diff --git a/Dockerfile b/Dockerfile index 0634299..f315c5c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,29 +1,33 @@ +# InsightFlow - Audio to Knowledge Graph Platform +# Phase 3: Memory & Growth + FROM python:3.11-slim WORKDIR /app -# Install uv -COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ - -# Install system deps +# Install system dependencies RUN apt-get update && apt-get install -y \ - ffmpeg \ - git \ + gcc \ + libpq-dev \ && rm -rf /var/lib/apt/lists/* -# Copy project files -COPY backend/pyproject.toml backend/uv.lock ./ +# Copy backend requirements +COPY backend/requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt -# Install dependencies using uv sync -RUN uv sync --frozen --no-install-project - -# Copy code +# Copy application code COPY backend/ ./backend/ COPY frontend/ ./frontend/ -# Install project -RUN uv sync --frozen +# Create data directory +RUN mkdir -p /app/data +# Set environment variables +ENV PYTHONPATH=/app +ENV DB_PATH=/app/data/insightflow.db + +# Expose port EXPOSE 8000 -CMD ["uv", "run", "python", "backend/main.py"] +# Run the application +CMD ["python", "-m", "uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"] diff --git a/README.md b/README.md index bd7bd7b..0c3aacd 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,88 @@ -# InsightFlow +# InsightFlow - Audio to Knowledge Graph Platform -音频与文档的领域知识构建平台 +## Phase 3: Memory & Growth - Completed ✅ -## 产品定位 -将会议录音和文档转化为结构化的知识图谱，通过人机回圈(Human-in-the-Loop)实现知识持续生长。 +### 新增功能 -## 核心特性 -- 🎙️ ASR 语音识别 + 热词注入 -- 🧠 LLM 实体抽取与解释 -- 🔗 双视图联动（文档视图 + 图谱视图） -- 📈 知识生长（多文件实体对齐） +#### 1. 多文件图谱融合 ✅ +- 支持上传多个音频文件到同一项目 +- 系统自动对齐实体，合并图谱 +- 实体提及跨文件追踪 +- 文件选择器切换不同转录内容 -## 技术栈 -- 前端: Next.js + Tailwind -- 后端: Node.js / Python -- 数据库: MySQL + Neo4j -- ASR: Whisper -- LLM: OpenAI / Kimi +#### 2. 实体对齐算法优化 ✅ +- 新增 `entity_aligner.py` 模块 +- 支持使用 Kimi API embedding 进行语义相似度匹配 +- 余弦相似度计算 +- 自动别名建议 +- 批量实体对齐 API -## 开发阶段 -- [ ] Phase 1: 骨架与单体分析 (MVP) -- [ ] Phase 2: 交互与纠错工作台 -- [ ] Phase 3: 记忆与生长 +#### 3. PDF/DOCX 文档导入 ✅ +- 新增 `document_processor.py` 模块 +- 支持 PDF、DOCX、TXT、MD 格式 +- 文档文本提取并参与实体提取 +- 文档类型标记（音频/文档） -## 文档 -- [PRD v2.0](docs/PRD-v2.0.md) +#### 4. 项目知识库面板 ✅ +- 全新的知识库视图 +- 统计面板：实体数、关系数、文件数、术语数 +- 实体网格展示（带提及统计） +- 关系列表展示 +- 术语表管理（添加/删除） +- 文件列表展示 + +### 技术栈 +- 后端: FastAPI + SQLite +- 前端: 原生 HTML/JS + D3.js +- ASR: 阿里云听悟 +- LLM: Kimi API +- 文档处理: PyPDF2, python-docx + +### 部署 + +```bash +# 构建 Docker 镜像 +docker build -t insightflow:phase3 . + +# 运行容器 +docker run -d \ + -p 18000:8000 \ + -v /opt/data:/app/data \ + -e KIMI_API_KEY=your_key \ + -e ALIYUN_ACCESS_KEY_ID=your_key \ + -e ALIYUN_ACCESS_KEY_SECRET=your_secret \ + insightflow:phase3 +``` + +### API 文档 + +#### 新增 API + +**文档上传** +``` +POST /api/v1/projects/{project_id}/upload-document +Content-Type: multipart/form-data +file: <文件> +``` + +**知识库查询** +``` +GET /api/v1/projects/{project_id}/knowledge-base +``` + +**术语表管理** +``` +POST /api/v1/projects/{project_id}/glossary +GET /api/v1/projects/{project_id}/glossary +DELETE /api/v1/glossary/{term_id} +``` + +**实体对齐** +``` +POST /api/v1/projects/{project_id}/align-entities?threshold=0.85 +``` + +### 数据库 Schema 更新 +- `transcripts` 表新增 `type` 字段（audio/document） +- `entities` 表新增 `embedding` 字段 +- 新增索引优化查询性能 diff --git a/STATUS.md b/STATUS.md index 7d92a31..8f40b44 100644 --- a/STATUS.md +++ b/STATUS.md @@ -4,7 +4,7 @@ ## 当前阶段 -Phase 2: 交互与纠错工作台 - **已完成 ✅** +Phase 3: 记忆与生长 - **已完成 ✅** ## 已完成 @@ -64,22 +64,70 @@ Phase 2: 交互与纠错工作台 - **已完成 ✅** - ✅ update_relation() - 更新关系 - ✅ update_transcript() - 更新转录文本 -## Phase 3 计划 (记忆与生长) - **即将开始** +### Phase 3: 记忆与生长 ✅ -- 多文件图谱融合 -- 实体对齐算法优化 -- PDF/DOCX 文档导入 -- 项目知识库面板 +#### 多文件图谱融合 +- ✅ 支持上传多个音频文件到同一项目 +- ✅ 系统自动对齐实体，合并图谱 +- ✅ 实体提及跨文件追踪 +- ✅ 文件选择器切换不同转录内容 +- ✅ 转录列表 API 返回文件类型 + +#### 实体对齐算法优化 +- ✅ 新增 `entity_aligner.py` 模块 +- ✅ 使用 Kimi API embedding 进行语义相似度匹配 +- ✅ 余弦相似度计算 +- ✅ 自动别名建议 +- ✅ 批量实体对齐 API +- ✅ 实体对齐回退机制（字符串匹配） + +#### PDF/DOCX 文档导入 +- ✅ 新增 `document_processor.py` 模块 +- ✅ 支持 PDF、DOCX、TXT、MD 格式 +- ✅ 文档文本提取并参与实体提取 +- ✅ 文档上传 API (/api/v1/projects/{id}/upload-document) +- ✅ 文档类型标记（audio/document） + +#### 项目知识库面板 +- ✅ 全新的知识库视图 +- ✅ 侧边栏导航切换（工作台/知识库） +- ✅ 统计面板：实体数、关系数、文件数、术语数 +- ✅ 实体网格展示（带提及统计） +- ✅ 关系列表展示 +- ✅ 术语表管理（添加/删除） +- ✅ 文件列表展示（区分音频/文档） + +#### 术语表功能 +- ✅ 术语表数据库表 (glossary) +- ✅ 添加术语 API +- ✅ 获取术语列表 API +- ✅ 删除术语 API +- ✅ 前端术语表管理界面 + +#### 数据库更新 +- ✅ transcripts 表新增 `type` 字段 +- ✅ entities 表新增 `embedding` 字段 +- ✅ 新增 glossary 表 +- ✅ 新增索引优化查询性能 ## 技术债务 - 听悟 SDK fallback 到 mock 需要更好的错误处理 -- 实体相似度匹配目前只是简单字符串包含，需要 embedding 方案 - 前端需要状态管理（目前使用全局变量） - 需要添加 API 文档 (OpenAPI/Swagger) +- Embedding 缓存需要持久化 +- 实体对齐算法需要更多测试 ## 部署信息 - 服务器: 122.51.127.111 - 项目路径: /opt/projects/insightflow - 端口: 18000 +- Docker 镜像: insightflow:phase3 + +## 下一步 (Phase 4) + +- 知识推理与问答 +- 实体属性扩展 +- 时间线视图 +- 导出功能（PDF/图片） diff --git a/backend/db_manager.py b/backend/db_manager.py index 0ef5322..7aba584 100644 --- a/backend/db_manager.py +++ b/backend/db_manager.py @@ -1,7 +1,8 @@ #!/usr/bin/env python3 """ -InsightFlow Database Manager +InsightFlow Database Manager - Phase 3 处理项目、实体、关系的持久化 +支持文档类型和多文件融合 """ import os @@ -166,6 +167,18 @@ class DatabaseManager: (target_id, source_id) ) + # 更新关系 - source 作为 source_entity_id + conn.execute( + "UPDATE entity_relations SET source_entity_id = ? WHERE source_entity_id = ?", + (target_id, source_id) + ) + + # 更新关系 - source 作为 target_entity_id + conn.execute( + "UPDATE entity_relations SET target_entity_id = ? WHERE target_entity_id = ?", + (target_id, source_id) + ) + # 删除源实体 conn.execute("DELETE FROM entities WHERE id = ?", (source_id,)) @@ -222,13 +235,13 @@ class DatabaseManager: return [EntityMention(**dict(r)) for r in rows] # Transcript operations - def save_transcript(self, transcript_id: str, project_id: str, filename: str, full_text: str): + def save_transcript(self, transcript_id: str, project_id: str, filename: str, full_text: str, transcript_type: str = "audio"): """保存转录记录""" conn = self.get_conn() now = datetime.now().isoformat() conn.execute( - "INSERT INTO transcripts (id, project_id, filename, full_text, created_at) VALUES (?, ?, ?, ?, ?)", - (transcript_id, project_id, filename, full_text, now) + "INSERT INTO transcripts (id, project_id, filename, full_text, type, created_at) VALUES (?, ?, ?, ?, ?, ?)", + (transcript_id, project_id, filename, full_text, transcript_type, now) ) conn.commit() conn.close() @@ -388,6 +401,58 @@ class DatabaseManager: conn.close() return dict(row) if row else None + + # Phase 3: Glossary operations + def add_glossary_term(self, project_id: str, term: str, pronunciation: str = "") -> str: + """添加术语到术语表""" + conn = self.get_conn() + + # 检查是否已存在 + existing = conn.execute( + "SELECT * FROM glossary WHERE project_id = ? AND term = ?", + (project_id, term) + ).fetchone() + + if existing: + # 更新频率 + conn.execute( + "UPDATE glossary SET frequency = frequency + 1 WHERE id = ?", + (existing['id'],) + ) + conn.commit() + conn.close() + return existing['id'] + + term_id = str(uuid.uuid4())[:8] + conn.execute( + "INSERT INTO glossary (id, project_id, term, pronunciation, frequency) VALUES (?, ?, ?, ?, ?)", + (term_id, project_id, term, pronunciation, 1) + ) + conn.commit() + conn.close() + return term_id + + def list_glossary(self, project_id: str) -> List[dict]: + """列出项目术语表""" + conn = self.get_conn() + rows = conn.execute( + "SELECT * FROM glossary WHERE project_id = ? ORDER BY frequency DESC", + (project_id,) + ).fetchall() + conn.close() + return [dict(r) for r in rows] + + def delete_glossary_term(self, term_id: str): + """删除术语""" + conn = self.get_conn() + conn.execute("DELETE FROM glossary WHERE id = ?", (term_id,)) + conn.commit() + conn.close() + + # Phase 3: Get all entities for embedding + def get_all_entities_for_embedding(self, project_id: str) -> List[Entity]: + """获取所有实体用于 embedding 计算""" + return self.list_project_entities(project_id) # Singleton instance diff --git a/backend/document_processor.py b/backend/document_processor.py new file mode 100644 index 0000000..84cb1bf --- /dev/null +++ b/backend/document_processor.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python3 +""" +Document Processor - Phase 3 +支持 PDF 和 DOCX 文档导入 +""" + +import os +import io +from typing import Dict, Optional + +class DocumentProcessor: + """文档处理器 - 提取 PDF/DOCX 文本""" + + def __init__(self): + self.supported_formats = { + '.pdf': self._extract_pdf, + '.docx': self._extract_docx, + '.doc': self._extract_docx, + '.txt': self._extract_txt, + '.md': self._extract_txt, + } + + def process(self, content: bytes, filename: str) -> Dict[str, str]: + """ + 处理文档并提取文本 + + Args: + content: 文件二进制内容 + filename: 文件名 + + Returns: + {"text": "提取的文本内容", "format": "文件格式"} + """ + ext = os.path.splitext(filename.lower())[1] + + if ext not in self.supported_formats: + raise ValueError(f"Unsupported file format: {ext}. Supported: {list(self.supported_formats.keys())}") + + extractor = self.supported_formats[ext] + text = extractor(content) + + # 清理文本 + text = self._clean_text(text) + + return { + "text": text, + "format": ext, + "filename": filename + } + + def _extract_pdf(self, content: bytes) -> str: + """提取 PDF 文本""" + try: + import PyPDF2 + pdf_file = io.BytesIO(content) + reader = PyPDF2.PdfReader(pdf_file) + + text_parts = [] + for page in reader.pages: + page_text = page.extract_text() + if page_text: + text_parts.append(page_text) + + return "\n\n".join(text_parts) + except ImportError: + # Fallback: 尝试使用 pdfplumber + try: + import pdfplumber + text_parts = [] + with pdfplumber.open(io.BytesIO(content)) as pdf: + for page in pdf.pages: + page_text = page.extract_text() + if page_text: + text_parts.append(page_text) + return "\n\n".join(text_parts) + except ImportError: + raise ImportError("PDF processing requires PyPDF2 or pdfplumber. Install with: pip install PyPDF2") + except Exception as e: + raise ValueError(f"PDF extraction failed: {str(e)}") + + def _extract_docx(self, content: bytes) -> str: + """提取 DOCX 文本""" + try: + import docx + doc_file = io.BytesIO(content) + doc = docx.Document(doc_file) + + text_parts = [] + for para in doc.paragraphs: + if para.text.strip(): + text_parts.append(para.text) + + # 提取表格中的文本 + for table in doc.tables: + for row in table.rows: + row_text = [] + for cell in row.cells: + if cell.text.strip(): + row_text.append(cell.text.strip()) + if row_text: + text_parts.append(" | ".join(row_text)) + + return "\n\n".join(text_parts) + except ImportError: + raise ImportError("DOCX processing requires python-docx. Install with: pip install python-docx") + except Exception as e: + raise ValueError(f"DOCX extraction failed: {str(e)}") + + def _extract_txt(self, content: bytes) -> str: + """提取纯文本""" + # 尝试多种编码 + encodings = ['utf-8', 'gbk', 'gb2312', 'latin-1'] + + for encoding in encodings: + try: + return content.decode(encoding) + except UnicodeDecodeError: + continue + + # 如果都失败了，使用 latin-1 并忽略错误 + return content.decode('latin-1', errors='ignore') + + def _clean_text(self, text: str) -> str: + """清理提取的文本""" + if not text: + return "" + + # 移除多余的空白字符 + lines = text.split('\n') + cleaned_lines = [] + + for line in lines: + line = line.strip() + # 移除空行，但保留段落分隔 + if line: + cleaned_lines.append(line) + + # 合并行，保留段落结构 + text = '\n\n'.join(cleaned_lines) + + # 移除多余的空格 + text = ' '.join(text.split()) + + # 移除控制字符 + text = ''.join(char for char in text if ord(char) >= 32 or char in '\n\r\t') + + return text.strip() + + def is_supported(self, filename: str) -> bool: + """检查文件格式是否支持""" + ext = os.path.splitext(filename.lower())[1] + return ext in self.supported_formats + + +# 简单的文本提取器（不需要外部依赖） +class SimpleTextExtractor: + """简单的文本提取器，用于测试""" + + def extract(self, content: bytes, filename: str) -> str: + """尝试提取文本""" + encodings = ['utf-8', 'gbk', 'latin-1'] + + for encoding in encodings: + try: + return content.decode(encoding) + except UnicodeDecodeError: + continue + + return content.decode('latin-1', errors='ignore') + + +if __name__ == "__main__": + # 测试 + processor = DocumentProcessor() + + # 测试文本提取 + test_text = "Hello World\n\nThis is a test document.\n\nMultiple paragraphs." + result = processor.process(test_text.encode('utf-8'), "test.txt") + print(f"Text extraction test: {len(result['text'])} chars") + print(result['text'][:100]) diff --git a/backend/entity_aligner.py b/backend/entity_aligner.py new file mode 100644 index 0000000..e3b5ace --- /dev/null +++ b/backend/entity_aligner.py @@ -0,0 +1,372 @@ +#!/usr/bin/env python3 +""" +Entity Aligner - Phase 3 +使用 embedding 进行实体对齐 +""" + +import os +import json +import httpx +import numpy as np +from typing import List, Optional, Dict +from dataclasses import dataclass + +# API Keys +KIMI_API_KEY = os.getenv("KIMI_API_KEY", "") +KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding") + +@dataclass +class EntityEmbedding: + entity_id: str + name: str + definition: str + embedding: List[float] + +class EntityAligner: + """实体对齐器 - 使用 embedding 进行相似度匹配""" + + def __init__(self, similarity_threshold: float = 0.85): + self.similarity_threshold = similarity_threshold + self.embedding_cache: Dict[str, List[float]] = {} + + def get_embedding(self, text: str) -> Optional[List[float]]: + """ + 使用 Kimi API 获取文本的 embedding + + Args: + text: 输入文本 + + Returns: + embedding 向量或 None + """ + if not KIMI_API_KEY: + return None + + # 检查缓存 + cache_key = hash(text) + if cache_key in self.embedding_cache: + return self.embedding_cache[cache_key] + + try: + response = httpx.post( + f"{KIMI_BASE_URL}/v1/embeddings", + headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"}, + json={ + "model": "k2p5", + "input": text[:500] # 限制长度 + }, + timeout=30.0 + ) + response.raise_for_status() + result = response.json() + + embedding = result["data"][0]["embedding"] + self.embedding_cache[cache_key] = embedding + return embedding + + except Exception as e: + print(f"Embedding API failed: {e}") + return None + + def compute_similarity(self, embedding1: List[float], embedding2: List[float]) -> float: + """ + 计算两个 embedding 的余弦相似度 + + Args: + embedding1: 第一个向量 + embedding2: 第二个向量 + + Returns: + 相似度分数 (0-1) + """ + vec1 = np.array(embedding1) + vec2 = np.array(embedding2) + + # 余弦相似度 + dot_product = np.dot(vec1, vec2) + norm1 = np.linalg.norm(vec1) + norm2 = np.linalg.norm(vec2) + + if norm1 == 0 or norm2 == 0: + return 0.0 + + return float(dot_product / (norm1 * norm2)) + + def get_entity_text(self, name: str, definition: str = "") -> str: + """ + 构建用于 embedding 的实体文本 + + Args: + name: 实体名称 + definition: 实体定义 + + Returns: + 组合文本 + """ + if definition: + return f"{name}: {definition}" + return name + + def find_similar_entity( + self, + project_id: str, + name: str, + definition: str = "", + exclude_id: Optional[str] = None, + threshold: Optional[float] = None + ) -> Optional[object]: + """ + 查找相似的实体 + + Args: + project_id: 项目 ID + name: 实体名称 + definition: 实体定义 + exclude_id: 要排除的实体 ID + threshold: 相似度阈值 + + Returns: + 相似的实体或 None + """ + if threshold is None: + threshold = self.similarity_threshold + + try: + from db_manager import get_db_manager + db = get_db_manager() + except ImportError: + return None + + # 获取项目的所有实体 + entities = db.get_all_entities_for_embedding(project_id) + + if not entities: + return None + + # 获取查询实体的 embedding + query_text = self.get_entity_text(name, definition) + query_embedding = self.get_embedding(query_text) + + if query_embedding is None: + # 如果 embedding API 失败，回退到简单匹配 + return self._fallback_similarity_match(entities, name, exclude_id) + + best_match = None + best_score = threshold + + for entity in entities: + if exclude_id and entity.id == exclude_id: + continue + + # 获取实体的 embedding + entity_text = self.get_entity_text(entity.name, entity.definition) + entity_embedding = self.get_embedding(entity_text) + + if entity_embedding is None: + continue + + # 计算相似度 + similarity = self.compute_similarity(query_embedding, entity_embedding) + + if similarity > best_score: + best_score = similarity + best_match = entity + + return best_match + + def _fallback_similarity_match( + self, + entities: List[object], + name: str, + exclude_id: Optional[str] = None + ) -> Optional[object]: + """ + 回退到简单的相似度匹配（不使用 embedding） + + Args: + entities: 实体列表 + name: 查询名称 + exclude_id: 要排除的实体 ID + + Returns: + 最相似的实体或 None + """ + name_lower = name.lower() + + # 1. 精确匹配 + for entity in entities: + if exclude_id and entity.id == exclude_id: + continue + if entity.name.lower() == name_lower: + return entity + if entity.aliases and name_lower in [a.lower() for a in entity.aliases]: + return entity + + # 2. 包含匹配 + for entity in entities: + if exclude_id and entity.id == exclude_id: + continue + if name_lower in entity.name.lower() or entity.name.lower() in name_lower: + return entity + + return None + + def batch_align_entities( + self, + project_id: str, + new_entities: List[Dict], + threshold: Optional[float] = None + ) -> List[Dict]: + """ + 批量对齐实体 + + Args: + project_id: 项目 ID + new_entities: 新实体列表 [{"name": "...", "definition": "..."}] + threshold: 相似度阈值 + + Returns: + 对齐结果列表 [{"new_entity": {...}, "matched_entity": {...}, "similarity": 0.9}] + """ + if threshold is None: + threshold = self.similarity_threshold + + results = [] + + for new_ent in new_entities: + matched = self.find_similar_entity( + project_id, + new_ent["name"], + new_ent.get("definition", ""), + threshold=threshold + ) + + result = { + "new_entity": new_ent, + "matched_entity": None, + "similarity": 0.0, + "should_merge": False + } + + if matched: + # 计算相似度 + query_text = self.get_entity_text(new_ent["name"], new_ent.get("definition", "")) + matched_text = self.get_entity_text(matched.name, matched.definition) + + query_emb = self.get_embedding(query_text) + matched_emb = self.get_embedding(matched_text) + + if query_emb and matched_emb: + similarity = self.compute_similarity(query_emb, matched_emb) + result["matched_entity"] = { + "id": matched.id, + "name": matched.name, + "type": matched.type, + "definition": matched.definition + } + result["similarity"] = similarity + result["should_merge"] = similarity >= threshold + + results.append(result) + + return results + + def suggest_entity_aliases(self, entity_name: str, entity_definition: str = "") -> List[str]: + """ + 使用 LLM 建议实体的别名 + + Args: + entity_name: 实体名称 + entity_definition: 实体定义 + + Returns: + 建议的别名列表 + """ + if not KIMI_API_KEY: + return [] + + prompt = f"""为以下实体生成可能的别名或简称： + +实体名称：{entity_name} +定义：{entity_definition} + +请返回 JSON 格式的别名列表： +{{"aliases": ["别名1", "别名2", "别名3"]}} + +只返回 JSON，不要其他内容。""" + + try: + response = httpx.post( + f"{KIMI_BASE_URL}/v1/chat/completions", + headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"}, + json={ + "model": "k2p5", + "messages": [{"role": "user", "content": prompt}], + "temperature": 0.3 + }, + timeout=30.0 + ) + response.raise_for_status() + result = response.json() + content = result["choices"][0]["message"]["content"] + + import re + json_match = re.search(r'\{{.*?\}}', content, re.DOTALL) + if json_match: + data = json.loads(json_match.group()) + return data.get("aliases", []) + except Exception as e: + print(f"Alias suggestion failed: {e}") + + return [] + + +# 简单的字符串相似度计算（不使用 embedding） +def simple_similarity(str1: str, str2: str) -> float: + """ + 计算两个字符串的简单相似度 + + Args: + str1: 第一个字符串 + str2: 第二个字符串 + + Returns: + 相似度分数 (0-1) + """ + if str1 == str2: + return 1.0 + + if not str1 or not str2: + return 0.0 + + # 转换为小写 + s1 = str1.lower() + s2 = str2.lower() + + # 包含关系 + if s1 in s2 or s2 in s1: + return 0.8 + + # 计算编辑距离相似度 + from difflib import SequenceMatcher + return SequenceMatcher(None, s1, s2).ratio() + + +if __name__ == "__main__": + # 测试 + aligner = EntityAligner() + + # 测试 embedding + test_text = "Kubernetes 容器编排平台" + embedding = aligner.get_embedding(test_text) + if embedding: + print(f"Embedding dimension: {len(embedding)}") + print(f"First 5 values: {embedding[:5]}") + else: + print("Embedding API not available") + + # 测试相似度计算 + emb1 = [1.0, 0.0, 0.0] + emb2 = [0.9, 0.1, 0.0] + sim = aligner.compute_similarity(emb1, emb2) + print(f"Similarity: {sim:.4f}") diff --git a/backend/main.py b/backend/main.py index d878b3b..1ca47dc 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,7 +1,7 @@ #!/usr/bin/env python3 """ -InsightFlow Backend - Phase 3 (Production Ready) -Knowledge Growth: Multi-file fusion + Entity Alignment +InsightFlow Backend - Phase 3 (Memory & Growth) +Knowledge Growth: Multi-file fusion + Entity Alignment + Document Import ASR: 阿里云听悟 + OSS """ @@ -9,6 +9,7 @@ import os import json import httpx import uuid +import re from fastapi import FastAPI, File, UploadFile, HTTPException, Form from fastapi.middleware.cors import CORSMiddleware from fastapi.staticfiles import StaticFiles @@ -35,6 +36,18 @@ try: except ImportError: DB_AVAILABLE = False +try: + from document_processor import DocumentProcessor + DOC_PROCESSOR_AVAILABLE = True +except ImportError: + DOC_PROCESSOR_AVAILABLE = False + +try: + from entity_aligner import EntityAligner + ALIGNER_AVAILABLE = True +except ImportError: + ALIGNER_AVAILABLE = False + app = FastAPI(title="InsightFlow", version="0.3.0") app.add_middleware( @@ -90,9 +103,29 @@ class EntityMergeRequest(BaseModel): source_entity_id: str target_entity_id: str +class GlossaryTermCreate(BaseModel): + term: str + pronunciation: Optional[str] = "" + # API Keys KIMI_API_KEY = os.getenv("KIMI_API_KEY", "") -KIMI_BASE_URL = "https://api.kimi.com/coding" +KIMI_BASE_URL = os.getenv("KIMI_BASE_URL", "https://api.kimi.com/coding") + +# Phase 3: Entity Aligner singleton +_aligner = None +def get_aligner(): + global _aligner + if _aligner is None and ALIGNER_AVAILABLE: + _aligner = EntityAligner() + return _aligner + +# Phase 3: Document Processor singleton +_doc_processor = None +def get_doc_processor(): + global _doc_processor + if _doc_processor is None and DOC_PROCESSOR_AVAILABLE: + _doc_processor = DocumentProcessor() + return _doc_processor # Phase 2: Entity Edit API @app.put("/api/v1/entities/{entity_id}") @@ -406,12 +439,21 @@ def extract_entities_with_llm(text: str) -> tuple[List[dict], List[dict]]: return [], [] -def align_entity(project_id: str, name: str, db) -> Optional[Entity]: - """实体对齐""" +def align_entity(project_id: str, name: str, db, definition: str = "") -> Optional[Entity]: + """实体对齐 - Phase 3: 使用 embedding 对齐""" + # 1. 首先尝试精确匹配 existing = db.get_entity_by_name(project_id, name) if existing: return existing + # 2. 使用 embedding 对齐（如果可用） + aligner = get_aligner() + if aligner: + similar = aligner.find_similar_entity(project_id, name, definition) + if similar: + return similar + + # 3. 回退到简单相似度匹配 similar = db.find_similar_entities(project_id, name) if similar: return similar[0] @@ -443,7 +485,7 @@ async def list_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""" + """上传音频到指定项目 - Phase 3: 支持多文件融合""" if not DB_AVAILABLE: raise HTTPException(status_code=500, detail="Database not available") @@ -471,12 +513,12 @@ async def upload_audio(project_id: str, file: UploadFile = File(...)): full_text=tw_result["full_text"] ) - # 实体对齐并保存 + # 实体对齐并保存 - Phase 3: 使用增强对齐 aligned_entities = [] entity_name_to_id = {} # 用于关系映射 for raw_ent in raw_entities: - existing = align_entity(project_id, raw_ent["name"], db) + existing = align_entity(project_id, raw_ent["name"], db, raw_ent.get("definition", "")) if existing: ent_model = EntityModel( @@ -551,6 +593,302 @@ async def upload_audio(project_id: str, file: UploadFile = File(...)): created_at=datetime.now().isoformat() ) +# Phase 3: Document Upload API +@app.post("/api/v1/projects/{project_id}/upload-document") +async def upload_document(project_id: str, file: UploadFile = File(...)): + """上传 PDF/DOCX 文档到指定项目""" + if not DB_AVAILABLE: + raise HTTPException(status_code=500, detail="Database not available") + + if not DOC_PROCESSOR_AVAILABLE: + raise HTTPException(status_code=500, detail="Document processor 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() + + # 处理文档 + processor = get_doc_processor() + try: + result = processor.process(content, file.filename) + except Exception as e: + raise HTTPException(status_code=400, detail=f"Document processing failed: {str(e)}") + + # 保存文档转录记录 + transcript_id = str(uuid.uuid4())[:8] + db.save_transcript( + transcript_id=transcript_id, + project_id=project_id, + filename=file.filename, + full_text=result["text"], + transcript_type="document" + ) + + # 提取实体和关系 + raw_entities, raw_relations = extract_entities_with_llm(result["text"]) + + # 实体对齐并保存 + aligned_entities = [] + entity_name_to_id = {} + + for raw_ent in raw_entities: + existing = align_entity(project_id, raw_ent["name"], db, raw_ent.get("definition", "")) + + if existing: + entity_name_to_id[raw_ent["name"]] = existing.id + aligned_entities.append(EntityModel( + id=existing.id, + name=existing.name, + type=existing.type, + definition=existing.definition, + aliases=existing.aliases + )) + else: + 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", "") + )) + entity_name_to_id[raw_ent["name"]] = new_ent.id + aligned_entities.append(EntityModel( + id=new_ent.id, + name=new_ent.name, + type=new_ent.type, + definition=new_ent.definition + )) + + # 保存实体提及位置 + full_text = result["text"] + name = raw_ent["name"] + start_pos = 0 + while True: + pos = full_text.find(name, start_pos) + if pos == -1: + break + mention = EntityMention( + id=str(uuid.uuid4())[:8], + entity_id=entity_name_to_id[name], + transcript_id=transcript_id, + start_pos=pos, + end_pos=pos + len(name), + text_snippet=full_text[max(0, pos-20):min(len(full_text), pos+len(name)+20)], + confidence=1.0 + ) + db.add_mention(mention) + start_pos = pos + 1 + + # 保存关系 + for rel in raw_relations: + source_id = entity_name_to_id.get(rel.get("source", "")) + target_id = entity_name_to_id.get(rel.get("target", "")) + if source_id and target_id: + db.create_relation( + project_id=project_id, + source_entity_id=source_id, + target_entity_id=target_id, + relation_type=rel.get("type", "related"), + evidence=result["text"][:200], + transcript_id=transcript_id + ) + + return { + "transcript_id": transcript_id, + "project_id": project_id, + "filename": file.filename, + "text_length": len(result["text"]), + "entities": [e.dict() for e in aligned_entities], + "created_at": datetime.now().isoformat() + } + +# Phase 3: Knowledge Base API +@app.get("/api/v1/projects/{project_id}/knowledge-base") +async def get_knowledge_base(project_id: str): + """获取项目知识库 - 包含所有实体、关系、术语表""" + 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") + + # 获取所有实体 + entities = db.list_project_entities(project_id) + + # 获取所有关系 + relations = db.list_project_relations(project_id) + + # 获取所有转录 + transcripts = db.list_project_transcripts(project_id) + + # 获取术语表 + glossary = db.list_glossary(project_id) + + # 构建实体统计 + entity_stats = {} + for ent in entities: + mentions = db.get_entity_mentions(ent.id) + entity_stats[ent.id] = { + "mention_count": len(mentions), + "transcript_ids": list(set([m.transcript_id for m in mentions])) + } + + # 构建实体名称映射 + entity_map = {e.id: e.name for e in entities} + + return { + "project": { + "id": project.id, + "name": project.name, + "description": project.description + }, + "stats": { + "entity_count": len(entities), + "relation_count": len(relations), + "transcript_count": len(transcripts), + "glossary_count": len(glossary) + }, + "entities": [ + { + "id": e.id, + "name": e.name, + "type": e.type, + "definition": e.definition, + "aliases": e.aliases, + "mention_count": entity_stats.get(e.id, {}).get("mention_count", 0), + "appears_in": entity_stats.get(e.id, {}).get("transcript_ids", []) + } + for e in entities + ], + "relations": [ + { + "id": r["id"], + "source_id": r["source_entity_id"], + "source_name": entity_map.get(r["source_entity_id"], "Unknown"), + "target_id": r["target_entity_id"], + "target_name": entity_map.get(r["target_entity_id"], "Unknown"), + "type": r["relation_type"], + "evidence": r["evidence"] + } + for r in relations + ], + "glossary": [ + { + "id": g["id"], + "term": g["term"], + "pronunciation": g["pronunciation"], + "frequency": g["frequency"] + } + for g in glossary + ], + "transcripts": [ + { + "id": t["id"], + "filename": t["filename"], + "type": t.get("type", "audio"), + "created_at": t["created_at"] + } + for t in transcripts + ] + } + +# Phase 3: Glossary API +@app.post("/api/v1/projects/{project_id}/glossary") +async def add_glossary_term(project_id: str, term: GlossaryTermCreate): + """添加术语到项目术语表""" + 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") + + term_id = db.add_glossary_term( + project_id=project_id, + term=term.term, + pronunciation=term.pronunciation + ) + + return { + "id": term_id, + "term": term.term, + "pronunciation": term.pronunciation, + "success": True + } + +@app.get("/api/v1/projects/{project_id}/glossary") +async def get_glossary(project_id: str): + """获取项目术语表""" + if not DB_AVAILABLE: + raise HTTPException(status_code=500, detail="Database not available") + + db = get_db_manager() + glossary = db.list_glossary(project_id) + return glossary + +@app.delete("/api/v1/glossary/{term_id}") +async def delete_glossary_term(term_id: str): + """删除术语""" + if not DB_AVAILABLE: + raise HTTPException(status_code=500, detail="Database not available") + + db = get_db_manager() + db.delete_glossary_term(term_id) + return {"success": True} + +# Phase 3: Entity Alignment API +@app.post("/api/v1/projects/{project_id}/align-entities") +async def align_project_entities(project_id: str, threshold: float = 0.85): + """运行实体对齐算法，合并相似实体""" + if not DB_AVAILABLE: + raise HTTPException(status_code=500, detail="Database not available") + + aligner = get_aligner() + if not aligner: + raise HTTPException(status_code=500, detail="Entity aligner not available") + + db = get_db_manager() + entities = db.list_project_entities(project_id) + + merged_count = 0 + merged_pairs = [] + + # 使用 embedding 对齐 + for i, entity in enumerate(entities): + # 跳过已合并的实体 + existing = db.get_entity(entity.id) + if not existing: + continue + + similar = aligner.find_similar_entity( + project_id, + entity.name, + entity.definition, + exclude_id=entity.id, + threshold=threshold + ) + + if similar: + # 合并实体 + db.merge_entities(similar.id, entity.id) + merged_count += 1 + merged_pairs.append({ + "source": entity.name, + "target": similar.name + }) + + return { + "success": True, + "merged_count": merged_count, + "merged_pairs": merged_pairs + } + @app.get("/api/v1/projects/{project_id}/entities") async def get_project_entities(project_id: str): """获取项目的全局实体列表""" @@ -559,7 +897,7 @@ async def get_project_entities(project_id: str): db = get_db_manager() entities = db.list_project_entities(project_id) - return [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition} for e in entities] + return [{"id": e.id, "name": e.name, "type": e.type, "definition": e.definition, "aliases": e.aliases} for e in entities] @app.get("/api/v1/projects/{project_id}/relations") @@ -597,6 +935,7 @@ async def get_project_transcripts(project_id: str): return [{ "id": t["id"], "filename": t["filename"], + "type": t.get("type", "audio"), "created_at": t["created_at"], "preview": t["full_text"][:100] + "..." if len(t["full_text"]) > 100 else t["full_text"] } for t in transcripts] @@ -619,42 +958,18 @@ async def get_entity_mentions(entity_id: str): "confidence": m.confidence } for m in mentions] -@app.post("/api/v1/entities/{entity_id}/merge") -async def merge_entities_endpoint(entity_id: str, merge_req: EntityMergeRequest): - """合并两个实体""" - if not DB_AVAILABLE: - raise HTTPException(status_code=500, detail="Database not available") - - db = get_db_manager() - - # 验证两个实体都存在 - source = db.get_entity(merge_req.source_entity_id) - target = db.get_entity(merge_req.target_entity_id) - - if not source or not target: - raise HTTPException(status_code=404, detail="Entity not found") - - result = db.merge_entities(merge_req.target_entity_id, merge_req.source_entity_id) - return { - "success": True, - "merged_entity": { - "id": result.id, - "name": result.name, - "type": result.type, - "definition": result.definition, - "aliases": result.aliases - } - } - # Health check @app.get("/health") async def health_check(): return { "status": "ok", "version": "0.3.0", + "phase": "Phase 3 - Memory & Growth", "oss_available": OSS_AVAILABLE, "tingwu_available": TINGWU_AVAILABLE, - "db_available": DB_AVAILABLE + "db_available": DB_AVAILABLE, + "doc_processor_available": DOC_PROCESSOR_AVAILABLE, + "aligner_available": ALIGNER_AVAILABLE } # Serve frontend diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..fe2afdc --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,24 @@ +# InsightFlow Backend Dependencies + +# Web Framework +fastapi==0.109.0 +uvicorn[standard]==0.27.0 +python-multipart==0.0.6 + +# HTTP Client +httpx==0.26.0 + +# Document Processing +PyPDF2==3.0.1 +python-docx==1.1.0 + +# Data Processing +numpy==1.26.3 + +# Aliyun SDK +aliyun-python-sdk-core==2.14.0 +aliyun-python-sdk-oss==2.18.5 +oss2==2.18.5 + +# Utilities +python-dotenv==1.0.0 diff --git a/backend/schema.sql b/backend/schema.sql index 04f0843..7cc1a92 100644 --- a/backend/schema.sql +++ b/backend/schema.sql @@ -16,7 +16,9 @@ CREATE TABLE IF NOT EXISTS transcripts ( project_id TEXT NOT NULL, filename TEXT, full_text TEXT, + type TEXT DEFAULT 'audio', -- 'audio' 或 'document' created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, FOREIGN KEY (project_id) REFERENCES projects(id) ); @@ -29,6 +31,7 @@ CREATE TABLE IF NOT EXISTS entities ( type TEXT, definition TEXT, aliases TEXT, -- JSON 数组：["别名1", "别名2"] + embedding TEXT, -- JSON 数组：实体名称+定义的 embedding created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, FOREIGN KEY (project_id) REFERENCES projects(id) @@ -71,3 +74,12 @@ CREATE TABLE IF NOT EXISTS glossary ( frequency INTEGER DEFAULT 1, FOREIGN KEY (project_id) REFERENCES projects(id) ); + +-- 创建索引以提高查询性能 +CREATE INDEX IF NOT EXISTS idx_entities_project ON entities(project_id); +CREATE INDEX IF NOT EXISTS idx_entities_name ON entities(name); +CREATE INDEX IF NOT EXISTS idx_transcripts_project ON transcripts(project_id); +CREATE INDEX IF NOT EXISTS idx_mentions_entity ON entity_mentions(entity_id); +CREATE INDEX IF NOT EXISTS idx_mentions_transcript ON entity_mentions(transcript_id); +CREATE INDEX IF NOT EXISTS idx_relations_project ON entity_relations(project_id); +CREATE INDEX IF NOT EXISTS idx_glossary_project ON glossary(project_id); diff --git a/frontend/app.js b/frontend/app.js index e8dc04f..330a32e 100644 --- a/frontend/app.js +++ b/frontend/app.js @@ -1,4 +1,5 @@ -// InsightFlow Frontend - Phase 2 (Interactive Workbench) +// InsightFlow Frontend - Phase 3 (Memory & Growth) +// Knowledge Growth: Multi-file fusion + Entity Alignment + Document Import const API_BASE = '/api/v1'; let currentProject = null; @@ -7,8 +8,11 @@ let selectedEntity = null; let projectRelations = []; let projectEntities = []; let currentTranscript = null; +let projectTranscripts = []; let editMode = false; let contextMenuTarget = null; +let currentUploadTab = 'audio'; +let knowledgeBaseData = null; // Init document.addEventListener('DOMContentLoaded', () => { @@ -70,6 +74,49 @@ async function uploadAudio(file) { return await res.json(); } +// Phase 3: Document Upload API +async function uploadDocument(file) { + const formData = new FormData(); + formData.append('file', file); + + const res = await fetch(`${API_BASE}/projects/${currentProject.id}/upload-document`, { + method: 'POST', + body: formData + }); + + if (!res.ok) { + const error = await res.json(); + throw new Error(error.detail || 'Document upload failed'); + } + return await res.json(); +} + +// Phase 3: Knowledge Base API +async function fetchKnowledgeBase() { + const res = await fetch(`${API_BASE}/projects/${currentProject.id}/knowledge-base`); + if (!res.ok) throw new Error('Failed to fetch knowledge base'); + return await res.json(); +} + +// Phase 3: Glossary API +async function addGlossaryTerm(term, pronunciation = '') { + const res = await fetch(`${API_BASE}/projects/${currentProject.id}/glossary`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ term, pronunciation }) + }); + if (!res.ok) throw new Error('Failed to add glossary term'); + return await res.json(); +} + +async function deleteGlossaryTerm(termId) { + const res = await fetch(`${API_BASE}/glossary/${termId}`, { + method: 'DELETE' + }); + if (!res.ok) throw new Error('Failed to delete glossary term'); + return await res.json(); +} + // Phase 2: Entity Edit API async function updateEntity(entityId, data) { const res = await fetch(`${API_BASE}/entities/${entityId}`, { @@ -147,7 +194,7 @@ async function updateTranscript(transcriptId, fullText) { async function loadProjectData() { try { - // 并行加载实体和关系 + // 并行加载实体、关系和转录列表 const [entitiesRes, relationsRes, transcriptsRes] = await Promise.all([ fetch(`${API_BASE}/projects/${currentProject.id}/entities`), fetch(`${API_BASE}/projects/${currentProject.id}/relations`), @@ -160,32 +207,173 @@ async function loadProjectData() { if (relationsRes.ok) { projectRelations = await relationsRes.json(); } + if (transcriptsRes.ok) { + projectTranscripts = await transcriptsRes.json(); + } // 加载最新的转录 - if (transcriptsRes.ok) { - const transcripts = await transcriptsRes.json(); - if (transcripts.length > 0) { - currentTranscript = await getTranscript(transcripts[0].id); - currentData = { - transcript_id: currentTranscript.id, - project_id: currentProject.id, - segments: [{ speaker: '全文', text: currentTranscript.full_text }], - entities: projectEntities, - full_text: currentTranscript.full_text, - created_at: currentTranscript.created_at - }; - renderTranscript(); - } + if (projectTranscripts.length > 0) { + currentTranscript = await getTranscript(projectTranscripts[0].id); + currentData = { + transcript_id: currentTranscript.id, + project_id: currentProject.id, + segments: [{ speaker: '全文', text: currentTranscript.full_text }], + entities: projectEntities, + full_text: currentTranscript.full_text, + created_at: currentTranscript.created_at + }; + renderTranscript(); } renderGraph(); renderEntityList(); + renderTranscriptDropdown(); } catch (err) { console.error('Load project data failed:', err); } } +// Phase 3: View Switching +window.switchView = function(viewName) { + // Update sidebar buttons + document.querySelectorAll('.sidebar-btn').forEach(btn => { + btn.classList.remove('active'); + }); + event.target.classList.add('active'); + + if (viewName === 'workbench') { + document.getElementById('workbenchView').style.display = 'flex'; + document.getElementById('knowledgeBaseView').classList.remove('show'); + } else if (viewName === 'knowledge-base') { + document.getElementById('workbenchView').style.display = 'none'; + document.getElementById('knowledgeBaseView').classList.add('show'); + loadKnowledgeBase(); + } +}; + +// Phase 3: Load Knowledge Base +async function loadKnowledgeBase() { + try { + knowledgeBaseData = await fetchKnowledgeBase(); + renderKnowledgeBase(); + } catch (err) { + console.error('Load knowledge base failed:', err); + } +} + +// Phase 3: Render Knowledge Base +function renderKnowledgeBase() { + if (!knowledgeBaseData) return; + + // Update stats + document.getElementById('kbEntityCount').textContent = knowledgeBaseData.stats.entity_count; + document.getElementById('kbRelationCount').textContent = knowledgeBaseData.stats.relation_count; + document.getElementById('kbTranscriptCount').textContent = knowledgeBaseData.stats.transcript_count; + document.getElementById('kbGlossaryCount').textContent = knowledgeBaseData.stats.glossary_count; + + // Render entities + const entityGrid = document.getElementById('kbEntityGrid'); + entityGrid.innerHTML = knowledgeBaseData.entities.map(e => ` +
+ ${e.type} +
${e.name}
+
${e.definition || '暂无定义'}
+
提及 ${e.mention_count} 次 | 出现在 ${e.appears_in.length} 个文件中
+
+ `).join(''); + + // Render relations + const relationsList = document.getElementById('kbRelationsList'); + relationsList.innerHTML = knowledgeBaseData.relations.map(r => ` +
+
+ ${r.source_name} + → ${r.type} → + ${r.target_name} +
${r.evidence || '无证据'}
+
+
+ `).join(''); + + // Render glossary + const glossaryList = document.getElementById('kbGlossaryList'); + glossaryList.innerHTML = knowledgeBaseData.glossary.map(g => ` +
+
+ ${g.term} + ${g.pronunciation ? ` (${g.pronunciation})` : ''} + 出现 ${g.frequency} 次 +
+ +
+ `).join(''); + + // Render transcripts + const transcriptsList = document.getElementById('kbTranscriptsList'); + transcriptsList.innerHTML = knowledgeBaseData.transcripts.map(t => ` +
+
+ ${t.type === 'audio' ? '🎵' : '📄'} + ${t.filename} +
+ ${new Date(t.created_at).toLocaleDateString()} +
+ `).join(''); +} + +// Phase 3: KB Tab Switching +window.switchKBTab = function(tabName) { + document.querySelectorAll('.kb-nav-item').forEach(item => { + item.classList.remove('active'); + }); + event.target.classList.add('active'); + + document.querySelectorAll('.kb-section').forEach(section => { + section.classList.remove('active'); + }); + document.getElementById(`kb${tabName.charAt(0).toUpperCase() + tabName.slice(1)}Section`).classList.add('active'); +}; + +// Phase 3: Transcript Dropdown +window.toggleTranscriptDropdown = function() { + const dropdown = document.getElementById('transcriptDropdown'); + dropdown.classList.toggle('show'); +}; + +function renderTranscriptDropdown() { + const dropdown = document.getElementById('transcriptDropdown'); + if (!dropdown || projectTranscripts.length === 0) return; + + dropdown.innerHTML = projectTranscripts.map(t => ` +
+ ${(t.type || 'audio') === 'audio' ? '🎵' : '📄'} + ${t.filename} +
+ `).join(''); +} + +window.switchTranscript = async function(transcriptId) { + try { + currentTranscript = await getTranscript(transcriptId); + currentData = { + transcript_id: currentTranscript.id, + project_id: currentProject.id, + segments: [{ speaker: '全文', text: currentTranscript.full_text }], + entities: projectEntities, + full_text: currentTranscript.full_text, + created_at: currentTranscript.created_at + }; + renderTranscript(); + renderTranscriptDropdown(); + document.getElementById('transcriptDropdown').classList.remove('show'); + } catch (err) { + console.error('Switch transcript failed:', err); + alert('切换文件失败'); + } +}; + // Phase 2: Transcript Edit Mode window.toggleEditMode = function() { editMode = !editMode; @@ -255,7 +443,7 @@ function renderTranscript() { const div = document.createElement('div'); div.className = 'segment'; div.innerHTML = ` -
转录文本
+
${currentTranscript.filename || '转录文本'}
${text}
`; @@ -311,7 +499,7 @@ function renderGraph() { .attr('y', '50%') .attr('text-anchor', 'middle') .attr('fill', '#666') - .text('暂无实体数据，请上传音频'); + .text('暂无实体数据，请上传音频或文档'); return; } @@ -458,7 +646,7 @@ function renderEntityList() { container.innerHTML = '

项目实体

'; if (!projectEntities || projectEntities.length === 0) { - container.innerHTML += '

暂无实体，请上传音频文件

'; + container.innerHTML += '

暂无实体，请上传音频或文档文件

'; return; } @@ -788,6 +976,28 @@ window.createEntityFromSelection = async function() { } }; +// Phase 3: Upload Tab Switching +window.switchUploadTab = function(tab) { + currentUploadTab = tab; + document.querySelectorAll('.upload-tab').forEach(t => t.classList.remove('active')); + event.target.classList.add('active'); + + const hint = document.getElementById('uploadHint'); + if (tab === 'audio') { + hint.textContent = '支持 MP3, WAV, M4A (最大 500MB)'; + } else { + hint.textContent = '支持 PDF, DOCX, DOC, TXT, MD'; + } +}; + +window.triggerFileSelect = function() { + if (currentUploadTab === 'audio') { + document.getElementById('fileInput').click(); + } else { + document.getElementById('docInput').click(); + } +}; + // Show/hide upload window.showUpload = function() { const el = document.getElementById('uploadOverlay'); @@ -799,49 +1009,120 @@ window.hideUpload = function() { if (el) el.classList.remove('show'); }; +// Phase 3: Glossary Modal +window.showAddTermModal = function() { + document.getElementById('glossaryModal').classList.add('show'); +}; + +window.hideGlossaryModal = function() { + document.getElementById('glossaryModal').classList.remove('show'); + document.getElementById('glossaryTerm').value = ''; + document.getElementById('glossaryPronunciation').value = ''; +}; + +window.saveGlossaryTerm = async function() { + const term = document.getElementById('glossaryTerm').value.trim(); + const pronunciation = document.getElementById('glossaryPronunciation').value.trim(); + + if (!term) { + alert('请输入术语'); + return; + } + + try { + await addGlossaryTerm(term, pronunciation); + hideGlossaryModal(); + loadKnowledgeBase(); + } catch (err) { + console.error('Add term failed:', err); + alert('添加术语失败: ' + err.message); + } +}; + // Upload handling function initUpload() { - const input = document.getElementById('fileInput'); + // Audio upload + const audioInput = document.getElementById('fileInput'); + if (audioInput) { + audioInput.addEventListener('change', async (e) => { + if (!e.target.files.length) return; + await handleFileUpload(e.target.files[0], 'audio'); + }); + } + + // Document upload + const docInput = document.getElementById('docInput'); + if (docInput) { + docInput.addEventListener('change', async (e) => { + if (!e.target.files.length) return; + await handleFileUpload(e.target.files[0], 'document'); + }); + } +} + +async function handleFileUpload(file, type) { const overlay = document.getElementById('uploadOverlay'); - if (!input) return; + overlay.innerHTML = ` +
+

正在分析...

+

${file.name}

+

${type === 'audio' ? 'ASR转录 + 实体提取中' : '文档解析 + 实体提取中'}

+
+ `; - input.addEventListener('change', async (e) => { - if (!e.target.files.length) return; + try { + let result; + if (type === 'audio') { + result = await uploadAudio(file); + } else { + result = await uploadDocument(file); + } - const file = e.target.files[0]; - if (overlay) { - overlay.innerHTML = ` -
-

正在分析...

-

${file.name}

-

ASR转录 + 实体提取中

+ // 更新当前数据 + currentData = result; + + // 重新加载项目数据 + await loadProjectData(); + + // 重置上传界面 + overlay.innerHTML = ` +
+

上传文件

+
+
🎵 音频
+
📄 文档
- `; - } +

支持 MP3, WAV, M4A (最大 500MB)

+ + + +

+ +
+ `; - try { - const result = await uploadAudio(file); - - // 更新当前数据 - currentData = result; - - // 重新加载项目数据 - await loadProjectData(); - - if (overlay) overlay.classList.remove('show'); - - } catch (err) { - console.error('Upload failed:', err); - if (overlay) { - overlay.innerHTML = ` -
-

分析失败

-

${err.message}

- -
- `; - } - } - }); + // 重新绑定事件 + initUpload(); + overlay.classList.remove('show'); + + } catch (err) { + console.error('Upload failed:', err); + overlay.innerHTML = ` +
+

分析失败

+

${err.message}

+ +
+ `; + } } + +// Close dropdown when clicking outside +document.addEventListener('click', (e) => { + const dropdown = document.getElementById('transcriptDropdown'); + const selector = document.querySelector('.transcript-selector'); + if (dropdown && selector && !selector.contains(e.target)) { + dropdown.classList.remove('show'); + } +}); diff --git a/frontend/workbench.html b/frontend/workbench.html index 8524173..3ed7cbe 100644 --- a/frontend/workbench.html +++ b/frontend/workbench.html @@ -3,7 +3,7 @@ - InsightFlow - 知识工作台 (Phase 2) + InsightFlow - 知识工作台 (Phase 3) @@ -381,35 +592,113 @@ ← 返回项目列表 加载中...
- +
+ +
-
-
- 📄 转录文本 -
- - -
-
-
-
-

暂无转录内容

- -
-
+ +
+ +
-
-
- 🔗 知识图谱 - 右键节点编辑 | 拖拽建立关系 + +
+ +
+
+
+
+ 📄 转录文本 +
+ +
+
+
+
+ + +
+
+
+
+

暂无转录内容

+ +
+
+
+ +
+
+ 🔗 知识图谱 + 右键节点编辑 | 拖拽建立关系 +
+ +
+

项目实体

+

暂无实体数据

+
+
- -
-

项目实体

-

暂无实体数据

+ + +
+
+

📚 项目知识库

+
+
+
0
+
实体
+
+
+
0
+
关系
+
+
+
0
+
文件
+
+
+
0
+
术语
+
+
+
+
+
+
🏷️ 实体
+
🔗 关系
+
📖 术语表
+
📁 文件
+
+
+ +
+

所有实体

+
+
+ +
+

所有关系

+
+
+ +
+
+

术语表

+ +
+
+
+ +
+

所有文件

+
+
+
+
@@ -417,10 +706,15 @@
-

上传音频分析

-

支持 MP3, WAV, M4A (最大 500MB)

+

上传文件

+
+
🎵 音频
+
📄 文档
+
+

支持 MP3, WAV, M4A (最大 500MB)

- + +

@@ -514,6 +808,25 @@
+ + +
✏️ 编辑实体