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
This commit is contained in:
OpenClaw Bot
293
backend/main.py
293
backend/main.py
@@ -48,6 +48,12 @@ try:
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except ImportError:
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ALIGNER_AVAILABLE = False
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try:
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from llm_client import get_llm_client, ChatMessage
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LLM_CLIENT_AVAILABLE = True
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except ImportError:
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LLM_CLIENT_AVAILABLE = False
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app = FastAPI(title="InsightFlow", version="0.3.0")
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app.add_middleware(
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@@ -99,6 +105,13 @@ class RelationCreate(BaseModel):
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class TranscriptUpdate(BaseModel):
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full_text: str
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class AgentQuery(BaseModel):
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query: str
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stream: bool = False
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class AgentCommand(BaseModel):
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command: str
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class EntityMergeRequest(BaseModel):
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source_entity_id: str
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target_entity_id: str
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@@ -963,13 +976,14 @@ async def get_entity_mentions(entity_id: str):
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async def health_check():
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return {
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"status": "ok",
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"version": "0.3.0",
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"phase": "Phase 3 - Memory & Growth",
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"version": "0.4.0",
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"phase": "Phase 4 - Agent Assistant",
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"oss_available": OSS_AVAILABLE,
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"tingwu_available": TINGWU_AVAILABLE,
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"db_available": DB_AVAILABLE,
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"doc_processor_available": DOC_PROCESSOR_AVAILABLE,
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"aligner_available": ALIGNER_AVAILABLE
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"aligner_available": ALIGNER_AVAILABLE,
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"llm_client_available": LLM_CLIENT_AVAILABLE
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}
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# Serve frontend
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@@ -978,3 +992,276 @@ app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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# ==================== Phase 4: Agent 助手 API ====================
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@app.post("/api/v1/projects/{project_id}/agent/query")
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async def agent_query(project_id: str, query: AgentQuery):
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"""Agent RAG 问答"""
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if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
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raise HTTPException(status_code=500, detail="Service not available")
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db = get_db_manager()
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llm = get_llm_client()
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project = db.get_project(project_id)
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if not project:
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raise HTTPException(status_code=404, detail="Project not found")
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# 获取项目上下文
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project_context = db.get_project_summary(project_id)
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# 构建上下文
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context_parts = []
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for t in project_context.get('recent_transcripts', []):
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context_parts.append(f"【{t['filename']}】\n{t['full_text'][:1000]}")
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context = "\n\n".join(context_parts)
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if query.stream:
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from fastapi.responses import StreamingResponse
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import json
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async def stream_response():
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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=f"""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.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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]
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async for chunk in llm.chat_stream(messages):
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yield f"data: {json.dumps({'content': chunk})}\n\n"
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yield "data: [DONE]\n\n"
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return StreamingResponse(stream_response(), media_type="text/event-stream")
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else:
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answer = await llm.rag_query(query.query, context, project_context)
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return {"answer": answer, "project_id": project_id}
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@app.post("/api/v1/projects/{project_id}/agent/command")
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async def agent_command(project_id: str, command: AgentCommand):
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"""Agent 指令执行 - 解析并执行自然语言指令"""
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if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
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raise HTTPException(status_code=500, detail="Service not available")
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db = get_db_manager()
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llm = get_llm_client()
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project = db.get_project(project_id)
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if not project:
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raise HTTPException(status_code=404, detail="Project not found")
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# 获取项目上下文
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project_context = db.get_project_summary(project_id)
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# 解析指令
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parsed = await llm.agent_command(command.command, project_context)
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intent = parsed.get("intent", "unknown")
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params = parsed.get("params", {})
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result = {"intent": intent, "explanation": parsed.get("explanation", "")}
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# 执行指令
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if intent == "merge_entities":
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# 合并实体
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source_names = params.get("source_names", [])
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target_name = params.get("target_name", "")
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target_entity = None
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source_entities = []
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# 查找目标实体
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for e in project_context.get("top_entities", []):
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if e["name"] == target_name or target_name in e["name"]:
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target_entity = db.get_entity_by_name(project_id, e["name"])
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break
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# 查找源实体
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for name in source_names:
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for e in project_context.get("top_entities", []):
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if e["name"] == name or name in e["name"]:
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ent = db.get_entity_by_name(project_id, e["name"])
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if ent and (not target_entity or ent.id != target_entity.id):
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source_entities.append(ent)
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break
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merged = []
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if target_entity:
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for source in source_entities:
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try:
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db.merge_entities(target_entity.id, source.id)
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merged.append(source.name)
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except Exception as e:
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print(f"Merge failed: {e}")
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result["action"] = "merge_entities"
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result["target"] = target_entity.name if target_entity else None
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result["merged"] = merged
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result["success"] = len(merged) > 0
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elif intent == "answer_question":
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# 问答 - 调用 RAG
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answer = await llm.rag_query(params.get("question", command.command), "", project_context)
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result["action"] = "answer"
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result["answer"] = answer
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elif intent == "edit_entity":
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# 编辑实体
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entity_name = params.get("entity_name", "")
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field = params.get("field", "")
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value = params.get("value", "")
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entity = db.get_entity_by_name(project_id, entity_name)
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if entity:
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updated = db.update_entity(entity.id, **{field: value})
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result["action"] = "edit_entity"
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result["entity"] = {"id": updated.id, "name": updated.name} if updated else None
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result["success"] = updated is not None
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else:
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result["success"] = False
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result["error"] = "Entity not found"
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else:
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result["action"] = "none"
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result["message"] = "无法理解的指令，请尝试：\n- 合并实体：把所有'客户端'合并到'App'\n- 提问：张总对项目的态度如何？\n- 编辑：修改'K8s'的定义为..."
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return result
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@app.get("/api/v1/projects/{project_id}/agent/suggest")
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async def agent_suggest(project_id: str):
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"""获取 Agent 建议 - 基于项目数据提供洞察"""
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if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
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raise HTTPException(status_code=500, detail="Service not available")
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db = get_db_manager()
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llm = get_llm_client()
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project_context = db.get_project_summary(project_id)
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# 生成建议
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prompt = f"""基于以下项目数据，提供3-5条分析建议：
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{json.dumps(project_context, ensure_ascii=False, indent=2)}
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请提供：
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1. 数据洞察发现
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2. 建议的操作（如合并相似实体、补充定义等）
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3. 值得关注的关键信息
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返回 JSON 格式：{{"suggestions": [{{"type": "insight|action", "title": "...", "description": "..."}}]}}"""
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messages = [ChatMessage(role="user", content=prompt)]
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content = await llm.chat(messages, temperature=0.3)
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import re
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json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
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if json_match:
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try:
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data = json.loads(json_match.group())
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return data
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except:
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pass
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return {"suggestions": []}
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# ==================== Phase 4: 知识溯源 API ====================
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@app.get("/api/v1/relations/{relation_id}/provenance")
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async def get_relation_provenance(relation_id: str):
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"""获取关系的知识溯源信息"""
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if not DB_AVAILABLE:
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raise HTTPException(status_code=500, detail="Database not available")
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db = get_db_manager()
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relation = db.get_relation_with_details(relation_id)
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if not relation:
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raise HTTPException(status_code=404, detail="Relation not found")
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return {
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"relation_id": relation_id,
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"source": relation.get("source_name"),
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"target": relation.get("target_name"),
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"type": relation.get("relation_type"),
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"evidence": relation.get("evidence"),
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"transcript": {
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"id": relation.get("transcript_id"),
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"filename": relation.get("transcript_filename"),
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} if relation.get("transcript_id") else None
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}
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@app.get("/api/v1/entities/{entity_id}/details")
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async def get_entity_details(entity_id: str):
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"""获取实体详情，包含所有提及位置"""
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if not DB_AVAILABLE:
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raise HTTPException(status_code=500, detail="Database not available")
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db = get_db_manager()
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entity = db.get_entity_with_mentions(entity_id)
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if not entity:
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raise HTTPException(status_code=404, detail="Entity not found")
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return entity
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@app.get("/api/v1/entities/{entity_id}/evolution")
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async def get_entity_evolution(entity_id: str):
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"""分析实体的演变和态度变化"""
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if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
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raise HTTPException(status_code=500, detail="Service not available")
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db = get_db_manager()
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llm = get_llm_client()
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entity = db.get_entity_with_mentions(entity_id)
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if not entity:
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raise HTTPException(status_code=404, detail="Entity not found")
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# 分析演变
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analysis = await llm.analyze_entity_evolution(entity["name"], entity.get("mentions", []))
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return {
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"entity_id": entity_id,
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"entity_name": entity["name"],
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"mention_count": entity.get("mention_count", 0),
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"analysis": analysis,
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"timeline": [
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{
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"date": m.get("transcript_date"),
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"snippet": m.get("text_snippet"),
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"transcript_id": m.get("transcript_id"),
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"filename": m.get("filename")
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}
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for m in entity.get("mentions", [])
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]
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}
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# ==================== Phase 4: 实体管理增强 API ====================
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@app.get("/api/v1/projects/{project_id}/entities/search")
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async def search_entities(project_id: str, q: str):
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"""搜索实体"""
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if not DB_AVAILABLE:
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raise HTTPException(status_code=500, detail="Database not available")
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db = get_db_manager()
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entities = db.search_entities(project_id, q)
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return [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition} for e in entities]
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