41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
import time
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from .results import AgentResult
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from .structured_output import build_mock_structured_output
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from .tool_registry import run_declared_tools
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from .rag.retriever import retrieve
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def run_agent(scenario_config: dict, user_input: str, options: dict | None = None) -> AgentResult:
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started_at = time.perf_counter()
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options = options or {}
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output_type = scenario_config.get("output", {}).get("type", "general_answer")
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references = []
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rag_config = scenario_config.get("rag", {})
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if rag_config.get("enabled"):
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references = retrieve(
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scenario_id=scenario_config.get("id", ""),
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query=user_input,
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collection=rag_config.get("collection", scenario_config.get("id", "")),
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top_k=rag_config.get("top_k", 5),
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document_ids=options.get("document_ids"),
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store_path=options.get("rag_store_path"),
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)
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tool_calls = run_declared_tools(scenario_config.get("tools", []), user_input)
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structured_output = build_mock_structured_output(output_type, user_input, references)
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answer = f"已根据「{scenario_config.get('name', '当前场景')}」生成模拟回答：{user_input}"
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latency_ms = int((time.perf_counter() - started_at) * 1000)
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return AgentResult(
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answer=answer,
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structured_output=structured_output,
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references=references,
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tool_calls=tool_calls,
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raw_output=answer,
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model_name=options.get("model_name", "mock-model"),
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latency_ms=latency_ms,
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status="success",
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)
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