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
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backend/__pycache__/llm_client.cpython-312.pyc
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backend/__pycache__/llm_client.cpython-312.pyc
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@@ -453,6 +453,168 @@ class DatabaseManager:
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def get_all_entities_for_embedding(self, project_id: str) -> List[Entity]:
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"""获取所有实体用于 embedding 计算"""
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return self.list_project_entities(project_id)
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# Phase 4: Agent & Provenance methods
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def get_relation_with_details(self, relation_id: str) -> Optional[dict]:
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"""获取关系详情，包含源文档信息"""
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conn = self.get_conn()
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row = conn.execute(
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"""SELECT r.*,
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s.name as source_name, t.name as target_name,
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tr.filename as transcript_filename, tr.full_text as transcript_text
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FROM entity_relations r
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JOIN entities s ON r.source_entity_id = s.id
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JOIN entities t ON r.target_entity_id = t.id
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LEFT JOIN transcripts tr ON r.transcript_id = tr.id
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WHERE r.id = ?""",
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(relation_id,)
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).fetchone()
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conn.close()
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if row:
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return dict(row)
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return None
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def get_entity_with_mentions(self, entity_id: str) -> Optional[dict]:
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"""获取实体详情及所有提及位置"""
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conn = self.get_conn()
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# 获取实体信息
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entity_row = conn.execute(
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"SELECT * FROM entities WHERE id = ?", (entity_id,)
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).fetchone()
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if not entity_row:
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conn.close()
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return None
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entity = dict(entity_row)
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entity['aliases'] = json.loads(entity['aliases']) if entity['aliases'] else []
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# 获取提及位置
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mentions = conn.execute(
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"""SELECT m.*, t.filename, t.created_at as transcript_date
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FROM entity_mentions m
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JOIN transcripts t ON m.transcript_id = t.id
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WHERE m.entity_id = ?
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ORDER BY t.created_at, m.start_pos""",
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(entity_id,)
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).fetchall()
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entity['mentions'] = [dict(m) for m in mentions]
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entity['mention_count'] = len(mentions)
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# 获取相关关系
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relations = conn.execute(
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"""SELECT r.*,
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s.name as source_name, t.name as target_name
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FROM entity_relations r
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JOIN entities s ON r.source_entity_id = s.id
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JOIN entities t ON r.target_entity_id = t.id
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WHERE r.source_entity_id = ? OR r.target_entity_id = ?
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ORDER BY r.created_at DESC""",
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(entity_id, entity_id)
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).fetchall()
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entity['relations'] = [dict(r) for r in relations]
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conn.close()
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return entity
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def search_entities(self, project_id: str, query: str) -> List[Entity]:
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"""搜索实体"""
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conn = self.get_conn()
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rows = conn.execute(
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"""SELECT * FROM entities
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WHERE project_id = ? AND
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(name LIKE ? OR definition LIKE ? OR aliases LIKE ?)
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ORDER BY name""",
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(project_id, f'%{query}%', f'%{query}%', f'%{query}%')
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).fetchall()
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conn.close()
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entities = []
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for row in rows:
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data = dict(row)
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data['aliases'] = json.loads(data['aliases']) if data['aliases'] else []
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entities.append(Entity(**data))
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return entities
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def get_project_summary(self, project_id: str) -> dict:
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"""获取项目摘要信息，用于 RAG 上下文"""
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conn = self.get_conn()
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# 项目基本信息
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project = conn.execute(
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"SELECT * FROM projects WHERE id = ?", (project_id,)
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).fetchone()
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# 统计信息
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entity_count = conn.execute(
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"SELECT COUNT(*) as count FROM entities WHERE project_id = ?",
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(project_id,)
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).fetchone()['count']
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transcript_count = conn.execute(
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"SELECT COUNT(*) as count FROM transcripts WHERE project_id = ?",
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(project_id,)
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).fetchone()['count']
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relation_count = conn.execute(
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"SELECT COUNT(*) as count FROM entity_relations WHERE project_id = ?",
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(project_id,)
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).fetchone()['count']
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# 获取最近的转录文本片段
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recent_transcripts = conn.execute(
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"""SELECT filename, full_text, created_at
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FROM transcripts
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WHERE project_id = ?
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ORDER BY created_at DESC
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LIMIT 5""",
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(project_id,)
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).fetchall()
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# 获取高频实体
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top_entities = conn.execute(
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"""SELECT e.name, e.type, e.definition, COUNT(m.id) as mention_count
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FROM entities e
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LEFT JOIN entity_mentions m ON e.id = m.entity_id
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WHERE e.project_id = ?
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GROUP BY e.id
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ORDER BY mention_count DESC
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LIMIT 10""",
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(project_id,)
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).fetchall()
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conn.close()
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return {
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'project': dict(project) if project else {},
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'statistics': {
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'entity_count': entity_count,
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'transcript_count': transcript_count,
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'relation_count': relation_count
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},
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'recent_transcripts': [dict(t) for t in recent_transcripts],
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'top_entities': [dict(e) for e in top_entities]
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}
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def get_transcript_context(self, transcript_id: str, position: int, context_chars: int = 200) -> str:
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"""获取转录文本的上下文"""
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conn = self.get_conn()
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row = conn.execute(
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"SELECT full_text FROM transcripts WHERE id = ?",
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(transcript_id,)
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).fetchone()
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conn.close()
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if not row:
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return ""
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text = row['full_text']
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start = max(0, position - context_chars)
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end = min(len(text), position + context_chars)
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return text[start:end]
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# Singleton instance
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255
backend/llm_client.py
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255
backend/llm_client.py
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@@ -0,0 +1,255 @@
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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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|
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|
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# Singleton instance
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_llm_client = None
|
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|
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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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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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|
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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
|
||||
except ImportError:
|
||||
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):
|
||||
full_text: str
|
||||
|
||||
class AgentQuery(BaseModel):
|
||||
query: str
|
||||
stream: bool = False
|
||||
|
||||
class AgentCommand(BaseModel):
|
||||
command: str
|
||||
|
||||
class EntityMergeRequest(BaseModel):
|
||||
source_entity_id: str
|
||||
target_entity_id: str
|
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@@ -963,13 +976,14 @@ async def get_entity_mentions(entity_id: str):
|
||||
async def health_check():
|
||||
return {
|
||||
"status": "ok",
|
||||
"version": "0.3.0",
|
||||
"phase": "Phase 3 - Memory & Growth",
|
||||
"version": "0.4.0",
|
||||
"phase": "Phase 4 - Agent Assistant",
|
||||
"oss_available": OSS_AVAILABLE,
|
||||
"tingwu_available": TINGWU_AVAILABLE,
|
||||
"db_available": DB_AVAILABLE,
|
||||
"doc_processor_available": DOC_PROCESSOR_AVAILABLE,
|
||||
"aligner_available": ALIGNER_AVAILABLE
|
||||
"aligner_available": ALIGNER_AVAILABLE,
|
||||
"llm_client_available": LLM_CLIENT_AVAILABLE
|
||||
}
|
||||
|
||||
# Serve frontend
|
||||
@@ -978,3 +992,276 @@ app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
|
||||
|
||||
# ==================== Phase 4: Agent 助手 API ====================
|
||||
|
||||
@app.post("/api/v1/projects/{project_id}/agent/query")
|
||||
async def agent_query(project_id: str, query: AgentQuery):
|
||||
"""Agent RAG 问答"""
|
||||
if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Service not available")
|
||||
|
||||
db = get_db_manager()
|
||||
llm = get_llm_client()
|
||||
|
||||
project = db.get_project(project_id)
|
||||
if not project:
|
||||
raise HTTPException(status_code=404, detail="Project not found")
|
||||
|
||||
# 获取项目上下文
|
||||
project_context = db.get_project_summary(project_id)
|
||||
|
||||
# 构建上下文
|
||||
context_parts = []
|
||||
for t in project_context.get('recent_transcripts', []):
|
||||
context_parts.append(f"【{t['filename']}】\n{t['full_text'][:1000]}")
|
||||
|
||||
context = "\n\n".join(context_parts)
|
||||
|
||||
if query.stream:
|
||||
from fastapi.responses import StreamingResponse
|
||||
import json
|
||||
|
||||
async def stream_response():
|
||||
messages = [
|
||||
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。"),
|
||||
ChatMessage(role="user", content=f"""基于以下项目信息回答问题：
|
||||
|
||||
## 项目信息
|
||||
{json.dumps(project_context, ensure_ascii=False, indent=2)}
|
||||
|
||||
## 相关上下文
|
||||
{context[:4000]}
|
||||
|
||||
## 用户问题
|
||||
{query.query}
|
||||
|
||||
请用中文回答，保持简洁专业。如果信息不足，请明确说明。""")
|
||||
]
|
||||
|
||||
async for chunk in llm.chat_stream(messages):
|
||||
yield f"data: {json.dumps({'content': chunk})}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
return StreamingResponse(stream_response(), media_type="text/event-stream")
|
||||
else:
|
||||
answer = await llm.rag_query(query.query, context, project_context)
|
||||
return {"answer": answer, "project_id": project_id}
|
||||
|
||||
|
||||
@app.post("/api/v1/projects/{project_id}/agent/command")
|
||||
async def agent_command(project_id: str, command: AgentCommand):
|
||||
"""Agent 指令执行 - 解析并执行自然语言指令"""
|
||||
if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Service not available")
|
||||
|
||||
db = get_db_manager()
|
||||
llm = get_llm_client()
|
||||
|
||||
project = db.get_project(project_id)
|
||||
if not project:
|
||||
raise HTTPException(status_code=404, detail="Project not found")
|
||||
|
||||
# 获取项目上下文
|
||||
project_context = db.get_project_summary(project_id)
|
||||
|
||||
# 解析指令
|
||||
parsed = await llm.agent_command(command.command, project_context)
|
||||
|
||||
intent = parsed.get("intent", "unknown")
|
||||
params = parsed.get("params", {})
|
||||
|
||||
result = {"intent": intent, "explanation": parsed.get("explanation", "")}
|
||||
|
||||
# 执行指令
|
||||
if intent == "merge_entities":
|
||||
# 合并实体
|
||||
source_names = params.get("source_names", [])
|
||||
target_name = params.get("target_name", "")
|
||||
|
||||
target_entity = None
|
||||
source_entities = []
|
||||
|
||||
# 查找目标实体
|
||||
for e in project_context.get("top_entities", []):
|
||||
if e["name"] == target_name or target_name in e["name"]:
|
||||
target_entity = db.get_entity_by_name(project_id, e["name"])
|
||||
break
|
||||
|
||||
# 查找源实体
|
||||
for name in source_names:
|
||||
for e in project_context.get("top_entities", []):
|
||||
if e["name"] == name or name in e["name"]:
|
||||
ent = db.get_entity_by_name(project_id, e["name"])
|
||||
if ent and (not target_entity or ent.id != target_entity.id):
|
||||
source_entities.append(ent)
|
||||
break
|
||||
|
||||
merged = []
|
||||
if target_entity:
|
||||
for source in source_entities:
|
||||
try:
|
||||
db.merge_entities(target_entity.id, source.id)
|
||||
merged.append(source.name)
|
||||
except Exception as e:
|
||||
print(f"Merge failed: {e}")
|
||||
|
||||
result["action"] = "merge_entities"
|
||||
result["target"] = target_entity.name if target_entity else None
|
||||
result["merged"] = merged
|
||||
result["success"] = len(merged) > 0
|
||||
|
||||
elif intent == "answer_question":
|
||||
# 问答 - 调用 RAG
|
||||
answer = await llm.rag_query(params.get("question", command.command), "", project_context)
|
||||
result["action"] = "answer"
|
||||
result["answer"] = answer
|
||||
|
||||
elif intent == "edit_entity":
|
||||
# 编辑实体
|
||||
entity_name = params.get("entity_name", "")
|
||||
field = params.get("field", "")
|
||||
value = params.get("value", "")
|
||||
|
||||
entity = db.get_entity_by_name(project_id, entity_name)
|
||||
if entity:
|
||||
updated = db.update_entity(entity.id, **{field: value})
|
||||
result["action"] = "edit_entity"
|
||||
result["entity"] = {"id": updated.id, "name": updated.name} if updated else None
|
||||
result["success"] = updated is not None
|
||||
else:
|
||||
result["success"] = False
|
||||
result["error"] = "Entity not found"
|
||||
|
||||
else:
|
||||
result["action"] = "none"
|
||||
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..."
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@app.get("/api/v1/projects/{project_id}/agent/suggest")
|
||||
async def agent_suggest(project_id: str):
|
||||
"""获取 Agent 建议 - 基于项目数据提供洞察"""
|
||||
if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Service not available")
|
||||
|
||||
db = get_db_manager()
|
||||
llm = get_llm_client()
|
||||
|
||||
project_context = db.get_project_summary(project_id)
|
||||
|
||||
# 生成建议
|
||||
prompt = f"""基于以下项目数据，提供3-5条分析建议：
|
||||
|
||||
{json.dumps(project_context, ensure_ascii=False, indent=2)}
|
||||
|
||||
请提供：
|
||||
1. 数据洞察发现
|
||||
2. 建议的操作（如合并相似实体、补充定义等）
|
||||
3. 值得关注的关键信息
|
||||
|
||||
返回 JSON 格式：{{"suggestions": [{{"type": "insight|action", "title": "...", "description": "..."}}]}}"""
|
||||
|
||||
messages = [ChatMessage(role="user", content=prompt)]
|
||||
content = await llm.chat(messages, temperature=0.3)
|
||||
|
||||
import re
|
||||
json_match = re.search(r'\{{.*?\}}', content, re.DOTALL)
|
||||
if json_match:
|
||||
try:
|
||||
data = json.loads(json_match.group())
|
||||
return data
|
||||
except:
|
||||
pass
|
||||
|
||||
return {"suggestions": []}
|
||||
|
||||
|
||||
# ==================== Phase 4: 知识溯源 API ====================
|
||||
|
||||
@app.get("/api/v1/relations/{relation_id}/provenance")
|
||||
async def get_relation_provenance(relation_id: str):
|
||||
"""获取关系的知识溯源信息"""
|
||||
if not DB_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Database not available")
|
||||
|
||||
db = get_db_manager()
|
||||
relation = db.get_relation_with_details(relation_id)
|
||||
|
||||
if not relation:
|
||||
raise HTTPException(status_code=404, detail="Relation not found")
|
||||
|
||||
return {
|
||||
"relation_id": relation_id,
|
||||
"source": relation.get("source_name"),
|
||||
"target": relation.get("target_name"),
|
||||
"type": relation.get("relation_type"),
|
||||
"evidence": relation.get("evidence"),
|
||||
"transcript": {
|
||||
"id": relation.get("transcript_id"),
|
||||
"filename": relation.get("transcript_filename"),
|
||||
} if relation.get("transcript_id") else None
|
||||
}
|
||||
|
||||
|
||||
@app.get("/api/v1/entities/{entity_id}/details")
|
||||
async def get_entity_details(entity_id: str):
|
||||
"""获取实体详情，包含所有提及位置"""
|
||||
if not DB_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Database not available")
|
||||
|
||||
db = get_db_manager()
|
||||
entity = db.get_entity_with_mentions(entity_id)
|
||||
|
||||
if not entity:
|
||||
raise HTTPException(status_code=404, detail="Entity not found")
|
||||
|
||||
return entity
|
||||
|
||||
|
||||
@app.get("/api/v1/entities/{entity_id}/evolution")
|
||||
async def get_entity_evolution(entity_id: str):
|
||||
"""分析实体的演变和态度变化"""
|
||||
if not DB_AVAILABLE or not LLM_CLIENT_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Service not available")
|
||||
|
||||
db = get_db_manager()
|
||||
llm = get_llm_client()
|
||||
|
||||
entity = db.get_entity_with_mentions(entity_id)
|
||||
if not entity:
|
||||
raise HTTPException(status_code=404, detail="Entity not found")
|
||||
|
||||
# 分析演变
|
||||
analysis = await llm.analyze_entity_evolution(entity["name"], entity.get("mentions", []))
|
||||
|
||||
return {
|
||||
"entity_id": entity_id,
|
||||
"entity_name": entity["name"],
|
||||
"mention_count": entity.get("mention_count", 0),
|
||||
"analysis": analysis,
|
||||
"timeline": [
|
||||
{
|
||||
"date": m.get("transcript_date"),
|
||||
"snippet": m.get("text_snippet"),
|
||||
"transcript_id": m.get("transcript_id"),
|
||||
"filename": m.get("filename")
|
||||
}
|
||||
for m in entity.get("mentions", [])
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
# ==================== Phase 4: 实体管理增强 API ====================
|
||||
|
||||
@app.get("/api/v1/projects/{project_id}/entities/search")
|
||||
async def search_entities(project_id: str, q: str):
|
||||
"""搜索实体"""
|
||||
if not DB_AVAILABLE:
|
||||
raise HTTPException(status_code=500, detail="Database not available")
|
||||
|
||||
db = get_db_manager()
|
||||
entities = db.search_entities(project_id, q)
|
||||
return [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition} for e in entities]
|
||||
|
||||
Reference in New Issue
Block a user