The subjectOf triples were redundant with the subgraph provenance model
introduced in e8407b34. Entity-to-source lineage can be traced via
tg:contains -> subgraph -> prov:wasDerivedFrom -> chunk, making the
direct subjectOf edges unnecessary metadata polluting the knowledge graph.
Removed from all three extractors (agent, definitions, relationships),
cleaned up the SUBJECT_OF constant and vocabulary label, and updated
tests accordingly.
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
Add unified explainability support and librarian storage for all retrieval engines
Implements consistent explainability/provenance tracking
across GraphRAG, DocumentRAG, and Agent retrieval
engines. All large content (answers, thoughts, observations)
is now stored in librarian rather than as inline literals in
the knowledge graph.
Explainability API:
- New explainability.py module with entity classes (Question,
Exploration, Focus, Synthesis, Analysis, Conclusion) and
ExplainabilityClient
- Quiescence-based eventual consistency handling for trace
fetching
- Content fetching from librarian with retry logic
CLI updates:
- tg-invoke-graph-rag -x/--explainable flag returns
explain_id
- tg-invoke-document-rag -x/--explainable flag returns
explain_id
- tg-invoke-agent -x/--explainable flag returns explain_id
- tg-list-explain-traces uses new explainability API
- tg-show-explain-trace handles all three trace types
Agent provenance:
- Records session, iterations (think/act/observe), and conclusion
- Stores thoughts and observations in librarian with document
references
- New predicates: tg:thoughtDocument, tg:observationDocument
DocumentRAG provenance:
- Records question, exploration (chunk retrieval), and synthesis
- Stores answers in librarian with document references
Schema changes:
- AgentResponse: added explain_id, explain_graph fields
- RetrievalResponse: added explain_id, explain_graph fields
- agent_iteration_triples: supports thought_document_id,
observation_document_id
Update tests.
Update RAG and Agent clients for streaming message handling
GraphRAG now sends multiple message types in a stream:
- 'explain' messages with explain_id and explain_graph for
provenance
- 'chunk' messages with response text fragments
- end_of_session marker for stream completion
Updated all clients to handle this properly:
CLI clients (trustgraph-base/trustgraph/clients/):
- graph_rag_client.py: Added chunk_callback and explain_callback
- document_rag_client.py: Added chunk_callback and explain_callback
- agent_client.py: Added think, observe, answer_callback,
error_callback
Internal clients (trustgraph-base/trustgraph/base/):
- graph_rag_client.py: Async callbacks for streaming
- agent_client.py: Async callbacks for streaming
All clients now:
- Route messages by chunk_type/message_type
- Stream via optional callbacks for incremental delivery
- Wait for proper completion signals
(end_of_dialog/end_of_session/end_of_stream)
- Accumulate and return complete response for callers not using
callbacks
Updated callers:
- extract/kg/agent/extract.py: Uses new invoke(question=...) API
- tests/integration/test_agent_kg_extraction_integration.py:
Updated mocks
This fixes the agent infinite loop issue where knowledge_query was
returning the first 'explain' message (empty response) instead of
waiting for the actual answer chunks.
Concurrency in triples query
The metadata field (list of triples) in the pipeline Metadata class
was redundant. Document metadata triples already flow directly from
librarian to triple-store via emit_document_provenance() - they don't
need to pass through the extraction pipeline.
Additionally, chunker and PDF decoder were overwriting metadata to []
anyway, so any metadata passed through the pipeline was being
discarded.
Changes:
- Remove metadata field from Metadata dataclass
(schema/core/metadata.py)
- Update all Metadata instantiations to remove metadata=[]
parameter
- Remove metadata handling from translators (document_loading,
knowledge)
- Remove metadata consumption from extractors (ontology, agent)
- Update gateway serializers and import handlers
- Update all unit, integration, and contract tests
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
* Don't emit graph embeddings if there aren't any.
* Don't store graph embeddings in a knowledge store if there's an empty list.
* Translate between Cassandra's 'null' representing an empty list and an
empty list which is what the surrounding code wants (and stored in the
first place).
* Avoid emitting empty embedding lists
* Avoid output empty triple lists
* Fix tests
* Changed schema for Value -> Term, majorly breaking change
* Following the schema change, Value -> Term into all processing
* Updated Cassandra for g, p, s, o index patterns (7 indexes)
* Reviewed and updated all tests
* Neo4j, Memgraph and FalkorDB remain broken, will look at once settled down
* Removed legacy storage management cruft. Tidied tech specs.
* Fix deletion of last collection
* Storage processor ignores data on the queue which is for a deleted collection
* Updated tests
* Tech spec
* Address multi-tenant queue option problems in CLI
* Modified collection service to use config
* Changed storage management to use the config service definition
* Updates for agent API with streaming support
* Added tg-dump-queues tool to dump Pulsar queues to a log
* Updated tg-invoke-agent, incremental output
* Queue dumper CLI - might be useful for debug
* Updating for tests
* Tidy up duplicate tech specs in doc directory
* Streaming LLM text-completion service tech spec.
* text-completion and prompt interfaces
* streaming change applied to all LLMs, so far tested with VertexAI
* Skip Pinecone unit tests, upstream module issue is affecting things, tests are passing again
* Added agent streaming, not working and has broken tests
* Tweak the structured query schema
* Structure query service
* Gateway support for nlp-query and structured-query
* API support
* Added CLI
* Update tests
* More tests
* Tech spec for graceful shutdown
* Graceful shutdown of importers/exporters
* Update socket to include graceful shutdown orchestration
* Adding tests for conditions tracked in this PR
* Implement KG extraction agent (kg-extract-agent)
* Using ReAct framework (agent-manager-react)
* ReAct manager had an issue when emitting JSON, which conflicts which ReAct manager's own JSON messages, so refactored ReAct manager to use traditional ReAct messages, non-JSON structure.
* Minor refactor to take the prompt template client out of prompt-template so it can be more readily used by other modules. kg-extract-agent uses this framework.
* Updated CLI invocation and config model for tools and mcp
* CLI anomalies
* Tweaked the MCP tool implementation for new model
* Update agent implementation to match the new model
* Fix agent tools, now all tested
* Fixed integration tests
* Fix MCP delete tool params
