agent-orchestrator: add explainability provenance for all agent
patterns
Extend the provenance/explainability system to provide
human-readable reasoning traces for the orchestrator's three
agent patterns. Previously only ReAct emitted provenance
(session, iteration, conclusion). Now each pattern records its
cognitive steps as typed RDF entities in the knowledge graph,
using composable mixin types (e.g. Finding + Answer).
New provenance chains:
- Supervisor: Question → Decomposition → Finding ×N → Synthesis
- Plan-then-Execute: Question → Plan → StepResult ×N → Synthesis
- ReAct: Question → Analysis ×N → Conclusion (unchanged)
New RDF types: Decomposition, Finding, Plan, StepResult.
New predicates: tg:subagentGoal, tg:planStep.
Reuses existing Synthesis + Answer mixin for final answers.
Provenance library (trustgraph-base):
- Triple builders, URI generators, vocabulary labels for new types
- Client dataclasses with from_triples() dispatch
- fetch_agent_trace() follows branching provenance chains
- API exports updated
Orchestrator (trustgraph-flow):
- PatternBase emit methods for decomposition, finding, plan, step result, and synthesis
- SupervisorPattern emits decomposition during fan-out
- PlanThenExecutePattern emits plan and step results
- Service emits finding triples on subagent completion
- Synthesis provenance replaces generic final triples
CLI (trustgraph-cli):
- invoke_agent -x displays new entity types inline
Add universal document decoder with multi-format support
using 'unstructured'.
New universal decoder service powered by the unstructured
library, handling DOCX, XLSX, PPTX, HTML, Markdown, CSV, RTF,
ODT, EPUB and more through a single service. Tables are preserved
as HTML markup for better downstream extraction. Images are
stored in the librarian but excluded from the text
pipeline. Configurable section grouping strategies
(whole-document, heading, element-type, count, size) for non-page
formats. Page-based formats (PDF, PPTX, XLSX) are automatically
grouped by page.
All four decoders (PDF, Mistral OCR, Tesseract OCR, universal)
now share the "document-decoder" ident so they are
interchangeable. PDF-only decoders fetch document metadata to
check MIME type and gracefully skip unsupported formats.
Librarian changes: removed MIME type whitelist validation so any
document format can be ingested. Simplified routing so text/plain
goes to text-load and everything else goes to document-load.
Removed dual inline/streaming data paths — documents always use
document_id for content retrieval.
New provenance entity types (tg:Section, tg:Image) and metadata
predicates (tg:elementTypes, tg:tableCount, tg:imageCount) for
richer explainability.
Universal decoder is in its own package (trustgraph-unstructured)
and container image (trustgraph-unstructured).
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.
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
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
