cybermaggedon
7a6197d8c3
GraphRAG Query-Time Explainability ( #677 )
...
Implements full explainability pipeline for GraphRAG queries, enabling
traceability from answers back to source documents.
Renamed throughout for clarity:
- provenance_callback → explain_callback
- provenance_id → explain_id
- provenance_collection → explain_collection
- message_type "provenance" → "explain"
- Queue name "provenance" → "explainability"
GraphRAG queries now emit explainability events as they execute:
1. Session - query text and timestamp
2. Retrieval - edges retrieved from subgraph
3. Selection - selected edges with LLM reasoning (JSONL with id +
reasoning)
4. Answer - reference to synthesized response
Events stream via explain_callback during query(), enabling
real-time UX.
- Answers stored in librarian service (not inline in graph - too large)
- Document ID as URN: urn:trustgraph:answer:{session_id}
- Graph stores tg:document reference (IRI) to librarian document
- Added librarian producer/consumer to graph-rag service
- get_labelgraph() now returns (labeled_edges, uri_map)
- uri_map maps edge_id(label_s, label_p, label_o) →
(uri_s, uri_p, uri_o)
- Explainability data stores original URIs, not labels
- Enables tracing edges back to reifying statements via tg:reifies
- Added serialize_triple() to query service (matches storage format)
- get_term_value() now handles TRIPLE type terms
- Enables querying by quoted triple in object position:
?stmt tg:reifies <<s p o>>
- Displays real-time explainability events during query
- Resolves rdfs:label for edge components (s, p, o)
- Traces source chain via prov:wasDerivedFrom to root document
- Output: "Source: Chunk 1 → Page 2 → Document Title"
- Label caching to avoid repeated queries
GraphRagResponse:
- explain_id: str | None
- explain_collection: str | None
- message_type: str ("chunk" or "explain")
- end_of_session: bool
trustgraph-base/trustgraph/provenance/:
- namespaces.py - Added TG_DOCUMENT predicate
- triples.py - answer_triples() supports document_id reference
- uris.py - Added edge_selection_uri()
trustgraph-base/trustgraph/schema/services/retrieval.py:
- GraphRagResponse with explain_id, explain_collection, end_of_session
trustgraph-flow/trustgraph/retrieval/graph_rag/:
- graph_rag.py - URI preservation, streaming answer accumulation
- rag.py - Librarian integration, real-time explain emission
trustgraph-flow/trustgraph/query/triples/cassandra/service.py:
- Quoted triple serialization for query matching
trustgraph-cli/trustgraph/cli/invoke_graph_rag.py:
- Full explainability display with label resolution and source tracing
2026-03-10 10:00:01 +00:00
cybermaggedon
2b9232917c
Fix/extraction prov ( #662 )
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Quoted triple fixes, including...
1. Updated triple_provenance_triples() in triples.py:
- Now accepts a Triple object directly
- Creates the reification triple using TRIPLE term type: stmt_uri tg:reifies
<<extracted_triple>>
- Includes it in the returned provenance triples
2. Updated definitions extractor:
- Added imports for provenance functions and component version
- Added ParameterSpec for optional llm-model and ontology flow parameters
- For each definition triple, generates provenance with reification
3. Updated relationships extractor:
- Same changes as definitions extractor
2026-03-06 12:23:58 +00:00
cybermaggedon
cd5580be59
Extract-time provenance ( #661 )
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1. Shared Provenance Module - URI generators, namespace constants,
triple builders, vocabulary bootstrap
2. Librarian - Emits document metadata to graph on processing
initiation (vocabulary bootstrap + PROV-O triples)
3. PDF Extractor - Saves pages as child documents, emits parent-child
provenance edges, forwards page IDs
4. Chunker - Saves chunks as child documents, emits provenance edges,
forwards chunk ID + content
5. Knowledge Extractors (both definitions and relationships):
- Link entities to chunks via SUBJECT_OF (not top-level document)
- Removed duplicate metadata emission (now handled by librarian)
- Get chunk_doc_id and chunk_uri from incoming Chunk message
6. Embedding Provenance:
- EntityContext schema has chunk_id field
- EntityEmbeddings schema has chunk_id field
- Definitions extractor sets chunk_id when creating EntityContext
- Graph embeddings processor passes chunk_id through to
EntityEmbeddings
Provenance Flow:
Document → Page (PDF) → Chunk → Extracted Facts/Embeddings
↓ ↓ ↓ ↓
librarian librarian librarian (chunk_id reference)
+ graph + graph + graph
Each artifact is stored in librarian with parent-child linking, and PROV-O
edges are emitted to the knowledge graph for full traceability from any
extracted fact back to its source document.
Also, updating tests
2026-03-05 18:36:10 +00:00
