fix(docker): address terrascan findings, drop apt-get upgrade

Two terrascan/GHAS findings on the previous commit: - AC_DOCKER_0052 (no apt-get upgrade in Dockerfiles): dropped it. It also wasn't fixing anything - Debian's openssl fix for CVE-2026-14456 is still in trixie-proposed-updates, not reachable via a normal upgrade. Pin both base images by digest instead (matches #1329's approach) so the docker Dependabot ecosystem bumps them once Debian ships a rebuilt image with the fix, and document why the QUIC DoS isn't reachable here regardless (HTTP-only via uvicorn). - AC_DOCKER_0010 (pin pip package versions): setuptools was `>=78.1.1`; pinned to the exact 84.0.0 already used by pyproject.toml/requirements-ci.txt. Also fixes two build breaks this introduces on its own: requirements-ci.txt wasn't in .dockerignore's allowlist or container-scan.yml's path trigger, so the COPY in the prior commit would have failed the image build outright.
fix(docker): extract requirements-ci.txt pins with Python instead of sed
2026-09-01 04:00:28 +00:00 · 2026-08-31 14:06:28 +05:30 · 2026-08-31 14:06:02 +05:30 · 2026-08-31 14:06:02 +05:30 · 2026-08-31 14:04:28 +05:30 · 2026-08-30 21:39:30 +05:30
94 changed files with 11215 additions and 369 deletions
@@ -6,6 +6,7 @@
 !README.md
 !LICENSE
 !MANIFEST.in
+!requirements-ci.txt
 !semantica/
 !semantica/**
 !integrations/
@@ -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"
@@ -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: "/"
@@ -33,15 +33,29 @@ 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.
+          pip install -e ".[explorer]" pytest==9.1.1
+      - 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
@@ -58,7 +72,7 @@ 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
+      - run: pip install build==1.6.0
      # 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
@@ -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"
@@ -0,0 +1,74 @@
+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'
+      - 'requirements-ci.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
@@ -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 }}

@@ -82,7 +84,7 @@ jobs:
        }

    - 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
@@ -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')
+          "
@@ -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
@@ -40,7 +40,7 @@ jobs:
      # 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
+      - run: pip install build==1.6.0
      # 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
@@ -63,11 +63,29 @@ jobs:

          print("Explorer frontend is packaged")
          PY
+      - name: Verify PyPI long-description will render
+        run: |
+          pip install twine==7.0.0
+          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
@@ -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
@@ -52,7 +52,7 @@ jobs:
          # 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 safety==3.8.1 bandit==1.9.4 semgrep==1.175.0 jq==1.12.0

      - name: Run Safety Check (Package Vulnerabilities)
        run: |
@@ -37,6 +37,6 @@ jobs:
      # 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 install pip-audit==2.10.1
      - run: pip-audit -r requirements-ci.txt
        continue-on-error: ${{ github.event_name != 'pull_request' }}
@@ -213,6 +213,15 @@ 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
@@ -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
@@ -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 +33,27 @@ 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 requirements-ci.txt ./
 COPY semantica/ ./semantica/
 COPY integrations/ ./integrations/
 COPY --from=frontend-builder /app/semantica/static ./semantica/static

-RUN pip install --no-cache-dir ".[explorer]" \
+# The base image ships an outdated setuptools (CVE-2025-47273); upgrade it
+# explicitly since nothing in our own dependency tree otherwise pulls a
+# newer copy. Pinned to the exact version requirements-ci.txt/pyproject.toml
+# already build against, rather than a floor, per terrascan AC_DOCKER_0010.
+# requirements-ci.txt itself carries the audited, CVE-checked pins for every
+# transitive dependency (see security-scan.yml / security.yml) - feed them
+# in as an unhashed constraints file (pip's hash-checking mode rejects the
+# unhashable local source directory this installs) so the image lands on
+# the same patched versions CI verified, e.g. msgpack>=1.2.1, rather than
+# letting pip freely re-resolve and pick up an unpatched transitive version.
+# (Extracted with Python's re module rather than sed/grep so there's no
+# line-continuation-backslash stripping to get subtly wrong.)
+RUN pip install --no-cache-dir "setuptools==84.0.0" \
+    && python -c "import re, pathlib; pins = re.findall(r'^([A-Za-z0-9._-]+==\S+)', pathlib.Path('requirements-ci.txt').read_text(), re.M); pathlib.Path('/tmp/constraints.txt').write_text('\n'.join(pins))" \
+    && pip install --no-cache-dir -c /tmp/constraints.txt ".[explorer]" \
+    && rm -f /tmp/constraints.txt requirements-ci.txt \
    && chown -R semantica:semantica /app

 USER semantica
@@ -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).
@@ -26,7 +26,7 @@

 #### 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)

@@ -1534,6 +1534,20 @@ 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
@@ -106,7 +106,8 @@
              "integrations/langchain",
              "integrations/docling",
              "integrations/snowflake",
-              "integrations/databricks"
+              "integrations/databricks",
+              "integrations/salesforce"
            ]
          },
          {
@@ -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>
@@ -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.
@@ -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).
@@ -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
@@ -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
@@ -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"'
@@ -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()
@@ -2083,9 +2083,9 @@
      }
    },
    "node_modules/brace-expansion": {
-      "version": "5.0.8",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.8.tgz",
-      "integrity": "sha512-JZyDyq3D4AUifKTPOB7DELf6XsB3WdPuNxCtob1vFXPsSXhdAiHBWJ/tJ8HAc9aH84BK+5JFZLNkJKx3G9kzQg==",
+      "version": "5.0.9",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-5.0.9.tgz",
+      "integrity": "sha512-ScQ4IuvIEF1TMlP7Zt+vjJ//9zlPb2SDcxWxM3bk8s6t6GGdJ7KO1dCcTidOPJKePW30LE/2cT7wCyPho9/Wxg==",
      "dev": true,
      "license": "MIT",
      "dependencies": {
@@ -4250,9 +4250,9 @@
      "license": "MIT"
    },
    "node_modules/nanoid": {
-      "version": "3.3.16",
-      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.16.tgz",
-      "integrity": "sha512-bzlKTyNJ7+LdGIIwy8ijFpIqEQIvafahV7eYykJ8Cvh42EdJeODoJ6gUJXpQJvej1BddH8OqTXZNE/KfbWAu8Q==",
+      "version": "3.3.18",
+      "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.18.tgz",
+      "integrity": "sha512-DTg4MJbGMWkfi6VZFdNt2/caMbQy4Ou+Op/hJQvGEWcnVfoA1QA+xzRKAzw9jD6+GVOOeYr/mIcuDSdug6F6+w==",
      "dev": true,
      "funding": [
        {
@@ -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",
+    "test:deterministic-e2e": "node --import tsx --test tests/deterministicExplorerRendering.e2e.ts",
    "test:plugin-registry": "node --import tsx --test tests/pluginRegistry.temporal.test.mjs"
  },
  "dependencies": {
@@ -41,6 +41,7 @@ import {
 } from "./plugins";
 import { explorationEffectsShouldLoad, neighborhoodPanelShouldLoad, temporalOverlayShouldLoad } from "./pluginRegistryPredicates";
 import { shouldFetchTemporalBounds, shouldFetchTemporalSnapshot } from "./temporalLifecyclePredicates";
+import { createTemporalSnapshotGuards, type TemporalSnapshotResponse } from "./temporalSnapshotGuards";
 import type { LinkPrediction, PathResponse } from "./GraphInspectorPanel";
 import type { GraphSceneHandle, GraphSceneRuntime } from "./scene";
 import type {
@@ -1479,6 +1480,23 @@ 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 +1506,67 @@ 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 +1576,8 @@ 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,
@@ -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;
+    },
+  };
+}
@@ -318,6 +318,20 @@ 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 +349,7 @@ 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 +404,7 @@ 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();
@@ -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`);
+  }
+});
@@ -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);
+  }
+});
@@ -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
+});
@@ -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 ----
@@ -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)

@@ -1286,13 +1286,19 @@ 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 +1314,9 @@ 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
@@ -306,10 +306,10 @@ class JSONExporter:

        self.logger.debug(f"Exporting {len(entities)} entity(ies) to JSON")

-        # Build JSON data with JSON-LD context
+        # Build JSON data with JSON-LD context. No @vocab: it would expand
+        # every bare key in the caller's entity dicts into ns# (#1146).
        json_data = {
            "@context": {
-                "@vocab": "https://semantica.dev/vocab/",
                "semantica": SEMANTICA_NS,
                "entities": {"@id": "semantica:entities", "@container": "@list"},
            },
@@ -339,7 +339,6 @@ class JSONExporter:
        """
        json_data = {
            "@context": {
-                "@vocab": "https://semantica.dev/vocab/",
                "semantica": SEMANTICA_NS,
                "relationships": {
                    "@id": "semantica:relationships",
@@ -434,11 +433,14 @@ class JSONExporter:
        Returns:
            Dictionary in JSON-LD format with @context, @graph/@value, and metadata
        """
-        # Initialize JSON-LD structure with context
+        # Initialize JSON-LD structure with context. No @vocab: for a generic
+        # payload it turned whatever bare keys the caller happened to use into
+        # ns# terms (#1146). Undeclared terms now simply expand to nothing,
+        # which is standard JSON-LD behaviour for a context that does not
+        # know them; the raw payload is still in the document.
        jsonld = {
            "@context": {
-                "@vocab": "https://semantica.dev/vocab/",
-                "semantica": "https://semantica.dev/ns#",
+                "semantica": SEMANTICA_NS,
            }
        }

@@ -598,13 +600,21 @@ class JSONExporter:
        Returns:
            Dictionary in JSON-LD format with @context, @id, @type, and graph data
        """
-        # Initialize JSON-LD structure with RDF context
+        # Initialize JSON-LD structure with RDF context. No @vocab: it applied
+        # to every bare term in caller data, so an extracted type like "ORG"
+        # became ns#ORG and a metadata key like "source" collided with the
+        # real sem:source object property (#1146). Only explicit semantica:
+        # terms resolve now, and the caller's metadata dict is typed @json so
+        # it survives as one rdf:JSON literal instead of expanding its keys.
        jsonld = {
            "@context": {
-                "@vocab": "https://semantica.dev/vocab/",
-                "semantica": "https://semantica.dev/ns#",
+                "semantica": SEMANTICA_NS,
                "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
                "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
+                "semantica:metadata": {
+                    "@id": "semantica:metadata",
+                    "@type": "@json",
+                },
            },
            # Minted from the graph's own content rather than the wall clock
            # (#1147): re-exporting an unchanged graph must produce the same
@@ -664,14 +674,23 @@ class JSONExporter:
        entity_text = entity.get("text") or entity.get("label", "unknown")
        entity_id = entity.get("id") or mint_entity_iri(entity_text)

+        # The caller's type label is data, not a class we define: minting it
+        # into @type expanded it through @vocab into ns#ORG and friends, terms
+        # that look official but do not exist (#1146). The node is always a
+        # semantica:Entity and the label travels as semantica:type, exactly
+        # how _relationship_to_jsonld has always carried the relationship type.
        jsonld = {
            "@id": entity_id,
-            "@type": entity.get("type") or "semantica:Entity",
+            "@type": "semantica:Entity",
            "semantica:text": entity.get("text") or entity.get("label", ""),
            "semantica:confidence": entity.get("confidence", 1.0),
        }
+        entity_type = entity.get("type")
+        if entity_type:
+            jsonld["semantica:type"] = entity_type

-        # Add metadata if present
+        # Add metadata if present. The @json term definition on
+        # semantica:metadata keeps the whole dict one rdf:JSON literal.
        if "metadata" in entity:
            jsonld["semantica:metadata"] = entity["metadata"]

@@ -1226,11 +1226,14 @@ class RDFSerializer:
        metadata_terms = _resolve_metadata_terms(options.pop("metadata_terms", None))
        graph_uri: Optional[str] = options.pop("graph_uri", None)

-        # Initialize JSON-LD structure with context
+        # Initialize JSON-LD structure with context. No @vocab: it applied to
+        # every bare term in caller data, so an extracted type like "ORG"
+        # became ns#ORG and a metadata key like "source" collided with the
+        # real sem:source object property (#1146). Only explicit semantica:
+        # terms resolve now.
        jsonld = {
            "@context": {
-                "@vocab": "https://semantica.dev/vocab/",
-                "semantica": "https://semantica.dev/ns#",
+                "semantica": SEMANTICA_NS,
                "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
                "rdfs": "http://www.w3.org/2000/01/rdf-schema#",
            },
@@ -1252,11 +1255,20 @@ class RDFSerializer:
            # and was dropped in full by a JSON-LD parser, silently.
            entity_id = entity.get("id") or mint_entity_iri(entity.get("text", ""))

+            # The caller's type label is data, not a class we define: minting
+            # it into @type expanded it through @vocab into ns#ORG and
+            # friends, terms that look official but do not exist (#1146).
+            # The node is always a semantica:Entity and the label travels as
+            # semantica:type, matching the relationship node below and
+            # JSONExporter._entity_to_jsonld.
            node = {
                "@id": entity_id,
-                "@type": entity.get("type", "semantica:Entity"),
+                "@type": "semantica:Entity",
                "semantica:text": entity.get("text") or entity.get("label", ""),
            }
+            entity_type = entity.get("type")
+            if entity_type:
+                node["semantica:type"] = entity_type
            confidence = normalize_confidence(entity.get("confidence", 1.0))
            if confidence is None:
                self.logger.warning(
@@ -130,7 +130,10 @@ Example Usage:
 from __future__ import annotations

 import importlib
-from typing import Any, Dict, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Tuple
+
+if TYPE_CHECKING:
+    from .salesforce_ingestor import SalesforceConnector, SalesforceData, SalesforceIngestor

 from .config import IngestConfig, ingest_config
 from .file_ingestor import (
@@ -152,6 +155,7 @@ from .methods import (
    ingest_parquet,
    ingest_public_api,
    ingest_repository,
+    ingest_salesforce,
    ingest_stream,
    ingest_web,
    ingest_xml,
@@ -235,6 +239,10 @@ _LAZY_EXPORTS: Dict[str, Tuple[str, str]] = {
    # XML ingestion
    "XMLIngestor": (".xml_ingestor", "XMLIngestor"),
    "XMLIngestionData": (".xml_ingestor", "XMLIngestionData"),
+    # Salesforce ingestion
+    "SalesforceIngestor": (".salesforce_ingestor", "SalesforceIngestor"),
+    "SalesforceData": (".salesforce_ingestor", "SalesforceData"),
+    "SalesforceConnector": (".salesforce_ingestor", "SalesforceConnector"),
 }

 _OPTIONAL_DEPENDENCY_MESSAGES = {
@@ -262,6 +270,11 @@ _OPTIONAL_DEPENDENCY_MESSAGES = {
        "Arrow ingestion requires optional dependency 'pyarrow'. "
        "Install it before importing ArrowIngestor or using ingest_arrow()."
    ),
+    ".salesforce_ingestor": (
+        "Salesforce ingestion requires optional dependency 'simple-salesforce'. "
+        "Install it with: pip install \"semantica[db-salesforce]\" "
+        "or: pip install simple-salesforce>=1.12.0"
+    ),
 }


@@ -276,7 +289,7 @@ def __getattr__(name: str) -> Any:
    except ModuleNotFoundError as exc:
        message = _OPTIONAL_DEPENDENCY_MESSAGES.get(module_name)
        missing_name = getattr(exc, "name", None)
-        if message and missing_name in {"git", "bs4", "pyarrow"}:
+        if message and missing_name in {"git", "bs4", "pyarrow", "simple_salesforce"}:
            raise ImportError(message) from exc
        raise

@@ -366,6 +379,10 @@ __all__ = [
    # XML ingestion
    "XMLIngestor",
    "XMLIngestionData",
+    # Salesforce ingestion
+    "SalesforceIngestor",
+    "SalesforceData",
+    "SalesforceConnector",
    # Registry and Methods
    "MethodRegistry",
    "method_registry",
@@ -377,6 +394,7 @@ __all__ = [
    "ingest_repository",
    "ingest_email",
    "ingest_database",
+    "ingest_salesforce",
    "ingest_ontology",
    "ingest_arrow",
    "ingest_parquet",
@@ -186,8 +186,13 @@ class IngestConfig:
        self._method_configs[method] = config

    def get_method_config(self, method: str) -> Dict:
-        """Get method-specific configuration."""
-        return self._method_configs.get(method, {})
+        """Get method-specific configuration.
+
+        Returns a **copy** of the stored method configuration so callers can
+        safely mutate it (e.g. to merge per-call options) without poisoning the
+        global configuration for subsequent calls.
+        """
+        return dict(self._method_configs.get(method, {}))

    def get_all(self) -> Dict[str, Any]:
        """Get all configuration."""
@@ -875,56 +875,95 @@ schema = connector.get_schema(engine)
        print(f"  {table_name}: {[col['name'] for col in columns]}")
 ```

-## SAP OData Ingestion
+## Salesforce CRM Ingestion

-`SAPIngestor` reads an Entity Set from a SAP OData service — S/4HANA Cloud,
-SuccessFactors, or an on-prem NetWeaver Gateway over its REST surface. It
-follows OData v2/v4 server-driven pagination and flattens each record into a
-document dict via `export_as_documents()`.
+Salesforce ingestion requires `simple-salesforce`:

-Install with `pip install 'semantica[ingest-sap]'`.
-
-### Connector Construction & Authentication
-
-```python
-from semantica.ingest import SAPIngestor
-
-# OAuth2 client-credentials (BTP / S/4HANA Cloud)
-ing = SAPIngestor(
-    base_url="https://my-sap.example.com/sap/opu/odata/sap/API_BUSINESS_PARTNER",
-    client_id="...", client_secret="...",
-    token_url="https://my-sap.example.com/oauth/token",
-)
-# On-prem NetWeaver often uses Basic auth instead — swap the block above for:
-# ing = SAPIngestor(base_url="...", username="erp_user", password="...")
+```bash
+pip install "semantica[db-salesforce]"
 ```

-### Entity-Set Ingestion & Document Export
+### Basic Usage

 ```python
-# 1. Discover entity sets + field types from $metadata
-sets = ing.discover_service()
+from semantica.ingest import SalesforceIngestor
+import os

-# 2. Page-walk an Entity Set (v2/v4 next links handled automatically)
-partners = ing.ingest_entity_set(
-    entity_set="A_BusinessPartnerSet",
-    select="BusinessPartner,BusinessPartnerFullName",
-    top=1000,
+ingestor = SalesforceIngestor(
+    username=os.getenv("SALESFORCE_USERNAME"),
+    password=os.getenv("SALESFORCE_PASSWORD"),
+    security_token=os.getenv("SALESFORCE_SECURITY_TOKEN"),
+    domain="login",   # "test" for sandbox
 )

-# 3. Flatten to document dicts that GraphBuilder can consume directly
-docs = ing.export_as_documents(partners)
+# Ingest Account records
+data = ingestor.ingest_sobject(
+    "Account",
+    fields=["Id", "Name", "Industry", "BillingCity"],
+    where="Type = 'Customer'",
+    limit=5000,
+)
+print(f"Retrieved {data.row_count} of {data.total_size} matching records")
 ```

- Use `expand="to_Item"` on a sales-order header set to pull nested line items
-  in one request — handy for modeling order → line-item → material relations.
- Every outbound request, including the OAuth2 token exchange, is routed through
-  the SSRF guard, and pagination never follows a next link that points to a
-  different host than the service root.
+`SalesforceIngestor()` with no arguments reads from `SALESFORCE_USERNAME`, `SALESFORCE_PASSWORD`, `SALESFORCE_SECURITY_TOKEN`, and `SALESFORCE_DOMAIN` environment variables automatically.
+
+### Custom Objects and Raw SOQL
+
+```python
+# Custom object (API name ends in __c)
+data = ingestor.ingest_sobject("My_Custom_Object__c", fields=["Id", "Name", "Custom_Field__c"])
+
+# Raw SOQL GÇö pagination is handled automatically
+data = ingestor.ingest_query("""
+    SELECT Id, Name, StageName, Amount
+    FROM Opportunity
+    WHERE IsClosed = false
+    ORDER BY CloseDate ASC
+""")
+print(f"Open opportunities: {data.row_count}")
+```
+
+### Document Export
+
+```python
+documents = ingestor.export_as_documents(
+    data,
+    id_field="Id",                      # Salesforce 18-char record Id
+    text_fields=["Name", "Description"],
+)
+# Each document: {"id": "001...", "text": "...", "metadata": {"source": "salesforce", ...}}
+```
+
+### Convenience Function
+
+```python
+from semantica.ingest import ingest_salesforce
+
+# Fetch records
+data = ingest_salesforce(
+    method="sobject",
+    sobject_name="Account",
+    fields=["Id", "Name"],
+    limit=500,
+)
+
+# Ingest and export as documents in one call
+docs = ingest_salesforce(
+    method="documents",
+    sobject_name="Account",
+    text_fields=["Name", "Description"],
+)
+
+# Using the unified dispatcher
+from semantica.ingest import ingest
+result = ingest(None, source_type="salesforce", method="sobject",
+                sobject_name="Account", fields=["Id", "Name"])
+data = result["data"]
+```
+
+See [Salesforce Integration](https://docs.getsemantica.ai/integrations/salesforce) for full documentation including sandbox, schema discovery, pagination details, and troubleshooting.

-> **Security Note:** Never hardcode credentials (`client_secret`, `password`);
-> pass them via environment variables (`SAP_CLIENT_SECRET`, `SAP_PASSWORD`) or a
-> secrets manager.

 ## MCP Server Ingestion

@@ -1664,3 +1703,54 @@ for source_type, source_list in sources.items():
   for batch in process_in_batches(large_dataset, batch_size=1000):
       result = ingest(batch)
   ```
+
+## SAP OData Ingestion
+
+`SAPIngestor` reads an Entity Set from a SAP OData service â€” S/4HANA Cloud,
+SuccessFactors, or an on-prem NetWeaver Gateway over its REST surface. It
+follows OData v2/v4 server-driven pagination and flattens each record into a
+document dict via `export_as_documents()`.
+
+Install with `pip install 'semantica[ingest-sap]'`.
+
+### Connector Construction & Authentication
+
+```python
+from semantica.ingest import SAPIngestor
+
+# OAuth2 client-credentials (BTP / S/4HANA Cloud)
+ing = SAPIngestor(
+    base_url="https://my-sap.example.com/sap/opu/odata/sap/API_BUSINESS_PARTNER",
+    client_id="...", client_secret="...",
+    token_url="https://my-sap.example.com/oauth/token",
+)
+# On-prem NetWeaver often uses Basic auth instead â€” swap the block above for:
+# ing = SAPIngestor(base_url="...", username="erp_user", password="...")
+```
+
+### Entity-Set Ingestion & Document Export
+
+```python
+# 1. Discover entity sets + field types from $metadata
+sets = ing.discover_service()
+
+# 2. Page-walk an Entity Set (v2/v4 next links handled automatically)
+partners = ing.ingest_entity_set(
+    entity_set="A_BusinessPartnerSet",
+    select="BusinessPartner,BusinessPartnerFullName",
+    top=1000,
+)
+
+# 3. Flatten to document dicts that GraphBuilder can consume directly
+docs = ing.export_as_documents(partners)
+```
+
+- Use `expand="to_Item"` on a sales-order header set to pull nested line items
+  in one request â€” handy for modeling order â†’ line-item â†’ material relations.
+- Every outbound request, including the OAuth2 token exchange, is routed through
+  the SSRF guard, and pagination never follows a next link that points to a
+  different host than the service root.
+
+> **Security Note:** Never hardcode credentials (`client_secret`, `password`);
+> pass them via environment variables (`SAP_CLIENT_SECRET`, `SAP_PASSWORD`) or a
+> secrets manager.
@@ -203,6 +203,7 @@ if TYPE_CHECKING:
    from .ontology_ingestor import OntologyData
    from .parquet_ingestor import ParquetData
    from .public_api_ingestor import PublicAPIDetection
+    from .salesforce_ingestor import SalesforceData
    from .stream_ingestor import StreamProcessor
    from .web_ingestor import WebContent
    from .xml_ingestor import XMLIngestionData
@@ -1126,6 +1127,213 @@ def ingest_database(
        raise


+def ingest_salesforce(
+    source: Optional[Dict[str, Any]] = None,
+    method: str = "sobject",
+    **kwargs,
+) -> Union["SalesforceData", List[Dict[str, Any]], Dict[str, Any]]:
+    """Ingest data from Salesforce CRM (convenience function).
+
+    A user-friendly wrapper around :class:`~semantica.ingest.SalesforceIngestor`
+    that connects, ingests, and returns data in a single call.
+
+    Args:
+        source: Optional credential/configuration dictionary.  Keys mirror the
+            :class:`~semantica.ingest.SalesforceConnector` constructor:
+            ``username``, ``password``, ``security_token``, ``domain``
+            (``"login"`` for production, ``"test"`` for sandbox),
+            ``instance_url``, ``session_id``, ``api_version``.
+            When ``None``, credentials are read from environment variables
+            (``SALESFORCE_USERNAME`` / ``SALESFORCE_PASSWORD`` /
+            ``SALESFORCE_SECURITY_TOKEN`` etc.).
+        method: Ingestion method:
+
+            * ``"sobject"`` *(default)* — fetch records from a named sObject
+              (requires ``sobject_name`` kwarg).
+            * ``"query"`` — execute a raw SOQL query string (requires
+              ``soql`` kwarg).
+            * ``"list_sobjects"`` — return a sorted list of accessible sObject
+              API names.
+            * ``"schema"`` — return field metadata for a named sObject
+              (requires ``sobject_name`` kwarg).
+            * ``"documents"`` — ingest an sObject and convert to the Semantica
+              document format in one step (requires ``sobject_name`` kwarg;
+              optional ``text_fields`` and ``id_field`` kwargs).
+
+        **kwargs: Additional options forwarded to the ingestor method.
+            Common kwargs for ``"sobject"`` / ``"documents"``:
+
+            * ``sobject_name`` — Salesforce sObject API name (e.g.
+              ``"Account"``, ``"My_Custom__c"``).
+            * ``fields`` — list of field API names to select.  When omitted
+              all selectable fields are fetched via ``describe()``.
+            * ``where`` — SOQL ``WHERE`` clause fragment (trusted input only).
+            * ``order_by`` — SOQL ``ORDER BY`` clause fragment.
+            * ``limit`` — maximum number of records.
+
+            For ``"query"``:
+
+            * ``soql`` — full SOQL query string.
+
+            For ``"schema"``:
+
+            * ``sobject_name`` — sObject to describe.
+
+    Returns:
+        * ``"sobject"`` / ``"query"`` → :class:`~semantica.ingest.SalesforceData`
+        * ``"documents"`` → ``List[Dict[str, Any]]`` (Semantica document format)
+        * ``"list_sobjects"`` → ``List[str]``
+        * ``"schema"`` → ``Dict[str, Any]``
+
+    Raises:
+        :class:`~semantica.utils.exceptions.ConfigurationError`: If
+            ``simple-salesforce`` is not installed.
+        :class:`~semantica.utils.exceptions.ValidationError`: If credentials
+            are incomplete or an sObject / field name is invalid.
+        :class:`~semantica.utils.exceptions.ProcessingError`: If the
+            Salesforce API call fails.
+
+    Examples::
+
+        >>> from semantica.ingest import ingest_salesforce
+
+        >>> # Fetch Account records (credentials from env vars)
+        >>> data = ingest_salesforce(
+        ...     method="sobject",
+        ...     sobject_name="Account",
+        ...     fields=["Id", "Name", "Industry"],
+        ...     limit=500,
+        ... )
+
+        >>> # Execute a raw SOQL query (credentials from environment variables)
+        >>> data = ingest_salesforce(
+        ...     method="query",
+        ...     soql="SELECT Id, Name FROM Contact WHERE IsActive = true",
+        ... )
+
+        >>> # Ingest and export as documents for GraphBuilder in one step
+        >>> docs = ingest_salesforce(
+        ...     method="documents",
+        ...     sobject_name="Account",
+        ...     text_fields=["Name", "Description"],
+        ...     limit=1000,
+        ... )
+
+        >>> # List all accessible sObjects in the connected org
+        >>> sobject_names = ingest_salesforce(method="list_sobjects")
+
+        >>> # Use sandbox org
+        >>> data = ingest_salesforce(
+        ...     method="sobject",
+        ...     sobject_name="Account",
+        ... )  # set SALESFORCE_DOMAIN=test in environment for sandbox
+    """
+    # Registry hook — allows callers to register a custom "salesforce" method
+    custom_method = method_registry.get("salesforce", method)
+    if custom_method and custom_method != ingest_salesforce:
+        fallback = kwargs.pop("fallback_on_custom_error", False)
+        result = call_custom_method(
+            logger, method, custom_method, source,
+            fallback_on_custom_error=fallback, **kwargs,
+        )
+        if result is not CUSTOM_METHOD_FELL_BACK:
+            return result
+
+    try:
+        from .salesforce_ingestor import SalesforceIngestor
+    except ModuleNotFoundError as exc:
+        if _is_missing_dependency(exc, "simple_salesforce"):
+            raise _missing_optional_dependency(
+                "Salesforce ingestion", "simple-salesforce"
+            ) from exc
+        raise
+
+    # Unpack credential dict (if given); everything else stays in kwargs.
+    creds: Dict[str, Any] = {}
+    if source is not None:
+        if not isinstance(source, dict):
+            raise ProcessingError(
+                "ingest_salesforce() source must be a credential dict or None. "
+                "Pass sobject_name / soql as keyword arguments."
+            )
+        creds = dict(source)
+
+    # Merge any ingest_config method config under "salesforce".
+    # get_method_config() now returns a copy, so this dict is safe to mutate.
+    # We build the final connector config in order of increasing priority:
+    #   1. base method config (lowest — global defaults set by operator)
+    #   2. per-call credential dict supplied via `source`
+    #   3. per-call connector params supplied as kwargs
+    # Credentials are extracted from kwargs and removed so they don't also
+    # flow into the ingest method call (which doesn't understand them).
+    _CONNECTOR_PARAMS = frozenset({
+        "username", "password", "security_token", "domain",
+        "instance_url", "session_id", "api_version",
+    })
+    connector_kwargs = {k: v for k, v in kwargs.items() if k in _CONNECTOR_PARAMS}
+    for k in _CONNECTOR_PARAMS:
+        kwargs.pop(k, None)
+
+    # Build a fresh per-call config dict — never mutate the global store.
+    config: Dict[str, Any] = {
+        **ingest_config.get_method_config("salesforce"),  # base (already a copy)
+        **creds,                                           # source dict credentials
+        **connector_kwargs,                                # kwarg credentials
+    }
+
+    ingestor = SalesforceIngestor(**config)
+
+    if method == "sobject":
+        sobject_name = kwargs.pop("sobject_name", None)
+        if not sobject_name:
+            raise ProcessingError(
+                "ingest_salesforce() with method='sobject' requires "
+                "sobject_name keyword argument."
+            )
+        return ingestor.ingest_sobject(sobject_name, **kwargs)
+
+    elif method == "query":
+        soql = kwargs.pop("soql", None)
+        if not soql:
+            raise ProcessingError(
+                "ingest_salesforce() with method='query' requires "
+                "soql keyword argument."
+            )
+        return ingestor.ingest_query(soql, **kwargs)
+
+    elif method == "list_sobjects":
+        return ingestor.list_sobjects()
+
+    elif method == "schema":
+        sobject_name = kwargs.pop("sobject_name", None)
+        if not sobject_name:
+            raise ProcessingError(
+                "ingest_salesforce() with method='schema' requires "
+                "sobject_name keyword argument."
+            )
+        return ingestor.get_sobject_schema(sobject_name)
+
+    elif method == "documents":
+        sobject_name = kwargs.pop("sobject_name", None)
+        if not sobject_name:
+            raise ProcessingError(
+                "ingest_salesforce() with method='documents' requires "
+                "sobject_name keyword argument."
+            )
+        id_field = kwargs.pop("id_field", "Id")
+        text_fields = kwargs.pop("text_fields", None)
+        data = ingestor.ingest_sobject(sobject_name, **kwargs)
+        return ingestor.export_as_documents(data, id_field=id_field,
+                                            text_fields=text_fields)
+
+    else:
+        raise ProcessingError(
+            f"Unknown ingest_salesforce method: {method!r}. "
+            "Valid methods: 'sobject', 'query', 'list_sobjects', 'schema', "
+            "'documents'."
+        )
+
+
 def ingest_mcp(
    source: Union[str, Dict[str, Any]],
    method: str = "resources",
@@ -1306,6 +1514,7 @@ def ingest(
            - "ontology": Ontology ingestion
            - "parquet": Apache Parquet file or directory ingestion
            - "xml": XML file or directory ingestion
+            - "salesforce": Salesforce CRM ingestion (pass credentials via kwargs)
        method: Optional specific ingestion method
        **kwargs: Additional options passed to ingestor

@@ -1428,6 +1637,9 @@ def ingest(
        return {"ontology": ingest_ontology(sources, method=method or "file", **kwargs)}
    elif source_type == "mcp":
        return {"data": ingest_mcp(sources, method=method or "resources", **kwargs)}
+    elif source_type == "salesforce":
+        return {"data": ingest_salesforce(sources,
+                                          method=method or "sobject", **kwargs)}
    else:
        raise ProcessingError(f"Unknown source type: {source_type}")

@@ -1540,3 +1752,9 @@ method_registry.register("ontology", "file", ingest_ontology)
 method_registry.register("ontology", "directory", ingest_ontology)
 method_registry.register("ingest", "default", ingest)
 method_registry.register("ingest", "unified", ingest)
+method_registry.register("salesforce", "default", ingest_salesforce)
+method_registry.register("salesforce", "sobject", ingest_salesforce)
+method_registry.register("salesforce", "query", ingest_salesforce)
+method_registry.register("salesforce", "list_sobjects", ingest_salesforce)
+method_registry.register("salesforce", "schema", ingest_salesforce)
+method_registry.register("salesforce", "documents", ingest_salesforce)
@@ -66,6 +66,7 @@ class MethodRegistry:
        "parquet": {},
        "arrow": {},
        "xml": {},
+        "salesforce": {},
        "ingest": {},
    }

@@ -10,28 +10,53 @@ Supported Providers:
    - OpenAI: OpenAI API (GPT-3.5, GPT-4, etc.)
    - HuggingFaceLLM: HuggingFace Transformers for local LLM inference
    - LiteLLM: Unified interface to 100+ LLM providers (OpenAI, Anthropic, Groq, Azure, Bedrock, Vertex AI, etc.)
+    - Anthropic: Anthropic Claude API (Claude sonnet, Opus, Haiku, etc.)
+    - Gemini: Google Gemini API
+    - Ollama: Local models served through Ollama
+    - DeepSeek: DeepSeek's OpenAI-compatible API
+    - Novita: Novita AI's OpenAI-compatible API

 Example Usage:
-    >>> from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM
-    >>> 
+    >>> from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM, Anthropic
+    >>>
    >>> # Groq provider
    >>> groq = Groq(model="llama-3.1-8b-instant", api_key="your-key")
    >>> response = groq.generate("Hello, world!")
-    >>> 
+    >>>
    >>> # OpenAI provider
    >>> openai = OpenAI(model="gpt-4", api_key="your-key")
    >>> response = openai.generate("Hello, world!")
-    >>> 
+    >>>
    >>> # HuggingFace LLM provider
    >>> hf = HuggingFaceLLM(model_name="gpt2")
    >>> response = hf.generate("Hello, world!")
-    >>> 
+    >>>
    >>> # LiteLLM provider (supports 100+ LLMs)
    >>> llm = LiteLLM(model="openai/gpt-4o", api_key="your-key")
    >>> response = llm.generate("Hello, world!")
    >>> # Or use other providers via LiteLLM
    >>> llm = LiteLLM(model="anthropic/claude-sonnet-4-20250514")
    >>> response = llm.generate("Hello, world!")
+    >>>
+    >>> # Anthropic provider
+    >>> claude = Anthropic(model="claude-sonnet-4-6", api_key="the-key")
+    >>> response = claude.generate("Hello, world!")
+    >>>
+    >>> # Gemini provider
+    >>> gemini = Gemini(model="gemini-pro", api_key="your-key")
+    >>> response = gemini.generate("Hello, world!")
+    >>>
+    >>> # Ollama provider (local, no api_key)
+    >>> ollama = Ollama(model="llama2")
+    >>> response = ollama.generate("Hello, world!")
+    >>>
+    >>> # DeepSeek provider
+    >>> deepseek = DeepSeek(model="deepseek-chat", api_key="your-key")
+    >>> response = deepseek.generate("Hello, world!")
+    >>>
+    >>> # Novita provider
+    >>> novita = Novita(model="deepseek/deepseek-v3.2", api_key="your-key")
+    >>> response = novita.generate("Hello, world!")

 Author: Semantica Contributors
 License: MIT
@@ -41,6 +66,20 @@ from .groq import Groq
 from .openai import OpenAI
 from .huggingface import HuggingFaceLLM
 from .litellm import LiteLLM
+from .anthropic import Anthropic
+from .gemini import Gemini
+from .ollama import Ollama
+from .deepseek import DeepSeek
+from .novita import Novita

-__all__ = ["Groq", "OpenAI", "HuggingFaceLLM", "LiteLLM"]
-
+__all__ = [
+    "Groq",
+    "OpenAI",
+    "HuggingFaceLLM",
+    "LiteLLM",
+    "Anthropic",
+    "Gemini",
+    "Ollama",
+    "DeepSeek",
+    "Novita",
+]
@@ -0,0 +1,111 @@
+"""
+Anthropic LLM Provider
+
+Wrapper for Anthropic Claude API provider with clean interface
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+from ..semantic_extract.providers import AnthropicProvider
+from ..utils.exceptions import ProcessingError
+from ..utils.logging import get_logger
+
+logger = get_logger("llms.anthropic")
+
+
+class Anthropic:
+    """
+    Anthropic Claude LLM provider wrapper.
+
+    Provides clean interface to Anthropic's Claude API.
+
+    Example:
+        >>> from semantica.llms import Anthropic
+        >>> claude = Anthropic(model="claude-sonnet-4-6", api_key="the-key")
+        >>> response = claude.generate("What is API key?")
+    """
+
+    def __init__(
+        self,
+        model: str = "claude-sonnet-4-6",
+        api_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Initialize Anthropic provider.
+
+        Args:
+            model: Model name (default: claude-sonnet-4-6)
+            api_key: Anthropic API key (default: from ANTHROPIC_API_KEY env var)
+            **kwargs: Additional provider options
+        """
+        self.provider = AnthropicProvider(api_key=api_key, model=model, **kwargs)
+        self.model = model
+        self.api_key = api_key
+
+    def is_available(self) -> bool:
+        """Check if Anthropic provider is available."""
+        return self.provider.is_available()
+
+    def generate(self, prompt: str, **kwargs) -> str:
+        """
+        Generate text from prompt.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options (temperature, max_tokens, etc.)
+
+        Returns:
+            Generated text response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Anthropic provider not available. Set ANTHROPIC_API_KEY or pass api_key."
+            )
+        return self.provider.generate(prompt, **kwargs)
+
+    def generate_structured(self, prompt: str, **kwargs) -> Union[Dict[str, Any], List[Any]]:
+        """
+        Generates structured JSON output.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options
+
+        Returns:
+            Parsed JSON response. A dict for a top-level JSON object, or a
+            list if the model returns a top-level JSON array.
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Anthropic provider not available. Set ANTHROPIC_API_KEY or pass api_key."
+            )
+        return self.provider.generate_structured(prompt, **kwargs)
+
+    def generate_typed(self, prompt: str, schema: Any, max_retries: int = 3, **kwargs) -> Any:
+        """
+        Generate output validated against a Pydantic schema.
+
+        Args:
+            prompt: Input prompt text
+            schema: Pydantic model class to validate the output against
+            max_retries: Number of retries if validation fails (default: 3)
+            **kwargs: Generation options
+
+        Returns:
+            An instance of `schema`, populated from the model's response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Anthropic provider not available. Set ANTHROPIC_API_KEY or pass api_key."
+            )
+        return self.provider.generate_typed(prompt, schema, max_retries=max_retries, **kwargs)
@@ -0,0 +1,111 @@
+"""
+DeepSeek LLM Provider
+
+Wrapper for DeepSeek API provider with clean interface.
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+from ..semantic_extract.providers import DeepSeekProvider
+from ..utils.exceptions import ProcessingError
+from ..utils.logging import get_logger
+
+logger = get_logger("llms.deepseek")
+
+
+class DeepSeek:
+    """
+    DeepSeek LLM provider wrapper.
+
+    Provides clean interface to DeepSeek's OpenAI-compatible API.
+
+    Example:
+        >>> from semantica.llms import DeepSeek
+        >>> llm = DeepSeek(model="deepseek-chat", api_key="your-key")
+        >>> response = llm.generate("What is AI?")
+    """
+
+    def __init__(
+        self,
+        model: str = "deepseek-chat",
+        api_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Initialize DeepSeek provider.
+
+        Args:
+            model: Model name (default: "deepseek-chat")
+            api_key: DeepSeek API key (default: from DEEPSEEK_API_KEY env var)
+            **kwargs: Additional provider options
+        """
+        self.provider = DeepSeekProvider(api_key=api_key, model=model, **kwargs)
+        self.model = model
+        self.api_key = api_key
+
+    def is_available(self) -> bool:
+        """Check if DeepSeek provider is available."""
+        return self.provider.is_available()
+
+    def generate(self, prompt: str, **kwargs) -> str:
+        """
+        Generate text from prompt.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options (temperature, max_tokens, etc.)
+
+        Returns:
+            Generated text response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "DeepSeek provider not available. Set DEEPSEEK_API_KEY or pass api_key."
+            )
+        return self.provider.generate(prompt, **kwargs)
+
+    def generate_structured(self, prompt: str, **kwargs) -> Union[Dict[str, Any], List[Any]]:
+        """
+        Generate structured JSON output.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options
+
+        Returns:
+            Parsed JSON response. A dict for a top-level JSON object, or a
+            list if the model returns a top-level JSON array.
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "DeepSeek provider not available. Set DEEPSEEK_API_KEY or pass api_key."
+            )
+        return self.provider.generate_structured(prompt, **kwargs)
+
+    def generate_typed(self, prompt: str, schema: Any, max_retries: int = 3, **kwargs) -> Any:
+        """
+        Generate output validated against a Pydantic schema.
+
+        Args:
+            prompt: Input prompt text
+            schema: Pydantic model class to validate the output against
+            max_retries: Number of retries if validation fails (default: 3)
+            **kwargs: Generation options
+
+        Returns:
+            An instance of `schema`, populated from the model's response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "DeepSeek provider not available. Set DEEPSEEK_API_KEY or pass api_key."
+            )
+        return self.provider.generate_typed(prompt, schema, max_retries=max_retries, **kwargs)
@@ -0,0 +1,111 @@
+"""
+Gemini LLM Provider
+
+Wrapper for Google Gemini API provider with clean interface.
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+from ..semantic_extract.providers import GeminiProvider
+from ..utils.exceptions import ProcessingError
+from ..utils.logging import get_logger
+
+logger = get_logger("llms.gemini")
+
+
+class Gemini:
+    """
+    Google Gemini LLM provider wrapper.
+
+    Provides clean interface to Google's Gemini API.
+
+    Example:
+        >>> from semantica.llms import Gemini
+        >>> gemini = Gemini(model="gemini-pro", api_key="your-key")
+        >>> response = gemini.generate("What is AI?")
+    """
+
+    def __init__(
+        self,
+        model: str = "gemini-pro",
+        api_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Initialize Gemini provider.
+
+        Args:
+            model: Model name (default: "gemini-pro")
+            api_key: Gemini API key (default: from GEMINI_API_KEY env var)
+            **kwargs: Additional provider options
+        """
+        self.provider = GeminiProvider(api_key=api_key, model=model, **kwargs)
+        self.model = model
+        self.api_key = api_key
+
+    def is_available(self) -> bool:
+        """Check if Gemini provider is available."""
+        return self.provider.is_available()
+
+    def generate(self, prompt: str, **kwargs) -> str:
+        """
+        Generate text from prompt.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options (temperature, max_tokens, etc.)
+
+        Returns:
+            Generated text response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Gemini provider not available. Set GEMINI_API_KEY or pass api_key."
+            )
+        return self.provider.generate(prompt, **kwargs)
+
+    def generate_structured(self, prompt: str, **kwargs) -> Union[Dict[str, Any], List[Any]]:
+        """
+        Generate structured JSON output.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options
+
+        Returns:
+            Parsed JSON response. A dict for a top-level JSON object, or a
+            list if the model returns a top-level JSON array.
+
+        Raises:
+            ProcessingError: If provider is not available or parsing fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Gemini provider not available. Set GEMINI_API_KEY or pass api_key."
+            )
+        return self.provider.generate_structured(prompt, **kwargs)
+
+    def generate_typed(self, prompt: str, schema: Any, max_retries: int = 3, **kwargs) -> Any:
+        """
+        Generate output validated against a Pydantic schema.
+
+        Args:
+            prompt: Input prompt text
+            schema: Pydantic model class to validate the output against
+            max_retries: Number of retries if validation fails (default: 3)
+            **kwargs: Generation options
+
+        Returns:
+            An instance of `schema`, populated from the model's response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Gemini provider not available. Set GEMINI_API_KEY or pass api_key."
+            )
+        return self.provider.generate_typed(prompt, schema, max_retries=max_retries, **kwargs)
@@ -0,0 +1,111 @@
+"""
+Novita LLM Provider
+
+Wrapper for Novita AI's OpenAI-compatible API with clean interface.
+"""
+
+from typing import Any, Dict, List, Optional, Union
+
+from ..semantic_extract.providers import NovitaProvider
+from ..utils.exceptions import ProcessingError
+from ..utils.logging import get_logger
+
+logger = get_logger("llms.novita")
+
+
+class Novita:
+    """
+    Novita AI LLM provider wrapper.
+
+    Provides clean interface to Novita's OpenAI-compatible API.
+
+    Example:
+        >>> from semantica.llms import Novita
+        >>> llm = Novita(model="deepseek/deepseek-v3.2", api_key="your-key")
+        >>> response = llm.generate("What is AI?")
+    """
+
+    def __init__(
+        self,
+        model: str = "deepseek/deepseek-v3.2",
+        api_key: Optional[str] = None,
+        **kwargs
+    ):
+        """
+        Initialize Novita provider.
+
+        Args:
+            model: Model name (default: "deepseek/deepseek-v3.2")
+            api_key: Novita API key (default: from NOVITA_API_KEY env var)
+            **kwargs: Additional provider options
+        """
+        self.provider = NovitaProvider(api_key=api_key, model=model, **kwargs)
+        self.model = model
+        self.api_key = api_key
+
+    def is_available(self) -> bool:
+        """Check if Novita provider is available."""
+        return self.provider.is_available()
+
+    def generate(self, prompt: str, **kwargs) -> str:
+        """
+        Generate text from prompt.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options (temperature, max_tokens, etc.)
+
+        Returns:
+            Generated text response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Novita provider not available. Set NOVITA_API_KEY or pass api_key."
+            )
+        return self.provider.generate(prompt, **kwargs)
+
+    def generate_structured(self, prompt: str, **kwargs) -> Union[Dict[str, Any], List[Any]]:
+        """
+        Generate structured JSON output.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options
+
+        Returns:
+            Parsed JSON response. A dict for a top-level JSON object, or a
+            list if the model returns a top-level JSON array.
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Novita provider not available. Set NOVITA_API_KEY or pass api_key."
+            )
+        return self.provider.generate_structured(prompt, **kwargs)
+
+    def generate_typed(self, prompt: str, schema: Any, max_retries: int = 3, **kwargs) -> Any:
+        """
+        Generate output validated against a Pydantic schema.
+
+        Args:
+            prompt: Input prompt text
+            schema: Pydantic model class to validate the output against
+            max_retries: Number of retries if validation fails (default: 3)
+            **kwargs: Generation options
+
+        Returns:
+            An instance of `schema`, populated from the model's response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Novita provider not available. Set NOVITA_API_KEY or pass api_key."
+            )
+        return self.provider.generate_typed(prompt, schema, max_retries=max_retries, **kwargs)
@@ -0,0 +1,116 @@
+"""
+Ollama LLM Provider
+
+Wrapper for local Ollama models with clean interface.
+"""
+
+from typing import Any, Dict, List, Union
+
+from ..semantic_extract.providers import OllamaProvider
+from ..utils.exceptions import ProcessingError
+from ..utils.logging import get_logger
+
+logger = get_logger("llms.ollama")
+
+
+class Ollama:
+    """
+    Ollama LLM provider wrapper.
+
+    Provides clean interface to a local Ollama server. Unlike the other
+    providers here, this one has no API key. It talks to an Ollama
+    instance over HTTP, so make sure `ollama serve` is running first.
+
+    Example:
+        >>> from semantica.llms import Ollama
+        >>> llm = Ollama(model="llama2")
+        >>> response = llm.generate("What is AI?")
+    """
+
+    def __init__(
+        self,
+        model: str = "llama2",
+        base_url: str = "http://localhost:11434",
+        **kwargs
+    ):
+        """
+        Initialize Ollama provider.
+
+        Args:
+            model: Model name (default: "llama2")
+            base_url: Ollama server URL (default: "http://localhost:11434")
+            **kwargs: Additional provider options
+        """
+        self.provider = OllamaProvider(base_url=base_url, model=model, **kwargs)
+        self.model = model
+        self.base_url = base_url
+
+    def is_available(self) -> bool:
+        """Check if Ollama provider is available."""
+        return self.provider.is_available()
+
+    def generate(self, prompt: str, **kwargs) -> str:
+        """
+        Generate text from prompt.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options (temperature, max_tokens, etc.)
+
+        Returns:
+            Generated text response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Ollama provider not available. Make sure Ollama is running "
+                "and reachable at the configured base_url."
+            )
+        return self.provider.generate(prompt, **kwargs)
+
+    def generate_structured(self, prompt: str, **kwargs) -> Union[Dict[str, Any], List[Any]]:
+        """
+        Generate structured JSON output.
+
+        Args:
+            prompt: Input prompt text
+            **kwargs: Generation options
+
+        Returns:
+            Parsed JSON response. A dict for a top-level JSON object, or a
+            list if the model returns a top-level JSON array.
+
+        Raises:
+            ProcessingError: If provider is not available or parsing fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Ollama provider not available. Make sure Ollama is running "
+                "and reachable at the configured base_url."
+            )
+        return self.provider.generate_structured(prompt, **kwargs)
+
+    def generate_typed(self, prompt: str, schema: Any, max_retries: int = 3, **kwargs) -> Any:
+        """
+        Generate output validated against a Pydantic schema.
+
+        Args:
+            prompt: Input prompt text
+            schema: Pydantic model class to validate the output against
+            max_retries: Number of retries if validation fails (default: 3)
+            **kwargs: Generation options
+
+        Returns:
+            An instance of `schema`, populated from the model's response
+
+        Raises:
+            ProcessingError: If provider is not available or generation fails
+        """
+        if not self.is_available():
+            raise ProcessingError(
+                "Ollama provider not available. Make sure Ollama is running "
+                "and reachable at the configured base_url."
+            )
+        return self.provider.generate_typed(prompt, schema, max_retries=max_retries, **kwargs)
@@ -117,6 +117,8 @@ class PropertyGenerator:
            data_properties = self._infer_data_properties(entities, classes, **options)
            properties.extend(data_properties)

+            properties = self._coalesce_normalized_properties(properties)
+
            self.progress_tracker.stop_tracking(
                tracking_id,
                status="completed",
@@ -193,6 +195,67 @@ class PropertyGenerator:

        return properties

+    def _coalesce_normalized_properties(
+        self, properties: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
+        """Merge same-kind properties that normalize to the same name."""
+        property_kinds = defaultdict(set)
+        for prop in properties:
+            property_kinds[prop["name"]].add(prop.get("type"))
+
+        collisions = {
+            name: sorted(kind for kind in kinds if kind is not None)
+            for name, kinds in property_kinds.items()
+            if len({kind for kind in kinds if kind is not None}) > 1
+        }
+        if collisions:
+            raise ValidationError(
+                "Normalized property names cannot be shared by object and "
+                "data properties.",
+                validation_context={"property_kind_collisions": collisions},
+            )
+
+        merged: Dict[tuple, Dict[str, Any]] = {}
+        result = []
+        for prop in properties:
+            key = (prop.get("type"), prop["name"])
+            existing = merged.get(key)
+            if existing is None:
+                merged[key] = prop
+                result.append(prop)
+                continue
+
+            existing["domain"] = self._merge_property_values(
+                existing.get("domain", []), prop.get("domain", [])
+            )
+            if prop.get("type") == "object":
+                existing["range"] = self._merge_property_values(
+                    existing.get("range", []), prop.get("range", [])
+                )
+                existing_metadata = existing.setdefault("metadata", {})
+                existing_metadata["occurrence_count"] = (
+                    existing_metadata.get("occurrence_count", 0)
+                    + prop.get("metadata", {}).get("occurrence_count", 0)
+                )
+            elif existing.get("range") != prop.get("range"):
+                existing["range"] = self._get_more_general_type(
+                    existing["range"], prop["range"]
+                )
+
+        return result
+
+    @staticmethod
+    def _merge_property_values(current: Any, incoming: Any) -> List[Any]:
+        """Merge scalar-or-list property values while preserving input order."""
+        values = list(current) if isinstance(current, list) else [current]
+        incoming_values = (
+            incoming if isinstance(incoming, list) else [incoming]
+        )
+        for value in incoming_values:
+            if value not in values:
+                values.append(value)
+        return [value for value in values if value is not None]
+
    def _infer_data_properties(
        self, entities: List[Dict[str, Any]], classes: List[Dict[str, Any]], **options
    ) -> List[Dict[str, Any]]:
@@ -70,7 +70,8 @@ sem:metadata a owl:AnnotationProperty ;
    rdfs:label "metadata" ;
    rdfs:comment """Free-form metadata carried through from extraction. An
 annotation property because its value is an arbitrary structure rather than a
-modelled one.""" ;
+modelled one; in the JSON-LD export the whole mapping is written as one
+rdf:JSON literal so caller keys never expand into this namespace (#1146).""" ;
    rdfs:isDefinedBy <https://semantica.dev/ns> .

 # ── Relationship terms (JSON-LD export) ──────────────────────────────────────
@@ -96,10 +97,10 @@ sem:target a owl:ObjectProperty ;

 sem:type a owl:DatatypeProperty ;
    rdfs:label "type" ;
-    rdfs:comment """The relationship type as a label, as emitted in the JSON-LD
-export. Distinct from rdf:type, which relates a node to a class rather than to
-a string.""" ;
-    rdfs:domain sem:Relationship ;
+    rdfs:comment """The entity or relationship type as a label, as emitted in
+the JSON-LD export. Distinct from rdf:type, which relates a node to a class
+rather than to a string. Emitted for both entities and relationships, so the
+domain is left open rather than tied to sem:Relationship.""" ;
    rdfs:range xsd:string ;
    rdfs:isDefinedBy <https://semantica.dev/ns> .

@@ -41,69 +41,134 @@ from ..utils.logging import get_logger
 from ..utils.progress_tracker import get_progress_tracker
 from .reasoner import Fact, Rule, _make_activation_key

+logger = get_logger("rete_engine")

-def _extract_bindings(condition: Any, fact: Fact) -> Dict[str, Any]:
-    """Extract ``?var`` bindings by matching a condition pattern against a fact.

-    ``condition`` is the pattern stored on the alpha node (typically a string
-    like ``"Person(?x)"``); ``fact`` is the working-memory :class:`Fact`. The
-    fact's canonical string form (``Predicate(arg1, arg2, ...)``) is matched
-    against the pattern using the same ``?\\w+`` placeholder convention as the
-    Reasoner, so downstream actions receive real bindings (e.g. ``{"x": "John"}``)
-    instead of the empty dict that previously left ``?x`` placeholders
-    unsubstituted.
+def _build_condition_regex(
+    pattern: str,
+    initial_bindings: Optional[Dict[str, str]] = None,
+) -> str:
+    """Build an anchored regex string for a condition pattern.

-    Returns an empty dict when the condition is not a string pattern or does
-    not match -- callers treat that as "no bindings extracted".
+    Splits the pattern on ``?var`` placeholders, escaping the literal
+    segments so surrounding parentheses/commas match literally. Variables
+    become named groups (or backreferences when repeated); variables already
+    present in ``initial_bindings`` are inlined as their literal value.
+
+    Args:
+        pattern: The condition pattern string (e.g. ``"Person(?x)"``).
+        initial_bindings: Bindings already established upstream. Variables
+            already bound are matched as literals rather than captured.
+
+    Returns:
+        An anchored regex string (``^...$``) suitable for ``re.compile`` /
+        ``re.match``.
    """
-    if not isinstance(condition, str):
-        return {}
-
-    segments = re.split(r"(\?\w+)", condition)
+    bindings = initial_bindings or {}
+    segments = re.split(r"(\?\w+)", pattern)
    seen_vars: Set[str] = set()
    p_regex = ""
    for seg in segments:
        if seg.startswith("?"):
            var_name = seg[1:]
-            if var_name in seen_vars:
+            if var_name in bindings:
+                # Already bound — require the exact literal value.
+                p_regex += re.escape(bindings[var_name])
+            elif var_name in seen_vars:
+                # Same variable used twice — enforce a backreference.
                p_regex += f"(?P={var_name})"
            else:
                p_regex += f"(?P<{var_name}>.+?)"
                seen_vars.add(var_name)
        else:
            p_regex += re.escape(seg)
-    p_regex = f"^{p_regex}$"
+    return f"^{p_regex}$"
+
+
+def unify_condition(
+    condition: Any,
+    fact: Fact,
+    initial_bindings: Optional[Dict[str, str]] = None,
+) -> Optional[Dict[str, str]]:
+    """Unify a condition pattern against a fact.
+
+    A condition is a pattern string such as ``"Person(?x)"`` or
+    ``"knows(?x, ?y)"`` where tokens beginning with ``?`` are variables.
+    The fact is rendered via its ``__str__`` representation
+    (``predicate(arg1, arg2)``) and matched against the pattern.
+
+    This mirrors ``Reasoner._match_pattern`` but is self-contained so the
+    RETE engine does not need a live ``Reasoner`` instance.
+
+    Args:
+        condition: The condition pattern (string). Non-string conditions
+            are stringified before matching.
+        fact: The fact to test.
+        initial_bindings: Bindings already established upstream. Variables
+            already bound must match the corresponding literal in the fact.
+
+    Returns:
+        A dict of variable bindings if the fact unifies with the condition,
+        otherwise ``None``.
+    """
+    bindings = dict(initial_bindings or {})
+    pattern = condition if isinstance(condition, str) else str(condition)
+    fact_str = str(fact)
+
+    # Build the anchored regex once (variables already bound are inlined as
+    # literals). See ``_build_condition_regex`` for the segment handling.
+    p_regex = _build_condition_regex(pattern, bindings)

    try:
-        match = re.match(p_regex, str(fact))
-    except re.error:
-        return {}
+        match = re.match(p_regex, fact_str)
+    except re.error as e:
+        logger.warning(
+            "unify_condition failed to compile/match condition "
+            "%r (regex: %r) against fact %r: %s",
+            pattern,
+            p_regex,
+            fact_str,
+            e,
+        )
+        return None
+    except Exception as e:  # noqa: BLE001 - mirror Reasoner._match_pattern
+        logger.warning(
+            "unify_condition unexpected error matching condition "
+            "%r (regex: %r) against fact %r: %s",
+            pattern,
+            p_regex,
+            fact_str,
+            e,
+        )
+        return None
    if not match:
-        return {}
-    return {k: v for k, v in match.groupdict().items() if v is not None}
+        return None

-
-def _bindings_for_rule(rule: Rule, facts: List[Fact]) -> Dict[str, Any]:
-    """Merge ``?var`` bindings from matching a rule's conditions against facts.
-
-    Each fact is matched against every condition of the rule; the first
-    condition that yields bindings for a fact contributes them. Bindings from
-    all facts are merged so multi-condition (joined) rules receive the full
-    variable environment. Later conflicting values do not overwrite earlier
-    ones, preserving the binding that a join already validated.
-    """
-    bindings: Dict[str, Any] = {}
-    for fact in facts:
-        for condition in rule.conditions:
-            extracted = _extract_bindings(condition, fact)
-            if not extracted:
-                continue
-            for key, value in extracted.items():
-                bindings.setdefault(key, value)
-            break
+    for var, value in match.groupdict().items():
+        if var in bindings and bindings[var] != value:
+            return None  # Binding conflict.
+        bindings[var] = value
    return bindings


+@dataclass
+class Token:
+    """A partial match flowing through the Rete network.
+
+    A token represents an ordered collection of concrete facts that have
+    been unified so far, together with the consistent variable bindings
+    accumulated across those facts.
+
+    Alpha nodes emit single-fact tokens. Beta nodes merge a left token and
+    a right token into a new token whose ``facts`` are the concatenation of
+    both sides (preserving condition order) and whose ``bindings`` are the
+    consistent union of both sides.
+    """
+
+    facts: List[Fact] = field(default_factory=list)
+    bindings: Dict[str, str] = field(default_factory=dict)
+
+
@dataclass
 class Match:
    """Pattern match."""
@@ -128,19 +193,68 @@ class AlphaNode(ReteNode):
    def __init__(self, node_id: str, condition: Any):
        super().__init__(node_id)
        self.condition = condition
-        self.matches: List[Fact] = []
+        # Single-fact tokens produced by unifying each matched fact with
+        # this node's condition.
+        self.tokens: List[Token] = []
+        # Pre-compile the condition regex once. Alpha nodes never have
+        # initial bindings, so the pattern is stable for the node's lifetime
+        # and every incoming fact reuses this compiled matcher instead of
+        # rebuilding it (avoids repeated regex construction overhead).
+        pattern = condition if isinstance(condition, str) else str(condition)
+        self._compiled: Optional[re.Pattern] = None
+        try:
+            self._compiled = re.compile(_build_condition_regex(pattern))
+        except re.error as e:
+            logger.warning(
+                "AlphaNode %r failed to compile condition %r: %s; "
+                "node will never match",
+                node_id,
+                pattern,
+                e,
+            )

-    def add_fact(self, fact: Fact) -> bool:
-        """Add fact if it matches condition."""
-        if self._matches(fact):
-            self.matches.append(fact)
-            return True
-        return False
+    def add_fact(self, fact: Fact) -> Optional[Token]:
+        """Add fact if it matches the condition, returning its token.

-    def _matches(self, fact: Fact) -> bool:
-        """Check if fact matches condition."""
-        # Simple matching - can be enhanced
-        return True
+        Returns the single-fact ``Token`` produced by unification when the
+        fact matches, otherwise ``None``.
+        """
+        bindings = self._matches(fact)
+        if bindings is not None:
+            token = Token(facts=[fact], bindings=dict(bindings))
+            self.tokens.append(token)
+            return token
+        return None
+
+    def _matches(self, fact: Fact) -> Optional[Dict[str, str]]:
+        """Check if fact matches the alpha node condition.
+
+        Uses the pre-compiled regex built in ``__init__`` for performance,
+        since RETE evaluates many facts against every alpha node.
+
+        Returns the variable bindings produced by unification if the fact
+        matches, otherwise ``None``. An empty dict signals a match with no
+        variables (still distinct from ``None``).
+        """
+        if self._compiled is None:
+            # Compilation failed at build time; treat as non-matching.
+            return None
+        fact_str = str(fact)
+        try:
+            match = self._compiled.match(fact_str)
+        except Exception as e:  # noqa: BLE001 - mirror unify_condition
+            logger.warning(
+                "AlphaNode %r unexpected error matching condition "
+                "%r against fact %r: %s",
+                self.node_id,
+                self.condition,
+                fact_str,
+                e,
+            )
+            return None
+        if not match:
+            return None
+        return match.groupdict()


 class BetaNode(ReteNode):
@@ -150,19 +264,28 @@ class BetaNode(ReteNode):
        super().__init__(node_id)
        self.left = left
        self.right = right
-        self.matches: List[Tuple[Fact, Fact]] = []
+        # Token memories for each side. Incoming tokens are stored here so
+        # that later-arriving tokens on the opposite side can be joined
+        # against every token already seen (chained joins).
+        self.left_tokens: List[Token] = []
+        self.right_tokens: List[Token] = []

-    def join(self, left_fact: Fact, right_fact: Fact) -> bool:
-        """Join facts from left and right nodes."""
-        if self._can_join(left_fact, right_fact):
-            self.matches.append((left_fact, right_fact))
-            return True
-        return False
+    def join(self, left_token: Token, right_token: Token) -> Optional[Token]:
+        """Join a left token with a right token.

-    def _can_join(self, left_fact: Fact, right_fact: Fact) -> bool:
-        """Check if facts can be joined."""
-        # Simple join logic - can be enhanced
-        return True
+        Returns a new merged ``Token`` (facts concatenated in condition
+        order, bindings unified) when the two tokens are consistent,
+        otherwise ``None`` on a binding conflict.
+        """
+        merged = dict(left_token.bindings)
+        for var, value in right_token.bindings.items():
+            if var in merged and merged[var] != value:
+                return None  # Binding conflict — cannot join.
+            merged[var] = value
+        return Token(
+            facts=list(left_token.facts) + list(right_token.facts),
+            bindings=merged,
+        )


 class TerminalNode(ReteNode):
@@ -248,12 +371,16 @@ class ReteEngine:
                self._add_rule_to_network(rule)

            self.logger.info(
-                f"Built Rete network with {len(self.network)} nodes for {len(rules)} rules"
+                f"Built Rete network with {len(self.network)} nodes "
+                f"for {len(rules)} rules"
            )
            self.progress_tracker.stop_tracking(
                tracking_id,
                status="completed",
-                message=f"Built Rete network with {len(self.network)} nodes for {len(rules)} rules",
+                message=(
+                    f"Built Rete network with {len(self.network)} nodes "
+                    f"for {len(rules)} rules"
+                ),
            )

        except Exception as e:
@@ -281,6 +408,10 @@ class ReteEngine:
                self.node_counter += 1
                beta_node = BetaNode(node_id, current, alpha_nodes[i])
                self.network[node_id] = beta_node
+                # Wire the beta node as a child of both its inputs so facts
+                # propagating from either side reach the join.
+                current.children.append(beta_node)
+                alpha_nodes[i].children.append(beta_node)
                current = beta_node
            final_node = current
        else:
@@ -311,40 +442,58 @@ class ReteEngine:
        # Find matching alpha nodes
        for node_id, node in self.network.items():
            if isinstance(node, AlphaNode):
-                if node.add_fact(fact):
-                    # Propagate to children
-                    self._propagate_from_alpha(node, fact)
+                token = node.add_fact(fact)
+                if token is not None:
+                    # Propagate the single-fact token to children.
+                    self._propagate_token(node, token)

-    def _propagate_from_alpha(self, alpha_node: AlphaNode, fact: Fact) -> None:
-        """Propagate from alpha node to children."""
-        for child in alpha_node.children:
+    def _propagate_token(self, source: ReteNode, token: Token) -> None:
+        """Propagate ``token`` (arriving from ``source``) to its children.
+
+        A ``Token`` carries the ordered facts and consistent bindings of a
+        partial match. Beta children attempt joins and, on success, emit a
+        new merged token downstream; terminal children turn the token into a
+        rule activation using the token's complete facts and bindings.
+        """
+        for child in source.children:
            if isinstance(child, BetaNode):
-                # Join with matches from left side
-                for left_fact in alpha_node.matches:
-                    if child.join(left_fact, fact):
-                        # Propagate to children
-                        for grandchild in child.children:
-                            if isinstance(grandchild, TerminalNode):
-                                facts = [left_fact, fact]
-                                match = Match(
-                                    rule=grandchild.rule,
-                                    facts=facts,
-                                    bindings=_bindings_for_rule(
-                                        grandchild.rule, facts
-                                    ),
-                                    confidence=1.0,
-                                )
-                                grandchild.activate(match)
+                self._propagate_to_beta(child, source, token)
            elif isinstance(child, TerminalNode):
-                # Direct activation
                match = Match(
                    rule=child.rule,
-                    facts=[fact],
-                    bindings=_bindings_for_rule(child.rule, [fact]),
+                    facts=list(token.facts),
+                    bindings=dict(token.bindings),
                    confidence=1.0,
                )
                child.activate(match)

+    def _propagate_to_beta(
+        self,
+        beta: "BetaNode",
+        source: ReteNode,
+        token: Token,
+    ) -> None:
+        """Attempt joins at ``beta`` for a token arriving from one side.
+
+        The incoming token is stored in the corresponding side's memory,
+        then joined against every token already recorded on the opposite
+        side. Each successful join produces a new merged token that is
+        propagated further downstream, enabling correct chained joins across
+        three or more conditions.
+        """
+        if source is beta.left:
+            beta.left_tokens.append(token)
+            for right_token in list(beta.right_tokens):
+                merged = beta.join(token, right_token)
+                if merged is not None:
+                    self._propagate_token(beta, merged)
+        elif source is beta.right:
+            beta.right_tokens.append(token)
+            for left_token in list(beta.left_tokens):
+                merged = beta.join(left_token, token)
+                if merged is not None:
+                    self._propagate_token(beta, merged)
+
    def match_patterns(self, facts: Optional[List[Fact]] = None) -> List[Match]:
        """
        Match patterns using Rete algorithm.
@@ -468,8 +617,11 @@ class ReteEngine:
        self.facts.clear()
        self.reset_action_history()
        for node in self.network.values():
-            if isinstance(node, AlphaNode) or isinstance(node, BetaNode):
-                node.matches.clear()
+            if isinstance(node, AlphaNode):
+                node.tokens.clear()
+            elif isinstance(node, BetaNode):
+                node.left_tokens.clear()
+                node.right_tokens.clear()
            elif isinstance(node, TerminalNode):
                node.activations.clear()

@@ -673,6 +673,7 @@ class GeminiProvider(BaseProvider):
        self.model = model
        self.client = None
        self._use_new_genai = False
+        self._legacy_model_cache: Dict[str, Any] = {}
        self._init_client()

    def _init_client(self):
@@ -694,6 +695,38 @@ class GeminiProvider(BaseProvider):
            self.client = None
            self.logger.warning("Gemini SDK not installed. Install with: pip install semantica[llm-gemini]")

+    def _legacy_client_for(self, requested_model: str):
+        """Return a legacy-SDK GenerativeModel bound to this instance's own
+        API key, for the given model name.
+
+        The legacy google-generativeai package keeps its API key as
+        module-level state (genai.configure()), so any GenerativeModel built
+        by a different GeminiProvider instance in the same process can leave
+        that state pointing at a different key. Re-asserting configure()
+        with this instance's key right before use, instead of only once at
+        construction, keeps sequential calls across instances from reading
+        each other's credentials. A cache keyed by model name avoids
+        rebuilding a GenerativeModel on every call for the common case of
+        one model being reused.
+        """
+        try:
+            import google.generativeai as old_genai
+            old_genai.configure(api_key=self.api_key)
+        except Exception:
+            # _init_client() already required this import to reach the
+            # legacy path in the first place, so this only happens when
+            # self.client was injected directly (tests). Fall back to it
+            # without reasserting credentials rather than failing calls
+            # that never needed the real SDK.
+            return self.client
+        if requested_model == self.model:
+            return self.client
+        cached = self._legacy_model_cache.get(requested_model)
+        if cached is None:
+            cached = old_genai.GenerativeModel(requested_model)
+            self._legacy_model_cache[requested_model] = cached
+        return cached
+
    def is_available(self) -> bool:
        """Check if provider is available."""
        return self.client is not None
@@ -724,7 +757,8 @@ class GeminiProvider(BaseProvider):
            )
            return self._resp_text(resp)
        else:
-            response = self.client.generate_content(prompt, generation_config=config or None)
+            legacy_client = self._legacy_client_for(kwargs.get("model", self.model))
+            response = legacy_client.generate_content(prompt, generation_config=config or None)
            return self._resp_text(response)

    def generate_structured(self, prompt: str, **kwargs) -> dict:
@@ -733,15 +767,24 @@ class GeminiProvider(BaseProvider):
            raise ProcessingError("Gemini client not initialized.")

        json_prompt = f"{prompt}\n\nReturn the response as valid JSON only."
+
+        config = {}
+        self._add_if_set(config, kwargs, "temperature", "top_p", "top_k", "stop_sequences", "candidate_count")
+        if "max_tokens" in kwargs:
+            config["max_output_tokens"] = kwargs["max_tokens"]
+
        if self._use_new_genai:
            model = kwargs.get("model", self.model)
-            resp = self.client.models.generate_content(model=model, contents=json_prompt)
+            resp = self.client.models.generate_content(
+                model=model, contents=json_prompt, config=config or None
+            )
            try:
                return self._parse_json(self._resp_text(resp))
            except Exception as e:
                raise ProcessingError(f"Failed to parse JSON from Gemini response: {e}")
        else:
-            response = self.client.generate_content(json_prompt)
+            legacy_client = self._legacy_client_for(kwargs.get("model", self.model))
+            response = legacy_client.generate_content(json_prompt, generation_config=config or None)
            try:
                return self._parse_json(self._resp_text(response))
            except Exception as e:
@@ -967,6 +1010,8 @@ class OllamaProvider(BaseProvider):

    def is_available(self) -> bool:
        """Check if provider is available."""
+        if self.client is None:
+            self._init_client()
        return self.client is not None

    def _build_options(self, kwargs: dict) -> Optional[dict]:
@@ -1018,7 +1063,6 @@ class DeepSeekProvider(BaseProvider):
        self.api_key = api_key or config.get_api_key("deepseek")
        self.base_url = "https://api.deepseek.com/v1"
        self.model = model
-        self.base_url = "https://api.deepseek.com/v1"
        self.client = None
        self._init_client()

@@ -1045,7 +1089,7 @@ class DeepSeekProvider(BaseProvider):
            "model": kwargs.get("model", self.model),
            "messages": [{"role": "user", "content": prompt}],
        }
-        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens")
+        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "user")

        response = self.client.chat.completions.create(**create_kwargs)
        return response.choices[0].message.content
@@ -1058,8 +1102,9 @@ class DeepSeekProvider(BaseProvider):
        create_kwargs = {
            "model": kwargs.get("model", self.model),
            "messages": [{"role": "user", "content": prompt}],
+            "response_format": {"type": "json_object"},
        }
-        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens")
+        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "user")

        response = self.client.chat.completions.create(**create_kwargs)
        try:
@@ -1103,7 +1148,7 @@ class NovitaProvider(BaseProvider):
            "model": kwargs.get("model", self.model),
            "messages": [{"role": "user", "content": prompt}],
        }
-        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens")
+        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "user")

        response = self.client.chat.completions.create(**create_kwargs)
        return response.choices[0].message.content
@@ -1118,7 +1163,7 @@ class NovitaProvider(BaseProvider):
            "messages": [{"role": "user", "content": prompt}],
            "response_format": {"type": "json_object"},
        }
-        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens")
+        self._add_if_set(create_kwargs, kwargs, "temperature", "max_tokens", "top_p", "frequency_penalty", "presence_penalty", "seed", "stop", "user")

        response = self.client.chat.completions.create(**create_kwargs)
        try:
@@ -64,6 +64,8 @@ class SlidingWindowChunker:
            raise ValidationError("overlap must be non-negative")
        if self.overlap >= self.chunk_size:
            raise ValidationError("overlap must be less than chunk_size")
+        if self.stride <= 0:
+            raise ValidationError("stride must be positive")

    def chunk(self, text: str, **options) -> List[Chunk]:
        """
@@ -215,15 +217,20 @@ class SlidingWindowChunker:
        Returns:
            list: List of chunks
        """
+        if overlap_size is None:
+            return self.chunk(text)
+        if overlap_size < 0:
+            raise ValidationError("overlap_size must be non-negative")
+        if overlap_size >= self.chunk_size:
+            raise ValidationError("overlap_size must be less than chunk_size")
+
        original_overlap = self.overlap
-        if overlap_size is not None:
+        original_stride = self.stride
+
+        try:
            self.overlap = overlap_size
            self.stride = self.chunk_size - self.overlap
-
-        chunks = self.chunk(text)
-
-        # Restore original overlap
-        self.overlap = original_overlap
-        self.stride = self.chunk_size - self.overlap
-
-        return chunks
+            return self.chunk(text)
+        finally:
+            self.overlap = original_overlap
+            self.stride = original_stride
@@ -43,7 +43,7 @@ from contextlib import contextmanager
 from dataclasses import dataclass, field
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, TextIO, Tuple, Union

 from .logging import get_logger

@@ -144,14 +144,37 @@ class ProgressDisplay(ABC):
 class ConsoleProgressDisplay(ProgressDisplay):
    """Console progress display with real-time updates."""

-    def __init__(self, use_emoji: bool = True, update_interval: float = 0.1):
+    def __init__(
+        self,
+        use_emoji: bool = True,
+        update_interval: float = 0.1,
+        stream: Optional[TextIO] = None,
+    ):
+        """Initialize the console display.
+
+        Args:
+            use_emoji: Whether to decorate output with emoji.
+            update_interval: Minimum seconds between redraws.
+            stream: Where progress is written. Defaults to ``sys.stderr``.
+
+                Progress is diagnostic output, so stderr is the correct stream
+                for it, and stdout must stay clean for programs that carry a
+                machine-readable protocol on it — the stdio MCP servers put
+                newline-delimited JSON-RPC there, and a progress bar on stdout
+                corrupts that stream.
+
+                Left as ``None``, the stream is resolved on each write rather
+                than captured here, so a later rebinding of ``sys.stderr``
+                (pytest capture, for instance) is honoured.
+        """
        self.use_emoji = use_emoji
-        
-        # Check if stdout supports emojis (especially on Windows)
+        self._stream = stream
+
+        # Check if the target stream supports emojis (especially on Windows)
        if self.use_emoji:
            try:
-                # Try encoding a test emoji with stdout's encoding
-                encoding = getattr(sys.stdout, "encoding", None)
+                # Try encoding a test emoji with the stream's encoding
+                encoding = getattr(self.stream, "encoding", None)
                if encoding:
                    "🧠".encode(encoding)
            except (UnicodeEncodeError, LookupError, AttributeError):
@@ -162,6 +185,11 @@ class ConsoleProgressDisplay(ProgressDisplay):
        self.current_lines: Dict[str, str] = {}
        self.lock = threading.Lock()

+    @property
+    def stream(self) -> TextIO:
+        """The stream progress is written to; ``sys.stderr`` unless overridden."""
+        return self._stream if self._stream is not None else sys.stderr
+
    def _should_update(self) -> bool:
        """Check if enough time has passed for update."""
        now = time.time()
@@ -259,15 +287,16 @@ class ConsoleProgressDisplay(ProgressDisplay):
        return f"{base_msg}: {message}"

    def _safe_write(self, text: str) -> None:
-        """Safely write text to stdout handling encoding errors."""
+        """Safely write text to the progress stream handling encoding errors."""
+        stream = self.stream
        try:
-            sys.stdout.write(text)
+            stream.write(text)
        except UnicodeEncodeError:
            # Fallback: encode with replacement and write decoded
            # Use ascii as safe fallback if encoding is unknown or caused error
-            encoding = getattr(sys.stdout, "encoding", None) or "ascii"
+            encoding = getattr(stream, "encoding", None) or "ascii"
            safe_text = text.encode(encoding, errors="replace").decode(encoding)
-            sys.stdout.write(safe_text)
+            stream.write(safe_text)

    def update(self, item: ProgressItem) -> None:
        """Update console progress display."""
@@ -302,11 +331,11 @@ class ConsoleProgressDisplay(ProgressDisplay):
                    self._display_item_line(pipeline_item)
                    self._safe_write("\n")
                
-                sys.stdout.flush()
+                self.stream.flush()
            else:
                # Original single-item display
                self._display_item_line(item)
-                sys.stdout.flush()
+                self.stream.flush()
    
    def _display_item_line(self, item: ProgressItem) -> None:
        """Display a single progress item line."""
@@ -468,13 +497,13 @@ class ConsoleProgressDisplay(ProgressDisplay):
                    f"Completed: {completed} | Failed: {failed} | Total Time: {total_time:.2f}s\n"
                )
            self._safe_write("=" * 80 + "\n")
-            sys.stdout.flush()
+            self.stream.flush()

    def clear(self) -> None:
        """Clear console display."""
        with self.lock:
            self._safe_write("\r" + " " * 100 + "\r")
-            sys.stdout.flush()
+            self.stream.flush()
            self.current_lines.clear()


@@ -66,12 +66,32 @@ class FAISSIndex:
        self.metadata: Dict[str, Dict[str, Any]] = {}

    def add_vectors(self, vectors: np.ndarray, ids: Optional[List[str]] = None):
-        """Add vectors to index."""
+        """
+        Add vectors to index.
+
+        Skips any id already present in vector_ids rather than appending a
+        second physical vector under the same id. FAISS indices here don't
+        support removing or replacing a single vector in place, so an
+        "update" isn't possible; without this check, re-running an add for
+        ids that already exist (e.g. retrying an interrupted migration)
+        would silently duplicate vectors under the same id on every retry.
+        """
        if ids is None:
            ids = [f"vec_{i}" for i in range(len(vectors))]

-        self.index.add(vectors.astype(np.float32))
-        self.vector_ids.extend(ids)
+        new_rows = []
+        new_ids = []
+        existing = set(self.vector_ids)
+        for row, vec_id in zip(vectors, ids):
+            if vec_id in existing:
+                continue
+            new_rows.append(row)
+            new_ids.append(vec_id)
+            existing.add(vec_id)
+
+        if new_rows:
+            self.index.add(np.array(new_rows, dtype=np.float32))
+            self.vector_ids.extend(new_ids)

    def search(
        self, query_vectors: np.ndarray, k: int = 10
@@ -305,6 +325,12 @@ class FAISSStore:
        """
        Add vectors to index.

+        Any id that already exists in the index is skipped rather than
+        stored as a second physical vector under the same id (see
+        FAISSIndex.add_vectors), so calling this again with ids from a
+        previous call is safe and doesn't accumulate duplicates. Metadata
+        for those ids is still updated.
+
        Args:
            vectors: List of vectors or numpy array
            ids: Vector IDs
@@ -312,7 +338,8 @@ class FAISSStore:
            **options: Additional options

        Returns:
-            List of vector IDs
+            List of vector IDs (including ids that were already present
+            and therefore not re-added as new vectors)
        """
        num_vectors = len(vectors) if isinstance(vectors, (list, np.ndarray)) else 1
        tracking_id = self.progress_tracker.start_tracking(
@@ -526,6 +553,30 @@ class FAISSStore:

        return results

+    def scan_vectors(self, offset: int = 0, limit: int = 100) -> List[Dict[str, Any]]:
+        """
+        Page through stored vectors in insertion order.
+
+        Args:
+            offset: Number of vectors to skip
+            limit: Maximum number of vectors to return
+
+        Returns:
+            List of result dicts with 'id', 'metadata', and 'vector'
+        """
+        if self.index is None or limit <= 0:
+            return []
+
+        ids_page = self.index.vector_ids[offset:offset + limit]
+        return [
+            {
+                "id": vector_id,
+                "metadata": self.get_metadata(vector_id) or {},
+                "vector": self.get_vector(vector_id),
+            }
+            for vector_id in ids_page
+        ]
+
    def get_stats(self) -> Dict[str, Any]:
        """Get index statistics."""
        if self.index is None:
@@ -656,6 +656,52 @@ class PgVectorStore:
            self.logger.warning(f"Failed to get metadata for {vector_id}: {e}")
            return None

+    def scan_vectors(self, offset: int = 0, limit: int = 100) -> List[Dict[str, Any]]:
+        """
+        Page through stored vectors ordered by id.
+
+        Args:
+            offset: Number of rows to skip
+            limit: Maximum number of rows to return
+
+        Returns:
+            List of result dicts with 'id', 'metadata', and 'vector'
+        """
+        if not PSYCOPG3_AVAILABLE and not PSYCOPG2_AVAILABLE:
+            raise ProcessingError(
+                "Neither psycopg3 nor psycopg2 is available. "
+                "Install with: pip install psycopg[binary] or psycopg2-binary"
+            )
+
+        if limit <= 0:
+            return []
+
+        scan_sql = psycopg_sql.SQL("""
+            SELECT id, vector, metadata
+            FROM {}
+            ORDER BY id
+            LIMIT %s OFFSET %s
+        """).format(psycopg_sql.Identifier(self.table_name))
+
+        with self._get_connection() as conn:
+            try:
+                cur = conn.cursor()
+                cur.execute(scan_sql, (limit, offset))
+                rows = cur.fetchall()
+                cur.close()
+
+                results = []
+                for row in rows:
+                    vec_id, vec, meta = row
+                    results.append({
+                        "id": vec_id,
+                        "metadata": meta if isinstance(meta, dict) else json.loads(meta) if meta else {},
+                        "vector": np.array(vec) if vec is not None else None,
+                    })
+                return results
+            except Exception as e:
+                raise ProcessingError(f"Failed to scan vectors: {str(e)}") from e
+
    def filter_by_metadata(
        self, filters: Dict[str, Any], limit: int = 10
    ) -> List[Dict[str, Any]]:
@@ -616,6 +616,49 @@ class SQLiteVecStore:
            self.logger.warning(f"Failed to get metadata for {vector_id}: {e}")
            return None

+    def scan_vectors(self, offset: int = 0, limit: int = 100) -> List[Dict[str, Any]]:
+        """
+        Page through stored vectors ordered by id.
+
+        Args:
+            offset: Number of rows to skip
+            limit: Maximum number of rows to return
+
+        Returns:
+            List of result dicts with 'id', 'metadata', and 'vector'
+        """
+        if limit <= 0:
+            return []
+
+        query_sql = f"""
+            SELECT id, embedding, metadata
+            FROM {self.table_name}
+            ORDER BY id
+            LIMIT ? OFFSET ?
+        """
+
+        with self._lock, self._get_connection() as conn:
+            try:
+                cur = conn.cursor()
+                cur.execute(query_sql, (limit, offset))
+                rows = cur.fetchall()
+                cur.close()
+
+                results = []
+                for row in rows:
+                    vec_id, embedding_blob, meta_json = row
+                    vec = None
+                    if embedding_blob:
+                        vec = np.frombuffer(embedding_blob, dtype=np.float32).copy()
+                    results.append({
+                        "id": vec_id,
+                        "metadata": json.loads(meta_json) if meta_json else {},
+                        "vector": vec,
+                    })
+                return results
+            except Exception as e:
+                raise ProcessingError(f"Failed to scan vectors: {str(e)}") from e
+
    def filter_by_metadata(
        self, filters: Dict[str, Any], limit: int = 10
    ) -> List[Dict[str, Any]]:
@@ -824,6 +824,64 @@ class VectorStore:
        else:
            raise NotImplementedError(f"Backend store {type(self._backend_store).__name__} does not implement get_metadata")

+    def scan_vectors(self, offset: int = 0, limit: int = 100) -> List[Dict[str, Any]]:
+        """
+        Page through stored vectors, backend-agnostic.
+
+        Follows the get_vector()/get_metadata() precedent (#843): the inmemory
+        backend pages its local dict directly, a persistent backend delegates
+        to a scan_vectors() on the wrapped store when available, and one that
+        cannot enumerate its contents raises NotImplementedError rather than
+        silently returning an empty page.
+
+        Args:
+            offset: Number of vectors to skip
+            limit: Maximum number of vectors to return
+
+        Returns:
+            List of result dicts with 'id', 'metadata', and 'vector'
+        """
+        if limit <= 0:
+            return []
+
+        if self.backend == "inmemory":
+            ids_page = list(self.vectors.keys())[offset:offset + limit]
+            return [
+                {
+                    "id": vec_id,
+                    "metadata": self.metadata.get(vec_id, {}),
+                    "vector": self.vectors.get(vec_id),
+                }
+                for vec_id in ids_page
+            ]
+        elif self._backend_store and hasattr(self._backend_store, "scan_vectors"):
+            return self._backend_store.scan_vectors(offset=offset, limit=limit)
+        else:
+            raise NotImplementedError(
+                f"Backend store {type(self._backend_store).__name__} does not "
+                "implement scan_vectors(). Add a scan_vectors() method to the "
+                "backend store adapter to enable enumeration for this backend."
+            )
+
+    def iter_vectors(self, batch_size: int = 500):
+        """
+        Iterate over every stored vector, one page at a time.
+
+        Args:
+            batch_size: Number of vectors to fetch per underlying scan_vectors() call
+
+        Yields:
+            Result dicts with 'id', 'metadata', and 'vector', in scan order
+        """
+        offset = 0
+        while True:
+            page = self.scan_vectors(offset=offset, limit=batch_size)
+            if not page:
+                return
+            for item in page:
+                yield item
+            offset += len(page)
+
    def count(self) -> int:
        """Return the number of vectors in the store, backend-agnostic.

@@ -0,0 +1,42 @@
+"""AgentMemory.find_by_entity returns all matches by default (#1018).
+
+The previous default limit of 10 silently truncated results, making erasure
+workflows incomplete for entities with more than 10 memories: a caller
+computing "what references this entity" from a truncated page would leave
+the remainder live. The unbounded default is deliberate — an erasure check
+cannot paginate — while callers that want a page still pass an explicit
+limit. (Previously lived in tests/test_seed_manager.py; moved to the
+AgentMemory area per review.)
+"""
+
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..")))
+
+from semantica.context.agent_memory import AgentMemory
+
+
+@pytest.fixture
+def memory_with_15():
+    mem = AgentMemory()
+    for i in range(15):
+        mem.store(
+            content=f"fact {i} about entity",
+            entities=[{"id": "e1", "name": "Entity", "type": "thing"}],
+        )
+    return mem
+
+
+class TestFindByEntityLimit:
+    def test_returns_all_matches_by_default(self, memory_with_15):
+        results = memory_with_15.find_by_entity("e1")
+        assert len(results) == 15, f"expected 15 (all), got {len(results)}"
+
+    def test_explicit_limit_still_works(self, memory_with_15):
+        assert len(memory_with_15.find_by_entity("e1", limit=5)) == 5
+
+    def test_no_matches_returns_empty(self):
+        assert AgentMemory().find_by_entity("nonexistent") == []
@@ -291,8 +291,8 @@ class TestDeduplication(unittest.TestCase):
 class TestProgressTrackerEncoding(unittest.TestCase):
    """Regression tests for issue #531 — Unicode crash on cp1252 Windows consoles."""

-    def _make_cp1252_stdout(self):
-        """Return a stdout-like object that raises UnicodeEncodeError for non-cp1252 chars."""
+    def _make_cp1252_stream(self):
+        """Return a stream that raises UnicodeEncodeError for non-cp1252 chars."""
        class CP1252Writer:
            encoding = "cp1252"
            def write(self, text):
@@ -304,39 +304,39 @@ class TestProgressTrackerEncoding(unittest.TestCase):
    def test_safe_write_does_not_crash_on_cp1252(self):
        """_safe_write must not raise UnicodeEncodeError on a cp1252 console."""
        display = ConsoleProgressDisplay()
-        orig = sys.stdout
-        sys.stdout = self._make_cp1252_stdout()
+        orig = sys.stderr
+        sys.stderr = self._make_cp1252_stream()
        try:
            display._safe_write("🧠 Semantica - 📊 Current Progress\n")
        except UnicodeEncodeError:
-            self.fail("_safe_write raised UnicodeEncodeError on cp1252 stdout")
+            self.fail("_safe_write raised UnicodeEncodeError on a cp1252 stream")
        finally:
-            sys.stdout = orig
+            sys.stderr = orig

    def test_update_pipeline_header_does_not_crash_on_cp1252(self):
        """update() pipeline header write must not crash on a cp1252 console (issue #531)."""
        from semantica.utils.progress_tracker import ProgressItem
        display = ConsoleProgressDisplay()
        display.use_emoji = True  # force emoji path to exercise the fixed branch
-        orig = sys.stdout
-        sys.stdout = self._make_cp1252_stdout()
+        orig = sys.stderr
+        sys.stderr = self._make_cp1252_stream()
        try:
            display._safe_write("🧠 Semantica - 📊 Current Progress\n")
            display._safe_write("=" * 150 + "\n")
        except UnicodeEncodeError:
            self.fail("Pipeline header write raised UnicodeEncodeError on cp1252 stdout")
        finally:
-            sys.stdout = orig
+            sys.stderr = orig

    def test_emoji_detection_disables_on_cp1252(self):
-        """ConsoleProgressDisplay should auto-disable emoji when stdout is cp1252."""
-        orig = sys.stdout
-        sys.stdout = self._make_cp1252_stdout()
+        """ConsoleProgressDisplay should auto-disable emoji when the progress stream is cp1252."""
+        orig = sys.stderr
+        sys.stderr = self._make_cp1252_stream()
        try:
            display = ConsoleProgressDisplay()
-            self.assertFalse(display.use_emoji, "use_emoji should be False on cp1252 stdout")
+            self.assertFalse(display.use_emoji, "use_emoji should be False on a cp1252 progress stream")
        finally:
-            sys.stdout = orig
+            sys.stderr = orig


 class TestResultLimiting(unittest.TestCase):
@@ -24,14 +24,19 @@ import math
 from datetime import datetime

 import pytest
-from fastapi.testclient import TestClient
-from pydantic import ValidationError
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the imports below, which need that extra.
+pytest.importorskip("fastapi")

-from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.routes.decisions import _node_to_decision
-from semantica.explorer.schemas import DecisionResponse
-from semantica.explorer.session import GraphSession
+from fastapi.testclient import TestClient  # noqa: E402
+from pydantic import ValidationError  # noqa: E402
+
+from semantica.context.context_graph import ContextGraph  # noqa: E402
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.routes.decisions import _node_to_decision  # noqa: E402
+from semantica.explorer.schemas import DecisionResponse  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402


 # ---------------------------------------------------------------------------
@@ -9,16 +9,15 @@ import networkx as nx
 import pytest

 from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.session import GraphSession
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")

-try:
-    from starlette.testclient import TestClient
-except ImportError:
-    pytest.skip(
-        "starlette TestClient is required for explorer tests. Install semantica[explorer].",
-        allow_module_level=True,
-    )
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402
+
+from starlette.testclient import TestClient  # noqa: E402



@@ -1104,7 +1103,7 @@ class TestBidirectionalPathRoute:
 # _classify_distance unit tests — issue #472
 # ---------------------------------------------------------------------------

-from semantica.utils.helpers import classify_path_distance
+from semantica.utils.helpers import classify_path_distance  # noqa: E402


 class _FakeSimilarity:
@@ -13,16 +13,15 @@ browsers can't set custom headers on a WebSocket handshake.
 import pytest

 from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.session import GraphSession
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")

-try:
-    from starlette.testclient import TestClient
-except ImportError:
-    pytest.skip(
-        "starlette TestClient is required for explorer tests. Install semantica[explorer].",
-        allow_module_level=True,
-    )
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402
+
+from starlette.testclient import TestClient  # noqa: E402


 def _build_sample_graph() -> ContextGraph:
@@ -0,0 +1,224 @@
+"""
+End-to-end integration test for deterministic Explorer rendering (Issue #1037).
+
+Validates:
+1. Building the canonical 4-node, 3-edge graph using ContextGraph.
+2. Serialization via save_to_file().
+3. Reloading via load_from_file() and GraphSession.from_file() without mutation.
+4. Explorer HTTP API serving exact nodes, edges, edge types, and connectivity.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the explorer imports below, which pull fastapi in transitively.
+pytest.importorskip("fastapi")
+
+from semantica.context.context_graph import ContextGraph  # noqa: E402
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402
+
+try:
+    from starlette.testclient import TestClient
+except ImportError:
+    pytest.skip(
+        "starlette TestClient required. Install semantica[explorer].",
+        allow_module_level=True,
+    )
+
+
+def _build_deterministic_graph() -> ContextGraph:
+    """Build the exact graph requested in Semantica #1037."""
+    graph = ContextGraph(advanced_analytics=False)
+
+    graph.add_node("alice", "Person", content="Alice")
+    graph.add_node("bob", "Person", content="Bob")
+    graph.add_node("acme", "Organization", content="Acme")
+    graph.add_node("new_york", "Location", content="New York")
+
+    graph.add_edge("alice", "acme", edge_type="WORKS_AT")
+    graph.add_edge("bob", "alice", edge_type="KNOWS")
+    graph.add_edge("acme", "new_york", edge_type="LOCATED_IN")
+
+    return graph
+
+
+class TestExplorerDeterministicRenderingE2E:
+    """E2E suite for deterministic graph rendering and session loading."""
+
+    def test_build_and_in_memory_structure(self):
+        graph = _build_deterministic_graph()
+
+        assert len(graph.nodes) == 4
+        assert len(graph.edges) == 3
+
+        node_map = {
+            nid: (node.node_type, node.content) for nid, node in graph.nodes.items()
+        }
+        assert node_map["alice"] == ("Person", "Alice")
+        assert node_map["bob"] == ("Person", "Bob")
+        assert node_map["acme"] == ("Organization", "Acme")
+        assert node_map["new_york"] == ("Location", "New York")
+
+        edge_tuples = {(e.source_id, e.target_id, e.edge_type) for e in graph.edges}
+        assert edge_tuples == {
+            ("alice", "acme", "WORKS_AT"),
+            ("bob", "alice", "KNOWS"),
+            ("acme", "new_york", "LOCATED_IN"),
+        }
+
+    def test_serialization_and_deserialization(self, tmp_path: Path):
+        graph = _build_deterministic_graph()
+        output_file = tmp_path / "explorer_deterministic_graph.json"
+
+        graph.save_to_file(str(output_file))
+        assert output_file.exists()
+
+        # Validate JSON structure
+        with open(output_file, "r", encoding="utf-8") as f:
+            raw_data = json.load(f)
+
+        assert "nodes" in raw_data
+        assert "edges" in raw_data
+        assert len(raw_data["nodes"]) == 4
+        assert len(raw_data["edges"]) == 3
+
+        # Reload with ContextGraph.load_from_file
+        reloaded_graph = ContextGraph(advanced_analytics=False)
+        reloaded_graph.load_from_file(str(output_file))
+
+        assert len(reloaded_graph.nodes) == 4
+        assert len(reloaded_graph.edges) == 3
+        reloaded_nodes = {
+            nid: (node.node_type, node.content)
+            for nid, node in reloaded_graph.nodes.items()
+        }
+        assert reloaded_nodes["alice"] == ("Person", "Alice")
+        assert reloaded_nodes["bob"] == ("Person", "Bob")
+        assert reloaded_nodes["acme"] == ("Organization", "Acme")
+        assert reloaded_nodes["new_york"] == ("Location", "New York")
+
+        reloaded_edges = {
+            (e.source_id, e.target_id, e.edge_type) for e in reloaded_graph.edges
+        }
+        assert reloaded_edges == {
+            ("alice", "acme", "WORKS_AT"),
+            ("bob", "alice", "KNOWS"),
+            ("acme", "new_york", "LOCATED_IN"),
+        }
+
+    def test_session_loading_and_stats(self, tmp_path: Path):
+        graph = _build_deterministic_graph()
+        output_file = tmp_path / "explorer_deterministic_graph.json"
+        graph.save_to_file(str(output_file))
+
+        session = GraphSession.from_file(str(output_file))
+        stats = session.get_stats()
+
+        assert stats["node_count"] == 4
+        assert stats["edge_count"] == 3
+
+    def test_explorer_api_endpoints_with_deterministic_graph(self, tmp_path: Path):
+        graph = _build_deterministic_graph()
+        output_file = tmp_path / "explorer_deterministic_graph.json"
+        graph.save_to_file(str(output_file))
+
+        session = GraphSession.from_file(str(output_file))
+        app = create_app(session=session)
+
+        with TestClient(app) as client:
+            # 1. Health & Info
+            health = client.get("/api/health")
+            assert health.status_code == 200
+            assert health.json() == {"status": "ok"}
+
+            info = client.get("/api/info")
+            assert info.status_code == 200
+            assert info.json()["status"] == "active"
+
+            # 2. Stats
+            stats = client.get("/api/graph/stats")
+            assert stats.status_code == 200
+            stats_data = stats.json()
+            assert stats_data["node_count"] == 4
+            assert stats_data["edge_count"] == 3
+
+            # 3. Nodes endpoint
+            nodes_res = client.get("/api/graph/nodes")
+            assert nodes_res.status_code == 200
+            nodes_data = nodes_res.json()
+            assert nodes_data["total"] == 4
+            assert len(nodes_data["nodes"]) == 4
+
+            returned_nodes = {
+                n["id"]: (n["type"], n["content"]) for n in nodes_data["nodes"]
+            }
+            assert returned_nodes["alice"] == ("Person", "Alice")
+            assert returned_nodes["bob"] == ("Person", "Bob")
+            assert returned_nodes["acme"] == ("Organization", "Acme")
+            assert returned_nodes["new_york"] == ("Location", "New York")
+
+            # 4. Individual node lookups
+            for node_id in ["alice", "bob", "acme", "new_york"]:
+                node_res = client.get(f"/api/graph/node/{node_id}")
+                assert node_res.status_code == 200
+                assert node_res.json()["id"] == node_id
+
+            # 5. Edges endpoint
+            edges_res = client.get("/api/graph/edges")
+            assert edges_res.status_code == 200
+            edges_data = edges_res.json()
+            assert edges_data["total"] == 3
+            assert len(edges_data["edges"]) == 3
+
+            returned_edges = {
+                (e["source"], e["target"], e["type"]) for e in edges_data["edges"]
+            }
+            assert returned_edges == {
+                ("alice", "acme", "WORKS_AT"),
+                ("bob", "alice", "KNOWS"),
+                ("acme", "new_york", "LOCATED_IN"),
+            }
+
+    def test_deterministic_graph_auth_enforcement(self, tmp_path: Path, monkeypatch):
+        graph = _build_deterministic_graph()
+        output_file = tmp_path / "explorer_deterministic_graph.json"
+        graph.save_to_file(str(output_file))
+
+        session = GraphSession.from_file(str(output_file))
+        app = create_app(session=session)
+
+        with TestClient(app) as client:
+            # 1. Unconfigured auth (no SEMANTICA_ALLOW_ANONYMOUS, no SEMANTICA_API_KEY) -> 503
+            monkeypatch.delenv("SEMANTICA_ALLOW_ANONYMOUS", raising=False)
+            monkeypatch.delenv("SEMANTICA_API_KEY", raising=False)
+            unconfigured_res = client.get("/api/graph/stats")
+            assert unconfigured_res.status_code == 503
+
+            # 2. Configured SEMANTICA_API_KEY without header -> 401
+            test_key = "secret-test-key-1037"
+            monkeypatch.setenv("SEMANTICA_API_KEY", test_key)
+            unauthorized_res = client.get("/api/graph/nodes")
+            assert unauthorized_res.status_code == 401
+
+            # 3. Configured SEMANTICA_API_KEY with valid X-API-Key header -> 200
+            authorized_res = client.get(
+                "/api/graph/nodes",
+                headers={"X-API-Key": test_key},
+            )
+            assert authorized_res.status_code == 200
+            assert authorized_res.json()["total"] == 4
+
+            # 4. Explicit local development opt-in: SEMANTICA_ALLOW_ANONYMOUS=true -> 200 without header
+            monkeypatch.setenv("SEMANTICA_ALLOW_ANONYMOUS", "true")
+            monkeypatch.delenv("SEMANTICA_API_KEY", raising=False)
+            anon_res = client.get("/api/graph/edges")
+            assert anon_res.status_code == 200
+            assert anon_res.json()["total"] == 3
+
@@ -27,7 +27,12 @@ import threading

 import pytest

-from semantica.explorer.routes import ontology as ontology_mod
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")
+
+from semantica.explorer.routes import ontology as ontology_mod  # noqa: E402


 def _start_local_server():
@@ -21,7 +21,12 @@ from unittest.mock import MagicMock, patch

 import pytest

-from semantica.explorer.routes import ontology as ontology_mod
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")
+
+from semantica.explorer.routes import ontology as ontology_mod  # noqa: E402


 def _fake_getaddrinfo(host, *args, **kwargs):
@@ -6,17 +6,16 @@ from urllib.parse import quote
 import pytest

 from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.routes.ontology import OntologyEntry
-from semantica.explorer.session import GraphSession
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")

-try:
-    from starlette.testclient import TestClient
-except ImportError:
-    pytest.skip(
-        "starlette TestClient is required for explorer tests. Install semantica[explorer].",
-        allow_module_level=True,
-    )
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.routes.ontology import OntologyEntry  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402
+
+from starlette.testclient import TestClient  # noqa: E402


 def _build_ontology_graph() -> ContextGraph:
@@ -5,13 +5,18 @@ Tests for ProvenanceManager wiring into Explorer routes and application startup.
 from unittest.mock import patch

 import pytest
-from starlette.testclient import TestClient
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the starlette/explorer imports below, which need that extra.
+pytest.importorskip("fastapi")

-from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.session import GraphSession
-from semantica.provenance import ProvenanceManager
-from semantica.provenance.storage import SQLiteStorage
+from starlette.testclient import TestClient  # noqa: E402
+
+from semantica.context.context_graph import ContextGraph  # noqa: E402
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402
+from semantica.provenance import ProvenanceManager  # noqa: E402
+from semantica.provenance.storage import SQLiteStorage  # noqa: E402


@pytest.fixture
@@ -2,7 +2,14 @@

 from types import SimpleNamespace

-from semantica.explorer.routes.provenance import (
+import pytest
+
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")
+
+from semantica.explorer.routes.provenance import (  # noqa: E402
    _add_chain_edges,
    _build_provenance,
    _render_markdown,
@@ -14,18 +14,17 @@ from unittest.mock import patch
 import pytest

 from semantica.context.context_graph import ContextGraph
-from semantica.explorer.app import create_app
-from semantica.explorer.session import GraphSession
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")

-try:
-    from starlette.testclient import TestClient
-except ImportError:
-    pytest.skip(
-        "starlette TestClient is required for explorer tests. Install semantica[explorer].",
-        allow_module_level=True,
-    )
+from semantica.explorer.app import create_app  # noqa: E402
+from semantica.explorer.session import GraphSession  # noqa: E402

-import semantica.explorer.routes.sparql as sparql_mod
+from starlette.testclient import TestClient  # noqa: E402
+
+import semantica.explorer.routes.sparql as sparql_mod  # noqa: E402


 def _build_sample_graph() -> ContextGraph:
@@ -2,12 +2,19 @@

 from unittest.mock import MagicMock

-from fastapi import FastAPI
-from fastapi.testclient import TestClient
+import pytest

-from semantica.explorer.dependencies import get_session
-from semantica.explorer.routes.vocabulary import router
-from semantica.utils.skos import validate_skos_hierarchy
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this module
+# must skip rather than fail collection when it is absent. The guard has to sit
+# above the import below, which pulls fastapi in transitively.
+pytest.importorskip("fastapi")
+
+from fastapi import FastAPI  # noqa: E402
+from fastapi.testclient import TestClient  # noqa: E402
+
+from semantica.explorer.dependencies import get_session  # noqa: E402
+from semantica.explorer.routes.vocabulary import router  # noqa: E402
+from semantica.utils.skos import validate_skos_hierarchy  # noqa: E402

 app = FastAPI()
 app.include_router(router)
@@ -67,7 +67,7 @@ def test_merging_repeated_exports_yields_one_graph_node(tmp_path):
    assert len(kg_nodes) == 1

    entity_nodes = set(
-        merged.subjects(RDF.type, URIRef("https://semantica.dev/vocab/ORG"))
+        merged.subjects(RDF.type, URIRef("https://semantica.dev/ns#Entity"))
    )
    assert len(entity_nodes) == 1

@@ -0,0 +1,120 @@
+"""Caller data must never expand into the Semantica namespace (#1146).
+
+``@vocab`` used to sit in every JSON-LD context pointing at ``ns#``, so every
+bare term in caller data expanded into it: an extracted type like ``"ORG"``
+became ``ns#ORG`` (a term the vocabulary does not define), and a metadata key
+like ``"source"`` collided with the real ``sem:source`` object property,
+attaching a plain string to a property whose range is a resource. The fix
+removes ``@vocab`` outright: only explicit ``semantica:``-prefixed terms
+resolve, caller type labels travel as ``semantica:type`` strings, and caller
+metadata survives as one ``rdf:JSON`` literal.
+"""
+
+import json
+
+from rdflib import RDF, Graph, Literal, URIRef
+
+from semantica.export.json_exporter import JSONExporter
+from semantica.export.rdf_exporter import RDFExporter, SEMANTICA_NS
+
+NS = SEMANTICA_NS
+E1 = "https://example.org/e1"
+
+KG = {
+    "entities": [
+        {
+            "id": E1,
+            "text": "Acme",
+            "type": "ORG",
+            "metadata": {"source": "crm_export_2024"},
+        }
+    ],
+    "relationships": [
+        {
+            "source_id": E1,
+            "target_id": "https://example.org/e2",
+            "type": "employs",
+        }
+    ],
+}
+
+
+def _jsonld_file(exporter, kind, tmp_path, name):
+    path = tmp_path / name
+    if kind == "knowledge_graph":
+        exporter.export_knowledge_graph(KG, path, format="json-ld")
+    elif kind == "entities":
+        exporter.export_entities(KG["entities"], path, format="json-ld")
+    elif kind == "relationships":
+        exporter.export_relationships(KG["relationships"], path, format="json-ld")
+    elif kind == "generic":
+        exporter.export({"note": "plain payload, no @id"}, path, format="json-ld")
+    else:
+        raise AssertionError(kind)
+    return json.loads(path.read_text())
+
+
+def test_no_jsonld_context_declares_a_vocab(tmp_path):
+    exporter = JSONExporter()
+    for kind in ("knowledge_graph", "entities", "relationships", "generic"):
+        context = _jsonld_file(exporter, kind, tmp_path, f"{kind}.jsonld")[
+            "@context"
+        ]
+        assert "@vocab" not in context, f"{kind}: @vocab expands caller data"
+
+    context = json.loads(RDFExporter().export_to_rdf(KG, format="jsonld"))[
+        "@context"
+    ]
+    assert "@vocab" not in context
+
+
+def test_extracted_type_labels_stay_out_of_the_namespace(tmp_path):
+    path = tmp_path / "kg.jsonld"
+    JSONExporter().export_knowledge_graph(KG, path, format="json-ld")
+
+    graph = Graph()
+    graph.parse(str(path), format="json-ld")
+
+    assert (None, RDF.type, URIRef(NS + "ORG")) not in graph, (
+        "the caller's type label was minted as a class in ns#"
+    )
+    assert (URIRef(E1), RDF.type, URIRef(NS + "Entity")) in graph
+    assert (URIRef(E1), URIRef(NS + "type"), Literal("ORG")) in graph, (
+        "the label itself must survive, as a string"
+    )
+
+
+def test_metadata_keys_stay_out_of_the_namespace(tmp_path):
+    path = tmp_path / "kg.jsonld"
+    JSONExporter().export_knowledge_graph(KG, path, format="json-ld")
+
+    graph = Graph()
+    graph.parse(str(path), format="json-ld")
+
+    assert (None, URIRef(NS + "source"), Literal("crm_export_2024")) not in (
+        graph
+    ), "caller metadata value attached to the real sem:source object property"
+    for _, _, o in graph.triples((None, URIRef(NS + "source"), None)):
+        assert not isinstance(o, Literal), (
+            "sem:source has a resource range but received a plain literal"
+        )
+
+    literals = [
+        o
+        for o in graph.objects(None, URIRef(NS + "metadata"))
+        if isinstance(o, Literal)
+    ]
+    assert literals, "the metadata dict was dropped instead of preserved"
+    assert literals[0].datatype == RDF.JSON
+    assert json.loads(str(literals[0])) == {"source": "crm_export_2024"}
+
+
+def test_rdf_exporter_jsonld_keeps_type_labels_out_of_the_namespace():
+    graph = Graph()
+    graph.parse(
+        data=RDFExporter().export_to_rdf(KG, format="jsonld"), format="json-ld"
+    )
+
+    assert (None, RDF.type, URIRef(NS + "ORG")) not in graph
+    assert (URIRef(E1), RDF.type, URIRef(NS + "Entity")) in graph
+    assert (URIRef(E1), URIRef(NS + "type"), Literal("ORG")) in graph
@@ -0,0 +1 @@
+# tests/integrations/crewai package
@@ -0,0 +1 @@
+# tests/integrations/langchain package
@@ -0,0 +1,51 @@
+import pytest
+
+from semantica.ontology.class_inferrer import ClassInferrer
+from semantica.ontology.property_generator import PropertyGenerator
+from semantica.utils.exceptions import ValidationError
+
+
+def test_same_kind_normalized_object_properties_are_merged():
+    entities = [
+        {"id": "p1", "type": "Person", "name": "Alice"},
+        {"id": "o1", "type": "Organization", "name": "Acme"},
+    ]
+    classes = ClassInferrer(min_occurrences=1).infer_classes(entities)
+    relationships = [
+        {
+            "source_type": "Person",
+            "target_type": "Organization",
+            "type": "works_for",
+        },
+        {
+            "source_type": "Person",
+            "target_type": "Organization",
+            "type": "worksFor",
+        },
+    ]
+
+    properties = PropertyGenerator().infer_properties(
+        entities, relationships, classes, min_occurrences=1
+    )
+
+    works_for = [prop for prop in properties if prop["name"] == "worksFor"]
+    assert len(works_for) == 1
+    assert works_for[0]["domain"] == ["Person"]
+    assert works_for[0]["range"] == ["Organization"]
+
+
+def test_normalized_name_cannot_be_both_object_and_data_property():
+    entities = [
+        {"id": "p1", "type": "Person", "value": "Alice"},
+        {"id": "p2", "type": "Person", "value": "Bob"},
+    ]
+    classes = ClassInferrer(min_occurrences=1).infer_classes(entities)
+    relationships = [
+        {"source_type": "Person", "target_type": "Person", "type": "value"},
+        {"source_type": "Person", "target_type": "Person", "type": "value"},
+    ]
+
+    with pytest.raises(ValidationError, match="object and data"):
+        PropertyGenerator().infer_properties(
+            entities, relationships, classes, min_occurrences=1
+        )
@@ -0,0 +1,366 @@
+"""Tests for the RETE engine pattern matching (issue #300).
+
+These tests verify that ``AlphaNode._matches`` and ``BetaNode._can_join`` no
+longer behave like the old always-``True`` stubs, and that the network as a
+whole only fires rules whose conditions actually unify with the facts.
+"""
+
+import itertools
+import re
+import unittest
+from unittest import mock
+
+from semantica.reasoning import rete_engine
+from semantica.reasoning.reasoner import Fact, Rule
+from semantica.reasoning.rete_engine import (
+    AlphaNode,
+    BetaNode,
+    ReteEngine,
+    unify_condition,
+)
+
+
+class TestUnifyCondition(unittest.TestCase):
+    def test_single_variable_binds(self):
+        fact = Fact("f1", "Person", ["John"])
+        bindings = unify_condition("Person(?x)", fact)
+        self.assertEqual(bindings, {"x": "John"})
+
+    def test_predicate_mismatch_returns_none(self):
+        fact = Fact("f1", "Company", ["Google"])
+        self.assertIsNone(unify_condition("Person(?x)", fact))
+
+    def test_two_arguments_bind(self):
+        fact = Fact("f2", "Parent", ["John", "Mary"])
+        bindings = unify_condition("Parent(?x, ?y)", fact)
+        self.assertEqual(bindings, {"x": "John", "y": "Mary"})
+
+    def test_literal_argument_must_match(self):
+        fact = Fact("f3", "Parent", ["John", "Mary"])
+        self.assertIsNone(unify_condition("Parent(Bob, ?y)", fact))
+        self.assertEqual(unify_condition("Parent(John, ?y)", fact), {"y": "Mary"})
+
+    def test_repeated_variable_requires_equal_values(self):
+        loves_self = Fact("f4", "Loves", ["John", "John"])
+        loves_other = Fact("f5", "Loves", ["John", "Mary"])
+        self.assertEqual(unify_condition("Loves(?x, ?x)", loves_self), {"x": "John"})
+        self.assertIsNone(unify_condition("Loves(?x, ?x)", loves_other))
+
+    def test_regex_error_logs_warning_and_returns_none(self):
+        """A regex compilation error is logged with context and yields None."""
+        fact = Fact("f6", "Person", ["John"])
+        with mock.patch.object(
+            rete_engine.re,
+            "match",
+            side_effect=re.error("bad pattern"),
+        ), self.assertLogs("semantica.rete_engine", level="WARNING") as captured:
+            result = unify_condition("Person(?x)", fact)
+        self.assertIsNone(result)
+        joined = "\n".join(captured.output)
+        self.assertIn("Person(?x)", joined)
+        self.assertIn("Person(John)", joined)
+        self.assertIn("bad pattern", joined)
+
+    def test_unexpected_error_logs_warning_and_returns_none(self):
+        """An unexpected error is also logged and swallowed as None."""
+        fact = Fact("f7", "Person", ["John"])
+        with mock.patch.object(
+            rete_engine.re,
+            "match",
+            side_effect=RuntimeError("boom"),
+        ), self.assertLogs("semantica.rete_engine", level="WARNING") as captured:
+            result = unify_condition("Person(?x)", fact)
+        self.assertIsNone(result)
+        self.assertIn("boom", "\n".join(captured.output))
+
+
+class TestAlphaNode(unittest.TestCase):
+    def test_matches_stores_bindings(self):
+        node = AlphaNode("a1", "Person(?x)")
+        fact = Fact("f1", "Person", ["John"])
+        token = node.add_fact(fact)
+        self.assertIsNotNone(token)
+        assert token is not None  # narrow type for the checker
+        self.assertEqual(token.facts, [fact])
+        self.assertEqual(token.bindings, {"x": "John"})
+        self.assertIn(token, node.tokens)
+
+    def test_non_matching_fact_rejected(self):
+        node = AlphaNode("a1", "Person(?x)")
+        fact = Fact("f1", "Company", ["Google"])
+        self.assertIsNone(node.add_fact(fact))
+        self.assertEqual(node.tokens, [])
+
+    def test_uses_precompiled_regex(self):
+        """AlphaNode compiles its condition once and reuses it per fact."""
+        node = AlphaNode("a1", "Person(?x)")
+        self.assertIsNotNone(node._compiled)
+        # Matching goes through the compiled matcher, not unify_condition.
+        with mock.patch.object(rete_engine, "unify_condition") as unify:
+            fact = Fact("f1", "Person", ["John"])
+            token = node.add_fact(fact)
+        unify.assert_not_called()
+        self.assertIsNotNone(token)
+        assert token is not None
+        self.assertEqual(token.bindings, {"x": "John"})
+
+    def test_bad_condition_never_matches_and_logs(self):
+        """A condition that fails to compile logs a warning and never fires."""
+        with mock.patch.object(
+            rete_engine,
+            "_build_condition_regex",
+            return_value="(unbalanced",
+        ), self.assertLogs("semantica.rete_engine", level="WARNING") as captured:
+            node = AlphaNode("bad", "Person(?x)")
+        self.assertIsNone(node._compiled)
+        self.assertIn("failed to compile", "\n".join(captured.output))
+        fact = Fact("f1", "Person", ["John"])
+        self.assertIsNone(node.add_fact(fact))
+        self.assertEqual(node.tokens, [])
+
+
+class TestBetaNode(unittest.TestCase):
+    def test_join_consistent_bindings(self):
+        left = AlphaNode("a1", "Parent(?x, ?y)")
+        right = AlphaNode("a2", "Person(?x)")
+        beta = BetaNode("b1", left, right)
+
+        parent = Fact("f1", "Parent", ["John", "Mary"])
+        person = Fact("f2", "Person", ["John"])
+        left_token = left.add_fact(parent)
+        right_token = right.add_fact(person)
+        assert left_token is not None and right_token is not None
+
+        merged = beta.join(left_token, right_token)
+        self.assertIsNotNone(merged)
+        assert merged is not None  # narrow type for the checker
+        self.assertEqual(merged.bindings, {"x": "John", "y": "Mary"})
+        # Facts are concatenated left-then-right in condition order.
+        self.assertEqual(merged.facts, [parent, person])
+
+    def test_join_conflicting_bindings_rejected(self):
+        left = AlphaNode("a1", "Parent(?x, ?y)")
+        right = AlphaNode("a2", "Person(?x)")
+        beta = BetaNode("b1", left, right)
+
+        parent = Fact("f1", "Parent", ["John", "Mary"])
+        # ?x conflicts: John vs Alice
+        person = Fact("f2", "Person", ["Alice"])
+        left_token = left.add_fact(parent)
+        right_token = right.add_fact(person)
+        assert left_token is not None and right_token is not None
+
+        self.assertIsNone(beta.join(left_token, right_token))
+
+
+class TestReteEngineEndToEnd(unittest.TestCase):
+    def test_only_matching_rule_fires(self):
+        engine = ReteEngine()
+        rule = Rule(
+            rule_id="r1",
+            name="person rule",
+            conditions=["Person(?x)"],
+            conclusion="Mortal(?x)",
+        )
+        engine.build_network([rule])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Company", ["Google"]))  # should NOT fire
+
+        matches = engine.match_patterns()
+        self.assertEqual(len(matches), 1)
+        self.assertEqual(matches[0].bindings, {"x": "John"})
+
+    def test_multi_condition_join(self):
+        engine = ReteEngine()
+        rule = Rule(
+            rule_id="r1",
+            name="child rule",
+            conditions=["Person(?x)", "Parent(?x, ?y)"],
+            conclusion="Child(?y, ?x)",
+        )
+        engine.build_network([rule])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["John", "Mary"]))
+        # Unrelated parent whose ?x does not match any Person -> no activation.
+        engine.add_fact(Fact("f3", "Parent", ["Bob", "Sue"]))
+
+        matches = engine.match_patterns()
+        self.assertEqual(len(matches), 1)
+        self.assertEqual(matches[0].bindings, {"x": "John", "y": "Mary"})
+
+    def test_no_activation_when_join_inconsistent(self):
+        engine = ReteEngine()
+        rule = Rule(
+            rule_id="r1",
+            name="child rule",
+            conditions=["Person(?x)", "Parent(?x, ?y)"],
+            conclusion="Child(?y, ?x)",
+        )
+        engine.build_network([rule])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["Alice", "Mary"]))  # ?x mismatch
+
+        matches = engine.match_patterns()
+        self.assertEqual(matches, [])
+
+
+class TestThreeConditionChain(unittest.TestCase):
+    """Chained beta joins across three or more conditions (issue #300).
+
+    These exercise the Token model: a token must accumulate the ordered
+    facts and the consistent bindings of every condition, so that deep
+    chains neither drop bindings nor duplicate facts, and a conflict on the
+    third condition correctly suppresses activation.
+    """
+
+    def _three_condition_rule(self):
+        return Rule(
+            rule_id="r1",
+            name="location chain",
+            conditions=[
+                "Person(?x)",
+                "Parent(?x, ?y)",
+                "Located(?y, ?z)",
+            ],
+            conclusion="LivesNear(?x, ?z)",
+        )
+
+    def test_three_condition_valid_match(self):
+        engine = ReteEngine()
+        engine.build_network([self._three_condition_rule()])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["John", "Mary"]))
+        engine.add_fact(Fact("f3", "Located", ["Mary", "Paris"]))
+
+        matches = engine.match_patterns()
+        self.assertEqual(len(matches), 1)
+        self.assertEqual(
+            matches[0].bindings,
+            {"x": "John", "y": "Mary", "z": "Paris"},
+        )
+
+    def test_three_condition_third_level_conflict(self):
+        engine = ReteEngine()
+        engine.build_network([self._three_condition_rule()])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["John", "Mary"]))
+        # ?y is bound to Mary, so a Located fact about Bob must not join.
+        engine.add_fact(Fact("f3", "Located", ["Bob", "Paris"]))
+
+        matches = engine.match_patterns()
+        self.assertEqual(matches, [])
+
+    def test_fact_insertion_order_independent(self):
+        # Whatever order facts arrive, the same single match must result.
+        base_facts = [
+            Fact("f1", "Person", ["John"]),
+            Fact("f2", "Parent", ["John", "Mary"]),
+            Fact("f3", "Located", ["Mary", "Paris"]),
+        ]
+        expected = {"x": "John", "y": "Mary", "z": "Paris"}
+
+        for order in itertools.permutations(base_facts):
+            engine = ReteEngine()
+            engine.build_network([self._three_condition_rule()])
+            for fact in order:
+                engine.add_fact(fact)
+            matches = engine.match_patterns()
+            self.assertEqual(len(matches), 1, f"order={order}")
+            self.assertEqual(matches[0].bindings, expected)
+
+    def test_match_facts_complete_in_condition_order(self):
+        engine = ReteEngine()
+        engine.build_network([self._three_condition_rule()])
+
+        person = Fact("f1", "Person", ["John"])
+        parent = Fact("f2", "Parent", ["John", "Mary"])
+        located = Fact("f3", "Located", ["Mary", "Paris"])
+        engine.add_fact(person)
+        engine.add_fact(parent)
+        engine.add_fact(located)
+
+        matches = engine.match_patterns()
+        self.assertEqual(len(matches), 1)
+        # All three facts preserved, in condition order, no duplicates.
+        self.assertEqual(matches[0].facts, [person, parent, located])
+
+    def test_multiple_left_tokens_join_one_right_fact(self):
+        # Two Person/Parent chains sharing the same Located(?y, ?z) fact.
+        engine = ReteEngine()
+        engine.build_network([self._three_condition_rule()])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["John", "Mary"]))
+        engine.add_fact(Fact("f3", "Person", ["Alice"]))
+        engine.add_fact(Fact("f4", "Parent", ["Alice", "Mary"]))
+        # One right fact should join with both accumulated left tokens.
+        engine.add_fact(Fact("f5", "Located", ["Mary", "Paris"]))
+
+        matches = engine.match_patterns()
+        self.assertEqual(len(matches), 2)
+        result = {m.bindings["x"]: m.bindings["z"] for m in matches}
+        self.assertEqual(result, {"John": "Paris", "Alice": "Paris"})
+
+    def test_matches_reasoner_match_rule(self):
+        from semantica.reasoning.reasoner import Reasoner
+
+        rule = self._three_condition_rule()
+        facts = [
+            Fact("f1", "Person", ["John"]),
+            Fact("f2", "Parent", ["John", "Mary"]),
+            Fact("f3", "Located", ["Mary", "Paris"]),
+        ]
+
+        # Reasoner works over stringified facts and returns
+        # (conclusion, matched_facts, bindings) tuples from self.facts.
+        reasoner = Reasoner()
+        for fact in facts:
+            reasoner.add_fact(str(fact))
+        reasoner_matches = reasoner._match_rule(rule)
+
+        engine = ReteEngine()
+        engine.build_network([rule])
+        for fact in facts:
+            engine.add_fact(fact)
+        rete_matches = engine.match_patterns()
+
+        # Both engines must agree on the number of activations.
+        self.assertEqual(len(rete_matches), len(reasoner_matches))
+        self.assertEqual(len(rete_matches), 1)
+        self.assertEqual(
+            rete_matches[0].bindings,
+            {"x": "John", "y": "Mary", "z": "Paris"},
+        )
+        # The RETE match must carry the instantiated conclusion facts too.
+        conclusion, _, _ = reasoner_matches[0]
+        self.assertEqual(conclusion, "LivesNear(John, Paris)")
+
+    def test_reset_clears_all_token_memory(self):
+        engine = ReteEngine()
+        engine.build_network([self._three_condition_rule()])
+
+        engine.add_fact(Fact("f1", "Person", ["John"]))
+        engine.add_fact(Fact("f2", "Parent", ["John", "Mary"]))
+        engine.add_fact(Fact("f3", "Located", ["Mary", "Paris"]))
+        self.assertEqual(len(engine.match_patterns()), 1)
+
+        engine.reset()
+
+        # No stale facts, tokens or activations remain anywhere.
+        self.assertEqual(engine.facts, [])
+        for node in engine.network.values():
+            if isinstance(node, AlphaNode):
+                self.assertEqual(node.tokens, [])
+            elif isinstance(node, BetaNode):
+                self.assertEqual(node.left_tokens, [])
+                self.assertEqual(node.right_tokens, [])
+        self.assertEqual(engine.match_patterns(), [])
+
+
+if __name__ == "__main__":
+    unittest.main()
@@ -60,6 +60,11 @@ class TestSlidingWindowChunker:
        with pytest.raises(ValidationError):
            SlidingWindowChunker(chunk_size=100, overlap=100)

+    @pytest.mark.parametrize("stride", [0, -1])
+    def test_init_rejects_non_positive_stride(self, stride):
+        with pytest.raises(ValidationError, match="stride must be positive"):
+            SlidingWindowChunker(chunk_size=100, stride=stride)
+
    def test_empty_text_returns_empty(self):
        chunker = SlidingWindowChunker(chunk_size=50, overlap=10)
        assert chunker.chunk("") == []
@@ -97,6 +102,49 @@ class TestSlidingWindowChunker:
        chunks = chunker.chunk_with_overlap(text, overlap_size=15)
        assert len(chunks) >= 2
        assert chunker.overlap == 0
+        assert chunker.stride == 50
+
+    @pytest.mark.parametrize("overlap_size", [-1, 50, 51])
+    def test_chunk_with_overlap_rejects_invalid_override(self, overlap_size):
+        chunker = SlidingWindowChunker(chunk_size=50)
+
+        with pytest.raises(ValidationError):
+            chunker.chunk_with_overlap(
+                "non-empty input", overlap_size=overlap_size
+            )
+
+    def test_chunk_with_overlap_accepts_largest_valid_override(self):
+        chunker = SlidingWindowChunker(chunk_size=5)
+
+        chunks = chunker.chunk_with_overlap("abcdefghij", overlap_size=4)
+
+        assert [chunk.start_index for chunk in chunks] == list(range(10))
+
+    def test_chunk_with_overlap_restores_custom_stride(self):
+        chunker = SlidingWindowChunker(chunk_size=10, overlap=2, stride=3)
+
+        chunker.chunk_with_overlap(
+            "abcdefghijklmnopqrstuvwxyz", overlap_size=4
+        )
+
+        assert chunker.overlap == 2
+        assert chunker.stride == 3
+
+    def test_chunk_with_overlap_restores_state_when_chunk_raises(
+        self, monkeypatch
+    ):
+        chunker = SlidingWindowChunker(chunk_size=10, overlap=2, stride=3)
+
+        def raise_error(text):
+            raise RuntimeError("chunk failed")
+
+        monkeypatch.setattr(chunker, "chunk", raise_error)
+
+        with pytest.raises(RuntimeError, match="chunk failed"):
+            chunker.chunk_with_overlap("non-empty input", overlap_size=4)
+
+        assert chunker.overlap == 2
+        assert chunker.stride == 3

    def test_boundary_preservation_avoids_mid_word_when_possible(self):
        text = (
@@ -1359,6 +1359,101 @@ class TestStore:
        result = runner.invoke(cli_module.main, ["store", "migrate", "--from", "faiss"])
        assert result.exit_code != 0

+    def test_migrate_refuses_unsupported_backend_pair(self, runner):
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "faiss", "--to", "qdrant"])
+        assert result.exit_code != 0
+        assert "faiss, pgvector, sqlite" in result.output
+
+    def _fake_migrate_store_module(self, source_items, stored, dest_configs=None):
+        class _FakeBackendStore:
+            def __init__(self, dimension=None):
+                self.dimension = dimension
+
+        class _FakeStore:
+            def __init__(self, backend, config=None, **kw):
+                self.backend = backend
+                self._config = config or {}
+                dim = self._config.get("dimension")
+                self._backend_store = _FakeBackendStore(dimension=dim)
+                if dest_configs is not None:
+                    dest_configs[backend] = dict(self._config)
+
+            def iter_vectors(self, batch_size=500):
+                if self.backend == "sqlite":
+                    yield from source_items
+                    return
+                return
+                yield  # pragma: no cover - makes this a generator for other backends
+
+            def store_vectors(self, vectors, metadata, ids=None):
+                for vec_id, meta in zip(ids, metadata):
+                    stored[vec_id] = meta
+
+        return _fake_module(VectorStore=_FakeStore)
+
+    def test_migrate_runs_between_supported_backends(self, runner, monkeypatch):
+        source_items = [
+            {"id": "a", "vector": [0.1, 0.2], "metadata": {"tag": "x"}},
+            {"id": "b", "vector": [0.3, 0.4], "metadata": {}},
+        ]
+        stored = {}
+        fake_vs = self._fake_migrate_store_module(source_items, stored)
+        monkeypatch.setitem(__import__("sys").modules, "semantica.vector_store", fake_vs)
+
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "sqlite", "--to", "pgvector",
+                                      "--namespace", "prod", "--json"])
+        _ok(result)
+        data = _json_output(result)
+        assert data == {"from": "sqlite", "to": "pgvector", "migrated": 2}
+        assert stored == {"a": {"tag": "x", "namespace": "prod"}, "b": {"namespace": "prod"}}
+
+    def test_migrate_reports_zero_for_empty_source(self, runner, monkeypatch):
+        stored = {}
+        fake_vs = self._fake_migrate_store_module([], stored)
+        monkeypatch.setitem(__import__("sys").modules, "semantica.vector_store", fake_vs)
+
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "sqlite", "--to", "pgvector", "--json"])
+        _ok(result)
+        assert _json_output(result)["migrated"] == 0
+        assert stored == {}
+
+    def test_migrate_inherits_source_dimension_into_dest(self, runner, monkeypatch):
+        source_items = [{"id": "a", "vector": [0.1, 0.2, 0.3], "metadata": {}}]
+        stored = {}
+        dest_configs: dict = {}
+        fake_vs = self._fake_migrate_store_module(source_items, stored, dest_configs)
+        monkeypatch.setitem(__import__("sys").modules, "semantica.vector_store", fake_vs)
+        monkeypatch.setattr(
+            cli_module.Config, "to_dict",
+            lambda self: {"vector_store": {"sqlite": {"dimension": 3}, "pgvector": {}}},
+        )
+
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "sqlite", "--to", "pgvector", "--json"])
+        _ok(result)
+        assert dest_configs["pgvector"].get("dimension") == 3
+
+    def test_migrate_faiss_source_requires_index_path(self, runner, monkeypatch):
+        fake_vs = _fake_module(VectorStore=lambda **kw: MagicMock())
+        monkeypatch.setitem(__import__("sys").modules, "semantica.vector_store", fake_vs)
+
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "faiss", "--to", "sqlite"])
+        assert result.exit_code != 0
+        assert "index_path" in result.output
+
+    def test_migrate_faiss_dest_requires_index_path(self, runner, monkeypatch):
+        fake_vs = _fake_module(VectorStore=lambda **kw: MagicMock())
+        monkeypatch.setitem(__import__("sys").modules, "semantica.vector_store", fake_vs)
+
+        result = runner.invoke(cli_module.main, ["store", "migrate",
+                                      "--from", "sqlite", "--to", "faiss"])
+        assert result.exit_code != 0
+        assert "index_path" in result.output
+
    def test_flush_requires_confirm(self, runner):
        result = runner.invoke(cli_module.main, ["store", "flush"])
        assert result.exit_code != 0
@@ -0,0 +1,97 @@
+"""Tests for the Anthropic LLM provider wrapper (semantica.llms.Anthropic)."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from semantica.llms import Anthropic
+from semantica.utils.exceptions import ProcessingError
+
+
+def test_construction_stores_model_and_api_key():
+    """Anthropic(...) should not crash and should remember what it was given."""
+    claude = Anthropic(model="claude-sonnet-4-6", api_key="fake-key")
+    assert claude.model == "claude-sonnet-4-6"
+    assert claude.api_key == "fake-key"
+
+
+def test_is_available_false_with_no_key(monkeypatch):
+    """Without a real key, is_available() must be a real False, not truthy junk.
+
+    api_key=None alone isn't enough to prove this: AnthropicProvider falls
+    back to the ANTHROPIC_API_KEY environment variable, so this test has to
+    clear it too or it would pass/fail depending on whoever's machine or CI
+    runner happens to run it.
+    """
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    claude = Anthropic(api_key=None)
+    assert claude.is_available() is False
+
+
+def test_generate_raises_clear_error_when_unavailable(monkeypatch):
+    """generate() must fail loudly.
+
+    Clears ANTHROPIC_API_KEY for the same reason as test_is_available_false_with_no_key:
+    otherwise this test flakes depending on whether the runner's environment has a key set.
+    """
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    claude = Anthropic(api_key=None)
+    with pytest.raises(ProcessingError, match="Anthropic provider not available"):
+        claude.generate("hello")
+
+
+def test_generate_forwards_to_the_real_provider_when_available():
+    """When available, generate() must actually call through to the real provider."""
+    claude = Anthropic(api_key="fake-key")
+
+    claude.provider = MagicMock()
+    claude.provider.is_available.return_value = True
+    claude.provider.generate.return_value = "a fake response"
+
+    result = claude.generate("hello", temperature=0.5)
+
+    assert result == "a fake response"
+    claude.provider.generate.assert_called_once_with("hello", temperature=0.5)
+
+
+def test_generate_structured_forwards_to_the_real_provider():
+    claude = Anthropic(api_key="fake-key")
+    claude.provider = MagicMock()
+    claude.provider.is_available.return_value = True
+    claude.provider.generate_structured.return_value = {"key": "value"}
+
+    result = claude.generate_structured("hello")
+
+    assert result == {"key": "value"}
+    claude.provider.generate_structured.assert_called_once_with("hello")
+
+
+def test_generate_typed_forwards_schema_and_max_retries():
+    claude = Anthropic(api_key="fake-key")
+    claude.provider = MagicMock()
+    claude.provider.is_available.return_value = True
+    fake_schema = object()
+    claude.provider.generate_typed.return_value = "typed result"
+
+    result = claude.generate_typed("hello", fake_schema, max_retries=5)
+
+    assert result == "typed result"
+    claude.provider.generate_typed.assert_called_once_with(
+        "hello", fake_schema, max_retries=5
+    )
+
+
+def test_generate_structured_raises_clear_error_when_unavailable(monkeypatch):
+    """generate_structured() must fail loudly, same as generate()."""
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    claude = Anthropic(api_key=None)
+    with pytest.raises(ProcessingError, match="Anthropic provider not available"):
+        claude.generate_structured("hello")
+
+
+def test_generate_typed_raises_clear_error_when_unavailable(monkeypatch):
+    """generate_typed() must fail loudly, same as generate() and generate_structured()."""
+    monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
+    claude = Anthropic(api_key=None)
+    with pytest.raises(ProcessingError, match="Anthropic provider not available"):
+        claude.generate_typed("hello", object())
@@ -0,0 +1,80 @@
+"""Tests for the DeepSeek LLM provider wrapper (semantica.llms.DeepSeek)."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from semantica.llms import DeepSeek
+from semantica.utils.exceptions import ProcessingError
+
+
+def test_construction_stores_model_and_api_key():
+    llm = DeepSeek(model="deepseek-chat", api_key="fake-key")
+    assert llm.model == "deepseek-chat"
+    assert llm.api_key == "fake-key"
+
+
+def test_is_available_false_with_no_key(monkeypatch):
+    monkeypatch.delenv("DEEPSEEK_API_KEY", raising=False)
+    llm = DeepSeek(api_key=None)
+    assert llm.is_available() is False
+
+
+def test_generate_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("DEEPSEEK_API_KEY", raising=False)
+    llm = DeepSeek(api_key=None)
+    with pytest.raises(ProcessingError, match="DeepSeek provider not available"):
+        llm.generate("hello")
+
+
+def test_generate_forwards_to_the_real_provider_when_available():
+    llm = DeepSeek(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate.return_value = "a fake response"
+
+    result = llm.generate("hello", temperature=0.5)
+
+    assert result == "a fake response"
+    llm.provider.generate.assert_called_once_with("hello", temperature=0.5)
+
+
+def test_generate_structured_forwards_to_the_real_provider():
+    llm = DeepSeek(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate_structured.return_value = {"key": "value"}
+
+    result = llm.generate_structured("hello")
+
+    assert result == {"key": "value"}
+    llm.provider.generate_structured.assert_called_once_with("hello")
+
+
+def test_generate_typed_forwards_schema_and_max_retries():
+    llm = DeepSeek(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    fake_schema = object()
+    llm.provider.generate_typed.return_value = "typed result"
+
+    result = llm.generate_typed("hello", fake_schema, max_retries=5)
+
+    assert result == "typed result"
+    llm.provider.generate_typed.assert_called_once_with(
+        "hello", fake_schema, max_retries=5
+    )
+
+
+def test_generate_structured_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("DEEPSEEK_API_KEY", raising=False)
+    llm = DeepSeek(api_key=None)
+    with pytest.raises(ProcessingError, match="DeepSeek provider not available"):
+        llm.generate_structured("hello")
+
+
+def test_generate_typed_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("DEEPSEEK_API_KEY", raising=False)
+    llm = DeepSeek(api_key=None)
+    with pytest.raises(ProcessingError, match="DeepSeek provider not available"):
+        llm.generate_typed("hello", object())
@@ -0,0 +1,80 @@
+"""Tests for the Gemini LLM provider wrapper (semantica.llms.Gemini)."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from semantica.llms import Gemini
+from semantica.utils.exceptions import ProcessingError
+
+
+def test_construction_stores_model_and_api_key():
+    gemini = Gemini(model="gemini-pro", api_key="fake-key")
+    assert gemini.model == "gemini-pro"
+    assert gemini.api_key == "fake-key"
+
+
+def test_is_available_false_with_no_key(monkeypatch):
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    gemini = Gemini(api_key=None)
+    assert gemini.is_available() is False
+
+
+def test_generate_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    gemini = Gemini(api_key=None)
+    with pytest.raises(ProcessingError, match="Gemini provider not available"):
+        gemini.generate("hello")
+
+
+def test_generate_forwards_to_the_real_provider_when_available():
+    gemini = Gemini(api_key="fake-key")
+    gemini.provider = MagicMock()
+    gemini.provider.is_available.return_value = True
+    gemini.provider.generate.return_value = "a fake response"
+
+    result = gemini.generate("hello", temperature=0.5)
+
+    assert result == "a fake response"
+    gemini.provider.generate.assert_called_once_with("hello", temperature=0.5)
+
+
+def test_generate_structured_forwards_to_the_real_provider():
+    gemini = Gemini(api_key="fake-key")
+    gemini.provider = MagicMock()
+    gemini.provider.is_available.return_value = True
+    gemini.provider.generate_structured.return_value = {"key": "value"}
+
+    result = gemini.generate_structured("hello")
+
+    assert result == {"key": "value"}
+    gemini.provider.generate_structured.assert_called_once_with("hello")
+
+
+def test_generate_typed_forwards_schema_and_max_retries():
+    gemini = Gemini(api_key="fake-key")
+    gemini.provider = MagicMock()
+    gemini.provider.is_available.return_value = True
+    fake_schema = object()
+    gemini.provider.generate_typed.return_value = "typed result"
+
+    result = gemini.generate_typed("hello", fake_schema, max_retries=5)
+
+    assert result == "typed result"
+    gemini.provider.generate_typed.assert_called_once_with(
+        "hello", fake_schema, max_retries=5
+    )
+
+
+def test_generate_structured_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    gemini = Gemini(api_key=None)
+    with pytest.raises(ProcessingError, match="Gemini provider not available"):
+        gemini.generate_structured("hello")
+
+
+def test_generate_typed_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("GEMINI_API_KEY", raising=False)
+    gemini = Gemini(api_key=None)
+    with pytest.raises(ProcessingError, match="Gemini provider not available"):
+        gemini.generate_typed("hello", object())
@@ -0,0 +1,80 @@
+"""Tests for the Novita LLM provider wrapper (semantica.llms.Novita)."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from semantica.llms import Novita
+from semantica.utils.exceptions import ProcessingError
+
+
+def test_construction_stores_model_and_api_key():
+    llm = Novita(model="deepseek/deepseek-v3.2", api_key="fake-key")
+    assert llm.model == "deepseek/deepseek-v3.2"
+    assert llm.api_key == "fake-key"
+
+
+def test_is_available_false_with_no_key(monkeypatch):
+    monkeypatch.delenv("NOVITA_API_KEY", raising=False)
+    llm = Novita(api_key=None)
+    assert llm.is_available() is False
+
+
+def test_generate_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("NOVITA_API_KEY", raising=False)
+    llm = Novita(api_key=None)
+    with pytest.raises(ProcessingError, match="Novita provider not available"):
+        llm.generate("hello")
+
+
+def test_generate_forwards_to_the_real_provider_when_available():
+    llm = Novita(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate.return_value = "a fake response"
+
+    result = llm.generate("hello", temperature=0.5)
+
+    assert result == "a fake response"
+    llm.provider.generate.assert_called_once_with("hello", temperature=0.5)
+
+
+def test_generate_structured_forwards_to_the_real_provider():
+    llm = Novita(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate_structured.return_value = {"key": "value"}
+
+    result = llm.generate_structured("hello")
+
+    assert result == {"key": "value"}
+    llm.provider.generate_structured.assert_called_once_with("hello")
+
+
+def test_generate_typed_forwards_schema_and_max_retries():
+    llm = Novita(api_key="fake-key")
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    fake_schema = object()
+    llm.provider.generate_typed.return_value = "typed result"
+
+    result = llm.generate_typed("hello", fake_schema, max_retries=5)
+
+    assert result == "typed result"
+    llm.provider.generate_typed.assert_called_once_with(
+        "hello", fake_schema, max_retries=5
+    )
+
+
+def test_generate_structured_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("NOVITA_API_KEY", raising=False)
+    llm = Novita(api_key=None)
+    with pytest.raises(ProcessingError, match="Novita provider not available"):
+        llm.generate_structured("hello")
+
+
+def test_generate_typed_raises_clear_error_when_unavailable(monkeypatch):
+    monkeypatch.delenv("NOVITA_API_KEY", raising=False)
+    llm = Novita(api_key=None)
+    with pytest.raises(ProcessingError, match="Novita provider not available"):
+        llm.generate_typed("hello", object())
@@ -0,0 +1,78 @@
+"""Tests for the Ollama LLM provider wrapper (semantica.llms.Ollama)."""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from semantica.llms import Ollama
+from semantica.utils.exceptions import ProcessingError
+
+
+def test_construction_stores_model_and_base_url():
+    llm = Ollama(model="llama2", base_url="http://localhost:11434")
+    assert llm.model == "llama2"
+    assert llm.base_url == "http://localhost:11434"
+
+
+def test_is_available_false_without_a_running_server():
+    """No api_key here, Ollama has none. Without a real server (or the ollama
+    package) reachable at base_url, this must be a real False."""
+    llm = Ollama(base_url="http://localhost:1")
+    assert llm.is_available() is False
+
+
+def test_generate_raises_clear_error_when_unavailable():
+    llm = Ollama(base_url="http://localhost:1")
+    with pytest.raises(ProcessingError, match="Ollama provider not available"):
+        llm.generate("hello")
+
+
+def test_generate_forwards_to_the_real_provider_when_available():
+    llm = Ollama()
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate.return_value = "a fake response"
+
+    result = llm.generate("hello", temperature=0.5)
+
+    assert result == "a fake response"
+    llm.provider.generate.assert_called_once_with("hello", temperature=0.5)
+
+
+def test_generate_structured_forwards_to_the_real_provider():
+    llm = Ollama()
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    llm.provider.generate_structured.return_value = {"key": "value"}
+
+    result = llm.generate_structured("hello")
+
+    assert result == {"key": "value"}
+    llm.provider.generate_structured.assert_called_once_with("hello")
+
+
+def test_generate_typed_forwards_schema_and_max_retries():
+    llm = Ollama()
+    llm.provider = MagicMock()
+    llm.provider.is_available.return_value = True
+    fake_schema = object()
+    llm.provider.generate_typed.return_value = "typed result"
+
+    result = llm.generate_typed("hello", fake_schema, max_retries=5)
+
+    assert result == "typed result"
+    llm.provider.generate_typed.assert_called_once_with(
+        "hello", fake_schema, max_retries=5
+    )
+
+
+def test_generate_structured_raises_clear_error_when_unavailable():
+    llm = Ollama(base_url="http://localhost:1")
+    with pytest.raises(ProcessingError, match="Ollama provider not available"):
+        llm.generate_structured("hello")
+
+
+def test_generate_typed_raises_clear_error_when_unavailable():
+    llm = Ollama(base_url="http://localhost:1")
+    with pytest.raises(ProcessingError, match="Ollama provider not available"):
+        llm.generate_typed("hello", object())
@@ -24,9 +24,20 @@ import pytest
 # rdf:/rdfs: namespaces) and neither the code nor this test caught it,
 # since both had the same bug. Importing the real function makes that class
 # of drift impossible.
-from semantica.explorer.routes.sparql import _is_read_only_query
+# fastapi ships in the optional `explorer` extra, not in `dev`, so this import
+# fails on a plain dev install. Only the SPARQL class below needs it; the Cypher,
+# XXE, vector-serialization and SSRF classes in this module are independent, so
+# the skip is scoped to the one class rather than the whole file.
+try:
+    from semantica.explorer.routes.sparql import _is_read_only_query
+except ImportError:  # pragma: no cover - depends on the installed extras
+    _is_read_only_query = None


+@pytest.mark.skipif(
+    _is_read_only_query is None,
+    reason="requires semantica[explorer] (fastapi)",
+)
 class TestSparqlReadOnlyValidation:
    """Regression tests for SPARQL injection prevention."""

@@ -443,4 +443,3 @@ def test_export_seed_data(seed_manager, temp_data_dir):
        rows = list(reader)
        assert len(rows) == 1
        assert rows[0]["id"] == "1"
-
@@ -0,0 +1,184 @@
+"""Console progress must be written to stderr, never to stdout.
+
+Progress is diagnostic output. Writing it to stdout corrupts any program that
+carries a machine-readable protocol there — the stdio MCP servers put
+newline-delimited JSON-RPC on stdout, and a progress bar interleaved with a
+response body makes that response unparseable (#1134).
+
+Both servers currently defend against this by setting
+SEMANTICA_DISABLE_PROGRESS, and a console display is only attached when the
+console is interactive. Those are containments, not the fix: they give up
+progress output entirely, and every future entry point has to remember them.
+Writing to the correct stream in the first place is what these tests pin.
+"""
+
+import importlib
+import io
+import sys
+
+import pytest
+
+
+@pytest.fixture
+def progress_module():
+    """Import the real progress_tracker, bypassing a mocked sys.modules entry.
+
+    tests/test_extractors_dispatch.py assigns a MagicMock over
+    'semantica.utils.progress_tracker' at import time and never restores it, so
+    a plain module-level import here returns mocks when that file has already
+    run. Dropping the cached entry re-imports the real module.
+
+    Both bindings are restored afterwards: importing a submodule also rebinds it
+    as an attribute of its parent package, so restoring only the sys.modules
+    entry would leave `semantica.utils.progress_tracker` and
+    `sys.modules["semantica.utils.progress_tracker"]` pointing at different
+    objects for every test that follows.
+    """
+    name = "semantica.utils.progress_tracker"
+    attr = name.rsplit(".", 1)[1]
+    parent = importlib.import_module("semantica.utils")
+
+    missing = object()
+    saved_entry = sys.modules.get(name, missing)
+    saved_attr = getattr(parent, attr, missing)
+
+    sys.modules.pop(name, None)
+    try:
+        module = importlib.import_module(name)
+        assert hasattr(module, "__file__"), "expected the real module, got a stand-in"
+        yield module
+    finally:
+        if saved_entry is missing:
+            sys.modules.pop(name, None)
+        else:
+            sys.modules[name] = saved_entry
+
+        if saved_attr is missing:
+            if hasattr(parent, attr):
+                delattr(parent, attr)
+        else:
+            setattr(parent, attr, saved_attr)
+
+
+@pytest.fixture
+def display_cls(progress_module):
+    return progress_module.ConsoleProgressDisplay
+
+
+@pytest.fixture
+def make_item(progress_module):
+    def _make(**overrides):
+        defaults = dict(
+            module="kg",
+            submodule="Reasoner",
+            message="Inferring facts",
+            status="running",
+            total_items=10,
+            processed_items=3,
+        )
+        defaults.update(overrides)
+        return progress_module.ProgressItem(**defaults)
+
+    return _make
+
+
+class TestProgressStreamDefaults:
+    def test_defaults_to_stderr(self, display_cls):
+        assert display_cls().stream is sys.stderr
+
+    def test_default_is_not_stdout(self, display_cls):
+        """The whole point: stdout stays clean for protocol traffic."""
+        assert display_cls().stream is not sys.stdout
+
+    def test_stream_follows_rebinding(self, display_cls, monkeypatch):
+        """Resolved per write, so pytest capture and later rebinds are honoured."""
+        display = display_cls()
+        replacement = io.StringIO()
+        monkeypatch.setattr(sys, "stderr", replacement)
+        assert display.stream is replacement
+
+    def test_explicit_stream_overrides_the_default(self, display_cls):
+        buffer = io.StringIO()
+        assert display_cls(stream=buffer).stream is buffer
+
+
+class TestProgressWritesGoToTheStream:
+    def test_update_writes_to_the_configured_stream(self, display_cls, make_item):
+        buffer = io.StringIO()
+        display = display_cls(stream=buffer, use_emoji=False, update_interval=0.0)
+
+        display.update(make_item())
+
+        assert buffer.getvalue(), "progress should have been rendered"
+
+    def test_update_writes_nothing_to_stdout(self, display_cls, make_item, monkeypatch):
+        """Regression guard for #1134: stdout must stay untouched."""
+        fake_stdout = io.StringIO()
+        monkeypatch.setattr(sys, "stdout", fake_stdout)
+        buffer = io.StringIO()
+
+        display = display_cls(stream=buffer, use_emoji=False, update_interval=0.0)
+        display.update(make_item())
+        display.clear()
+
+        assert fake_stdout.getvalue() == "", (
+            f"console progress leaked to stdout: {fake_stdout.getvalue()!r}"
+        )
+
+    def test_default_display_writes_nothing_to_stdout(
+        self, display_cls, make_item, monkeypatch
+    ):
+        """Same guard without an explicit stream, i.e. the real default path."""
+        fake_stdout = io.StringIO()
+        fake_stderr = io.StringIO()
+        monkeypatch.setattr(sys, "stdout", fake_stdout)
+        monkeypatch.setattr(sys, "stderr", fake_stderr)
+
+        display = display_cls(use_emoji=False, update_interval=0.0)
+        display.update(make_item())
+
+        assert fake_stdout.getvalue() == ""
+        assert fake_stderr.getvalue(), "progress should have gone to stderr"
+
+    def test_clear_flushes_the_stream_not_stdout(self, display_cls, monkeypatch):
+        flushed = []
+
+        class RecordingStream(io.StringIO):
+            def flush(self):
+                flushed.append("stream")
+
+        class ExplodingStdout(io.StringIO):
+            def flush(self):  # pragma: no cover - fails the test if reached
+                raise AssertionError("progress must not flush stdout")
+
+        monkeypatch.setattr(sys, "stdout", ExplodingStdout())
+        display = display_cls(
+            stream=RecordingStream(), use_emoji=False, update_interval=0.0
+        )
+
+        display.clear()
+
+        assert flushed == ["stream"]
+
+
+class TestEncodingFallback:
+    def test_falls_back_when_the_stream_cannot_encode(self, display_cls):
+        """A cp1252-style console must not raise on emoji; it degrades instead."""
+
+        class AsciiOnly(io.StringIO):
+            encoding = "ascii"
+
+            def write(self, text):
+                text.encode("ascii")  # raises UnicodeEncodeError on emoji
+                return super().write(text)
+
+        buffer = AsciiOnly()
+        display = display_cls(stream=buffer, use_emoji=True, update_interval=0.0)
+
+        display._safe_write("progress \U0001f504 bar\n")
+
+        assert "progress" in buffer.getvalue()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
@@ -3,7 +3,7 @@ from unittest.mock import MagicMock
 import numpy as np
 import pytest

-from semantica.vector_store.faiss_store import FAISSIndex
+from semantica.vector_store.faiss_store import FAISSIndex, FAISSStore


 def test_get_vector_reconstructs_from_flat_l2_index():
@@ -120,3 +120,90 @@ def test_get_vector_reconstructs_from_real_ivfflat_index_without_prior_direct_ma
    result = index.get_vector("vec_target")

    np.testing.assert_allclose(result, vectors[3], atol=1e-6)
+
+
+def _store_with_fake_index(ids, metadata_by_id=None):
+    backend_index = MagicMock()
+    backend_index.reconstruct.side_effect = lambda idx: [float(idx)] * 3
+    index = FAISSIndex(backend_index, dimension=3)
+    index.vector_ids = list(ids)
+    index.metadata = dict(metadata_by_id or {})
+
+    store = FAISSStore(dimension=3)
+    store.index = index
+    return store
+
+
+def test_scan_vectors_returns_all_across_pages():
+    store = _store_with_fake_index(["a", "b", "c", "d", "e"])
+
+    seen_ids = []
+    offset = 0
+    while True:
+        page = store.scan_vectors(offset=offset, limit=2)
+        if not page:
+            break
+        seen_ids.extend(p["id"] for p in page)
+        offset += len(page)
+
+    assert seen_ids == ["a", "b", "c", "d", "e"]
+
+
+def test_scan_vectors_includes_vector_and_metadata():
+    store = _store_with_fake_index(["a"], {"a": {"tag": "only"}})
+
+    page = store.scan_vectors(offset=0, limit=10)
+
+    assert len(page) == 1
+    assert page[0]["id"] == "a"
+    assert page[0]["metadata"] == {"tag": "only"}
+    np.testing.assert_array_equal(page[0]["vector"], np.array([0.0, 0.0, 0.0], dtype=np.float32))
+
+
+def test_scan_vectors_no_index_returns_empty_list():
+    store = FAISSStore(dimension=3)
+    assert store.scan_vectors(offset=0, limit=10) == []
+
+
+def test_scan_vectors_zero_limit_returns_empty_list():
+    store = _store_with_fake_index(["a"])
+    assert store.scan_vectors(offset=0, limit=0) == []
+
+
+def test_scan_vectors_offset_past_end_returns_empty_list():
+    store = _store_with_fake_index(["a"])
+    assert store.scan_vectors(offset=100, limit=10) == []
+
+
+def test_add_vectors_retry_with_same_ids_does_not_duplicate():
+    """Re-running add_vectors with ids already in the index (e.g. retrying
+    an interrupted migration) must not create a second physical vector
+    under the same id."""
+    backend_index = MagicMock()
+    store = FAISSStore(dimension=3)
+    store.index = FAISSIndex(backend_index, dimension=3)
+
+    vectors = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=np.float32)
+    ids = ["a", "b", "c", "d"]
+
+    store.add_vectors(vectors, ids=ids, metadata=[{"i": i} for i in range(4)])
+    assert store.count() == 4
+
+    store.add_vectors(vectors, ids=ids, metadata=[{"i": i} for i in range(4)])
+
+    assert store.count() == 4
+    assert store.index.vector_ids == ids
+
+
+def test_add_vectors_retry_with_partial_overlap_only_adds_new_ids():
+    backend_index = MagicMock()
+    store = FAISSStore(dimension=3)
+    store.index = FAISSIndex(backend_index, dimension=3)
+
+    store.add_vectors(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32), ids=["a", "b"])
+    store.add_vectors(np.array([[1, 2, 3], [7, 8, 9]], dtype=np.float32), ids=["a", "c"])
+
+    assert store.index.vector_ids == ["a", "b", "c"]
+    second_call_vectors = backend_index.add.call_args[0][0]
+    assert second_call_vectors.shape[0] == 1
+    np.testing.assert_array_equal(second_call_vectors[0], np.array([7, 8, 9], dtype=np.float32))
@@ -467,6 +467,48 @@ class TestPgVectorStoreDelete:
        assert success is True


+class TestPgVectorStoreScan:
+    """Test scan_vectors pagination."""
+
+    def test_scan_returns_all_vectors_across_pages(self, store):
+        vectors = [np.random.rand(128).astype(np.float32) for _ in range(5)]
+        ids = store.add(vectors, [{"index": i} for i in range(5)])
+
+        seen_ids = []
+        offset = 0
+        while True:
+            page = store.scan_vectors(offset=offset, limit=2)
+            if not page:
+                break
+            seen_ids.extend(p["id"] for p in page)
+            offset += len(page)
+
+        assert set(seen_ids) == set(ids)
+        assert len(seen_ids) == 5
+
+    def test_scan_page_includes_vector_and_metadata(self, store):
+        vectors = [np.random.rand(128).astype(np.float32)]
+        ids = store.add(vectors, [{"tag": "only"}])
+
+        page = store.scan_vectors(offset=0, limit=10)
+
+        assert len(page) == 1
+        assert page[0]["id"] == ids[0]
+        assert page[0]["metadata"] == {"tag": "only"}
+        assert page[0]["vector"] is not None
+
+    def test_scan_empty_store_returns_empty_list(self, store):
+        assert store.scan_vectors(offset=0, limit=10) == []
+
+    def test_scan_zero_limit_returns_empty_list(self, store):
+        store.add([np.random.rand(128).astype(np.float32)])
+        assert store.scan_vectors(offset=0, limit=0) == []
+
+    def test_scan_offset_past_end_returns_empty_list(self, store):
+        store.add([np.random.rand(128).astype(np.float32)])
+        assert store.scan_vectors(offset=100, limit=10) == []
+
+
 class TestPgVectorStoreIndex:
    """Test index creation operations."""

@@ -415,6 +415,48 @@ class TestSQLiteVecStoreStats:
        assert stats["vector_count"] == 4


+class TestSQLiteVecStoreScan:
+    """Test scan_vectors pagination."""
+
+    def test_scan_returns_all_vectors_across_pages(self, store):
+        vectors = [np.random.rand(128).astype(np.float32) for _ in range(5)]
+        ids = store.add(vectors, [{"index": i} for i in range(5)])
+
+        seen_ids = []
+        offset = 0
+        while True:
+            page = store.scan_vectors(offset=offset, limit=2)
+            if not page:
+                break
+            seen_ids.extend(p["id"] for p in page)
+            offset += len(page)
+
+        assert set(seen_ids) == set(ids)
+        assert len(seen_ids) == 5
+
+    def test_scan_page_includes_vector_and_metadata(self, store):
+        vectors = [np.random.rand(128).astype(np.float32)]
+        ids = store.add(vectors, [{"tag": "only"}])
+
+        page = store.scan_vectors(offset=0, limit=10)
+
+        assert len(page) == 1
+        assert page[0]["id"] == ids[0]
+        assert page[0]["metadata"] == {"tag": "only"}
+        assert page[0]["vector"] is not None
+
+    def test_scan_empty_store_returns_empty_list(self, store):
+        assert store.scan_vectors(offset=0, limit=10) == []
+
+    def test_scan_zero_limit_returns_empty_list(self, store):
+        store.add([np.random.rand(128).astype(np.float32)])
+        assert store.scan_vectors(offset=0, limit=0) == []
+
+    def test_scan_offset_past_end_returns_empty_list(self, store):
+        store.add([np.random.rand(128).astype(np.float32)])
+        assert store.scan_vectors(offset=100, limit=10) == []
+
+
 class TestSQLiteVecStoreFilterByMetadata:
    """Test filter_by_metadata, including list-valued metadata handling."""

@@ -120,6 +120,78 @@ class VectorStoreCountTests(unittest.TestCase):
        self.assertIn("count()", msg)


+# ---------------------------------------------------------------------------
+# VectorStore.scan_vectors() / iter_vectors() dispatch tests
+# ---------------------------------------------------------------------------
+
+class _ScanningBackendStore:
+    """Fake persistent backend store that supports scan_vectors()."""
+
+    def __init__(self, items):
+        self._items = items
+
+    def scan_vectors(self, offset=0, limit=100):
+        return self._items[offset:offset + limit]
+
+
+class _NonScanningBackendStore:
+    """Fake persistent backend store without any scan capability."""
+
+
+class VectorStoreScanVectorsTests(unittest.TestCase):
+    """VectorStore.scan_vectors() / iter_vectors() backend-agnostic accessors."""
+
+    def setUp(self):
+        self.vectors = [np.array([1.0, 0.0]), np.array([0.0, 1.0]), np.array([1.0, 1.0])]
+        self.metadata = [{"type": "a"}, {"type": "b"}, {"type": "c"}]
+
+    def test_scan_inmemory_pages_through_all_vectors(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        ids = store.store_vectors(self.vectors, self.metadata)
+
+        page1 = store.scan_vectors(offset=0, limit=2)
+        page2 = store.scan_vectors(offset=2, limit=2)
+
+        self.assertEqual([p["id"] for p in page1], ids[:2])
+        self.assertEqual([p["id"] for p in page2], ids[2:])
+        self.assertEqual(page2[0]["metadata"], {"type": "c"})
+
+    def test_scan_inmemory_empty_store(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        self.assertEqual(store.scan_vectors(offset=0, limit=10), [])
+
+    def test_scan_zero_limit_returns_empty_list(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        store.store_vectors(self.vectors, self.metadata)
+        self.assertEqual(store.scan_vectors(offset=0, limit=0), [])
+
+    def test_scan_delegates_to_backend_store(self):
+        items = [{"id": "a", "metadata": {}, "vector": None}]
+        store = VectorStore(backend="inmemory", dimension=2)
+        store.backend = "faiss"
+        store._backend_store = _ScanningBackendStore(items)
+        self.assertEqual(store.scan_vectors(offset=0, limit=10), items)
+
+    def test_scan_raises_not_implemented_without_backend_support(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        store.backend = "faiss"
+        store._backend_store = _NonScanningBackendStore()
+        with self.assertRaises(NotImplementedError):
+            store.scan_vectors(offset=0, limit=10)
+
+    def test_iter_vectors_walks_every_page(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        ids = store.store_vectors(self.vectors, self.metadata)
+
+        collected = list(store.iter_vectors(batch_size=2))
+
+        self.assertEqual([item["id"] for item in collected], ids)
+
+    def test_iter_vectors_empty_store_yields_nothing(self):
+        store = VectorStore(backend="inmemory", dimension=2)
+        self.assertEqual(list(store.iter_vectors(batch_size=2)), [])
+
+
 # ---------------------------------------------------------------------------
 # VectorManager tests — inmemory backend
 # ---------------------------------------------------------------------------