Commit graph

8 commits

Author SHA1 Message Date
cybermaggedon
96fd1eab15
Use UUID-based URNs for page and chunk IDs (#703)
Page and chunk document IDs were deterministic ({doc_id}/p{num},
{doc_id}/p{num}/c{num}), causing "Document already exists" errors
when reprocessing documents through different flows. Content may
differ between runs due to different parameters or extractors, so
deterministic IDs are incorrect.

Pages now use urn:page:{uuid}, chunks use
urn:chunk:{uuid}. Parent- child relationships are tracked via
librarian metadata and provenance triples.

Also brings Mistral OCR and Tesseract OCR decoders up to parity
with the PDF decoder: librarian fetch/save support, per-page
output with unique IDs, and provenance triple emission. Fixes
Mistral OCR bug where only the first 5 pages were processed.
2026-03-21 21:17:03 +00:00
cybermaggedon
a115ec06ab
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding (#697)
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding,
consistent PROV-O

GraphRAG:
- Split retrieval into 4 prompt stages: extract-concepts,
  kg-edge-scoring,
  kg-edge-reasoning, kg-synthesis (was single-stage)
- Add concept extraction (grounding) for per-concept embedding
- Filter main query to default graph, ignoring
  provenance/explainability edges
- Add source document edges to knowledge graph

DocumentRAG:
- Add grounding step with concept extraction, matching GraphRAG's
  pattern:
  Question → Grounding → Exploration → Synthesis
- Per-concept embedding and chunk retrieval with deduplication

Cross-pipeline:
- Make PROV-O derivation links consistent: wasGeneratedBy for first
  entity from Activity, wasDerivedFrom for entity-to-entity chains
- Update CLIs (tg-invoke-agent, tg-invoke-graph-rag,
  tg-invoke-document-rag) for new explainability structure
- Fix all affected unit and integration tests
2026-03-16 12:12:13 +00:00
cybermaggedon
64e3f6bd0d
Subgraph provenance (#694)
Replace per-triple provenance reification with subgraph model

Extraction provenance previously created a full reification (statement
URI, activity, agent) for every single extracted triple, producing ~13
provenance triples per knowledge triple.  Since each chunk is processed
by a single LLM call, this was both redundant and semantically
inaccurate.

Now one subgraph object is created per chunk extraction, with
tg:contains linking to each extracted triple.  For 20 extractions from
a chunk this reduces provenance from ~260 triples to ~33.

- Rename tg:reifies -> tg:contains, stmt_uri -> subgraph_uri
- Replace triple_provenance_triples() with subgraph_provenance_triples()
- Refactor kg-extract-definitions and kg-extract-relationships to
  generate provenance once per chunk instead of per triple
- Add subgraph provenance to kg-extract-ontology and kg-extract-agent
  (previously had none)
- Update CLI tools and tech specs to match

Also rename tg-show-document-hierarchy to tg-show-extraction-provenance.

Added extra typing for extraction provenance, fixed extraction prov CLI
2026-03-13 11:37:59 +00:00
cybermaggedon
312174eb88
Adding explainability to the ReACT agent (#689)
* Added tech spec

* Add provenance recording to React agent loop

Enables agent sessions to be traced and debugged using the same
explainability infrastructure as GraphRAG. Agent traces record:
- Session start with query and timestamp
- Each iteration's thought, action, arguments, and observation
- Final answer with derivation chain

Changes:
- Add session_id and collection fields to AgentRequest schema
- Add agent predicates (TG_THOUGHT, TG_ACTION, etc.) to namespaces
- Create agent provenance triple generators in provenance/agent.py
- Register explainability producer in agent service
- Emit provenance triples during agent execution
- Update CLI tools to detect and render agent traces alongside GraphRAG

* Updated explainability taxonomy:

GraphRAG: tg:Question → tg:Exploration → tg:Focus → tg:Synthesis

Agent: tg:Question → tg:Analysis(s) → tg:Conclusion

All entities also have their PROV-O type (prov:Activity or prov:Entity).

Updated commit message:

Add provenance recording to React agent loop

Enables agent sessions to be traced and debugged using the same
explainability infrastructure as GraphRAG.

Entity types follow human reasoning patterns:
- tg:Question - the user's query (shared with GraphRAG)
- tg:Analysis - each think/act/observe cycle
- tg:Conclusion - the final answer

Also adds explicit TG types to GraphRAG entities:
- tg:Question, tg:Exploration, tg:Focus, tg:Synthesis

All types retain their PROV-O base types (prov:Activity, prov:Entity).

Changes:
- Add session_id and collection fields to AgentRequest schema
- Add explainability entity types to namespaces.py
- Create agent provenance triple generators
- Register explainability producer in agent service
- Emit provenance triples during agent execution
- Update CLI tools to detect and render both trace types

* Document RAG explainability is now complete. Here's a summary of the
changes made:

Schema Changes:
- trustgraph-base/trustgraph/schema/services/retrieval.py: Added
  explain_id and explain_graph fields to DocumentRagResponse
- trustgraph-base/trustgraph/messaging/translators/retrieval.py:
  Updated translator to handle explainability fields

Provenance Changes:
- trustgraph-base/trustgraph/provenance/namespaces.py: Added
  TG_CHUNK_COUNT and TG_SELECTED_CHUNK predicates
- trustgraph-base/trustgraph/provenance/uris.py: Added
  docrag_question_uri, docrag_exploration_uri, docrag_synthesis_uri
  generators
- trustgraph-base/trustgraph/provenance/triples.py: Added
  docrag_question_triples, docrag_exploration_triples,
  docrag_synthesis_triples builders
- trustgraph-base/trustgraph/provenance/__init__.py: Exported all
  new Document RAG functions and predicates

Service Changes:
- trustgraph-flow/trustgraph/retrieval/document_rag/document_rag.py:
  Added explainability callback support and triple emission at each
  phase (Question → Exploration → Synthesis)
- trustgraph-flow/trustgraph/retrieval/document_rag/rag.py:
  Registered explainability producer and wired up the callback

Documentation:
- docs/tech-specs/agent-explainability.md: Added Document RAG entity
  types and provenance model documentation

Document RAG Provenance Model:
Question (urn:trustgraph:docrag:{uuid})
    │
    │  tg:query, prov:startedAtTime
    │  rdf:type = prov:Activity, tg:Question
    │
    ↓ prov:wasGeneratedBy
    │
Exploration (urn:trustgraph:docrag:{uuid}/exploration)
    │
    │  tg:chunkCount, tg:selectedChunk (multiple)
    │  rdf:type = prov:Entity, tg:Exploration
    │
    ↓ prov:wasDerivedFrom
    │
Synthesis (urn:trustgraph:docrag:{uuid}/synthesis)
    │
    │  tg:content = "The answer..."
    │  rdf:type = prov:Entity, tg:Synthesis

* Specific subtype that makes the retrieval mechanism immediately
obvious:

System: GraphRAG
TG Types on Question: tg:Question, tg:GraphRagQuestion
URI Pattern: urn:trustgraph:question:{uuid}
────────────────────────────────────────
System: Document RAG
TG Types on Question: tg:Question, tg:DocRagQuestion
URI Pattern: urn:trustgraph:docrag:{uuid}
────────────────────────────────────────
System: Agent
TG Types on Question: tg:Question, tg:AgentQuestion
URI Pattern: urn:trustgraph:agent:{uuid}
Files modified:
- trustgraph-base/trustgraph/provenance/namespaces.py - Added
TG_GRAPH_RAG_QUESTION, TG_DOC_RAG_QUESTION, TG_AGENT_QUESTION
- trustgraph-base/trustgraph/provenance/triples.py - Added subtype to
question_triples and docrag_question_triples
- trustgraph-base/trustgraph/provenance/agent.py - Added subtype to
agent_session_triples
- trustgraph-base/trustgraph/provenance/__init__.py - Exported new types
- docs/tech-specs/agent-explainability.md - Documented the subtypes

This allows:
- Query all questions: ?q rdf:type tg:Question
- Query only GraphRAG: ?q rdf:type tg:GraphRagQuestion
- Query only Document RAG: ?q rdf:type tg:DocRagQuestion
- Query only Agent: ?q rdf:type tg:AgentQuestion

* Fixed tests
2026-03-11 15:28:15 +00:00
cybermaggedon
e1bc4c04a4
Terminology Rename, and named-graphs for explainability (#682)
Terminology Rename, and named-graphs for explainability data

Changed terminology:
  - session -> question
  - retrieval -> exploration
  - selection -> focus
  - answer -> synthesis

- uris.py: Renamed query_session_uri → question_uri,
  retrieval_uri → exploration_uri, selection_uri → focus_uri,
  answer_uri → synthesis_uri
- triples.py: Renamed corresponding triple generation functions with
  updated labels ("GraphRAG question", "Exploration", "Focus",
  "Synthesis")
- namespaces.py: Added named graph constants GRAPH_DEFAULT,
  GRAPH_SOURCE, GRAPH_RETRIEVAL
- init.py: Updated exports
- graph_rag.py: Updated to use new terminology
- invoke_graph_rag.py: Updated CLI to display new stage names
  (Question, Exploration, Focus, Synthesis)

Query-Time Explainability → Named Graph
- triples.py: Added set_graph() helper function to set named graph
  on triples
- graph_rag.py: All explainability triples now use GRAPH_RETRIEVAL
  named graph
- rag.py: Explainability triples stored in user's collection (not
  separate collection) with named graph

Extraction Provenance → Named Graph
- relationships/extract.py: Provenance triples use GRAPH_SOURCE
  named graph
- definitions/extract.py: Provenance triples use GRAPH_SOURCE
  named graph
- chunker.py: Provenance triples use GRAPH_SOURCE named graph
- pdf_decoder.py: Provenance triples use GRAPH_SOURCE named graph

CLI Updates
- show_graph.py: Added -g/--graph option to filter by named graph and
  --show-graph to display graph column

Also:
- Fix knowledge core schemas
2026-03-10 14:35:21 +00:00
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)
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)
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