* feat(context): add to_kg_dict() adapter for canonical KG shape
Convert ContextGraph internal nodes/edges/source representation into the canonical entities/relationships/source_id shape consumed by RDFExporter and TemporalGraphQuery. Add entities_only filtering that drops dangling relationships, plus README examples and unit tests.
* fix(context): harden to_kg_dict against null props and non-str node ids
- Guard properties/metadata with 'or {}' so nodes loaded from JSON null
no longer raise TypeError when copied (Qodo bug 1)
- Coerce entity id to str(n.node_id) so it matches ContextEdge's
str-coerced endpoints, preventing valid relationships from being
dropped during entities_only filtering (Qodo bug 3)
* fix(kg): accept source_id/target_id endpoints in validator and temporal query
to_kg_dict() emits canonical source_id/target_id keys, but GraphValidator
and TemporalGraphQuery only read the legacy source/target keys, so its
output failed validation and lost relationships (Qodo bug 2).
- GraphValidator: resolve endpoints from either key variant and treat a
resolvable source/target (plus type) as satisfying required fields
- TemporalGraphQuery.analyze_evolution/find_paths: read either variant
- tests: add regression coverage for null props/metadata (bug 1),
non-string node ids (bug 3), and KG-utility consumability (bug 2)
---------
Co-authored-by: 江俊杰 <jiangjunjie.37@jd.com>
Moves a concise version of the system-level vs. foundation-model
explainability clarification up next to the opening pitch, so it's
visible before readers scroll to the high-stakes-domains section.
Adds a consistent scope note to README and docs (concepts, FAQ, index)
stating Semantica does not expose or reconstruct an LLM's internal
reasoning/chain-of-thought. It explains and audits the AI system
around the model: context, provenance, policies, decisions, and
execution history.
* feat(crewai): add first-class CrewAI integration (#962)
Add native CrewAI support so Crew agents can share a ContextGraph and
AgentContext via BaseTool subclasses and a BaseKnowledgeSource, matching
the existing agno integration pattern.
- SemanticaKGTool: 5 KG actions (extract_entities, extract_relations,
add_to_graph, query_graph, find_related) with sync run()/async arun()
- SemanticaDecisionTool: 5 decision-intelligence actions
(record_decision, find_precedents, trace_causal_chain,
analyze_impact, check_policy) over AgentContext
- SemanticaKnowledgeSource: serializes a ContextGraph into crew
knowledge storage; bridges legacy load_content() and current
validate_content()/aadd() contracts for crewai>=0.80.0
- All classes degrade gracefully when crewai is absent
- New pip extra crewai=... included in the all bundle
- 70 new tests (stub-based present-case + subprocess degradation path)
- Docs: integrations/crewai.md, docs.json nav, README matrix updates
* fix(crewai): harden tools against real Semantica dataclass shapes (#962)
Bugs found during live testing with crewai 1.15.16:
- SemanticaKGTool.add_to_graph crashed on real Entity/Relation dataclasses
('str' object has no attribute 'end_char'): string names were passed to
extract_relations(entities=...), which requires Entity objects, and the
tool read .name/.source/.target instead of Entity's .text/.label and
Relation's .subject/.object. Add shape-agnostic field helpers.
- SemanticaDecisionTool() created an AgentContext without a knowledge_graph,
so _decision_backend was never set and record_decision raised 'Decision
tracking is not enabled'. Wire in a ContextGraph.
- record_decision hard-failed when the agent omitted optional fields; fall
back to category='general', reasoning='agent decision',
outcome='recorded'.
Add tests covering real Entity/Relation dataclass shapes and the live
auto-created AgentContext path (now 77 crewai tests, 212 total).
* fix(crewai): make find_related traverse edges undirected (#962)
ContextGraph.get_neighbors only follows outgoing edges, so a node whose
only edge is incoming (A -> B) reported no related concepts. Rebuild a
bidirectional adjacency from find_edges() in SemanticaKGTool._find_related
so 'related' honors both directions.
* fix(crewai): harden tools for checkpoint serialization and correct action semantics (#962)
- Exclude live graph/context/extractor state from JSON serialization
(model_dump(mode="json")) so CrewAI checkpointing no longer raises
PydanticSerializationError; model_post_init self-heals defaults on restore
- query_graph now searches node content via graph.query() plus id/type
- trace_causal_chain returns an explicit error when causal tracing is
unavailable instead of substituting similarity precedents; call
trace_decision_causality(..., max_depth=...) with the correct kwarg name
- find_precedents propagates max_precedents/limit to the backend instead of
being silently capped at 10
- Serialize add_to_graph batches under a module lock to prevent concurrent
double-counting; skip nameless entities instead of creating repr()-junk nodes
- aadd() runs CPU-bound serialization in a thread executor
- Mirror crewai args_schema serialize/restore in the conftest stub and add
serialization regression tests (crewai: 92 tests)
* fix(crewai): correct check_policy coercion, guard causal tracing, and harden concurrency (#962)
- _eval_rule now coerces rule values type-aware: bool("false") was truthy, so
'enabled == false' reported a violation for enabled=false, and string datums
like "0.90" were compared lexicographically instead of numerically
- _trace_causal_chain no longer raises AttributeError (which escaped _run) when
the decision context lacks knowledge_graph; returns honest error JSON
- SemanticaKnowledgeSource storage failures log an actionable ERROR; without a
configured crew embedder agents previously retrieved nothing silently
- add_to_graph uses a per-graph re-entrant lock (WeakKeyDictionary) instead of a
process-global one: independent graphs no longer serialize each other and
re-entrant extractor callbacks cannot deadlock
- entity/relation confidence=None normalizes to 1.0 instead of failing the
whole extraction with float(None)
- add subprocess integration test against real crewai covering Crew-level
serialization round-trip and checkpoint restore (stub tests cannot see it)
- docs: embedder requirement for SemanticaKnowledgeSource; resume contract note
* fix(crewai): surface knowledge-source save failures at ERROR when storage is wired (#962)
Re-verification against real crewai showed the embedder-missing failure raises
ValueError even though storage IS wired, so the old except-ValueError branch
mislabeled it as 'storage not wired' and logged DEBUG — hiding the failure.
Distinguish by storage presence instead of exception type: storage is None ->
DEBUG keep-in-memory (legitimate standalone use); storage wired but save()
raises -> actionable ERROR. Add regression test mirroring real crewai's
ValueError-on-missing-embedder behavior.
* fix(crewai): expose run()/arun() entry points in degraded mode (#962)
The public crewai contract is run()/arun(); without crewai installed they were
missing (only the private _run existed), so the documented 'usable without
crewai' path raised AttributeError at the entry point. Define them in degraded
mode only, leaving crewai's BaseTool implementations untouched when present.
Extend the degradation subprocess test to exercise run() and arun().
* fix(crewai): standardize query shape, field-name rules, and restore-state flag
- _query_graph: id/type matches now return the same schema as content
matches (id/type/label/content/score) instead of a bare list
- _eval_rule: non-greedy field capture so hyphen/dot/space JSON keys
(e.g. "risk-score >= 0.9") are addressable in policy rules
- add had_live_state/reconstructed_state so checkpoint-restored tools
and knowledge sources signal that their live graph/context was lost
and an empty one reconstructed; knowledge source no longer hides the
loss by eagerly rebuilding its graph inside __init__ (pydantic calls
__init__ during model_validate)
* fix(crewai): address Qodo review — confidence errors, string trim, holistic availability
- record_decision: stop calling float() in _run, so malformed confidence
values surface as JSON errors (via _record_decision's handling) instead
of crashing the tool
- _coerce_value: return the stripped string for non-numeric literals so
whitespace-padded decision_data fields match policy rules
- centralize crewai availability in _availability.py so the exported
CREWAI_AVAILABLE flag is holistic across tools and knowledge source
(previously each module probed crewai independently and the package
flag came from decision_tool only)
* ci: regenerate requirements-ci.txt for the crewai extra
The crewai extra in pyproject.toml brings in crewai, crewai-tools and
transitive deps (chromadb, lancedb, ...). Recompile with
uv==0.12.1 per CONTRIBUTING.md so the CI staleness check passes.
* ci: keep crewai out of the locked CI dependency set
crewai (all versions) hard-requires chromadb~=1.1.0, which carries a
pre-authentication code-injection advisory (CVE-2026-45829 / GHSA-f4j7-r4q5-qw2c)
with NO fixed release — even the latest 1.5.9 is affected. Keeping crewai in
the 'all' extra failed pip-audit and the safety check on requirements-ci.txt.
- drop crewai from the 'all' aggregate (standalone semantica[crewai] extra is
unchanged and still installs crewai)
- stop listing crewai-tools in the extra: the integration only uses crewai core
(BaseTool, BaseKnowledgeSource) and crewai-tools pulled extra transitive deps
- regenerate requirements-ci.txt: OSV/pip-audit 0 vulnerabilities, safety 0
vulnerabilities, staleness check matches
* docs(crewai): document crewai extra scope and chromadb CVE-2026-45829
- CHANGELOG: extra is crewai>=0.80.0 only (no crewai-tools) and is not
part of the 'all' bundle, with the chromadb CVE-2026-45829 reason
- integrations/crewai/README.md: add a security warning that installing
the extra pulls chromadb~=1.1.0, which is affected by the unpatched
pre-auth code-injection CVE-2026-45829
---------
- README: get_table_lineage() takes table_name first, then catalog/schema
keyword args — the example had them in the wrong order, which would have
queried lineage for the wrong fully-qualified table when copy-pasted.
- modules.md: the ingest example used DatabricksIngestor without importing
it, causing a NameError if copy-pasted as-is.
- guides/ingest.md: corrected the claim that Databricks/Snowflake ingestors
return "the same shape as DBIngestor" — DBIngestor.execute_query() returns
a raw List[Dict] with no wrapper, unlike DatabricksData/SnowflakeData.
Makes enterprise lakehouse/warehouse ingestion (Databricks Unity Catalog +
Delta Lake, Snowflake) a first-class, prominently documented capability
across the README and guides, and adds matching runnable examples to
docs/guides/ingest.md. Also fixes several pre-existing inaccuracies caught
while auditing the ingest module docs against the actual source:
WebIngestor has no ingest_urls() (only singular ingest_url()), XMLIngestor's
XSD option is schema_path (not validate_xsd) and belongs on ingest() not the
constructor, and the "Available ingestors" list was missing DatabricksIngestor
while listing several classes not actually exported from semantica.ingest.
Promotes the Unreleased changelog section (Databricks connector, SQLite
vector store, SPARQL CONSTRUCT templates, JenaStore named-graph support)
to 0.6.0 and syncs version references across pyproject.toml, __init__.py,
and docs.
- TemporalGraphQuery.query_time_range() and RDFExporter.export() both
expect {entities/relationships} (or {relationships} with source_id/
target_id keys), not ContextGraph.to_dict()'s {nodes, edges} shape.
Map the output before passing it in, and add an actual temporally-
bounded edge to the Temporal Intelligence example so the query has
something to find.
- add_causal_relationship() only accepts relationship_type values of
CAUSED, INFLUENCED, or PRECEDENT_FOR; replace the invented "triggers"/
"enables" values used in Decision Intelligence and the audit-trail
recipe, which would otherwise raise ValueError immediately.
PipelineBuilder.add_step() returns the created PipelineStep, not the
builder, so chaining .add_step().add_step() raised AttributeError.
Only connect_steps() and set_parallelism() return the builder and can
be chained.
Consolidates the platform reference into a single, premium README with
collapsible module/recipe sections so the docs and the deep-dive reference
no longer live in two places. Every code example was checked against the
actual semantica/ source and corrected where the API had drifted:
resolve_conflicts, register_source, ValidationResult.valid, clean_data,
execute_pipeline, ParquetExporter/LPGExporter/ReportGenerator calls,
graph.to_dict(), TemporalGraphQuery/TemporalNormalizer usage, the
Reasoner/ExplanationGenerator API, and the REST endpoint paths. Also
removed duplicated titles, snippets, and repeated example scenarios that
had crept in during the merge.
Updates the tagline, adds Ontology Management/SKOS to the feature pills, swaps
the yellow-highlight subhead for a cleaner italic style, and surfaces a
regulated-domains teaser linking to the existing "Built for High-Stakes
Domains" section.
Trims the README from a full module/API dump into a scannable pitch (hero,
why-Semantica, quick start, architecture, decision intelligence, one flagship
audit-trail recipe) and moves the exhaustive per-module reference, extra
recipes, and full integrations matrix into a new PLATFORM_REFERENCE.md.
Add graph-apache-age extra (psycopg2-binary) which was previously
undeclared despite age_store.py depending on it, wire it into
graph-all, and document install commands for FalkorDB/AGE/Neptune
alongside Neo4j. Note that RDF triple stores need no extra since they
talk SPARQL over HTTP via the core `requests` dependency. Also align
README's "Triplet Stores" table label to "Triple Stores (RDF)" to
match the standard term used elsewhere in the docs, while keeping the
TripletStore interface name in backticks.
Semantica already ships both an RDF triplet-store stack (Blazegraph,
Apache Jena, Eclipse RDF4J via a unified TripletStore/SPARQL interface)
and an LPG graph-store stack (Neo4j, FalkorDB, Apache AGE, AWS Neptune
via Cypher), but the README only surfaced the LPG side. Add a hero
highlight line, a "What Semantica gives you" bullet, and split the
Features-at-a-Glance table row so both formats and all backends are
named explicitly.
Condense the CLI section to the essential install/usage snippet and
command groups, pointing to docs.getsemantica.ai for the full
reference instead of maintaining static terminal mockups in the README.
Consolidate the hero around a single category claim (Context and
Accountability Layer for AI agents), drop the named-competitor
comparison table (LangChain/LlamaIndex/Mem0/Zep/Palantir Foundry),
remove GitHub alert-box tips/notes, cut redundant module/changelog
sections, strip vanity feature counts, and trim em dashes for a
cleaner, more premium read.
Revert badge rows to flat-square (for-the-badge rendered as
mismatched oversized blocks), convert the subtitle to a native
blockquote for GitHub's built-in muted-grey text styling, and
trim "The" from the tagline.
Update the hero tagline, subtitle, and comparison table to frame
Semantica as Palantir-grade knowledge/decision intelligence that is
open source, self-hostable, and priced for startups through
Fortune 500, not just enterprise budgets.
* fix(explorer): resolve blank dashboard UI and ship frontend bundle in wheel
Fixes#631 — the Explorer server started successfully but the browser showed
a blank page because semantica/static/ was gitignored and never present after
a fresh install or clone.
Changes:
- ci.yml / release.yml: add Node 20 setup + npm ci && npm run build before
python -m build so every wheel contains a CI-built frontend bundle
- pyproject.toml: add package-data patterns (static/*, static/assets/*) so
setuptools includes the bundle in the wheel; add MANIFEST.in for sdist coverage
- app.py: replace silent empty-HTML fallback with a 200 page that clearly
explains the missing bundle and links to /docs; fix CORS allow_credentials
to default false, gated behind EXPLORER_CORS_CREDENTIALS env var to prevent
credentialed cross-origin requests on unauthenticated endpoints
- __init__.py: warn at startup when --host is non-loopback (unauthenticated
network exposure)
- explorer/README.md: full rewrite covering pip-install mode (primary path,
no Node required) and dev-server mode (contributors), CLI flags, env vars,
workspace table, troubleshooting for the blank-page symptom
- README.md: update Knowledge Explorer section with correct command and link
to the new setup guide
* fix(explorer): set build.target esnext to fix esbuild CI failure
esbuild >=0.28 (forced via npm overrides) conflicts with Vite 6 defaults on
Linux CI — it tries to lower destructuring syntax for the implicit browser
target list but errors out. Explicit target: 'esnext' tells esbuild to emit
native syntax unchanged, bypassing the transpilation error entirely. Safe for
a developer tool that runs in modern browsers.
* test(explorer): verify packaged frontend bundle
---------
Co-authored-by: Zohaib Hassnain <109234410+ZohaibHassan16@users.noreply.github.com>
Remove all horizontal rule dividers for a cleaner premium look.
Replace all Hawksight-AI references with semantica-agi org URLs and update footer attribution from Hawksight AI to Semantica.
Replaces the bare GIF with a structured "See Semantica in Action"
section featuring a clickable YouTube thumbnail for the Knowledge
Explorer Tour (https://youtu.be/QfnNZg4-dZA) above the original GIF,
with named subsections and a feature-list subtitle.
CLI section:
- Intro updated to mention startup dashboard and Rich polish
- Data In: added semantica watch examples; removed --watch flag from ingest
(watch is now its own command)
- Developer Tools: new subsection covering init, doctor, changelog, shell,
info with representative examples
What's New in v0.5.0:
- Added Modern CLI Experience subsection listing all 11 improvements:
startup dashboard, grouped help, doctor, init, watch, changelog, shell,
progress bars, elapsed timing, error cards, Windows UTF-8 fix
Covers all 22 command groups introduced in issue #568:
global flags, data in, processing, KG, intelligence (reason/decision/temporal),
provenance, validation, ontology, export, visualize, orchestration
(pipeline/store/backup), services (server/explorer/mcp), and shell completion.
Each section shows real invocation examples rather than flag tables.
