2026-01-15 15:12:32 +00:00
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"""
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TrustGraph API Client Library
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This package provides Python client interfaces for interacting with TrustGraph services.
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TrustGraph is a knowledge graph and RAG (Retrieval-Augmented Generation) platform that
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combines graph databases, vector embeddings, and LLM capabilities.
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The library offers both synchronous and asynchronous APIs for:
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- Flow management and execution
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- Knowledge graph operations (triples, entities, embeddings)
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- RAG queries (graph-based and document-based)
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- Agent interactions with streaming support
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- WebSocket-based real-time communication
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- Bulk import/export operations
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- Configuration and collection management
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Quick Start:
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```python
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from trustgraph.api import Api
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# Create API client
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api = Api(url="http://localhost:8088/")
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# Get a flow instance
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flow = api.flow().id("default")
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# Execute a graph RAG query
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response = flow.graph_rag(
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query="What are the main topics?",
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collection="default"
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)
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```
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For streaming and async operations:
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```python
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# WebSocket streaming
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socket = api.socket()
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flow = socket.flow("default")
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feat: workspace-based multi-tenancy, replacing user as tenancy axis (#840)
Introduces `workspace` as the isolation boundary for config, flows,
library, and knowledge data. Removes `user` as a schema-level field
throughout the code, API specs, and tests; workspace provides the
same separation more cleanly at the trusted flow.workspace layer
rather than through client-supplied message fields.
Design
------
- IAM tech spec (docs/tech-specs/iam.md) documents current state,
proposed auth/access model, and migration direction.
- Data ownership model (docs/tech-specs/data-ownership-model.md)
captures the workspace/collection/flow hierarchy.
Schema + messaging
------------------
- Drop `user` field from AgentRequest/Step, GraphRagQuery,
DocumentRagQuery, Triples/Graph/Document/Row EmbeddingsRequest,
Sparql/Rows/Structured QueryRequest, ToolServiceRequest.
- Keep collection/workspace routing via flow.workspace at the
service layer.
- Translators updated to not serialise/deserialise user.
API specs
---------
- OpenAPI schemas and path examples cleaned of user fields.
- Websocket async-api messages updated.
- Removed the unused parameters/User.yaml.
Services + base
---------------
- Librarian, collection manager, knowledge, config: all operations
scoped by workspace. Config client API takes workspace as first
positional arg.
- `flow.workspace` set at flow start time by the infrastructure;
no longer pass-through from clients.
- Tool service drops user-personalisation passthrough.
CLI + SDK
---------
- tg-init-workspace and workspace-aware import/export.
- All tg-* commands drop user args; accept --workspace.
- Python API/SDK (flow, socket_client, async_*, explainability,
library) drop user kwargs from every method signature.
MCP server
----------
- All tool endpoints drop user parameters; socket_manager no longer
keyed per user.
Flow service
------------
- Closure-based topic cleanup on flow stop: only delete topics
whose blueprint template was parameterised AND no remaining
live flow (across all workspaces) still resolves to that topic.
Three scopes fall out naturally from template analysis:
* {id} -> per-flow, deleted on stop
* {blueprint} -> per-blueprint, kept while any flow of the
same blueprint exists
* {workspace} -> per-workspace, kept while any flow in the
workspace exists
* literal -> global, never deleted (e.g. tg.request.librarian)
Fixes a bug where stopping a flow silently destroyed the global
librarian exchange, wedging all library operations until manual
restart.
RabbitMQ backend
----------------
- heartbeat=60, blocked_connection_timeout=300. Catches silently
dead connections (broker restart, orphaned channels, network
partitions) within ~2 heartbeat windows, so the consumer
reconnects and re-binds its queue rather than sitting forever
on a zombie connection.
Tests
-----
- Full test refresh: unit, integration, contract, provenance.
- Dropped user-field assertions and constructor kwargs across
~100 test files.
- Renamed user-collection isolation tests to workspace-collection.
2026-04-21 23:23:01 +01:00
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for chunk in flow.agent(question="Hello"):
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2026-01-15 15:12:32 +00:00
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print(chunk.content)
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# Async operations
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async with Api(url="http://localhost:8088/") as api:
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async_flow = api.async_flow()
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result = await async_flow.id("default").text_completion(
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system="You are helpful",
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prompt="Hello"
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)
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```
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"""
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2024-11-22 15:55:32 +00:00
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2025-12-04 17:38:57 +00:00
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# Core API
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from .api import Api
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# Flow clients
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from .flow import Flow, FlowInstance
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from .async_flow import AsyncFlow, AsyncFlowInstance
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# WebSocket clients
|
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
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|
from .socket_client import SocketClient, SocketFlowInstance, build_term
|
2025-12-04 17:38:57 +00:00
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from .async_socket_client import AsyncSocketClient, AsyncSocketFlowInstance
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# Bulk operation clients
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from .bulk_client import BulkClient
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from .async_bulk_client import AsyncBulkClient
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# Metrics clients
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|
from .metrics import Metrics
|
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|
|
|
from .async_metrics import AsyncMetrics
|
|
|
|
|
|
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
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|
# Explainability
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|
from .explainability import (
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ExplainabilityClient,
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ExplainEntity,
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Question,
|
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding (#697)
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
|
|
|
Grounding,
|
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
|
|
|
Exploration,
|
|
|
|
|
Focus,
|
|
|
|
|
Synthesis,
|
Enhance retrieval pipelines: 4-stage GraphRAG, DocRAG grounding (#697)
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
|
|
|
Reflection,
|
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
|
|
|
Analysis,
|
Split Analysis into Analysis+ToolUse and Observation, add message_id (#747)
Refactor agent provenance so that the decision (thought + tool
selection) and the result (observation) are separate DAG entities:
Question ← Analysis+ToolUse ← Observation ← ... ← Conclusion
Analysis gains tg:ToolUse as a mixin RDF type and is emitted
before tool execution via an on_action callback in react().
This ensures sub-traces (e.g. GraphRAG) appear after their
parent Analysis in the streaming event order.
Observation becomes a standalone prov:Entity with tg:Observation
type, emitted after tool execution. The linear DAG chain runs
through Observation — subsequent iterations and the Conclusion
derive from it, not from the Analysis.
message_id is populated on streaming AgentResponse for thought
and observation chunks, using the provenance URI of the entity
being built. This lets clients group streamed chunks by entity.
Wire changes:
- provenance/agent.py: Add ToolUse type, new
agent_observation_triples(), remove observation from iteration
- agent_manager.py: Add on_action callback between reason() and
tool execution
- orchestrator/pattern_base.py: Split emit, wire message_id,
chain through observation URIs
- orchestrator/react_pattern.py: Emit Analysis via on_action
before tool runs
- agent/react/service.py: Same for non-orchestrator path
- api/explainability.py: New Observation class, updated dispatch
and chain walker
- api/types.py: Add message_id to AgentThought/AgentObservation
- cli: Render Observation separately, [analysis: tool] labels
2026-03-31 17:51:22 +01:00
|
|
|
Observation,
|
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
|
|
|
Conclusion,
|
agent-orchestrator: add explainability provenance for all patterns (#744)
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
2026-03-31 12:54:51 +01:00
|
|
|
Decomposition,
|
|
|
|
|
Finding,
|
|
|
|
|
Plan,
|
|
|
|
|
StepResult,
|
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
|
|
|
EdgeSelection,
|
|
|
|
|
wire_triples_to_tuples,
|
|
|
|
|
extract_term_value,
|
|
|
|
|
)
|
|
|
|
|
|
2025-12-04 17:38:57 +00:00
|
|
|
# Types
|
|
|
|
|
from .types import (
|
|
|
|
|
Triple,
|
2026-02-16 19:18:40 +00:00
|
|
|
Uri,
|
|
|
|
|
Literal,
|
2025-12-04 17:38:57 +00:00
|
|
|
ConfigKey,
|
|
|
|
|
ConfigValue,
|
|
|
|
|
DocumentMetadata,
|
|
|
|
|
ProcessingMetadata,
|
|
|
|
|
CollectionMetadata,
|
|
|
|
|
StreamingChunk,
|
|
|
|
|
AgentThought,
|
|
|
|
|
AgentObservation,
|
|
|
|
|
AgentAnswer,
|
|
|
|
|
RAGChunk,
|
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
|
|
|
TextCompletionResult,
|
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
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ProvenanceEvent,
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2025-12-04 17:38:57 +00:00
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)
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# Exceptions
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2025-12-04 20:42:25 +00:00
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from .exceptions import (
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ProtocolException,
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TrustGraphException,
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AgentError,
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ConfigError,
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DocumentRagError,
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FlowError,
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GatewayError,
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GraphRagError,
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LLMError,
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LoadError,
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LookupError,
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NLPQueryError,
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2026-02-23 15:56:29 +00:00
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RowsQueryError,
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2025-12-04 20:42:25 +00:00
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RequestError,
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StructuredQueryError,
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UnexpectedError,
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# Legacy alias
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ApplicationException,
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)
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__all__ = [
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# Core API
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"Api",
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# Flow clients
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"Flow",
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"FlowInstance",
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"AsyncFlow",
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"AsyncFlowInstance",
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# WebSocket clients
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"SocketClient",
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"SocketFlowInstance",
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"AsyncSocketClient",
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"AsyncSocketFlowInstance",
|
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
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"build_term",
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2025-12-04 17:38:57 +00:00
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# Bulk operation clients
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"BulkClient",
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"AsyncBulkClient",
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# Metrics clients
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"Metrics",
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"AsyncMetrics",
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|
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
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# Explainability
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"ExplainabilityClient",
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"ExplainEntity",
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"Question",
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"Exploration",
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"Focus",
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"Synthesis",
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"Analysis",
|
Split Analysis into Analysis+ToolUse and Observation, add message_id (#747)
Refactor agent provenance so that the decision (thought + tool
selection) and the result (observation) are separate DAG entities:
Question ← Analysis+ToolUse ← Observation ← ... ← Conclusion
Analysis gains tg:ToolUse as a mixin RDF type and is emitted
before tool execution via an on_action callback in react().
This ensures sub-traces (e.g. GraphRAG) appear after their
parent Analysis in the streaming event order.
Observation becomes a standalone prov:Entity with tg:Observation
type, emitted after tool execution. The linear DAG chain runs
through Observation — subsequent iterations and the Conclusion
derive from it, not from the Analysis.
message_id is populated on streaming AgentResponse for thought
and observation chunks, using the provenance URI of the entity
being built. This lets clients group streamed chunks by entity.
Wire changes:
- provenance/agent.py: Add ToolUse type, new
agent_observation_triples(), remove observation from iteration
- agent_manager.py: Add on_action callback between reason() and
tool execution
- orchestrator/pattern_base.py: Split emit, wire message_id,
chain through observation URIs
- orchestrator/react_pattern.py: Emit Analysis via on_action
before tool runs
- agent/react/service.py: Same for non-orchestrator path
- api/explainability.py: New Observation class, updated dispatch
and chain walker
- api/types.py: Add message_id to AgentThought/AgentObservation
- cli: Render Observation separately, [analysis: tool] labels
2026-03-31 17:51:22 +01:00
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"Observation",
|
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
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"Conclusion",
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"EdgeSelection",
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"wire_triples_to_tuples",
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"extract_term_value",
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|
2025-12-04 17:38:57 +00:00
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# Types
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"Triple",
|
2026-02-16 19:18:40 +00:00
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"Uri",
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"Literal",
|
2025-12-04 17:38:57 +00:00
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"ConfigKey",
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"ConfigValue",
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"DocumentMetadata",
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"ProcessingMetadata",
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"CollectionMetadata",
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"StreamingChunk",
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"AgentThought",
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|
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|
"AgentObservation",
|
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"AgentAnswer",
|
|
|
|
|
"RAGChunk",
|
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
|
|
|
"TextCompletionResult",
|
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
|
|
|
"ProvenanceEvent",
|
2025-12-04 17:38:57 +00:00
|
|
|
|
|
|
|
|
# Exceptions
|
|
|
|
|
"ProtocolException",
|
2025-12-04 20:42:25 +00:00
|
|
|
"TrustGraphException",
|
|
|
|
|
"AgentError",
|
|
|
|
|
"ConfigError",
|
|
|
|
|
"DocumentRagError",
|
|
|
|
|
"FlowError",
|
|
|
|
|
"GatewayError",
|
|
|
|
|
"GraphRagError",
|
|
|
|
|
"LLMError",
|
|
|
|
|
"LoadError",
|
|
|
|
|
"LookupError",
|
|
|
|
|
"NLPQueryError",
|
2026-02-23 15:56:29 +00:00
|
|
|
"RowsQueryError",
|
2025-12-04 20:42:25 +00:00
|
|
|
"RequestError",
|
|
|
|
|
"StructuredQueryError",
|
|
|
|
|
"UnexpectedError",
|
|
|
|
|
"ApplicationException", # Legacy alias
|
2025-12-04 17:38:57 +00:00
|
|
|
]
|
2024-11-22 15:55:32 +00:00
|
|
|
|