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6 commits

Author SHA1 Message Date
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
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
f2ae0e8623
Embeddings API scores (#671)
- Put scores in all responses
- Remove unused 'middle' vector layer. Vector of texts -> vector of (vector embedding)
2026-03-09 10:53:44 +00:00
cybermaggedon
4fa7cc7d7c
Fix/embeddings integration 2 (#670) 2026-03-08 19:42:26 +00:00
cybermaggedon
3bf8a65409
Fix tests (#666) 2026-03-07 23:38:09 +00:00
cybermaggedon
2f7fddd206
Test suite executed from CI pipeline (#433)
* Test strategy & test cases

* Unit tests

* Integration tests
2025-07-14 14:57:44 +01:00