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6 commits

Author SHA1 Message Date
cybermaggedon
d2751553a3
Add agent explainability instrumentation and unify envelope field naming (#795)
Addresses recommendations from the UX developer's agent experience report.
Adds provenance predicates, DAG structure changes, error resilience, and
a published OWL ontology.

Explainability additions:

- Tool candidates: tg:toolCandidate on Analysis events lists the tools
  visible to the LLM for each iteration (names only, descriptions in config)
- Termination reason: tg:terminationReason on Conclusion/Synthesis events
  (final-answer, plan-complete, subagents-complete)
- Step counter: tg:stepNumber on iteration events
- Pattern decision: new tg:PatternDecision entity in the DAG between
  session and first iteration, carrying tg:pattern and tg:taskType
- Latency: tg:llmDurationMs on Analysis events, tg:toolDurationMs on
  Observation events
- Token counts on events: tg:inToken/tg:outToken/tg:llmModel on
  Grounding, Focus, Synthesis, and Analysis events
- Tool/parse errors: tg:toolError on Observation events with tg:Error
  mixin type. Parse failures return as error observations instead of
  crashing the agent, giving it a chance to retry.

Envelope unification:

- Rename chunk_type to message_type across AgentResponse schema,
  translator, SDK types, socket clients, CLI, and all tests.
  Agent and RAG services now both use message_type on the wire.

Ontology:

- specs/ontology/trustgraph.ttl — OWL vocabulary covering all 26 classes,
  7 object properties, and 36+ datatype properties including new predicates.

DAG structure tests:

- tests/unit/test_provenance/test_dag_structure.py verifies the
  wasDerivedFrom chain for GraphRAG, DocumentRAG, and all three agent
  patterns (react, plan, supervisor) including the pattern-decision link.
2026-04-13 16:16:42 +01:00
cybermaggedon
4fb0b4d8e8
Pub/sub abstraction: decouple from Pulsar (#751)
Remove Pulsar-specific concepts from application code so that
the pub/sub backend is swappable via configuration.

Rename translators:
- to_pulsar/from_pulsar → decode/encode across all translator
  classes, dispatch handlers, and tests (55+ files)
- from_response_with_completion → encode_with_completion
- Remove pulsar.schema.Record from translator base class

Queue naming (CLASS:TOPICSPACE:TOPIC):
- Replace topic() helper with queue() using new format:
  flow:tg:name, request:tg:name, response:tg:name, state:tg:name
- Queue class implies persistence/TTL (no QoS in names)
- Update Pulsar backend map_topic() to parse new format
- Librarian queues use flow class (persistent, for chunking)
- Config push uses state class (persistent, last-value)
- Remove 15 dead topic imports from schema files
- Update init_trustgraph.py namespace: config → state

Confine Pulsar to pulsar_backend.py:
- Delete legacy PulsarClient class from pubsub.py
- Move add_args to add_pubsub_args() with standalone flag
  for CLI tools (defaults to localhost)
- PulsarBackendConsumer.receive() catches _pulsar.Timeout,
  raises standard TimeoutError
- Remove Pulsar imports from: async_processor, flow_processor,
  log_level, all 11 client files, 4 storage writers, gateway
  service, gateway config receiver
- Remove log_level/LoggerLevel from client API
- Rewrite tg-monitor-prompts to use backend abstraction
- Update tg-dump-queues to use add_pubsub_args

Also: pubsub-abstraction.md tech spec covering problem statement,
design goals, as-is requirements, candidate broker assessment,
approach, and implementation order.
2026-04-01 20:16:53 +01:00
cybermaggedon
849987f0e6
Add multi-pattern orchestrator with plan-then-execute and supervisor (#739)
Introduce an agent orchestrator service that supports three
execution patterns (ReAct, plan-then-execute, supervisor) with
LLM-based meta-routing to select the appropriate pattern and task
type per request. Update the agent schema to support
orchestration fields (correlation, sub-agents, plan steps) and
remove legacy response fields (answer, thought, observation).
2026-03-31 00:32:49 +01:00
cybermaggedon
35128ff019
Add unified explainability support and librarian storage for (#693)
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.
2026-03-12 21:40:09 +00:00
cybermaggedon
7a6197d8c3
GraphRAG Query-Time Explainability (#677)
Implements full explainability pipeline for GraphRAG queries, enabling
traceability from answers back to source documents.

Renamed throughout for clarity:
- provenance_callback → explain_callback
- provenance_id → explain_id
- provenance_collection → explain_collection
- message_type "provenance" → "explain"
- Queue name "provenance" → "explainability"

GraphRAG queries now emit explainability events as they execute:
1. Session - query text and timestamp
2. Retrieval - edges retrieved from subgraph
3. Selection - selected edges with LLM reasoning (JSONL with id +
   reasoning)
4. Answer - reference to synthesized response

Events stream via explain_callback during query(), enabling
real-time UX.

- Answers stored in librarian service (not inline in graph - too large)
- Document ID as URN: urn:trustgraph:answer:{session_id}
- Graph stores tg:document reference (IRI) to librarian document
- Added librarian producer/consumer to graph-rag service

- get_labelgraph() now returns (labeled_edges, uri_map)
- uri_map maps edge_id(label_s, label_p, label_o) →
  (uri_s, uri_p, uri_o)
- Explainability data stores original URIs, not labels
- Enables tracing edges back to reifying statements via tg:reifies

- Added serialize_triple() to query service (matches storage format)
- get_term_value() now handles TRIPLE type terms
- Enables querying by quoted triple in object position:
  ?stmt tg:reifies <<s p o>>

- Displays real-time explainability events during query
- Resolves rdfs:label for edge components (s, p, o)
- Traces source chain via prov:wasDerivedFrom to root document
- Output: "Source: Chunk 1 → Page 2 → Document Title"
- Label caching to avoid repeated queries

GraphRagResponse:
- explain_id: str | None
- explain_collection: str | None
- message_type: str ("chunk" or "explain")
- end_of_session: bool

trustgraph-base/trustgraph/provenance/:
- namespaces.py - Added TG_DOCUMENT predicate
- triples.py - answer_triples() supports document_id reference
- uris.py - Added edge_selection_uri()

trustgraph-base/trustgraph/schema/services/retrieval.py:
- GraphRagResponse with explain_id, explain_collection, end_of_session

trustgraph-flow/trustgraph/retrieval/graph_rag/:
- graph_rag.py - URI preservation, streaming answer accumulation
- rag.py - Librarian integration, real-time explain emission

trustgraph-flow/trustgraph/query/triples/cassandra/service.py:
- Quoted triple serialization for query matching

trustgraph-cli/trustgraph/cli/invoke_graph_rag.py:
- Full explainability display with label resolution and source tracing
2026-03-10 10:00:01 +00:00
cybermaggedon
807f6cc4e2
Fix non streaming RAG problems (#607)
* Fix non-streaming failure in RAG services

* Fix non-streaming failure in API

* Fix agent non-streaming messaging

* Agent messaging unit & contract tests
2026-01-12 18:45:52 +00:00