Phase 4: Agent Assistant + Provenance + Entity Cards + Confidence Indicators
- Add llm_client.py for Kimi API integration with RAG and streaming support - Add Agent API endpoints: query, command, suggest - Add Provenance API for relation source tracking - Add Entity details API with mentions and relations - Add Entity evolution analysis API - Update workbench.html with Agent panel, entity cards, provenance modal - Update app.js with Agent chat, entity hover cards, relation provenance - Add low-confidence entity highlighting - Update STATUS.md with Phase 4 progress
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OpenClaw Bot
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backend/llm_client.py
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255
backend/llm_client.py
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#!/usr/bin/env python3
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"""
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InsightFlow LLM Client - Phase 4
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用于与 Kimi API 交互，支持 RAG 问答和 Agent 功能
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"""
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import os
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import json
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import httpx
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from typing import List, Dict, Optional, AsyncGenerator
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from dataclasses import dataclass
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KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
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KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding")
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@dataclass
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class ChatMessage:
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role: str
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content: str
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@dataclass
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class EntityExtractionResult:
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name: str
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type: str
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definition: str
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confidence: float
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@dataclass
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class RelationExtractionResult:
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source: str
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target: str
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type: str
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confidence: float
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class LLMClient:
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"""Kimi API 客户端"""
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def __init__(self, api_key: str = None, base_url: str = None):
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self.api_key = api_key or KIMI_API_KEY
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self.base_url = base_url or KIMI_BASE_URL
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self.headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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async def chat(self, messages: List[ChatMessage], temperature: float = 0.3, stream: bool = False) -> str:
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"""发送聊天请求"""
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if not self.api_key:
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raise ValueError("KIMI_API_KEY not set")
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payload = {
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"model": "k2p5",
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"messages": [{"role": m.role, "content": m.content} for m in messages],
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"temperature": temperature,
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"stream": stream
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}
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async with httpx.AsyncClient() as client:
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response = await client.post(
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f"{self.base_url}/v1/chat/completions",
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headers=self.headers,
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json=payload,
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timeout=120.0
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)
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response.raise_for_status()
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result = response.json()
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return result["choices"][0]["message"]["content"]
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async def chat_stream(self, messages: List[ChatMessage], temperature: float = 0.3) -> AsyncGenerator[str, None]:
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"""流式聊天请求"""
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if not self.api_key:
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raise ValueError("KIMI_API_KEY not set")
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payload = {
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"model": "k2p5",
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"messages": [{"role": m.role, "content": m.content} for m in messages],
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"temperature": temperature,
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"stream": True
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}
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async with httpx.AsyncClient() as client:
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async with client.stream(
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"POST",
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f"{self.base_url}/v1/chat/completions",
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headers=self.headers,
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json=payload,
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timeout=120.0
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) as response:
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response.raise_for_status()
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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data = line[6:]
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if data == "[DONE]":
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break
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try:
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chunk = json.loads(data)
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delta = chunk["choices"][0]["delta"]
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if "content" in delta:
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yield delta["content"]
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except:
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pass
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async def extract_entities_with_confidence(self, text: str) -> tuple[List[EntityExtractionResult], List[RelationExtractionResult]]:
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"""提取实体和关系，带置信度分数"""
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prompt = f"""从以下会议文本中提取关键实体和它们之间的关系，以 JSON 格式返回：
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文本：{text[:3000]}
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要求：
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1. entities: 每个实体包含 name(名称), type(类型: PROJECT/TECH/PERSON/ORG/OTHER), definition(一句话定义), confidence(置信度0-1)
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2. relations: 每个关系包含 source(源实体名), target(目标实体名), type(关系类型: belongs_to/works_with/depends_on/mentions/related), confidence(置信度0-1)
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3. 只返回 JSON 对象，格式: {{"entities": [...], "relations": [...]}}
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示例：
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{{
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"entities": [
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{{"name": "Project Alpha", "type": "PROJECT", "definition": "核心项目", "confidence": 0.95}},
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{{"name": "K8s", "type": "TECH", "definition": "Kubernetes容器编排平台", "confidence": 0.88}}
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],
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"relations": [
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{{"source": "Project Alpha", "target": "K8s", "type": "depends_on", "confidence": 0.82}}
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]
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}}"""
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messages = [ChatMessage(role="user", content=prompt)]
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content = await self.chat(messages, temperature=0.1)
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import re
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json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
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if not json_match:
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return [], []
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try:
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data = json.loads(json_match.group())
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entities = [
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EntityExtractionResult(
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name=e["name"],
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type=e.get("type", "OTHER"),
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definition=e.get("definition", ""),
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confidence=e.get("confidence", 0.8)
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)
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for e in data.get("entities", [])
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]
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relations = [
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RelationExtractionResult(
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source=r["source"],
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target=r["target"],
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type=r.get("type", "related"),
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confidence=r.get("confidence", 0.8)
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)
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for r in data.get("relations", [])
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]
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return entities, relations
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except Exception as e:
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print(f"Parse extraction result failed: {e}")
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return [], []
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async def rag_query(self, query: str, context: str, project_context: Dict) -> str:
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"""RAG 问答 - 基于项目上下文回答问题"""
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prompt = f"""你是一个专业的项目分析助手。基于以下项目信息回答问题：
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## 项目信息
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{json.dumps(project_context, ensure_ascii=False, indent=2)}
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## 相关上下文
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{context[:4000]}
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## 用户问题
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{query}
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请用中文回答，保持简洁专业。如果信息不足，请明确说明。"""
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messages = [
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ChatMessage(role="system", content="你是一个专业的项目分析助手，擅长从会议记录中提取洞察。"),
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ChatMessage(role="user", content=prompt)
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]
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return await self.chat(messages, temperature=0.3)
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async def agent_command(self, command: str, project_context: Dict) -> Dict:
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"""Agent 指令解析 - 将自然语言指令转换为结构化操作"""
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prompt = f"""解析以下用户指令，转换为结构化操作：
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## 项目信息
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{json.dumps(project_context, ensure_ascii=False, indent=2)}
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## 用户指令
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{command}
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请分析指令意图，返回 JSON 格式：
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{{
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"intent": "merge_entities|answer_question|edit_entity|create_relation|unknown",
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"params": {{
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// 根据 intent 不同，参数不同
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}},
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"explanation": "对用户指令的解释"
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}}
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意图说明：
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- merge_entities: 合并实体，params 包含 source_names(源实体名列表), target_name(目标实体名)
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- answer_question: 回答问题，params 包含 question(问题内容)
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- edit_entity: 编辑实体，params 包含 entity_name(实体名), field(字段), value(新值)
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- create_relation: 创建关系，params 包含 source(源实体), target(目标实体), relation_type(关系类型)
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"""
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messages = [ChatMessage(role="user", content=prompt)]
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content = await self.chat(messages, temperature=0.1)
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import re
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json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
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if not json_match:
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return {"intent": "unknown", "explanation": "无法解析指令"}
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try:
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return json.loads(json_match.group())
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except:
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return {"intent": "unknown", "explanation": "解析失败"}
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async def analyze_entity_evolution(self, entity_name: str, mentions: List[Dict]) -> str:
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"""分析实体在项目中的演变/态度变化"""
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mentions_text = "\n".join([
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f"[{m.get('created_at', '未知时间')}] {m.get('text_snippet', '')}"
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for m in mentions[:20] # 限制数量
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])
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prompt = f"""分析实体 "{entity_name}" 在项目中的演变和态度变化：
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## 提及记录
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{mentions_text}
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请分析：
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1. 该实体的角色/重要性变化
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2. 相关方对它的态度变化
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3. 关键时间节点
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4. 总结性洞察
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用中文回答，结构清晰。"""
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messages = [ChatMessage(role="user", content=prompt)]
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return await self.chat(messages, temperature=0.3)
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# Singleton instance
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_llm_client = None
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def get_llm_client() -> LLMClient:
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global _llm_client
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if _llm_client is None:
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_llm_client = LLMClient()
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return _llm_client
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