mirror of
https://github.com/trustgraph-ai/trustgraph.git
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d2751553a3
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.
80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
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from . request_response_spec import RequestResponse, RequestResponseSpec
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from .. schema import AgentRequest, AgentResponse
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from .. knowledge import Uri, Literal
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class AgentClient(RequestResponse):
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async def invoke(self, question, plan=None, state=None,
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history=[], think=None, observe=None, answer_callback=None,
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timeout=300):
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"""
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Invoke the agent with optional streaming callbacks.
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Args:
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question: The question to ask
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plan: Optional plan context
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state: Optional state context
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history: Conversation history
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think: Optional async callback(content, end_of_message) for thought chunks
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observe: Optional async callback(content, end_of_message) for observation chunks
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answer_callback: Optional async callback(content, end_of_message) for answer chunks
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timeout: Request timeout in seconds
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Returns:
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Complete answer text (accumulated from all answer chunks)
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"""
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accumulated_answer = []
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async def recipient(resp):
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if resp.error:
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raise RuntimeError(resp.error.message)
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# Handle thought chunks
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if resp.message_type == 'thought':
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if think:
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await think(resp.content, resp.end_of_message)
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return False # Continue receiving
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# Handle observation chunks
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if resp.message_type == 'observation':
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if observe:
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await observe(resp.content, resp.end_of_message)
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return False # Continue receiving
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# Handle answer chunks
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if resp.message_type == 'answer':
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if resp.content:
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accumulated_answer.append(resp.content)
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if answer_callback:
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await answer_callback(resp.content, resp.end_of_message)
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# Complete when dialog ends
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if resp.end_of_dialog:
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return True
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return False # Continue receiving
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await self.request(
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AgentRequest(
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question = question,
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state = state or "",
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history = history,
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),
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recipient=recipient,
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timeout=timeout,
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)
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return "".join(accumulated_answer)
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class AgentClientSpec(RequestResponseSpec):
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def __init__(
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self, request_name, response_name,
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):
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super(AgentClientSpec, self).__init__(
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request_name = request_name,
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request_schema = AgentRequest,
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response_name = response_name,
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response_schema = AgentResponse,
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impl = AgentClient,
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)
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