104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
from __future__ import annotations
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import json
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import re
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import uuid
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from typing import Any, Dict, List
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from .base import AgentProvider, AssistantTurn, ToolCall
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class RuleBasedMeetingProvider(AgentProvider):
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"""Offline provider for demos. It still uses the same tool-call loop as an LLM provider."""
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def generate(self, messages: List[Dict[str, Any]], tools: List[Dict[str, Any]]) -> AssistantTurn:
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last = messages[-1]
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if last.get("role") == "tool":
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return AssistantTurn(content=_summarize_tool_result(last.get("name", ""), last.get("content", "{}")))
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user_message = _last_user_message(messages)
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knowledge_base_id = _extract_knowledge_base_id(user_message)
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path = _extract_path(user_message)
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if _looks_like_list_bases(user_message):
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return AssistantTurn(
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tool_calls=[ToolCall(id=f"call_{uuid.uuid4().hex}", name="list_knowledge_bases", arguments={})]
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)
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if _looks_like_import(user_message) and path:
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args: dict[str, Any] = {"file_path": path}
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if knowledge_base_id:
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args["knowledge_base_id"] = knowledge_base_id
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meeting_date = _extract_date(user_message)
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if meeting_date:
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args["meeting_date"] = meeting_date
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return AssistantTurn(
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tool_calls=[ToolCall(id=f"call_{uuid.uuid4().hex}", name="import_meeting_transcript", arguments=args)]
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)
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query_args: dict[str, Any] = {"query": user_message, "limit": 8}
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if knowledge_base_id:
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query_args["knowledge_base_id"] = knowledge_base_id
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return AssistantTurn(
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tool_calls=[
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ToolCall(
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id=f"call_{uuid.uuid4().hex}",
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name="query_knowledge",
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arguments=query_args,
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)
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]
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)
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def _summarize_tool_result(tool_name: str, raw: str) -> str:
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try:
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data = json.loads(raw)
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except json.JSONDecodeError:
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return raw
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if tool_name == "import_meeting_transcript":
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counts = data.get("counts", {})
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return (
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"已导入会议并更新知识库周会台账。\n"
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f"- knowledge_base_id: {data.get('knowledge_base_id') or data.get('team_id')}\n"
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f"- meeting_id: {data.get('meeting_id')}\n"
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f"- ledger: {data.get('ledger_path')}\n"
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f"- 抽取：指标 {counts.get('metrics', 0)}，待办 {counts.get('actions', 0)}，风险 {counts.get('risks', 0)}，"
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f"完成事项 {counts.get('completed', 0)}，追踪项 {counts.get('followups', 0)}"
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)
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if tool_name == "query_knowledge":
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return data.get("answer", json.dumps(data, ensure_ascii=False, indent=2))
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if tool_name == "list_knowledge_bases":
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bases = data.get("knowledge_bases", [])
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return "当前已有知识库：" + ("、".join(bases) if bases else "暂无")
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return json.dumps(data, ensure_ascii=False, indent=2)
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def _last_user_message(messages: List[Dict[str, Any]]) -> str:
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for message in reversed(messages):
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if message.get("role") == "user":
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return str(message.get("content", ""))
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return ""
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def _looks_like_import(text: str) -> bool:
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return any(word in text for word in ("导入", "读取", "更新", "import")) and bool(_extract_path(text))
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def _extract_path(text: str) -> str:
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quoted = re.search(r"['\"]([^'\"]+\.(?:txt|md))['\"]", text)
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if quoted:
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return quoted.group(1)
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match = re.search(r"([A-Za-z]:\\[^\s，。；]+?\.(?:txt|md))", text)
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return match.group(1) if match else ""
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def _extract_knowledge_base_id(text: str) -> str | None:
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match = re.search(r"(?:knowledge_base_id|知识库|库|team_id|团队|team)[:：= ]*([A-Za-z0-9_\-\u4e00-\u9fff]+)", text)
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return match.group(1) if match else None
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def _extract_date(text: str) -> str:
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match = re.search(r"\d{4}-\d{2}-\d{2}", text)
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return match.group(0) if match else ""
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def _looks_like_list_bases(text: str) -> bool:
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return any(word in text for word in ("有哪些知识库", "知识库列表", "列出知识库", "可用知识库", "有哪些库"))
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