Fixes#826. Addresses all five points the maintainer called out in
the follow-up to #825.
Source change (trustgraph-flow/trustgraph/extract/kg/ontology/extract.py):
- Added `_is_subclass_of(cls, target, ontology_subset, max_depth=100)`
helper with visited-set cycle detection + a defensive depth cap.
LLM-generated ontologies may emit cycles (A subclass_of B,
B subclass_of A); the prior while-loop would infinite-loop on that.
- Replaced both near-identical domain and range subclass walks in
`is_valid_triple` with a single call to the new helper. Net is
-20 duplicated lines + 26-line helper.
Tests (tests/unit/test_extract/test_ontology/test_prompt_and_extraction.py):
- test_is_valid_triple_subclass_is_accepted: domain expects Recipe,
actual type is Cake (subclass), validates.
- test_is_valid_triple_handles_subclass_cycle_without_infinite_loop:
A subclass_of B, B subclass_of A; call returns False within the
depth cap rather than hanging.
- test_parse_and_validate_triples_collects_entity_types_from_rdf_type:
end-to-end path: rdf:type triples build the entity_types dict,
subsequent domain-check triples validate against it.
- test_is_valid_triple_entity_types_none_default: the None default
path now has explicit coverage.
156 existing tests in tests/unit/test_extract/test_ontology still pass.
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
* 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
