mirror of
https://github.com/trustgraph-ai/trustgraph.git
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2e83413cbe
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.
94 lines
2.8 KiB
Python
94 lines
2.8 KiB
Python
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from .. schema import AgentRequest, AgentResponse
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from .. schema import agent_request_queue
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from .. schema import agent_response_queue
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from . base import BaseClient
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# Ugly
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class AgentClient(BaseClient):
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def __init__(
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self,
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subscriber=None,
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input_queue=None,
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output_queue=None,
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pulsar_host="pulsar://pulsar:6650",
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pulsar_api_key=None,
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):
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if input_queue is None: input_queue = agent_request_queue
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if output_queue is None: output_queue = agent_response_queue
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super(AgentClient, self).__init__(
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subscriber=subscriber,
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input_queue=input_queue,
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output_queue=output_queue,
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pulsar_host=pulsar_host,
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input_schema=AgentRequest,
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output_schema=AgentResponse,
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pulsar_api_key=pulsar_api_key
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)
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def request(
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self,
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question,
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think=None,
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observe=None,
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answer_callback=None,
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error_callback=None,
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timeout=300
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):
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"""
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Request an agent query with optional streaming callbacks.
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Args:
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question: The question to ask
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think: Optional callback(content, end_of_message) for thought chunks
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observe: Optional callback(content, end_of_message) for observation chunks
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answer_callback: Optional callback(content, end_of_message) for answer chunks
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error_callback: Optional callback(content) for error messages
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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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def inspect(x):
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# Handle errors
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if x.message_type == 'error' or x.error:
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if error_callback:
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error_callback(x.content or (x.error.message if x.error else ""))
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# Continue to check end_of_dialog
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# Handle thought chunks
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elif x.message_type == 'thought':
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if think:
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think(x.content, x.end_of_message)
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# Handle observation chunks
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elif x.message_type == 'observation':
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if observe:
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observe(x.content, x.end_of_message)
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# Handle answer chunks
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elif x.message_type == 'answer':
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if x.content:
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accumulated_answer.append(x.content)
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if answer_callback:
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answer_callback(x.content, x.end_of_message)
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# Complete when dialog ends
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if x.end_of_dialog:
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return True
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return False # Continue receiving
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self.call(
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question=question, inspect=inspect, timeout=timeout
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)
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return "".join(accumulated_answer)
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