cybermaggedon
14e49d83c7
Expose LLM token usage across all service layers ( #782 )
...
Expose LLM token usage (in_token, out_token, model) across all
service layers
Propagate token counts from LLM services through the prompt,
text-completion, graph-RAG, document-RAG, and agent orchestrator
pipelines to the API gateway and Python SDK. All fields are Optional
— None means "not available", distinguishing from a real zero count.
Key changes:
- Schema: Add in_token/out_token/model to TextCompletionResponse,
PromptResponse, GraphRagResponse, DocumentRagResponse,
AgentResponse
- TextCompletionClient: New TextCompletionResult return type. Split
into text_completion() (non-streaming) and
text_completion_stream() (streaming with per-chunk handler
callback)
- PromptClient: New PromptResult with response_type
(text/json/jsonl), typed fields (text/object/objects), and token
usage. All callers updated.
- RAG services: Accumulate token usage across all prompt calls
(extract-concepts, edge-scoring, edge-reasoning,
synthesis). Non-streaming path sends single combined response
instead of chunk + end_of_session.
- Agent orchestrator: UsageTracker accumulates tokens across
meta-router, pattern prompt calls, and react reasoning. Attached
to end_of_dialog.
- Translators: Encode token fields when not None (is not None, not truthy)
- Python SDK: RAG and text-completion methods return
TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with
token fields (streaming)
- CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt,
tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
cybermaggedon
a115ec06ab
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding ( #697 )
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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 )
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* 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 )
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- 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 )
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* Test strategy & test cases
* Unit tests
* Integration tests
2025-07-14 14:57:44 +01:00
