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.
105 lines
No EOL
4.1 KiB
Python
105 lines
No EOL
4.1 KiB
Python
from typing import Dict, Any, Tuple
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from ...schema import AgentRequest, AgentResponse
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from .base import MessageTranslator
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from .primitives import TripleTranslator
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class AgentRequestTranslator(MessageTranslator):
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"""Translator for AgentRequest schema objects"""
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def decode(self, data: Dict[str, Any]) -> AgentRequest:
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return AgentRequest(
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question=data["question"],
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state=data.get("state", None),
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group=data.get("group", None),
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history=data.get("history", []),
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user=data.get("user", "trustgraph"),
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collection=data.get("collection", "default"),
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streaming=data.get("streaming", False),
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session_id=data.get("session_id", ""),
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conversation_id=data.get("conversation_id", ""),
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pattern=data.get("pattern", ""),
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task_type=data.get("task_type", ""),
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framing=data.get("framing", ""),
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correlation_id=data.get("correlation_id", ""),
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parent_session_id=data.get("parent_session_id", ""),
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subagent_goal=data.get("subagent_goal", ""),
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expected_siblings=data.get("expected_siblings", 0),
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)
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def encode(self, obj: AgentRequest) -> Dict[str, Any]:
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return {
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"question": obj.question,
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"state": obj.state,
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"group": obj.group,
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"history": obj.history,
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"user": obj.user,
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"collection": getattr(obj, "collection", "default"),
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"streaming": getattr(obj, "streaming", False),
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"session_id": getattr(obj, "session_id", ""),
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"conversation_id": getattr(obj, "conversation_id", ""),
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"pattern": getattr(obj, "pattern", ""),
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"task_type": getattr(obj, "task_type", ""),
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"framing": getattr(obj, "framing", ""),
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"correlation_id": getattr(obj, "correlation_id", ""),
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"parent_session_id": getattr(obj, "parent_session_id", ""),
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"subagent_goal": getattr(obj, "subagent_goal", ""),
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"expected_siblings": getattr(obj, "expected_siblings", 0),
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}
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class AgentResponseTranslator(MessageTranslator):
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"""Translator for AgentResponse schema objects"""
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def __init__(self):
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self.triple_translator = TripleTranslator()
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def decode(self, data: Dict[str, Any]) -> AgentResponse:
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raise NotImplementedError("Response translation to Pulsar not typically needed")
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def encode(self, obj: AgentResponse) -> Dict[str, Any]:
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result = {}
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if obj.message_type:
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result["message_type"] = obj.message_type
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if obj.content:
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result["content"] = obj.content
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result["end_of_message"] = getattr(obj, "end_of_message", False)
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result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
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if getattr(obj, "message_id", ""):
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result["message_id"] = obj.message_id
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# Include explainability fields if present
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explain_id = getattr(obj, "explain_id", None)
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if explain_id:
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result["explain_id"] = explain_id
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explain_graph = getattr(obj, "explain_graph", None)
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if explain_graph is not None:
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result["explain_graph"] = explain_graph
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# Include explain_triples for explain messages
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explain_triples = getattr(obj, "explain_triples", [])
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if explain_triples:
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result["explain_triples"] = [
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self.triple_translator.encode(t) for t in explain_triples
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]
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# Always include error if present
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if hasattr(obj, 'error') and obj.error and obj.error.message:
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result["error"] = {"message": obj.error.message, "code": obj.error.code}
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if obj.in_token is not None:
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result["in_token"] = obj.in_token
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if obj.out_token is not None:
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result["out_token"] = obj.out_token
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if obj.model is not None:
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result["model"] = obj.model
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return result
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def encode_with_completion(self, obj: AgentResponse) -> Tuple[Dict[str, Any], bool]:
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"""Returns (response_dict, is_final)"""
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is_final = getattr(obj, 'end_of_dialog', False)
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return self.encode(obj), is_final |