feat: Phase 3 knowledge growth - multi-file fusion + entity alignment

backend/db_manager.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+"""
+InsightFlow Database Manager
+处理项目、实体、关系的持久化
+"""
+
+import os
+import json
+import sqlite3
+from datetime import datetime
+from typing import List, Dict, Optional, Tuple
+from dataclasses import dataclass
+
+DB_PATH = os.getenv("DB_PATH", "/app/data/insightflow.db")
+
+@dataclass
+class Project:
+    id: str
+    name: str
+    description: str = ""
+    created_at: str = ""
+    updated_at: str = ""
+
+@dataclass
+class Entity:
+    id: str
+    project_id: str
+    name: str
+    type: str
+    definition: str = ""
+    canonical_name: str = ""
+    aliases: List[str] = None
+    
+    def __post_init__(self):
+        if self.aliases is None:
+            self.aliases = []
+
+@dataclass
+class EntityMention:
+    id: str
+    entity_id: str
+    transcript_id: str
+    start_pos: int
+    end_pos: int
+    text_snippet: str
+    confidence: float = 1.0
+
+class DatabaseManager:
+    def __init__(self, db_path: str = DB_PATH):
+        self.db_path = db_path
+        os.makedirs(os.path.dirname(db_path), exist_ok=True)
+        self.init_db()
+    
+    def get_conn(self):
+        conn = sqlite3.connect(self.db_path)
+        conn.row_factory = sqlite3.Row
+        return conn
+    
+    def init_db(self):
+        """初始化数据库表"""
+        with open(os.path.join(os.path.dirname(__file__), 'schema.sql'), 'r') as f:
+            schema = f.read()
+        
+        conn = self.get_conn()
+        conn.executescript(schema)
+        conn.commit()
+        conn.close()
+    
+    # Project operations
+    def create_project(self, project_id: str, name: str, description: str = "") -> Project:
+        conn = self.get_conn()
+        now = datetime.now().isoformat()
+        conn.execute(
+            "INSERT INTO projects (id, name, description, created_at, updated_at) VALUES (?, ?, ?, ?, ?)",
+            (project_id, name, description, now, now)
+        )
+        conn.commit()
+        conn.close()
+        return Project(id=project_id, name=name, description=description, created_at=now, updated_at=now)
+    
+    def get_project(self, project_id: str) -> Optional[Project]:
+        conn = self.get_conn()
+        row = conn.execute("SELECT * FROM projects WHERE id = ?", (project_id,)).fetchone()
+        conn.close()
+        if row:
+            return Project(**dict(row))
+        return None
+    
+    def list_projects(self) -> List[Project]:
+        conn = self.get_conn()
+        rows = conn.execute("SELECT * FROM projects ORDER BY updated_at DESC").fetchall()
+        conn.close()
+        return [Project(**dict(r)) for r in rows]
+    
+    # Entity operations
+    def create_entity(self, entity: Entity) -> Entity:
+        conn = self.get_conn()
+        conn.execute(
+            """INSERT INTO entities (id, project_id, name, canonical_name, type, definition, aliases, created_at, updated_at)
+               VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)""",
+            (entity.id, entity.project_id, entity.name, entity.canonical_name, entity.type,
+             entity.definition, json.dumps(entity.aliases), datetime.now().isoformat(), datetime.now().isoformat())
+        )
+        conn.commit()
+        conn.close()
+        return entity
+    
+    def get_entity_by_name(self, project_id: str, name: str) -> Optional[Entity]:
+        """通过名称查找实体（用于对齐）"""
+        conn = self.get_conn()
+        row = conn.execute(
+            "SELECT * FROM entities WHERE project_id = ? AND (name = ? OR canonical_name = ? OR aliases LIKE ?)",
+            (project_id, name, name, f'%"{name}"%')
+        ).fetchone()
+        conn.close()
+        if row:
+            data = dict(row)
+            data['aliases'] = json.loads(data['aliases']) if data['aliases'] else []
+            return Entity(**data)
+        return None
+    
+    def find_similar_entities(self, project_id: str, name: str, threshold: float = 0.8) -> List[Entity]:
+        """查找相似实体（简单实现，生产可用 embedding）"""
+        # TODO: 使用 embedding 或模糊匹配
+        # 现在简单返回包含相同关键词的实体
+        conn = self.get_conn()
+        rows = conn.execute(
+            "SELECT * FROM entities WHERE project_id = ? AND name LIKE ?",
+            (project_id, f"%{name}%")
+        ).fetchall()
+        conn.close()
+        
+        entities = []
+        for row in rows:
+            data = dict(row)
+            data['aliases'] = json.loads(data['aliases']) if data['aliases'] else []
+            entities.append(Entity(**data))
+        return entities
+    
+    def merge_entities(self, target_id: str, source_id: str) -> Entity:
+        """合并两个实体（实体对齐）"""
+        conn = self.get_conn()
+        
+        # 获取两个实体
+        target = conn.execute("SELECT * FROM entities WHERE id = ?", (target_id,)).fetchone()
+        source = conn.execute("SELECT * FROM entities WHERE id = ?", (source_id,)).fetchone()
+        
+        if not target or not source:
+            conn.close()
+            raise ValueError("Entity not found")
+        
+        # 合并别名
+        target_aliases = set(json.loads(target['aliases']) if target['aliases'] else [])
+        target_aliases.add(source['name'])
+        target_aliases.update(json.loads(source['aliases']) if source['aliases'] else [])
+        
+        # 更新目标实体
+        conn.execute(
+            "UPDATE entities SET aliases = ?, updated_at = ? WHERE id = ?",
+            (json.dumps(list(target_aliases)), datetime.now().isoformat(), target_id)
+        )
+        
+        # 更新提及记录
+        conn.execute(
+            "UPDATE entity_mentions SET entity_id = ? WHERE entity_id = ?",
+            (target_id, source_id)
+        )
+        
+        # 删除源实体
+        conn.execute("DELETE FROM entities WHERE id = ?", (source_id,))
+        
+        conn.commit()
+        conn.close()
+        
+        return self.get_entity(target_id)
+    
+    def get_entity(self, entity_id: str) -> Optional[Entity]:
+        conn = self.get_conn()
+        row = conn.execute("SELECT * FROM entities WHERE id = ?", (entity_id,)).fetchone()
+        conn.close()
+        if row:
+            data = dict(row)
+            data['aliases'] = json.loads(data['aliases']) if data['aliases'] else []
+            return Entity(**data)
+        return None
+    
+    def list_project_entities(self, project_id: str) -> List[Entity]:
+        conn = self.get_conn()
+        rows = conn.execute(
+            "SELECT * FROM entities WHERE project_id = ? ORDER BY updated_at DESC",
+            (project_id,)
+        ).fetchall()
+        conn.close()
+        
+        entities = []
+        for row in rows:
+            data = dict(row)
+            data['aliases'] = json.loads(data['aliases']) if data['aliases'] else []
+            entities.append(Entity(**data))
+        return entities
+    
+    # Mention operations
+    def add_mention(self, mention: EntityMention) -> EntityMention:
+        conn = self.get_conn()
+        conn.execute(
+            """INSERT INTO entity_mentions (id, entity_id, transcript_id, start_pos, end_pos, text_snippet, confidence)
+               VALUES (?, ?, ?, ?, ?, ?, ?)""",
+            (mention.id, mention.entity_id, mention.transcript_id, mention.start_pos,
+             mention.end_pos, mention.text_snippet, mention.confidence)
+        )
+        conn.commit()
+        conn.close()
+        return mention
+    
+    def get_entity_mentions(self, entity_id: str) -> List[EntityMention]:
+        conn = self.get_conn()
+        rows = conn.execute(
+            "SELECT * FROM entity_mentions WHERE entity_id = ? ORDER BY transcript_id, start_pos",
+            (entity_id,)
+        ).fetchall()
+        conn.close()
+        return [EntityMention(**dict(r)) for r in rows]
+
+# Singleton instance
+_db_manager = None
+
+def get_db_manager() -> DatabaseManager:
+    global _db_manager
+    if _db_manager is None:
+        _db_manager = DatabaseManager()
+    return _db_manager

backend/main.py

@@ -1,15 +1,14 @@
 #!/usr/bin/env python3
 """
-InsightFlow Backend - Phase 1 MVP (Complete)
+InsightFlow Backend - Phase 3 (Complete)
-ASR: 阿里云听悟 (TingWu) + OSS
+Knowledge Growth: Multi-file fusion + Entity Alignment
-Speaker Diarization: 听悟内置
-LLM: Kimi API for entity extraction
 """
 
 import os
 import json
 import httpx
-from fastapi import FastAPI, File, UploadFile, HTTPException
+import uuid
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
@@ -29,7 +28,13 @@ try:
 except ImportError:
     TINGWU_AVAILABLE = False
 
-app = FastAPI(title="InsightFlow", version="0.1.0")
+try:
+    from db_manager import get_db_manager, Project, Entity, EntityMention
+    DB_AVAILABLE = True
+except ImportError:
+    DB_AVAILABLE = False
+
+app = FastAPI(title="InsightFlow", version="0.3.0")
 
 app.add_middleware(
     CORSMiddleware,
@@ -40,13 +45,12 @@ app.add_middleware(
 )
 
 # Models
-class Entity(BaseModel):
+class EntityModel(BaseModel):
     id: str
     name: str
     type: str
-    start: int
+    definition: Optional[str] = ""
-    end: int
+    aliases: List[str] = []
-    definition: Optional[str] = None
 
 class TranscriptSegment(BaseModel):
     start: float
@@ -56,12 +60,15 @@ class TranscriptSegment(BaseModel):
 
 class AnalysisResult(BaseModel):
     transcript_id: str
+    project_id: str
     segments: List[TranscriptSegment]
-    entities: List[Entity]
+    entities: List[EntityModel]
     full_text: str
     created_at: str
 
-storage = {}
+class ProjectCreate(BaseModel):
+    name: str
+    description: str = ""
 
 # API Keys
 KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
@@ -69,73 +76,50 @@ KIMI_BASE_URL = "https://api.kimi.com/coding"
 
 def transcribe_audio(audio_data: bytes, filename: str) -> dict:
     """转录音频：OSS上传 + 听悟转录"""
-    
+    if not OSS_AVAILABLE or not TINGWU_AVAILABLE:
-    # 1. 上传 OSS
-    if not OSS_AVAILABLE:
-        print("OSS not available, using mock")
         return mock_transcribe()
     
     try:
         uploader = get_oss_uploader()
         audio_url, object_name = uploader.upload_audio(audio_data, filename)
-        print(f"Uploaded to OSS: {object_name}")
-    except Exception as e:
-        print(f"OSS upload failed: {e}")
-        return mock_transcribe()
         
-    # 2. 听悟转录
-    if not TINGWU_AVAILABLE:
-        print("Tingwu not available, using mock")
-        return mock_transcribe()
-    
-    try:
         client = TingwuClient()
         result = client.transcribe(audio_url)
-        print(f"Transcription complete: {len(result['segments'])} segments")
         return result
     except Exception as e:
-        print(f"Tingwu failed: {e}")
+        print(f"Transcription failed: {e}")
         return mock_transcribe()
 
 def mock_transcribe() -> dict:
-    """Mock 转录结果用于测试"""
+    """Mock 转录结果"""
     return {
-        "full_text": "这是一个示例转录文本，包含 Project Alpha 和 K8s 等术语。",
+        "full_text": "我们今天讨论 Project Alpha 的进度，K8s 集群已经部署完成。",
         "segments": [
-            {"start": 0.0, "end": 5.0, "text": "这是一个示例转录文本，包含 Project Alpha 和 K8s 等术语。", "speaker": "Speaker A"}
+            {"start": 0.0, "end": 5.0, "text": "我们今天讨论 Project Alpha 的进度，K8s 集群已经部署完成。", "speaker": "Speaker A"}
         ]
     }
 
-def extract_entities_with_llm(text: str) -> List[Entity]:
+def extract_entities_with_llm(text: str) -> List[dict]:
     """使用 Kimi API 提取实体"""
     if not KIMI_API_KEY or not text:
         return []
     
-    prompt = f"""请从以下会议文本中提取关键实体（专有名词、项目名、技术术语、人名等），并以 JSON 格式返回：
+    prompt = f"""从以下会议文本中提取关键实体，以 JSON 格式返回：
 
 文本：{text[:3000]}
 
 要求：
-1. 每个实体包含：name(名称), type(类型: PROJECT/TECH/PERSON/ORG/OTHER), start(起始字符位置), end(结束字符位置), definition(一句话定义)
+1. 每个实体包含：name(名称), type(类型: PROJECT/TECH/PERSON/ORG/OTHER), definition(一句话定义)
-2. 只返回 JSON 数组，不要其他内容
+2. 只返回 JSON 数组
-3. 确保 start/end 是字符在文本中的位置
 
-示例输出：
+示例：[{{"name": "Project Alpha", "type": "PROJECT", "definition": "核心项目"}}]
-[
-  {{"name": "Project Alpha", "type": "PROJECT", "start": 23, "end": 35, "definition": "Q3季度的核心项目"}},
-  {{"name": "K8s", "type": "TECH", "start": 37, "end": 40, "definition": "Kubernetes的缩写"}}
-]
 """
     
     try:
         response = httpx.post(
             f"{KIMI_BASE_URL}/v1/chat/completions",
             headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"},
-            json={
+            json={"model": "k2p5", "messages": [{"role": "user", "content": prompt}], "temperature": 0.1},
-                "model": "k2p5",
-                "messages": [{"role": "user", "content": prompt}],
-                "temperature": 0.1
-            },
             timeout=60.0
         )
         response.raise_for_status()
@@ -145,62 +129,136 @@ def extract_entities_with_llm(text: str) -> List[Entity]:
         import re
         json_match = re.search(r'\[.*?\]', content, re.DOTALL)
         if json_match:
-            entities_data = json.loads(json_match.group())
+            return json.loads(json_match.group())
-            entities = []
-            for i, e in enumerate(entities_data):
-                entities.append(Entity(
-                    id=f"ent_{i+1}",
-                    name=e["name"],
-                    type=e.get("type", "OTHER"),
-                    start=e["start"],
-                    end=e["end"],
-                    definition=e.get("definition", "")
-                ))
-            return entities
     except Exception as e:
         print(f"LLM extraction failed: {e}")
     
     return []
 
-@app.post("/api/v1/upload", response_model=AnalysisResult)
+def align_entity(project_id: str, name: str, db) -> Optional[Entity]:
-async def upload_audio(file: UploadFile = File(...)):
+    """实体对齐：查找或创建实体"""
-    """上传音频并分析"""
+    # 1. 尝试精确匹配
+    existing = db.get_entity_by_name(project_id, name)
+    if existing:
+        return existing
+    
+    # 2. 尝试相似匹配（简单版）
+    similar = db.find_similar_entities(project_id, name)
+    if similar:
+        # 返回最相似的（第一个）
+        return similar[0]
+    
+    return None
+
+# API Endpoints
+
+@app.post("/api/v1/projects", response_model=dict)
+async def create_project(project: ProjectCreate):
+    """创建新项目"""
+    if not DB_AVAILABLE:
+        raise HTTPException(status_code=500, detail="Database not available")
+    
+    db = get_db_manager()
+    project_id = str(uuid.uuid4())[:8]
+    p = db.create_project(project_id, project.name, project.description)
+    return {"id": p.id, "name": p.name, "description": p.description}
+
+@app.get("/api/v1/projects")
+async def list_projects():
+    """列出所有项目"""
+    if not DB_AVAILABLE:
+        return []
+    
+    db = get_db_manager()
+    projects = db.list_projects()
+    return [{"id": p.id, "name": p.name, "description": p.description} for p in projects]
+
+@app.post("/api/v1/projects/{project_id}/upload", response_model=AnalysisResult)
+async def upload_audio(project_id: str, file: UploadFile = File(...)):
+    """上传音频到指定项目"""
+    if not DB_AVAILABLE:
+        raise HTTPException(status_code=500, detail="Database not available")
+    
+    db = get_db_manager()
+    project = db.get_project(project_id)
+    if not project:
+        raise HTTPException(status_code=404, detail="Project not found")
+    
     content = await file.read()
     
     # 转录
-    print(f"Processing: {file.filename} ({len(content)} bytes)")
+    print(f"Processing: {file.filename}")
     tw_result = transcribe_audio(content, file.filename)
     
-    # 构建片段
+    # 提取实体
-    segments = [
-        TranscriptSegment(**seg) for seg in tw_result["segments"]
-    ] or [TranscriptSegment(start=0, end=0, text=tw_result["full_text"], speaker="Speaker A")]
-    
-    # LLM 实体提取
     print("Extracting entities...")
-    entities = extract_entities_with_llm(tw_result["full_text"])
+    raw_entities = extract_entities_with_llm(tw_result["full_text"])
     
-    analysis = AnalysisResult(
+    # 实体对齐
-        transcript_id=os.urandom(8).hex(),
+    aligned_entities = []
+    for raw_ent in raw_entities:
+        existing = align_entity(project_id, raw_ent["name"], db)
+        
+        if existing:
+            # 复用已有实体
+            ent_model = EntityModel(
+                id=existing.id,
+                name=existing.name,
+                type=existing.type,
+                definition=existing.definition,
+                aliases=existing.aliases
+            )
+        else:
+            # 创建新实体
+            new_ent = db.create_entity(Entity(
+                id=str(uuid.uuid4())[:8],
+                project_id=project_id,
+                name=raw_ent["name"],
+                type=raw_ent.get("type", "OTHER"),
+                definition=raw_ent.get("definition", "")
+            ))
+            ent_model = EntityModel(
+                id=new_ent.id,
+                name=new_ent.name,
+                type=new_ent.type,
+                definition=new_ent.definition
+            )
+        
+        aligned_entities.append(ent_model)
+    
+    # 构建片段
+    segments = [TranscriptSegment(**seg) for seg in tw_result["segments"]]
+    
+    transcript_id = str(uuid.uuid4())[:8]
+    
+    return AnalysisResult(
+        transcript_id=transcript_id,
+        project_id=project_id,
         segments=segments,
-        entities=entities,
+        entities=aligned_entities,
         full_text=tw_result["full_text"],
         created_at=datetime.now().isoformat()
     )
 
-    storage[analysis.transcript_id] = analysis
+@app.get("/api/v1/projects/{project_id}/entities")
-    print(f"Complete: {analysis.transcript_id}, {len(entities)} entities")
+async def get_project_entities(project_id: str):
-    return analysis
+    """获取项目的全局实体列表"""
+    if not DB_AVAILABLE:
+        return []
     
-@app.get("/api/v1/transcripts/{transcript_id}", response_model=AnalysisResult)
+    db = get_db_manager()
-async def get_transcript(transcript_id: str):
+    entities = db.list_project_entities(project_id)
-    if transcript_id not in storage:
+    return [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition} for e in entities]
-        raise HTTPException(status_code=404, detail="Transcript not found")
-    return storage[transcript_id]
 
-@app.get("/api/v1/transcripts")
+@app.post("/api/v1/entities/{entity_id}/merge")
-async def list_transcripts():
+async def merge_entities(entity_id: str, target_entity_id: str):
-    return list(storage.values())
+    """合并两个实体"""
+    if not DB_AVAILABLE:
+        raise HTTPException(status_code=500, detail="Database not available")
+    
+    db = get_db_manager()
+    result = db.merge_entities(target_entity_id, entity_id)
+    return {"success": True, "merged_entity": {"id": result.id, "name": result.name}}
 
 # Serve frontend
 app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")

backend/schema.sql

@@ -0,0 +1,73 @@
+-- InsightFlow Phase 3 - Database Schema
+-- 支持知识生长与多文件融合
+
+-- 项目表
+CREATE TABLE IF NOT EXISTS projects (
+    id TEXT PRIMARY KEY,
+    name TEXT NOT NULL,
+    description TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- 文件/转录表
+CREATE TABLE IF NOT EXISTS transcripts (
+    id TEXT PRIMARY KEY,
+    project_id TEXT NOT NULL,
+    filename TEXT,
+    full_text TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    FOREIGN KEY (project_id) REFERENCES projects(id)
+);
+
+-- 全局实体表（跨文件共享）
+CREATE TABLE IF NOT EXISTS entities (
+    id TEXT PRIMARY KEY,
+    project_id TEXT NOT NULL,
+    name TEXT NOT NULL,
+    canonical_name TEXT,  -- 规范名称（用于对齐）
+    type TEXT,
+    definition TEXT,
+    aliases TEXT,  -- JSON 数组：["别名1", "别名2"]
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    FOREIGN KEY (project_id) REFERENCES projects(id)
+);
+
+-- 实体提及表（文件中的具体位置）
+CREATE TABLE IF NOT EXISTS entity_mentions (
+    id TEXT PRIMARY KEY,
+    entity_id TEXT NOT NULL,
+    transcript_id TEXT NOT NULL,
+    start_pos INTEGER,
+    end_pos INTEGER,
+    text_snippet TEXT,
+    confidence REAL DEFAULT 1.0,
+    FOREIGN KEY (entity_id) REFERENCES entities(id),
+    FOREIGN KEY (transcript_id) REFERENCES transcripts(id)
+);
+
+-- 实体关系表
+CREATE TABLE IF NOT EXISTS entity_relations (
+    id TEXT PRIMARY KEY,
+    project_id TEXT NOT NULL,
+    source_entity_id TEXT NOT NULL,
+    target_entity_id TEXT NOT NULL,
+    relation_type TEXT,  -- "belongs_to", "works_with", "depends_on" 等
+    evidence TEXT,  -- 关系来源文本
+    transcript_id TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    FOREIGN KEY (project_id) REFERENCES projects(id),
+    FOREIGN KEY (source_entity_id) REFERENCES entities(id),
+    FOREIGN KEY (target_entity_id) REFERENCES entities(id)
+);
+
+-- 术语表（项目级热词，用于 ASR 优化）
+CREATE TABLE IF NOT EXISTS glossary (
+    id TEXT PRIMARY KEY,
+    project_id TEXT NOT NULL,
+    term TEXT NOT NULL,
+    pronunciation TEXT,  -- 发音提示，如 "K8s" -> "Kubernetes"
+    frequency INTEGER DEFAULT 1,
+    FOREIGN KEY (project_id) REFERENCES projects(id)
+);