61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
"""
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pipeline_service/intent_classifier.py — Reusable intent classification.
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DSPY can call: await intent_classify(model_name, message, intents, context)
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Registered on ServerEnv in pipeline_service/init.py
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"""
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import json
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import logging
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logger = logging.getLogger("pipeline.intent_classifier")
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INTENT_PROMPT = """你是一个意图分类器。分析用户输入，返回 JSON。
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可用意图：
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{intent_list}
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当前上下文：{context_str}
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返回纯 JSON（不要 markdown 包裹）：
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{{"intent":"...","confidence":0.0-1.0,"params":{{"key":"value"...}},"missing_info":"..."}}"""
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async def intent_classify(intents: list, message: str, context: dict = None) -> dict:
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"""Classify user intent. Returns {intent, confidence, params, missing_info}.
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Args:
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intents: [{"name":"new_project","description":"创建新项目"},...]
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message: user input text
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context: {"project_name":"...","iteration_name":"..."}
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"""
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if not intents:
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return {"intent": "chat", "confidence": 0.5, "params": {}, "missing_info": ""}
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intent_list = "\n".join(
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f"- {i['name']}: {i.get('description','')}" for i in intents
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)
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ctx_parts = []
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if context:
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for k, v in context.items():
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if v:
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ctx_parts.append(f"{k}={v}")
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context_str = ", ".join(ctx_parts) if ctx_parts else "无上下文"
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prompt = INTENT_PROMPT.format(intent_list=intent_list, context_str=context_str)
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try:
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from pipeline_service.llm_bridge import llm_call
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raw = await llm_call(
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prompt=f"{prompt}\n\n用户输入：{message}",
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model=None,
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temperature=0.2,
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)
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raw = raw.strip()
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if raw.startswith("```"):
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raw = raw.split("\n", 1)[1].rsplit("```", 1)[0]
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return json.loads(raw)
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except Exception as e:
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logger.warning("intent_classify failed: %s", e)
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return {"intent": "chat", "confidence": 0.3, "params": {}, "missing_info": ""}
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