        contextStyle.hidden,
+        false,
+        `Edge ${item.id} (${item.label}) in context state 'neighbor' must be visible in zoom tier '${tier}'`,
+      );
+      assert.ok(
+        contextStyle.size !== undefined && contextStyle.size > 0,
+        `Edge ${item.id} (${item.label}) must have positive render size in zoom tier '${tier}'`,
+      );
+    }
+  }
+
+  // 3. In selected state in inspection zoom tier (close examination of edge details and label):
+  for (const item of canonicalEdges) {
+    const attrs = graph.getEdgeAttributes(item.id);
+    const selectedStyle = resolveEdgeElementStyle(
+      GRAPH_THEME,
+      "inspection",
+      "selected",
+      attrs,
+      item.source,
+      item.target,
+      "full",
+      item.id,
+      "selected",
+    );
+
+    assert.equal(
+      selectedStyle.hidden,
+      false,
+      `Selected edge ${item.id} (${item.label}) must be visible in inspection zoom tier`,
+    );
+    assert.ok(
+      selectedStyle.size !== undefined && selectedStyle.size > 0,
+      `Selected edge ${item.id} (${item.label}) must have positive render size`,
+    );
+  }
+
+  // 4. Verify inspection zoom tier camera and arrow rendering settings
+  assert.equal(GRAPH_THEME.zoomTiers.inspection.showContextualArrows, true);
+  assert.equal(GRAPH_THEME.zoomTiers.inspection.showCurves, true);
+});
+
+test("node hover interaction preserves edge visibility and highlights canonical incident edge types", () => {
+  loadDeterministicTestGraph();
+
+  // Scenario 1: Hover Alice
+  // Incident edges: Alice -> Acme (WORKS_AT) and Bob -> Alice (KNOWS)
+  const aliceAttrs = graph.getNodeAttributes("alice");
+  const aliceVisual = resolveNodeVisualState("alice", "structure", "alice", "", "", new Set(), new Set(), new Set());
+  assert.equal(aliceVisual, "hovered");
+
+  const aliceStyle = resolveNodeElementStyle(GRAPH_THEME, "structure", "hovered", aliceAttrs, "Alice");
+  assert.equal(aliceStyle.forceLabel, true, "Hovered Alice must force-render label");
+  assert.equal(aliceStyle.label, "Alice");
+  assert.equal(aliceStyle.showHalo, true, "Hovered Alice must show interactive halo");
+
+  const aliceIncidentEdges = new Set(["edge_alice_acme", "edge_bob_alice"]);
+
+  // Edge Alice -> Acme (WORKS_AT) under Alice hover
+  const aliceAcmeAttrs = graph.getEdgeAttributes("edge_alice_acme");
+  assert.equal(aliceAcmeAttrs.edgeType, "WORKS_AT");
+  const aliceAcmeState = resolveEdgeVisualState(
+    "edge_alice_acme",
+    "alice",
+    "acme",
+    "structure",
+    "alice",
+    "",
+    "",
+    new Set(),
+    new Set(),
+    aliceIncidentEdges,
+  );
+  assert.equal(aliceAcmeState, "hovered");
+  const aliceAcmeStyle = resolveEdgeElementStyle(
+    GRAPH_THEME,
+    "structure",
+    "hovered",
+    aliceAcmeAttrs,
+    "alice",
+    "acme",
+    "full",
+    "edge_alice_acme",
+  );
+  assert.equal(aliceAcmeStyle.hidden, false, "Incident edge WORKS_AT must remain visible on hover");
+  assert.ok(aliceAcmeStyle.size !== undefined && aliceAcmeStyle.size > 0);
+
+  // Edge Bob -> Alice (KNOWS) under Alice hover
+  const bobAliceAttrs = graph.getEdgeAttributes("edge_bob_alice");
+  assert.equal(bobAliceAttrs.edgeType, "KNOWS");
+  const bobAliceState = resolveEdgeVisualState(
+    "edge_bob_alice",
+    "bob",
+    "alice",
+    "structure",
+    "alice",
+    "",
+    "",
+    new Set(),
+    new Set(),
+    aliceIncidentEdges,
+  );
+  assert.equal(bobAliceState, "hovered");
+  const bobAliceStyle = resolveEdgeElementStyle(
+    GRAPH_THEME,
+    "structure",
+    "hovered",
+    bobAliceAttrs,
+    "bob",
+    "alice",
+    "full",
+    "edge_bob_alice",
+  );
+  assert.equal(bobAliceStyle.hidden, false, "Incident edge KNOWS must remain visible on hover");
+
+  // Non-incident edge Acme -> New York (LOCATED_IN) under Alice hover
+  const acmeNyAttrs = graph.getEdgeAttributes("edge_acme_new_york");
+  assert.equal(acmeNyAttrs.edgeType, "LOCATED_IN");
+  const acmeNyState = resolveEdgeVisualState(
+    "edge_acme_new_york",
+    "acme",
+    "new_york",
+    "structure",
+    "alice",
+    "",
+    "",
+    new Set(),
+    new Set(),
+    aliceIncidentEdges,
+  );
+  assert.equal(acmeNyState, "muted");
+
+  // Scenario 2: Hover Acme
+  // Incident edges: Alice -> Acme (WORKS_AT) and Acme -> New York (LOCATED_IN)
+  const acmeAttrs = graph.getNodeAttributes("acme");
+  const acmeStyle = resolveNodeElementStyle(GRAPH_THEME, "structure", "hovered", acmeAttrs, "Acme");
+  assert.equal(acmeStyle.forceLabel, true);
+  assert.equal(acmeStyle.label, "Acme");
+
+  const acmeIncidentEdges = new Set(["edge_alice_acme", "edge_acme_new_york"]);
+  const acmeNyHoverState = resolveEdgeVisualState(
+    "edge_acme_new_york",
+    "acme",
+    "new_york",
+    "structure",
+    "acme",
+    "",
+    "",
+    new Set(),
+    new Set(),
+    acmeIncidentEdges,
+  );
+  assert.equal(acmeNyHoverState, "hovered");
+  const acmeNyHoverStyle = resolveEdgeElementStyle(
+    GRAPH_THEME,
+    "structure",
+    "hovered",
+    acmeNyAttrs,
+    "acme",
+    "new_york",
+    "full",
+    "edge_acme_new_york",
+  );
+  assert.equal(acmeNyHoverStyle.hidden, false, "Incident edge LOCATED_IN must remain visible on hover");
+
+  // Scenario 3: Hover Bob
+  // Incident edge: Bob -> Alice (KNOWS)
+  const bobAttrs = graph.getNodeAttributes("bob");
+  const bobStyle = resolveNodeElementStyle(GRAPH_THEME, "structure", "hovered", bobAttrs, "Bob");
+  assert.equal(bobStyle.forceLabel, true);
+  assert.equal(bobStyle.label, "Bob");
+
+  const bobIncidentEdges = new Set(["edge_bob_alice"]);
+  const bobAliceHoverState = resolveEdgeVisualState(
+    "edge_bob_alice",
+    "bob",
+    "alice",
+    "structure",
+    "bob",
+    "",
+    "",
+    new Set(),
+    new Set(),
+    bobIncidentEdges,
+  );
+  assert.equal(bobAliceHoverState, "hovered");
+});
+
+test("edge selection maintains canonical edge type labels and active visual state", () => {
+  loadDeterministicTestGraph();
+
+  const edgeCases = [
+    { id: "edge_alice_acme", source: "alice", target: "acme", label: "WORKS_AT" },
+    { id: "edge_bob_alice", source: "bob", target: "alice", label: "KNOWS" },
+    { id: "edge_acme_new_york", source: "acme", target: "new_york", label: "LOCATED_IN" },
+  ];
+
+  for (const { id, source, target, label } of edgeCases) {
+    const attrs = graph.getEdgeAttributes(id);
+    assert.equal(attrs.edgeType, label);
+
+    const visualState = resolveEdgeVisualState(
+      id,
+      source,
+      target,
+      "inspection",
+      null,
+      "",
+      id, // selected edge
+      new Set(),
+      new Set(),
+    );
+    assert.equal(visualState, "selected", `Selected edge ${id} must resolve to 'selected' state`);
+
+    const style = resolveEdgeElementStyle(
+      GRAPH_THEME,
+      "inspection",
+      "selected",
+      attrs,
+      source,
+      target,
+      "full",
+      id,
+      "selected",
+    );
+
+    assert.equal(style.hidden, false, `Selected edge ${id} (${label}) must not be hidden`);
+    assert.ok(
+      style.size !== undefined && style.size > 0,
+      `Selected edge ${id} (${label}) must have positive render size`,
+    );
+  }
+});
+
+test("node labels remain forced visible during hover, selection, and inspection zoom tier", () => {
+  loadDeterministicTestGraph();
+
+  const nodes = ["alice", "bob", "acme", "new_york"];
+
+  for (const nid of nodes) {
+    const attrs = graph.getNodeAttributes(nid);
+
+    // Hover state forces label visibility
+    const hoverForcesLabel = shouldForceNodeLabel(GRAPH_THEME, "structure", "hovered", attrs, 0);
+    assert.equal(hoverForcesLabel, true, `Node ${nid} label must force visible on hover`);
+
+    // Selected state forces label visibility
+    const selectForcesLabel = shouldForceNodeLabel(GRAPH_THEME, "structure", "selected", attrs, 0);
+    assert.equal(selectForcesLabel, true, `Node ${nid} label must force visible on selection`);
+
+    // Resolved style emits actual string label
+    const style = resolveNodeElementStyle(GRAPH_THEME, "inspection", "hovered", attrs, attrs.label);
+    assert.equal(style.forceLabel, true);
+    assert.equal(style.label, attrs.label);
+  }
+});

explorer/tests/graphSceneState.display.test.ts

@@ -407,6 +407,31 @@ test("resolveEdgeElementStyle applies full-graph LOD to directional background e
   assert.equal(style.hidden, true);
 });
 
+test("resolveEdgeElementStyle keeps small-graph relationships visible in overview", () => {
+  const style = resolveEdgeElementStyle(
+    GRAPH_THEME,
+    "overview",
+    "inactive",
+    {
+      edgeType: "related_to",
+      weight: 1,
+      properties: {},
+      edgeVariant: "directional",
+      visualPriority: 0.1,
+      baseSize: 0.5,
+      isSmallGraph: true,
+    },
+    "source",
+    "target",
+    "full",
+    "small-graph-low-priority",
+    "hidden",
+  );
+
+  assert.equal(style.hidden, false);
+  assert.ok(Number(style.size ?? 0) >= 0.9);
+});
+
 test("classifyFullGraphEdge applies deterministic priority order", () => {
   const edgeClass = classifyFullGraphEdge(
     "edge-priority",

explorer/tests/realtimeGraphAttributes.test.ts

@@ -0,0 +1,31 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import { buildRealtimeEdgeAttributes } from "../src/workspaces/GraphWorkspace/realtimeGraphAttributes.ts";
+
+const payload = {
+  id: "edge-live",
+  source_id: "source",
+  target_id: "target",
+  type: "related_to",
+  properties: {},
+};
+
+test("realtime edges retain the active small-graph visibility marker", () => {
+  const attributes = buildRealtimeEdgeAttributes(payload, {
+    isBidirectional: false,
+    isSmallGraph: true,
+  });
+
+  assert.equal(attributes.isSmallGraph, true);
+  assert.equal(attributes.edgeVariant, "directional");
+});
+
+test("realtime edges do not retain the marker after graph leaves small-graph mode", () => {
+  const attributes = buildRealtimeEdgeAttributes(payload, {
+    isBidirectional: false,
+    isSmallGraph: false,
+  });
+
+  assert.equal(attributes.isSmallGraph, false);
+});

explorer/tests/smallGraphLayout.test.ts

@@ -0,0 +1,100 @@
+import assert from "node:assert/strict";
+import test from "node:test";
+
+import {
+  SMALL_GRAPH_MAX_NODES,
+  buildSmallGraphSeedPositions,
+  resolveGraphLayoutDecision,
+  resolveNodeLayoutPosition,
+  shouldUseSmallGraphLayout,
+} from "../src/workspaces/GraphWorkspace/smallGraphLayout.ts";
+
+test("small graph layout is selected only when coordinates are not already usable", () => {
+  assert.equal(shouldUseSmallGraphLayout(12, 0), true);
+  assert.equal(shouldUseSmallGraphLayout(SMALL_GRAPH_MAX_NODES + 1, 0), false);
+  assert.equal(shouldUseSmallGraphLayout(12, 0.95), false);
+});
+
+test("small graph layout ignores isolated partial coordinates", () => {
+  const decision = resolveGraphLayoutDecision(12, 1 / 12);
+  assert.deepEqual(
+    resolveNodeLayoutPosition(decision, { x: 50_000, y: -50_000 }, { x: 24, y: -18 }),
+    { x: 24, y: -18 },
+  );
+  assert.deepEqual(
+    resolveNodeLayoutPosition(decision, { x: 50_000, y: null }, { x: -12, y: 36 }),
+    { x: -12, y: 36 },
+  );
+});
+
+test("small graph load is immediately ready and skips runtime stabilization", () => {
+  assert.deepEqual(resolveGraphLayoutDecision(12, 0), {
+    useProvidedCoordinates: false,
+    useSmallGraphLayout: true,
+    layoutReady: true,
+  });
+  assert.deepEqual(resolveGraphLayoutDecision(SMALL_GRAPH_MAX_NODES + 1, 0), {
+    useProvidedCoordinates: false,
+    useSmallGraphLayout: false,
+    layoutReady: false,
+  });
+  assert.deepEqual(resolveGraphLayoutDecision(12, 1), {
+    useProvidedCoordinates: true,
+    useSmallGraphLayout: false,
+    layoutReady: true,
+  });
+});
+
+test("small graph layout is deterministic and keeps connected nodes together", () => {
+  const nodes = ["Apple", "Steve", "Ronald", "Cupertino", "California"];
+  const edges = [
+    { source: "Apple", target: "Steve" },
+    { source: "Ronald", target: "Cupertino" },
+  ];
+  const first = buildSmallGraphSeedPositions(nodes, edges);
+  const second = buildSmallGraphSeedPositions([...nodes].reverse(), [...edges].reverse());
+
+  assert.deepEqual([...first.entries()].sort(), [...second.entries()].sort());
+  assert.equal(first.size, nodes.length);
+
+  const distance = (left: string, right: string) => {
+    const a = first.get(left);
+    const b = first.get(right);
+    assert.ok(a && b);
+    return Math.hypot(a.x - b.x, a.y - b.y);
+  };
+  assert.ok(distance("Apple", "Steve") < distance("Apple", "California"));
+  assert.ok(distance("Ronald", "Cupertino") < distance("Ronald", "California"));
+});
+
+test("small graph layout keeps maximum-radius components separated", () => {
+  const componentCount = 4;
+  const nodesPerComponent = 12;
+  const nodes = Array.from(
+    { length: componentCount * nodesPerComponent },
+    (_, index) => `component-${Math.floor(index / nodesPerComponent)}-node-${index % nodesPerComponent}`,
+  );
+  const edges = Array.from({ length: componentCount }).flatMap((_, componentIndex) => {
+    const prefix = `component-${componentIndex}-node-`;
+    return Array.from({ length: nodesPerComponent - 1 }, (_unused, nodeIndex) => ({
+      source: `${prefix}${nodeIndex}`,
+      target: `${prefix}${nodeIndex + 1}`,
+    }));
+  });
+  const positions = buildSmallGraphSeedPositions(nodes, edges);
+
+  for (let leftComponent = 0; leftComponent < componentCount; leftComponent += 1) {
+    for (let rightComponent = leftComponent + 1; rightComponent < componentCount; rightComponent += 1) {
+      let closestDistance = Number.POSITIVE_INFINITY;
+      for (let leftNode = 0; leftNode < nodesPerComponent; leftNode += 1) {
+        for (let rightNode = 0; rightNode < nodesPerComponent; rightNode += 1) {
+          const left = positions.get(`component-${leftComponent}-node-${leftNode}`);
+          const right = positions.get(`component-${rightComponent}-node-${rightNode}`);
+          assert.ok(left && right);
+          closestDistance = Math.min(closestDistance, Math.hypot(left.x - right.x, left.y - right.y));
+        }
+      }
+      assert.ok(closestDistance >= 48, `components are only ${closestDistance} units apart`);
+    }
+  }
+});

explorer/tests/temporalSnapshotGuards.test.ts

@@ -0,0 +1,150 @@
+import test from "node:test";
+import assert from "node:assert/strict";
+
+import { createTemporalSnapshotGuards } from "../src/workspaces/GraphWorkspace/temporalSnapshotGuards.ts";
+
+const POSITION_1 = new Date("2023-07-02T00:00:00Z").getTime();
+const POSITION_2 = new Date("2024-01-02T00:00:00Z").getTime();
+const POSITION_3 = new Date("2024-07-02T00:00:00Z").getTime();
+
+const SNAPSHOT = { active_node_ids: ["n1", "n2"], active_node_count: 2 };
+
+// ── begin: one request per scrubber position ─────────────────────────────────
+
+test("begin: a new position returns a fresh request sequence", () => {
+  const guards = createTemporalSnapshotGuards();
+  assert.deepEqual(guards.begin(POSITION_1), { seq: 1, cached: null });
+});
+
+test("begin: an identical in-flight request is deduplicated (no duplicate fetch)", () => {
+  const guards = createTemporalSnapshotGuards();
+  guards.begin(POSITION_1);
+  assert.deepEqual(guards.begin(POSITION_1), { seq: null, cached: null });
+});
+
+test("begin: distinct positions request independently", () => {
+  const guards = createTemporalSnapshotGuards();
+  assert.equal(guards.begin(POSITION_1).seq, 1);
+  assert.equal(guards.begin(POSITION_2).seq, 2);
+});
+
+test("begin: revisiting an applied position returns its cached snapshot", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.apply(POSITION_1, seq, SNAPSHOT);
+  const revisit = guards.begin(POSITION_1);
+  assert.equal(revisit.seq, 2);
+  assert.deepEqual(revisit.cached, SNAPSHOT);
+});
+
+test("begin: a failed position (finished) can be requested again", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.finish(POSITION_1, seq);
+  const retry = guards.begin(POSITION_1);
+  assert.equal(retry.seq, 2);
+  assert.equal(retry.cached, null);
+});
+
+test("finish: does not clear a position whose snapshot was already applied", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.apply(POSITION_1, seq, SNAPSHOT);
+  guards.finish(POSITION_1, seq);
+  assert.deepEqual(guards.begin(POSITION_1).cached, SNAPSHOT);
+});
+
+test("finish: a stale sequence cannot release a newer request's position", () => {
+  const guards = createTemporalSnapshotGuards();
+  const first = guards.begin(POSITION_1);
+  guards.finish(POSITION_1, first.seq);
+  guards.begin(POSITION_1); // seq 2, in flight again
+  guards.finish(POSITION_1, first.seq); // stale seq: must not release seq 2
+  assert.deepEqual(guards.begin(POSITION_1), { seq: null, cached: null });
+});
+
+// ── shouldApply: applied only while the scrubber is on that position ─────────
+
+test("shouldApply: the current position's response is applied", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  assert.equal(guards.shouldApply(POSITION_1, seq), true);
+});
+
+test("shouldApply: a response for a position the scrubber left is discarded", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq: seq1 } = guards.begin(POSITION_1);
+  guards.begin(POSITION_2);
+  assert.equal(guards.shouldApply(POSITION_1, seq1), false);
+  assert.equal(guards.shouldApply(POSITION_2, 2), true);
+});
+
+test("shouldApply: a late response for the position the scrubber returned to is applied", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq: seq1 } = guards.begin(POSITION_1);
+  const { seq: seq2 } = guards.begin(POSITION_2);
+  guards.begin(POSITION_1); // back to 1: deduplicated, no new request
+  assert.equal(guards.shouldApply(POSITION_1, seq1), true);
+  assert.equal(guards.shouldApply(POSITION_2, seq2), false);
+});
+
+test("shouldApply: an unknown sequence is discarded", () => {
+  const guards = createTemporalSnapshotGuards();
+  guards.begin(POSITION_1);
+  assert.equal(guards.shouldApply(POSITION_1, 99), false);
+});
+
+test("shouldApply: after a reset no pre-reset response applies", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.reset();
+  assert.equal(guards.shouldApply(POSITION_1, seq), false);
+});
+
+// ── apply: caching for revisits ─────────────────────────────────────────────
+
+test("apply: stores the snapshot so a revisit re-applies it without a request", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.apply(POSITION_1, seq, SNAPSHOT);
+  guards.begin(POSITION_2);
+  assert.deepEqual(guards.begin(POSITION_1).cached, SNAPSHOT);
+});
+
+test("apply: play wrap-around re-applies the wrapped-to position's snapshot", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.apply(POSITION_1, seq, SNAPSHOT);
+  guards.begin(POSITION_2);
+  guards.begin(POSITION_3);
+  const wrap = guards.begin(POSITION_1);
+  assert.deepEqual(wrap.cached, SNAPSHOT);
+  assert.equal(guards.shouldApply(POSITION_1, wrap.seq), true);
+});
+
+// ── reset: graph reload ─────────────────────────────────────────────────────
+
+test("reset: clears requested and cached state so positions refetch", () => {
+  const guards = createTemporalSnapshotGuards();
+  const { seq } = guards.begin(POSITION_1);
+  guards.apply(POSITION_1, seq, SNAPSHOT);
+  guards.reset();
+  const fresh = guards.begin(POSITION_1);
+  assert.equal(fresh.seq, 1);
+  assert.equal(fresh.cached, null);
+});
+
+// ── cache bound ─────────────────────────────────────────────────────────────
+
+test("cache: oldest positions are evicted when the cache is full", () => {
+  const guards = createTemporalSnapshotGuards();
+  const count = 300;
+  for (let i = 0; i < count; i++) {
+    const { seq } = guards.begin(POSITION_1 + i * 1000);
+    guards.apply(POSITION_1 + i * 1000, seq, SNAPSHOT);
+  }
+  const oldest = guards.begin(POSITION_1);
+  assert.equal(oldest.cached, null); // evicted: must refetch on revisit
+  const newest = guards.begin(POSITION_1 + (count - 1) * 1000);
+  assert.deepEqual(newest.cached, SNAPSHOT); // still cached
+});

mcp/README.md

@@ -8,8 +8,9 @@ Connects Claude Code, Cursor, Windsurf, Cline, Continue, VS Code (GitHub Copilot
 ## Quick start
 
 ```bash
-# From the repo root
-pip install -e ".[mcp]"
+# From the repo root — no extra install flag needed; the root mcp/ package is
+# part of the repository and does not require an external MCP SDK.
+pip install -e .
 
 # Test the server (type a JSON-RPC request, press Enter)
 python -m mcp
@@ -89,7 +90,14 @@ python -m mcp [--debug]
 
 ## Per-tool configuration
 
-### Claude Code (`~/.claude/settings.json`)
+### Claude Code (`~/.claude.json` or `.mcp.json`)
+
+Claude Code supports two MCP configuration scopes:
+
+- **User scope** — `~/.claude.json` applies across all projects for your user account.
+- **Project scope** — `.mcp.json` in your project root applies only to that project.
+
+Both files use the same `mcpServers` structure:
 
 ```json
 {
@@ -97,15 +105,33 @@ python -m mcp [--debug]
     "semantica": {
       "command": "python",
       "args": ["-m", "mcp"],
-      "cwd": "/path/to/semantica"
+      "env": {
+        "PYTHONPATH": "/path/to/semantica"
+      }
     }
   }
 }
 ```
 
-Or use the plugin bundle:
+> **Why `PYTHONPATH`?**  The root `mcp/` package is intentionally not included in
+> the installed wheel, so `python -m mcp` only works when the repository is on
+> Python's import path.  Setting `PYTHONPATH` here ensures this works regardless
+> of the working directory Claude uses when it launches the server.
+
+Or add it via the CLI (user scope):
+
 ```bash
-claude mcp add semantica python -m mcp --cwd /path/to/semantica
+claude mcp add --scope user semantica \
+  -e PYTHONPATH=/path/to/semantica \
+  -- python -m mcp
+```
+
+Or for project scope (omit `--scope user`):
+
+```bash
+claude mcp add semantica \
+  -e PYTHONPATH=/path/to/semantica \
+  -- python -m mcp
 ```
 
 ---
@@ -216,7 +242,7 @@ Add to your Q Developer MCP config:
 
 | Variable | Default | Description |
 |---|---|---|
-| `SEMANTICA_KG_PATH` | *(in-memory)* | Path to persist/load the graph (JSON file) |
+| `SEMANTICA_KG_PATH` | *(in-memory only)* | Path to a JSON file used to **load** the graph on startup and **persist** mutations (record decisions, add entities/relationships) back to disk after each change. When unset the graph lives in memory only and is lost when the server exits. |
 
 ---
 

mcp/session.py

@@ -16,6 +16,13 @@ log = logging.getLogger("semantica.mcp.session")
 
 _graph: Optional[Any] = None
 
+# Tracks whether the last graph initialisation successfully loaded the
+# configured SEMANTICA_KG_PATH file.  When True (or no path was configured)
+# mutation handlers are allowed to save.  When False an existing file failed
+# to load; saving would overwrite the original data with an empty graph, so
+# persistence is blocked until the process is restarted with a readable file.
+_load_ok: bool = True
+
 
 def get_graph() -> Any:
     """
@@ -24,24 +31,45 @@ def get_graph() -> Any:
     The graph is created with advanced_analytics=True so all centrality,
     community-detection, and embedding features are available.
     """
-    global _graph
+    global _graph, _load_ok
     if _graph is None:
         from semantica.context import ContextGraph
 
         _graph = ContextGraph(advanced_analytics=True)
+        _load_ok = True  # default: safe to persist
 
         kg_path = os.environ.get("SEMANTICA_KG_PATH", "").strip()
         if kg_path and os.path.exists(kg_path):
-            try:
-                _graph.load(kg_path)
-                log.info("Graph loaded from %s", kg_path)
-            except Exception as exc:
-                log.warning("Could not load graph from %s: %s", kg_path, exc)
+            # Only attempt to load if the file has content.  An empty file
+            # means the path was just created (e.g. a fresh tempfile) and
+            # should be treated as "start with empty graph" rather than a
+            # corrupt-file failure.
+            if os.path.getsize(kg_path) > 0:
+                try:
+                    _graph.load_from_file(kg_path)
+                    log.info("Graph loaded from %s", kg_path)
+                except Exception as exc:
+                    log.warning(
+                        "Could not load graph from %s: %s — persistence disabled "
+                        "to protect existing data; restart the server to retry.",
+                        kg_path, exc,
+                    )
+                    _load_ok = False  # do not overwrite the original file
 
     return _graph
 
 
+def is_persistence_safe() -> bool:
+    """Return True when it is safe to write mutations back to SEMANTICA_KG_PATH.
+
+    Returns False after a failed load so that mutation handlers do not
+    overwrite the original (possibly intact) file with a fresh empty graph.
+    """
+    return _load_ok
+
+
 def reset_graph() -> None:
     """Reset the singleton (mainly useful in tests)."""
-    global _graph
+    global _graph, _load_ok
     _graph = None
+    _load_ok = True

mcp/tools/decisions.py

@@ -5,6 +5,7 @@ Decision intelligence tools — record, query, precedents, causal chain, impact.
 from __future__ import annotations
 
 import logging
+import os
 
 from mcp.schemas import (
     ANALYZE_DECISION_IMPACT,
@@ -13,7 +14,7 @@ from mcp.schemas import (
     QUERY_DECISIONS,
     RECORD_DECISION,
 )
-from mcp.session import get_graph
+from mcp.session import get_graph, is_persistence_safe
 
 log = logging.getLogger("semantica.mcp.tools.decisions")
 
@@ -37,6 +38,39 @@ def handle_record_decision(args: dict) -> dict:
             valid_from=args.get("valid_from"),
             valid_until=args.get("valid_until"),
         )
+        # Persist back to disk so the decision survives server restarts.
+        # Skip when the initial load failed to avoid overwriting original data.
+        kg_path = os.environ.get("SEMANTICA_KG_PATH", "").strip()
+        if kg_path:
+            if not is_persistence_safe():
+                # Roll back the in-memory mutation so the client-visible state
+                # matches the persisted state (neither is saved).
+                if hasattr(graph, "_decisions") and decision_id in graph._decisions:
+                    del graph._decisions[decision_id]
+                    if hasattr(graph, "_decision_index"):
+                        cat = args.get("category", "")
+                        if cat in graph._decision_index:
+                            graph._decision_index[cat].discard(decision_id)
+                return {
+                    "error": (
+                        "Persistence blocked: the configured SEMANTICA_KG_PATH "
+                        "could not be loaded at startup. Restart the server with "
+                        "a readable graph file to re-enable persistence."
+                    )
+                }
+            try:
+                graph.save_to_file(kg_path)
+            except Exception as save_exc:
+                # Atomic write failed. Roll back the in-memory mutation so the
+                # client-visible and persisted states remain consistent.
+                if hasattr(graph, "_decisions") and decision_id in graph._decisions:
+                    del graph._decisions[decision_id]
+                    if hasattr(graph, "_decision_index"):
+                        cat = args.get("category", "")
+                        if cat in graph._decision_index:
+                            graph._decision_index[cat].discard(decision_id)
+                log.exception("save_to_file failed after record_decision; mutation rolled back")
+                return {"error": f"Mutation rolled back: could not persist graph: {save_exc}"}
         return {
             "decision_id": decision_id,
             "status": "recorded",

mcp/tools/graph.py

@@ -5,9 +5,10 @@ Graph tools — add entities/relationships, search, analytics, summary.
 from __future__ import annotations
 
 import logging
+import os
 
 from mcp.schemas import ADD_ENTITY, ADD_RELATIONSHIP, EMPTY, GET_ANALYTICS, SEARCH_GRAPH
-from mcp.session import get_graph
+from mcp.session import get_graph, is_persistence_safe
 
 log = logging.getLogger("semantica.mcp.tools.graph")
 
@@ -25,6 +26,35 @@ def handle_add_entity(args: dict) -> dict:
             node_type=args.get("type", "Entity"),
             metadata=args.get("metadata", {}),
         )
+        # Persist back to disk so the entity survives server restarts.
+        # Skip when the initial load failed to avoid overwriting original data.
+        kg_path = os.environ.get("SEMANTICA_KG_PATH", "").strip()
+        if kg_path:
+            if not is_persistence_safe():
+                # Roll back: remove the node we just added.
+                try:
+                    with graph._lock:
+                        graph._drop_node_from_indexes(node_id)
+                except Exception:
+                    pass
+                return {
+                    "error": (
+                        "Persistence blocked: the configured SEMANTICA_KG_PATH "
+                        "could not be loaded at startup. Restart the server with "
+                        "a readable graph file to re-enable persistence."
+                    )
+                }
+            try:
+                graph.save_to_file(kg_path)
+            except Exception as save_exc:
+                # Roll back: remove the node so in-memory and persisted state agree.
+                try:
+                    with graph._lock:
+                        graph._drop_node_from_indexes(node_id)
+                except Exception:
+                    pass
+                log.exception("save_to_file failed after add_entity; mutation rolled back")
+                return {"error": f"Mutation rolled back: could not persist graph: {save_exc}"}
         return {"status": "added", "id": node_id, "type": args.get("type", "Entity")}
     except Exception as exc:
         log.exception("add_entity failed")
@@ -46,6 +76,45 @@ def handle_add_relationship(args: dict) -> dict:
             edge_type=rel_type,
             metadata=args.get("metadata", {}),
         )
+        # Persist back to disk so the relationship survives server restarts.
+        # Skip when the initial load failed to avoid overwriting original data.
+        kg_path = os.environ.get("SEMANTICA_KG_PATH", "").strip()
+        if kg_path:
+            if not is_persistence_safe():
+                # Roll back: remove the edge we just added (last matching edge).
+                try:
+                    with graph._lock:
+                        for edge in reversed(list(graph.edges)):
+                            if (edge.source_id == source
+                                    and edge.target_id == target
+                                    and edge.edge_type == rel_type):
+                                graph._drop_edge_from_indexes(edge)
+                                break
+                except Exception:
+                    pass
+                return {
+                    "error": (
+                        "Persistence blocked: the configured SEMANTICA_KG_PATH "
+                        "could not be loaded at startup. Restart the server with "
+                        "a readable graph file to re-enable persistence."
+                    )
+                }
+            try:
+                graph.save_to_file(kg_path)
+            except Exception as save_exc:
+                # Roll back: remove the edge so in-memory and persisted state agree.
+                try:
+                    with graph._lock:
+                        for edge in reversed(list(graph.edges)):
+                            if (edge.source_id == source
+                                    and edge.target_id == target
+                                    and edge.edge_type == rel_type):
+                                graph._drop_edge_from_indexes(edge)
+                                break
+                except Exception:
+                    pass
+                log.exception("save_to_file failed after add_relationship; mutation rolled back")
+                return {"error": f"Mutation rolled back: could not persist graph: {save_exc}"}
         return {"status": "added", "source": source, "target": target, "type": rel_type}
     except Exception as exc:
         log.exception("add_relationship failed")

plugins/.claude-plugin/plugin.json

@@ -25,5 +25,9 @@
     "mcp"
   ],
   "skills": "./skills",
-  "agents": "./agents"
+  "agents": [
+    "./agents/decision-advisor.md",
+    "./agents/explainability.md",
+    "./agents/kg-assistant.md"
+  ]
 }

pyproject.toml

@@ -49,7 +49,14 @@ dependencies = [
   "scipy>=1.13.1",
   "scikit-learn>=1.7.2",
   "umap-learn>=0.5.12",
-  "spacy>=3.4.0",
+  # thinc (spacy's core dep) dropped Python 3.9 wheels at 8.3.10, and later
+  # spacy patch releases (3.8.8+) require thinc>=8.3.9-only-on-3.10+ ranges,
+  # which forces a source build that fails outright on 3.9 (see Install
+  # Matrix run history). Capping both keeps 3.9 on the last wheel-compatible
+  # pair; 3.10+ is left unconstrained to always get the latest spacy/thinc.
+  "spacy>=3.4.0,<3.8.8; python_version < '3.10'",
+  "spacy>=3.4.0; python_version >= '3.10'",
+  "thinc<8.3.5; python_version < '3.10'",
   "transformers>=4.20.0",
   "torch>=1.13.1",
   "sentence-transformers>=2.2.0",
@@ -107,11 +114,12 @@ llm-gemini = ["google-genai>=0.1.0"]
 llm-anthropic = ["anthropic>=0.122.0"]
 llm-ollama = ["ollama>=0.1.0"]
 llm-deepseek = ["openai>=1.0.0"]
+llm-novita = ["openai>=1.0.0"]
 llm-litellm = ["litellm>=1.83.9"]
 llm-instructor = ["instructor>=1.15.3"]
 
 llm-all = [
-  "semantica[llm-openai,llm-groq,llm-gemini,llm-anthropic,llm-ollama,llm-deepseek,llm-litellm,llm-instructor]"
+  "semantica[llm-openai,llm-groq,llm-gemini,llm-anthropic,llm-ollama,llm-deepseek,llm-novita,llm-litellm,llm-instructor]"
 ]
 
 # ---- Document Parsing ----
@@ -124,12 +132,13 @@ shacl = ["pyshacl>=0.25.0"]
 db-snowflake = ["snowflake-connector-python>=4.6.0", "cryptography>=49.0.0"]
 db-databricks = ["databricks-sdk>=0.60.0", "databricks-sql-connector>=4.0.0"]
 db-arrow = ["pyarrow>=24.0.0"]
+db-salesforce = ["simple-salesforce>=1.12.0"]
 ingest-parquet = ["pyarrow>=24.0.0"]
 ingest-arrow = ["pyarrow>=24.0.0"]
 ingest-sap = ["requests>=2.28.0"]
 
 db-all = [
-  "semantica[db-snowflake,db-databricks,db-arrow]"
+  "semantica[db-snowflake,db-databricks,db-salesforce,db-arrow]"
 ]
 
 # ---- Embedding / Models ----

semantica/cli.py

@@ -3714,19 +3714,61 @@ def store_stats(cli_ctx: CLIContext, backend: str, fmt: str, local_json: bool) -
     _run_with_error_handling(_action)
 
 
+_MIGRATE_SUPPORTED_BACKENDS = {"faiss", "sqlite", "pgvector"}
+_MIGRATE_BATCH_SIZE = 500
+
+
+def _migrate_backend_config(vs_cfg: Dict[str, Any], backend: str) -> Dict[str, Any]:
+    """Resolve per-backend config out of the vector_store config section.
+
+    Supports both a per-backend nested shape (``vector_store.faiss.dimension``)
+    and the common flat single-backend shape (``vector_store.backend`` +
+    sibling keys), since either can appear depending on how many backends a
+    user has configured.
+    """
+    nested = vs_cfg.get(backend)
+    if isinstance(nested, dict):
+        return dict(nested)
+    if vs_cfg.get("backend") == backend:
+        return {k: v for k, v in vs_cfg.items() if k != "backend"}
+    return {}
+
+
+def _require_faiss_index_path(cfg: Dict[str, Any], role: str) -> str:
+    """FAISS has no server to hold state between commands: a fresh FAISSStore
+    starts empty and nothing outside the process persists it, so migration
+    needs an explicit on-disk index to read from or write to."""
+    index_path = cfg.get("index_path")
+    if not index_path:
+        raise click.ClickException(
+            f"faiss as migration {role} requires 'index_path' in the vector_store "
+            f"config (vector_store.faiss.index_path or vector_store.index_path "
+            f"when faiss is the configured backend)."
+        )
+    return index_path
+
+
 @store.command("migrate")
 @click.option("--from", "from_backend", required=True)
 @click.option("--to", "to_backend", required=True)
 @click.option("--namespace", default=None)
 @click.option("--dry-run", "local_dry", is_flag=True, default=False)
+@click.option("--json", "local_json", is_flag=True, default=False)
 @click.pass_obj
 def store_migrate(cli_ctx: CLIContext, from_backend: str, to_backend: str,
-                  namespace: Optional[str], local_dry: bool) -> None:
+                  namespace: Optional[str], local_dry: bool, local_json: bool) -> None:
     """Migrate data between backends.
 
+    Direct migration is only wired up between faiss, sqlite, and pgvector -
+    these are the backends whose storage contract supports paging through
+    every stored vector. Migrating to or from qdrant, pinecone, milvus, or
+    weaviate still needs the export/reindex workaround below, since each of
+    those needs its own enumeration design (Qdrant scroll, Pinecone list,
+    etc.) that hasn't been built yet.
+
     \b
     Example:
-      semantica store migrate --from faiss --to qdrant --namespace production --dry-run
+      semantica store migrate --from faiss --to sqlite --namespace production --dry-run
     """
     cli_ctx = _require_ctx(cli_ctx)
 
@@ -3734,13 +3776,76 @@ def store_migrate(cli_ctx: CLIContext, from_backend: str, to_backend: str,
         if _is_dry(cli_ctx, local_dry):
             _dry(cli_ctx, "migrate", from_backend=from_backend, to_backend=to_backend)
             return
-        raise click.ClickException(
-            f"Direct backend migration ({from_backend} → {to_backend}) is not yet supported "
-            "by the vector store layer. To migrate, export your data first:\n"
-            "  semantica export --format parquet --output dump.parquet\n"
-            f"  semantica embed index dump.parquet --store {to_backend}"
-            + (f" --namespace {namespace}" if namespace else "")
-        )
+
+        if from_backend not in _MIGRATE_SUPPORTED_BACKENDS or to_backend not in _MIGRATE_SUPPORTED_BACKENDS:
+            raise click.ClickException(
+                f"Direct backend migration ({from_backend} → {to_backend}) is only supported "
+                f"between {', '.join(sorted(_MIGRATE_SUPPORTED_BACKENDS))}. To migrate involving "
+                "another backend, export your data first:\n"
+                "  semantica export --format parquet --output dump.parquet\n"
+                f"  semantica embed index dump.parquet --store {to_backend}"
+                + (f" --namespace {namespace}" if namespace else "")
+            )
+
+        from .vector_store import VectorStore
+
+        vs_cfg = cli_ctx.config.to_dict().get("vector_store", {}) or {}
+        source_cfg = _migrate_backend_config(vs_cfg, from_backend)
+        dest_cfg = _migrate_backend_config(vs_cfg, to_backend)
+
+        source_index_path = None
+        if from_backend == "faiss":
+            source_index_path = _require_faiss_index_path(source_cfg, "source")
+        dest_index_path = None
+        if to_backend == "faiss":
+            dest_index_path = _require_faiss_index_path(dest_cfg, "destination")
+
+        source = VectorStore(backend=from_backend, config=source_cfg)
+        if source_index_path:
+            source._backend_store.load_index(source_index_path)
+
+        source_dimension = getattr(source._backend_store, "dimension", None)
+        if source_dimension and "dimension" not in dest_cfg:
+            dest_cfg["dimension"] = source_dimension
+
+        dest = VectorStore(backend=to_backend, config=dest_cfg)
+        if dest_index_path and Path(dest_index_path).exists():
+            dest._backend_store.load_index(dest_index_path)
+
+        migrated = 0
+        vectors_batch: List[Any] = []
+        metadata_batch: List[Dict[str, Any]] = []
+        ids_batch: List[str] = []
+
+        def _flush() -> None:
+            nonlocal migrated
+            if not vectors_batch:
+                return
+            dest.store_vectors(list(vectors_batch), list(metadata_batch), ids=list(ids_batch))
+            migrated += len(vectors_batch)
+            vectors_batch.clear()
+            metadata_batch.clear()
+            ids_batch.clear()
+
+        for item in source.iter_vectors(batch_size=_MIGRATE_BATCH_SIZE):
+            meta = dict(item.get("metadata") or {})
+            if namespace and "namespace" not in meta:
+                meta["namespace"] = namespace
+            vectors_batch.append(item["vector"])
+            metadata_batch.append(meta)
+            ids_batch.append(item["id"])
+            if len(vectors_batch) >= _MIGRATE_BATCH_SIZE:
+                _flush()
+        _flush()
+
+        if dest_index_path and migrated:
+            dest._backend_store.save_index(dest_index_path)
+
+        result = {"from": from_backend, "to": to_backend, "migrated": migrated}
+        if _is_json(cli_ctx, local_json):
+            _jecho(result)
+        else:
+            _ok(cli_ctx, f"Migrated {migrated} vectors from {from_backend} to {to_backend}")
 
     _run_with_error_handling(_action)
 

semantica/context/__init__.py

@@ -111,6 +111,7 @@ from .context_graph import ContextEdge, ContextGraph, ContextNode
 from .context_retriever import ContextRetriever, RetrievedContext, TemporalGraphRetriever
 from .decision_context import DecisionContext
 from .entity_linker import EntityLink, EntityLinker, LinkedEntity
+from .erasure import ErasureCoordinator, ErasureReceipt
 
 # Decision tracking imports
 from .decision_models import (
@@ -145,6 +146,9 @@ __all__ = [
     "ContextRetriever",
     "RetrievedContext",
     "TemporalGraphRetriever",
+    # Cross-store erasure
+    "ErasureCoordinator",
+    "ErasureReceipt",
     # Decision tracking models
     "Decision",
     "DecisionContextModel", 

semantica/context/agent_memory.py

@@ -626,6 +626,27 @@ class AgentMemory:
         self.logger.debug(f"Deleted memory item: {memory_id}")
         return True
 
+    def vector_ids_for(self, memory_id: str) -> List[str]:
+        """Return the vector-store ids owned by a memory item.
+
+        Read-only view of the ids ``delete_memory()`` would remove for this
+        item, so a caller that needs to *report* on vector removal can delete
+        them itself rather than relying on ``delete_memory()``'s best-effort
+        cascade, which logs a vector-store failure and still returns ``True``.
+
+        Mirrors the fallback in ``delete_memory``: an item stored without
+        tracked vector ids is keyed in the vector store by its own memory id.
+
+        Args:
+            memory_id: Memory identifier.
+
+        Returns:
+            The item's vector ids, or ``[]`` if the item is unknown.
+        """
+        if memory_id not in self.memory_items:
+            return []
+        return list(self._vector_ids.get(memory_id, [])) or [memory_id]
+
     def clear_memory(self, **filters) -> int:
         """
         Clear memory items matching filters.
@@ -1286,13 +1307,19 @@ class AgentMemory:
         """
         return self.retrieve(content, max_results=limit, **kwargs)
 
-    def find_by_entity(self, entity_id: str, limit: int = 10) -> List[Dict[str, Any]]:
+    def find_by_entity(
+        self, entity_id: str, limit: Optional[int] = None
+    ) -> List[Dict[str, Any]]:
         """
         Find by entity.
 
         Args:
             entity_id: Entity ID to search for
-            limit: Maximum results (default: 10)
+            limit: Maximum results. None (the default) returns ALL matches.
+                The previous default of 10 silently truncated results — an
+                erasure workflow computing "what references this entity"
+                from a truncated page would leave the remainder live
+                (#1018). Callers that want pagination pass an explicit limit.
 
         Returns:
             List of memory dicts containing the entity
@@ -1308,9 +1335,9 @@ class AgentMemory:
                     if mem_dict:
                         results.append(mem_dict)
                     break
-            if len(results) >= limit:
+            if limit is not None and len(results) >= limit:
                 break
-        return results[:limit]
+        return results if limit is None else results[:limit]
 
     def find_by_relationship(
         self, relationship_type: str, limit: int = 10

semantica/context/context_graph.py

@@ -1203,8 +1203,30 @@ class ContextGraph:
                 "links": links_data,
             }
 
-        with open(path, "w", encoding="utf-8") as f:
-            json.dump(data, f, indent=2, ensure_ascii=False)
+        # Write atomically: serialize to a sibling temp file then replace the
+        # destination in one OS-level rename.  This guarantees the destination
+        # is either the old contents or the new contents — never a partial write
+        # — so a crash or disk-full error during json.dump cannot corrupt the
+        # sole persisted copy of the graph.
+        dest = Path(path)
+        dest.parent.mkdir(parents=True, exist_ok=True)
+        fd, tmp_path = tempfile.mkstemp(
+            dir=dest.parent, prefix=".kg_tmp_", suffix=".json"
+        )
+        try:
+            with os.fdopen(fd, "w", encoding="utf-8") as f:
+                json.dump(data, f, indent=2, ensure_ascii=False)
+                f.flush()
+                os.fsync(f.fileno())
+            os.replace(tmp_path, dest)
+        except Exception:
+            # Clean up the temp file on any failure so we don't litter the
+            # directory with partial writes.
+            try:
+                os.unlink(tmp_path)
+            except OSError:
+                pass
+            raise
 
         self.logger.info(f"Saved context graph to {path}")
 
@@ -2640,7 +2662,11 @@ class ContextGraph:
 
         Scope is this graph only. Copies held elsewhere (``AgentMemory``, a
         bound vector store, an exported file) are not reached, so this is one
-        step of an erasure workflow, not the whole of it.
+        step of an erasure workflow, not the whole of it. Callers who need the
+        whole workflow -- and a receipt recording which stores it actually
+        reached -- should drive this through
+        :class:`~semantica.context.erasure.ErasureCoordinator` rather than
+        treating a ``True`` here as proof the content is gone.
 
         Args:
             node_id: Node to purge.

semantica/context/context_usage.md

@@ -239,6 +239,82 @@ print(f"Python importance score: {importance.get('degree', 0)}")
 
 ---
 
+## 🧹 Erasing an Entity Everywhere - ErasureCoordinator
+
+`purge_node()` removes an entity from **one graph**. The same content can still be
+sitting in agent memory and in your vector store, so purge on its own is one step
+of an erasure workflow rather than the whole of it.
+
+`ErasureCoordinator` drives the whole cascade and hands you a receipt saying what
+it actually managed to erase.
+
+```python
+from semantica.context import AgentMemory, ContextGraph, ErasureCoordinator
+
+coordinator = ErasureCoordinator(graph=knowledge, memory=memory)
+
+receipt = coordinator.erase_entity(
+    "customer-4471",
+    reason="GDPR Art. 17 request #882",
+)
+
+if receipt.complete:
+    print("Erased everywhere")
+else:
+    print("Still holding data:", receipt.incomplete_stores)
+```
+
+### Always Check the Receipt
+
+The receipt is the point of the feature — **do not treat the call itself as proof
+the data is gone**. Each store reports one of five statuses:
+
+| Status | Meaning |
+|---|---|
+| `erased` | Reached, data removed (on the vectors leg: the store accepted the delete for the ids given) |
+| `not_found` | Reached, held nothing for this entity |
+| `not_configured` | No such store was bound — normal, not a failure |
+| `unsupported` | The store cannot delete at all; retrying will not help |
+| `failed` | The store was reached and the deletion did not succeed |
+
+```python
+receipt.to_dict()
+# {
+#   "entity_id": "customer-4471",
+#   "reason": "GDPR Art. 17 request #882",
+#   "erased_at": "2026-08-16T09:03:36.813220",
+#   "complete": False,
+#   "stores": {
+#     "vectors": {"status": "unsupported", "backend": "faiss",
+#                 "detail": "backend exposes no delete()/delete_vectors(); ..."},
+#     "memory":  {"status": "erased", "items": 14},
+#     "graph":   {"status": "erased", "nodes": 1, "edges": 3},
+#   },
+# }
+```
+
+`complete` is `False` when any store reports `unsupported` or `failed`, which is
+your signal to handle that store out of band. FAISS, Milvus and Weaviate expose
+no delete method today, so erasure genuinely cannot be completed on them — the
+coordinator says so rather than reporting a success it did not achieve.
+
+### Good to Know
+
+- **Order is vectors → memory → graph.** The graph tombstone is the durable record
+  that an erasure happened, so it is written last: a crash mid-cascade leaves the
+  node present and the receipt incomplete, rather than a tombstone claiming more
+  than actually happened.
+- **A failing store does not abort the rest.** Partial failure is recorded in the
+  receipt and the remaining stores are still erased.
+- **Every store is optional.** `ErasureCoordinator(graph=graph)` is fine; the other
+  legs report `not_configured`.
+- **It is idempotent.** Erasing the same entity twice returns a receipt saying
+  there was nothing left to do, rather than raising.
+- **Batch:** `coordinator.erase_entities([...], reason=...)` returns one receipt per
+  entity, in order, so one entity's failure does not stop the others.
+
+---
+
 ## 🔄 Using Both Together - The Complete Setup
 
 ### Your Smart Agent System

semantica/context/decision_models.py

@@ -76,11 +76,11 @@ Production Use Cases:
     - Insurance: Claim decisions, underwriting assessments
 """
 
-from dataclasses import dataclass, field
-from datetime import datetime
-from typing import Any, Dict, List, Optional
 import json
 import uuid
+from dataclasses import InitVar, dataclass, field
+from datetime import datetime
+from typing import Any, Dict, List, Optional
 
 
 @dataclass
@@ -100,8 +100,9 @@ class Decision:
     valid_from: Optional[str] = None
     valid_until: Optional[str] = None
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate decision data."""
         if auto_generate_id and not self.decision_id:  # Handle both None and empty string
             self.decision_id = str(uuid.uuid4())
@@ -146,8 +147,9 @@ class DecisionContext:
     risk_factors: List[str]
     cross_system_inputs: Dict[str, Any] = field(default_factory=dict)
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate decision context data."""
         if auto_generate_id and not self.context_id:  # Handle both None and empty string
             self.context_id = str(uuid.uuid4())
@@ -184,8 +186,9 @@ class Policy:
     created_at: datetime
     updated_at: datetime
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate policy data."""
         if auto_generate_id and not self.policy_id:  # Handle both None and empty string
             self.policy_id = str(uuid.uuid4())
@@ -227,8 +230,9 @@ class PolicyException:
     approval_timestamp: datetime
     justification: str
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate policy exception data."""
         if auto_generate_id and not self.exception_id:  # Handle both None and empty string
             self.exception_id = str(uuid.uuid4())
@@ -265,8 +269,9 @@ class Precedent:
     similarity_score: float
     relationship_type: str  # "similar_scenario", "same_policy", "exception_precedent"
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate precedent data."""
         if auto_generate_id and not self.precedent_id:  # Handle both None and empty string
             self.precedent_id = str(uuid.uuid4())
@@ -305,8 +310,9 @@ class ApprovalChain:
     approval_context: str
     timestamp: datetime
     metadata: Dict[str, Any] = field(default_factory=dict)
+    auto_generate_id: InitVar[bool] = True
     
-    def __post_init__(self, auto_generate_id: bool = True):
+    def __post_init__(self, auto_generate_id: bool) -> None:
         """Validate approval chain data."""
         if auto_generate_id and not self.approval_id:  # Handle both None and empty string
             self.approval_id = str(uuid.uuid4())