diff --git a/pyproject.toml b/pyproject.toml index 0227f596..a2ababb3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,7 @@ dependencies = [ "langchain-together>=0.3.0", "langgraph>=0.3.34", "langgraph-checkpoint-sqlite>=2.0.7", + "langgraph-cli[inmem]>=0.1.54", "langsmith>=0.3.37", "llama-index>=0.12.33", "llama-index-readers-file>=0.4.7", @@ -47,4 +48,4 @@ lint.select = [ # 选择的规则 "W", "UP", ] -lint.ignore = ["F401"] # 忽略的规则 \ No newline at end of file +lint.ignore = ["F401"] # 忽略的规则 diff --git a/server/models/kb_models.py b/server/models/kb_models.py index 5ea399d3..33499ed7 100644 --- a/server/models/kb_models.py +++ b/server/models/kb_models.py @@ -59,6 +59,11 @@ class KnowledgeFile(Base): database = relationship("KnowledgeDatabase", back_populates="files") nodes = relationship("KnowledgeNode", back_populates="file", cascade="all, delete-orphan") + @property + def computed_node_count(self): + """动态计算节点数量""" + return len(self.nodes) if self.nodes is not None else 0 + def to_dict(self): """转换为字典格式""" result = { @@ -67,6 +72,7 @@ class KnowledgeFile(Base): "path": self.path, "type": self.file_type, "status": self.status, + "node_count": self.computed_node_count, # 使用计算属性 "created_at": self.created_at.timestamp() if self.created_at else time.time() } diff --git a/server/routers/data_router.py b/server/routers/data_router.py index 7c556b6b..38c466cc 100644 --- a/server/routers/data_router.py +++ b/server/routers/data_router.py @@ -52,36 +52,42 @@ async def query_test(query: str = Body(...), meta: dict = Body(...), current_use return result @data.post("/file-to-chunk") -async def file_to_chunk(files: list[str] = Body(...), params: dict = Body(...), current_user: User = Depends(get_admin_user)): - logger.debug(f"File to chunk: {files} {params=}") - result = await knowledge_base.file_to_chunk(files, params=params) - return result +async def file_to_chunk(db_id: str = Body(...), files: list[str] = Body(...), params: dict = Body(...), current_user: User = Depends(get_admin_user)): + logger.debug(f"File to chunk for db_id {db_id}: {files} {params=}") + try: + processed_files = await knowledge_base.save_files_for_pending_indexing(db_id, files, params) + return {"message": "Files processed and pending indexing", "files": processed_files, "status": "success"} + except Exception as e: + logger.error(f"Failed to process files for pending indexing: {e}, {traceback.format_exc()}") + return {"message": f"Failed to process files for pending indexing: {e}", "status": "failed"} @data.post("/url-to-chunk") -async def url_to_chunk(urls: list[str] = Body(...), params: dict = Body(...), current_user: User = Depends(get_admin_user)): - logger.debug(f"Url to chunk: {urls}") - result = await knowledge_base.url_to_chunk(urls, params=params) - return result +async def url_to_chunk(db_id: str = Body(...), urls: list[str] = Body(...), params: dict = Body(...), current_user: User = Depends(get_admin_user)): + logger.debug(f"Url to chunk for db_id {db_id}: {urls} {params=}") + try: + processed_urls = await knowledge_base.save_urls_for_pending_indexing(db_id, urls, params) + return {"message": "URLs processed and pending indexing", "urls": processed_urls, "status": "success"} + except Exception as e: + logger.error(f"Failed to process URLs for pending indexing: {e}, {traceback.format_exc()}") + return {"message": f"Failed to process URLs for pending indexing: {e}", "status": "failed"} @data.post("/add-by-file") async def create_document_by_file(db_id: str = Body(...), files: list[str] = Body(...), current_user: User = Depends(get_admin_user)): - logger.debug(f"Add document in {db_id} by file: {files}") - try: - await knowledge_base.add_files(db_id, files) - return {"message": "文件添加完成", "status": "success"} - except Exception as e: - logger.error(f"添加文件失败: {e}, {traceback.format_exc()}") - return {"message": f"添加文件失败: {e}", "status": "failed"} + raise ValueError("This method is deprecated. Use /file-to-chunk and /index-file instead.") @data.post("/add-by-chunks") async def add_by_chunks(db_id: str = Body(...), file_chunks: dict = Body(...), current_user: User = Depends(get_admin_user)): - # logger.debug(f"Add chunks in {db_id}: {len(file_chunks)} chunks") + raise ValueError("This method is deprecated. Use /file-to-chunk and /index-file instead.") + +@data.post("/index-file") +async def index_file(db_id: str = Body(...), file_id: str = Body(...), current_user: User = Depends(get_admin_user)): + logger.debug(f"Indexing file_id {file_id} in db_id {db_id}") try: - await knowledge_base.add_chunks(db_id, file_chunks) - return {"message": "分块添加完成", "status": "success"} + result = await knowledge_base.trigger_file_indexing(db_id, file_id) + return {"message": f"File {file_id} indexing initiated", "details": result, "status": "success"} except Exception as e: - logger.error(f"添加分块失败: {e}, {traceback.format_exc()}") - return {"message": f"添加分块失败: {e}", "status": "failed"} + logger.error(f"Failed to index file {file_id}: {e}, {traceback.format_exc()}") + return {"message": f"Failed to index file {file_id}: {e}", "status": "failed"} @data.get("/info") async def get_database_info(db_id: str, current_user: User = Depends(get_admin_user)): diff --git a/src/core/knowledgebase.py b/src/core/knowledgebase.py index 30fac731..2f3d44dd 100644 --- a/src/core/knowledgebase.py +++ b/src/core/knowledgebase.py @@ -6,6 +6,7 @@ import shutil from sqlalchemy.orm import joinedload from pathlib import Path import asyncio +import random from pymilvus import MilvusClient, MilvusException @@ -36,6 +37,16 @@ class KnowledgeBase: """安全地将对象转换为字典,避免延迟加载问题""" if hasattr(obj, 'to_dict'): return obj.to_dict() + # For SQLAlchemy models not having a to_dict, or to customize + if isinstance(obj, (KnowledgeDatabase, KnowledgeFile, KnowledgeNode)): + # Basic serialization, customize as needed + d = obj.__dict__.copy() + d.pop('_sa_instance_state', None) + # Convert datetime objects to string or timestamp if necessary + for k, v in d.items(): + if isinstance(v, time.struct_time) or hasattr(v, 'isoformat'): # crude datetime check + d[k] = time.mktime(v.timetuple()) if isinstance(v, time.struct_time) else v.isoformat() + return d return obj def _check_migration(self): @@ -86,24 +97,18 @@ class KnowledgeBase: def get_all_databases(self): """获取所有知识库""" with db_manager.get_session_context() as session: - # 使用eager loading加载关联的files databases = session.query(KnowledgeDatabase).options( joinedload(KnowledgeDatabase.files) ).all() - - # 转换为字典并返回,避免后续延迟加载 - return [self._to_dict_safely(db) for db in databases] + return [db.to_dict() for db in databases] # Assuming to_dict handles files correctly def get_database_by_id(self, db_id): """根据ID获取知识库""" with db_manager.get_session_context() as session: - # 使用eager loading加载关联的files db = session.query(KnowledgeDatabase).options( joinedload(KnowledgeDatabase.files).joinedload(KnowledgeFile.nodes) ).filter_by(db_id=db_id).first() - - # 转换为字典并返回,避免后续延迟加载 - return self._to_dict_safely(db) if db else None + return db.to_dict() if db else None # Assuming to_dict handles files and nodes def create_database_record(self, db_id, name, description, embed_model=None, dimension=None, metadata=None): """在数据库中创建知识库记录""" @@ -114,21 +119,11 @@ class KnowledgeBase: description=description, embed_model=embed_model, dimension=dimension, - meta_info=metadata or {} # 存储到meta_info字段 + meta_info=metadata or {} ) session.add(db) - session.flush() # 立即写入数据库,获取ID - - # 手动将必要的数据加载到内存中 - db_dict = { - "db_id": db_id, - "name": name, - "description": description, - "embed_model": embed_model, - "dimension": dimension, - "metadata": metadata or {}, # 返回时使用metadata键 - "files": {} - } + session.flush() + db_dict = db.to_dict() # Use the model's to_dict return db_dict def delete_database_record(self, db_id): @@ -136,6 +131,11 @@ class KnowledgeBase: with db_manager.get_session_context() as session: db = session.query(KnowledgeDatabase).filter_by(db_id=db_id).first() if db: + # Manually delete associated files and nodes if cascade is not set up or to be sure + files = session.query(KnowledgeFile).filter_by(database_id=db_id).all() + for file_obj in files: + session.query(KnowledgeNode).filter_by(file_id=file_obj.file_id).delete() + session.delete(file_obj) session.delete(db) return True return False @@ -146,19 +146,15 @@ class KnowledgeBase: db = session.query(KnowledgeDatabase).filter_by(db_id=db_id).first() if not db: raise ValueError(f"数据库 {db_id} 不存在") - - # 更新字段 db.name = name db.description = description session.commit() - - # 返回更新后的数据库信息 - return self._to_dict_safely(db) + return db.to_dict() def add_file_record(self, db_id, file_id, filename, path, file_type, status="waiting"): """在数据库中添加文件记录""" with db_manager.get_session_context() as session: - file = KnowledgeFile( + file_obj = KnowledgeFile( file_id=file_id, database_id=db_id, filename=filename, @@ -166,35 +162,29 @@ class KnowledgeBase: file_type=file_type, status=status ) - session.add(file) + session.add(file_obj) session.flush() - - # 返回字典而非对象,避免会话关闭后的延迟加载问题 - return { - "file_id": file_id, - "filename": filename, - "path": path, - "type": file_type, - "status": status, - "created_at": file.created_at.timestamp() if file.created_at else None, - "nodes": [] - } + return file_obj.to_dict() def update_file_status(self, file_id, status): """更新文件状态""" with db_manager.get_session_context() as session: - file = session.query(KnowledgeFile).filter_by(file_id=file_id).first() - if file: - file.status = status + file_obj = session.query(KnowledgeFile).filter_by(file_id=file_id).first() + if file_obj: + file_obj.status = status + session.commit() return True return False def delete_file_record(self, file_id): - """从数据库中删除文件记录""" + """从数据库中删除文件记录及其关联的节点""" with db_manager.get_session_context() as session: - file = session.query(KnowledgeFile).filter_by(file_id=file_id).first() - if file: - session.delete(file) + # First, delete associated nodes + session.query(KnowledgeNode).filter_by(file_id=file_id).delete() + # Then, delete the file + file_obj = session.query(KnowledgeFile).filter_by(file_id=file_id).first() + if file_obj: + session.delete(file_obj) return True return False @@ -202,48 +192,38 @@ class KnowledgeBase: """获取知识库下的所有文件""" with db_manager.get_session_context() as session: files = session.query(KnowledgeFile).options( - joinedload(KnowledgeFile.nodes) + joinedload(KnowledgeFile.nodes) # Eager load nodes ).filter_by(database_id=db_id).all() - return [self._to_dict_safely(file) for file in files] + return [f.to_dict() for f in files] def get_file_by_id(self, file_id): - """根据ID获取文件""" + """根据ID获取文件及其节点""" with db_manager.get_session_context() as session: - file = session.query(KnowledgeFile).options( - joinedload(KnowledgeFile.nodes) + file_obj = session.query(KnowledgeFile).options( + joinedload(KnowledgeFile.nodes) # Eager load nodes ).filter_by(file_id=file_id).first() - return self._to_dict_safely(file) if file else None + return file_obj.to_dict() if file_obj else None def add_node(self, file_id, text, hash_value=None, start_char_idx=None, end_char_idx=None, metadata=None): - """添加知识块""" + """添加知识块 (原始文本节点)""" with db_manager.get_session_context() as session: node = KnowledgeNode( file_id=file_id, text=text, - hash=hash_value, + hash=hash_value or hashstr(text, with_salt=True), # Ensure hash is present start_char_idx=start_char_idx, end_char_idx=end_char_idx, meta_info=metadata or {} ) session.add(node) - session.flush() - - # 返回字典而非对象,避免会话关闭后的延迟加载问题 - return { - "id": node.id, - "file_id": file_id, - "text": text, - "hash": hash_value, - "start_char_idx": start_char_idx, - "end_char_idx": end_char_idx, - "metadata": metadata or {} - } + session.flush() # To get node.id + return node.to_dict() def get_nodes_by_file(self, file_id): """获取文件下的所有知识块""" with db_manager.get_session_context() as session: nodes = session.query(KnowledgeNode).filter_by(file_id=file_id).all() - return [self._to_dict_safely(node) for node in nodes] + return [node.to_dict() for node in nodes] def get_nodes_by_filter(self, file_id=None, search_text=None, limit=100): """根据条件筛选知识块""" @@ -254,14 +234,12 @@ class KnowledgeBase: if search_text: query = query.filter(KnowledgeNode.text.like(f"%{search_text}%")) nodes = query.limit(limit).all() - return [self._to_dict_safely(node) for node in nodes] + return [node.to_dict() for node in nodes] def create_database(self, database_name, description, dimension=None): """创建一个数据库(业务逻辑)""" dimension = dimension or self.embed_model.get_dimension() db_id = f"kb_{hashstr(database_name, with_salt=True)}" - - # 创建数据库记录 db_dict = self.create_database_record( db_id=db_id, name=database_name, @@ -269,17 +247,11 @@ class KnowledgeBase: embed_model=self.embed_model.embed_model_fullname, dimension=dimension ) - - # 创建数据库对应的文件夹 self._ensure_db_folders(db_id) - - # 在Milvus中创建集合 self.add_collection(db_id, dimension) - return db_dict def _ensure_db_folders(self, db_id): - """确保数据库文件夹存在""" db_folder = os.path.join(self.work_dir, db_id) uploads_folder = os.path.join(db_folder, "uploads") os.makedirs(db_folder, exist_ok=True) @@ -287,42 +259,37 @@ class KnowledgeBase: return db_folder, uploads_folder def get_db_upload_path(self, db_id=None): - """获取上传文件夹路径,如果没有指定db_id则使用默认路径""" - _, uploads_folder = self._ensure_db_folders(db_id) - return uploads_folder + if db_id: + _, uploads_folder = self._ensure_db_folders(db_id) + return uploads_folder + # Fallback for general uploads if db_id is not specified during upload + general_uploads = os.path.join(self.work_dir, "uploads") + os.makedirs(general_uploads, exist_ok=True) + return general_uploads def get_databases(self): assert config.enable_knowledge_base, "知识库未启用" - - # 从数据库获取所有知识库 databases = self.get_all_databases() - - # 检查和更新Milvus信息 databases_with_milvus = [] - for db in databases: - db_copy = db.copy() # 创建字典的副本以避免修改原始数据 - # 更新Milvus集合信息 + for db_data in databases: # db_data is already a dict from to_dict() + db_copy = db_data.copy() try: - milvus_info = self.get_collection_info(db["db_id"]) - db_copy["metadata"] = milvus_info - # logger.debug(f"获取知识库 {db['name']} (ID: {db['db_id']}) 的Milvus信息成功: {milvus_info}") + milvus_info = self.get_collection_info(db_copy["db_id"]) + # Merge Milvus info carefully, avoid overwriting existing keys like 'name', 'description' + for k, v in milvus_info.items(): + if k not in db_copy or k in ["row_count", "status", "error_message"]: # Milvus specific keys + db_copy[k] = v except Exception as e: - logger.warning(f"获取知识库 {db['name']} (ID: {db['db_id']}) 的Milvus信息失败: {e}") - # 添加一个默认的Milvus状态 - db_copy.update({ - "row_count": 0, - "status": "未连接", - "error": str(e) - }) + logger.warning(f"获取知识库 {db_copy.get('name')} (ID: {db_copy.get('db_id')}) 的Milvus信息失败: {e}") + db_copy.update({"row_count": 0, "status": "未连接", "error": str(e)}) - # 检查处理中的文件 - processing_files = [f for f_id, f in db_copy.get("files", {}).items() - if f["status"] in ["processing", "waiting"]] - if processing_files: - logger.info(f"数据库 {db['name']} 有 {len(processing_files)} 个文件正在处理中") + # files should be part of db_copy from to_dict() + db_copy_files = db_copy.get("files", {}).values() + processing_files_count = sum(1 for file_info in db_copy_files if file_info.get("status") in ["processing", "waiting"]) + if processing_files_count > 0: + logger.info(f"数据库 {db_copy.get('name')} 有 {processing_files_count} 个文件正在处理中或等待处理") databases_with_milvus.append(db_copy) - return {"databases": databases_with_milvus} def get_database_info(self, db_id): @@ -331,24 +298,15 @@ class KnowledgeBase: return None else: db_copy = db_dict.copy() + original_description = db_copy.get("description") try: - # 保存原始描述 - original_description = db_copy.get("description") - milvus_info = self.get_collection_info(db_id) db_copy.update(milvus_info) - - # 如果原始描述不为空,恢复它 - if original_description: + if original_description: # Preserve original if Milvus info overwrote it db_copy["description"] = original_description except Exception as e: logger.warning(f"获取知识库 ID: {db_id} 的Milvus信息失败: {e}") - # 添加一个默认的Milvus状态 - db_copy.update({ - "row_count": 0, - "status": "未连接", - "error": str(e) - }) + db_copy.update({"row_count": 0, "status": "未连接", "error": str(e)}) return db_copy def get_database_id(self): @@ -356,202 +314,230 @@ class KnowledgeBase: return [db["db_id"] for db in databases] def get_file_info(self, db_id, file_id): - db = self.get_database_by_id(db_id) - if db is None: - raise Exception(f"database not found, {db_id}") + # This method originally queried Milvus. For raw chunks, we query SQLite. + # If the intention is to show indexed chunks, this might need adjustment post-indexing. + # For now, let's assume it's for displaying raw, pre-indexed chunks if file status is 'waiting'. + file_record = self.get_file_by_id(file_id) + if not file_record: + raise Exception(f"File not found: {file_id}") - lines = self.client.query( - collection_name=db_id, - filter=f"file_id == '{file_id}'", - output_fields=None - ) - # 删除 vector 字段 - for line in lines: - line.pop("vector") + # Nodes are already part of file_record from get_file_by_id due to eager loading and to_dict + nodes = file_record.get("nodes", []) - lines.sort(key=lambda x: x.get("start_char_idx") or x.get("metadata", {}).get("chunk_idx", 0)) - return {"lines": lines} + if len(nodes) == 0: + nodes = self.client.query( + collection_name=db_id, + filter=f"file_id == '{file_id}'", + output_fields=None + ) + for node in nodes: + node.pop("vector") + + # Sort nodes if necessary, e.g., by start_char_idx or an explicit chunk_idx if available + nodes.sort(key=lambda x: x.get("start_char_idx") or x.get("metadata", {}).get("chunk_idx", 0)) + return {"lines": nodes} # Return nodes from SQLite for consistency with frontend expectations def get_kb_by_id(self, db_id): if not config.enable_knowledge_base: return None - return self.get_database_by_id(db_id) - async def file_to_chunk(self, files, params=None): - """将文件转换为分块 - - 这里主要是将文件转换为分块,但并不保存到数据库,仅仅返回分块后的信息,返回的信息里面也包含文件的id,文件名,文件类型,文件路径,文件状态,文件创建时间等。 - files: list of file path - params: params for chunking - - return: list of chunk info - """ - file_infos = {} - for file in files: - file_path = Path(file) - file_id = "file_" + hashstr(str(file_path) + str(time.time())) + async def save_files_for_pending_indexing(self, db_id, files_paths, params=None): + processed_files_info = [] + for file_path_str in files_paths: + file_path = Path(file_path_str) + file_id = "file_" + hashstr(str(file_path) + str(time.time()), 6) # Shorter hash file_type = file_path.suffix.lower().replace(".", "") - file_info_dict = { - "file_id": file_id, - "filename": file_path.name, - "path": str(file_path), - "type": file_type, - "status": "waiting", - "created_at": time.time(), - "nodes": [] - } - logger.debug(f"{file_info_dict=}") + + # Initial file record with status 'waiting' + file_record = self.add_file_record( + db_id=db_id, + file_id=file_id, + filename=file_path.name, + path=str(file_path), + file_type=file_type, + status="waiting" + ) + try: if file_type == "pdf": texts = await parse_pdf_async(file_path, params=params) - nodes = chunk_text(texts, params=params) + raw_nodes = chunk_text(texts, params=params) else: - nodes = chunk_with_parser(file_path, params=params) + raw_nodes = chunk_with_parser(file_path, params=params) - processed_nodes = [parse_node_data(node) for node in nodes] + parsed_nodes_data = [parse_node_data(node) for node in raw_nodes] + + for node_data in parsed_nodes_data: + self.add_node( + file_id=file_id, + text=node_data["text"], + hash_value=node_data["hash"], + start_char_idx=node_data.get("start_char_idx"), + end_char_idx=node_data.get("end_char_idx"), + metadata=node_data.get("metadata") + ) + + self.update_file_status(file_id, "pending_indexing") + file_record['status'] = "pending_indexing" # Ensure status is up-to-date - file_info_dict["nodes"] = processed_nodes except Exception as e: - logger.error(f"处理文件 {file_path} 时出错: {e}") - file_info_dict["status"] = "failed" - file_info_dict["error"] = str(e) - raise e + logger.error(f"处理文件 {file_path} 失败,无法保存待索引块: {e}, {traceback.format_exc()}") + self.update_file_status(file_id, "failed") # Mark file as failed + file_record['status'] = "failed" - finally: - file_infos[file_id] = file_info_dict + processed_files_info.append(file_record) + return processed_files_info - return file_infos - - async def url_to_chunk(self, urls, params=None): - """将url转换为分块,读取url的内容,并转换为分块 - - Args: - urls: list of urls - params: params for chunking - - Returns: - dict: 包含分块信息的字典 - """ + async def save_urls_for_pending_indexing(self, db_id, urls, params=None): try: from langchain_community.document_loaders import UnstructuredURLLoader except ImportError: - raise ImportError("请安装 langchain_community 和 unstructured 包:pip install langchain-community unstructured") - - file_infos = {} + logger.error("请安装 langchain_community 和 unstructured 包:pip install langchain-community unstructured") + raise ImportError("Required packages for URL processing are missing.") + processed_urls_info = [] for url in urls: - file_id = "url_" + hashstr(url + str(time.time())) - file_info_dict = { - "file_id": file_id, - "filename": gen_filename_from_url(url), - "path": url, - "type": "url", - "status": "waiting", - "created_at": time.time(), - "nodes": [] - } + file_id = "url_" + hashstr(url + str(time.time()), 6) # Shorter hash + filename = gen_filename_from_url(url) + + file_record = self.add_file_record( + db_id=db_id, + file_id=file_id, + filename=filename, + path=url, + file_type="url", + status="waiting" + ) + try: - # 加载单个URL内容 single_loader = UnstructuredURLLoader(urls=[url], continue_on_failure=False) documents = await single_loader.aload() - - # 合并文档内容 text_content = "\n\n".join([doc.page_content for doc in documents]) - file_info_dict["nodes"] = chunk_text(text_content, params=params) + + raw_nodes = chunk_text(text_content, params=params) + parsed_nodes_data = [parse_node_data(node) for node in raw_nodes] + + for node_data in parsed_nodes_data: + self.add_node( + file_id=file_id, + text=node_data["text"], + hash_value=node_data["hash"], + start_char_idx=node_data.get("start_char_idx"), + end_char_idx=node_data.get("end_char_idx"), + metadata=node_data.get("metadata") + ) + + self.update_file_status(file_id, "pending_indexing") + file_record['status'] = "pending_indexing" except Exception as e: - logger.error(f"处理URL {url} 时出错: {e}") - file_info_dict["status"] = "failed" - file_info_dict["error"] = str(e) - - file_infos[file_id] = file_info_dict - - return file_infos - - async def add_chunks(self, db_id, file_chunks): - """添加分块""" - db = self.get_kb_by_id(db_id) - assert self.check_embed_model(db_id), f"Embed model not match, {db['embed_model']} != {self.embed_model.embed_model_fullname}" - - for file_id, chunk_info in file_chunks.items(): - # 在数据库中创建文件记录 - self.add_file_record( - db_id=db_id, - file_id=file_id, - filename=chunk_info["filename"], - path=chunk_info["path"], - file_type=chunk_info["type"], - status="processing" - ) - - try: - await self.add_documents( - file_id=file_id, - collection_name=db_id, - docs=[node["text"] for node in chunk_info["nodes"]], - chunk_infos=chunk_info["nodes"]) - - # 更新文件状态为完成 - self.update_file_status(file_id, "done") - - except Exception as e: - logger.error(f"Failed to add documents to collection {db_id}, {e}, {traceback.format_exc()}") - # 更新文件状态为失败 + logger.error(f"处理URL {url} 失败,无法保存待索引块: {e}, {traceback.format_exc()}") self.update_file_status(file_id, "failed") + file_record['status'] = "failed" - async def add_files(self, db_id, files, params=None): - db = self.get_kb_by_id(db_id) - assert self.check_embed_model(db_id), f"Embed model not match, {db['embed_model']} != {self.embed_model.embed_model_fullname}" + processed_urls_info.append(file_record) + return processed_urls_info - # Preprocessing the files to the queue - new_files = await self.file_to_chunk(files, params=params) + async def trigger_file_indexing(self, db_id, file_id): + logger.info(f"开始为文件 {file_id} (数据库: {db_id}) 创建索引") + if not self.check_embed_model(db_id): + logger.error(f"文件 {file_id} 索引失败:向量模型不匹配。") + self.update_file_status(file_id, "failed") + return {"status": "failed", "message": "向量模型不匹配"} - for file_id, new_file in new_files.items(): - # 在数据库中创建文件记录 - self.add_file_record( - db_id=db_id, - file_id=file_id, - filename=new_file["filename"], - path=new_file["path"], - file_type=new_file["type"], - status="processing" - ) + try: + self.update_file_status(file_id, "processing") - try: - await self.add_documents( - file_id=file_id, - collection_name=db_id, - docs=[node["text"] for node in new_file["nodes"]], - chunk_infos=new_file["nodes"]) + nodes_to_index = self.get_nodes_by_file(file_id) + if not nodes_to_index: + logger.warning(f"文件 {file_id} 没有找到需要索引的块。") + self.update_file_status(file_id, "done") # Or 'failed' if this is an error condition + return {"status": "success", "message": "没有需要索引的块"} - # 更新文件状态为完成 - self.update_file_status(file_id, "done") + docs_text = [node["text"] for node in nodes_to_index] + + logger.info(f"正在为文件 {file_id} 的 {len(docs_text)} 个块生成向量...") + vectors = await self.embed_model.abatch_encode(docs_text) + logger.info(f"文件 {file_id} 的向量生成完毕。") + + data_to_insert = [] + for i, node in enumerate(nodes_to_index): + milvus_entry = { + "id": node["id"], # Using KnowledgeNode.id as Milvus PK + "vector": vectors[i], + "text": node["text"], + "file_id": file_id, # Explicitly ensure file_id is present + "hash": node["hash"], + # Spread other metadata stored in node["meta_info"] + **(node.get("meta_info") if isinstance(node.get("meta_info"), dict) else {}) + } + # Ensure start_char_idx and end_char_idx are included if they exist directly on node dict + if "start_char_idx" in node and node["start_char_idx"] is not None: + milvus_entry["start_char_idx"] = node["start_char_idx"] + if "end_char_idx" in node and node["end_char_idx"] is not None: + milvus_entry["end_char_idx"] = node["end_char_idx"] + + data_to_insert.append(milvus_entry) + + if data_to_insert: + logger.info(f"正在将文件 {file_id} 的 {len(data_to_insert)} 个向量插入 Milvus 集合 {db_id}...") + self.client.insert(collection_name=db_id, data=data_to_insert) + logger.info(f"文件 {file_id} 的向量成功插入 Milvus。") + + self.update_file_status(file_id, "done") + logger.info(f"文件 {file_id} 索引成功完成。") + return {"status": "success", "message": "文件索引成功"} + + except Exception as e: + logger.error(f"文件 {file_id} 索引过程中发生错误: {e}, {traceback.format_exc()}") + self.update_file_status(file_id, "failed") + return {"status": "failed", "message": f"索引失败: {str(e)}"} - except Exception as e: - logger.error(f"Failed to add documents to collection {db_id}, {e}, {traceback.format_exc()}") - # 更新文件状态为失败 - self.update_file_status(file_id, "failed") def delete_file(self, db_id, file_id): - # 从Milvus中删除文件的向量 - self.client.delete(collection_name=db_id, filter=f"file_id == '{file_id}'") + logger.info(f"Deleting file {file_id} from database {db_id}") + try: + # From Milvus + logger.info(f"Deleting vectors for file_id {file_id} from Milvus collection {db_id}") + self.client.delete(collection_name=db_id, filter=f"file_id == '{file_id}'") + logger.info(f"Milvus deletion successful for file_id {file_id}.") + except Exception as e: + logger.error(f"Error deleting file {file_id} from Milvus collection {db_id}: {e}") + # Decide if to proceed with DB deletion or not. For now, we proceed. + + # From SQLite (deletes file and its nodes) + if self.delete_file_record(file_id): + logger.info(f"Successfully deleted file record {file_id} and its nodes from SQLite.") + else: + logger.warning(f"File record {file_id} not found in SQLite for deletion or already deleted.") - # 从SQLite中删除文件记录 - self.delete_file_record(file_id) def delete_database(self, db_id): - # 从Milvus中删除集合 - self.client.drop_collection(collection_name=db_id) + logger.info(f"Deleting database {db_id}") + try: + if self.client.has_collection(collection_name=db_id): + logger.info(f"Dropping Milvus collection {db_id}") + self.client.drop_collection(collection_name=db_id) + logger.info(f"Milvus collection {db_id} dropped.") + else: + logger.warning(f"Milvus collection {db_id} not found, skipping drop.") + except Exception as e: + logger.error(f"Error dropping Milvus collection {db_id}: {e}") + # Decide if to proceed with DB deletion. For now, we proceed. - # 从SQLite中删除数据库记录 - self.delete_database_record(db_id) + if self.delete_database_record(db_id): # This now also handles deleting files/nodes + logger.info(f"Successfully deleted database record {db_id} and associated data from SQLite.") + else: + logger.warning(f"Database record {db_id} not found in SQLite for deletion.") - # 删除数据库对应的文件夹 db_folder = os.path.join(self.work_dir, db_id) if os.path.exists(db_folder): - shutil.rmtree(db_folder) - + try: + shutil.rmtree(db_folder) + logger.info(f"Successfully deleted database folder {db_folder}.") + except Exception as e: + logger.error(f"Error deleting database folder {db_folder}: {e}") return {"message": "删除成功"} def restart(self): @@ -561,37 +547,66 @@ class KnowledgeBase: #* Below is the code for retriever # ################################### - def query(self, query, db_id, **kwargs): - + def query(self, query_text, db_id, **kwargs): # Renamed 'query' to 'query_text' to avoid clash distance_threshold = kwargs.get("distance_threshold", self.default_distance_threshold) rerank_threshold = kwargs.get("rerank_threshold", self.default_rerank_threshold) max_query_count = kwargs.get("max_query_count", self.default_max_query_count) - all_db_result = self.search(query, db_id, limit=max_query_count) - all_db_result = [dict(r) for r in all_db_result] + all_db_result = self.search(query_text, db_id, limit=max_query_count) # Use query_text + all_db_result_dicts = [] + for res_item in all_db_result: # res is a list of SearchResult objects + # 将 Milvus SearchResult 对象转换为字典 + item_dict = { + "id": res_item.id, + "distance": res_item.distance, + "entity": {} + } + # 安全地获取实体字段 + if hasattr(res_item, "entity"): + entity_data = res_item.entity + if isinstance(entity_data, dict): + item_dict["entity"] = entity_data.copy() + else: + # 如果 entity 不是字典,尝试获取常见字段 + for field in ["text", "file_id", "hash", "start_char_idx", "end_char_idx"]: + try: + item_dict["entity"][field] = getattr(entity_data, field, None) + except: + continue - # 获取文件信息并添加到结果中 - for res in all_db_result: - file = self.get_file_by_id(res["entity"]["file_id"]) - if file: - res["file"] = file + all_db_result_dicts.append(item_dict) - db_result = [r for r in all_db_result if r["distance"] > distance_threshold] + for res_dict in all_db_result_dicts: + if res_dict.get("entity") and res_dict["entity"].get("file_id"): + file_info = self.get_file_by_id(res_dict["entity"]["file_id"]) # get_file_by_id returns a dict + if file_info: + # Add selective file info to avoid circular references or overly large objects + res_dict["file"] = { + "file_id": file_info.get("file_id"), + "filename": file_info.get("filename"), + "type": file_info.get("file_type") + } + else: + logger.warning(f"Missing entity or file_id in Milvus result: {res_dict}") - if config.enable_reranker and len(db_result) > 0 and self.reranker: - texts = [r["entity"]["text"] for r in db_result] - rerank_scores = self.reranker.compute_score([query, texts], normalize=False) - for i, r in enumerate(db_result): - r["rerank_score"] = rerank_scores[i] - db_result.sort(key=lambda x: x["rerank_score"], reverse=True) - db_result = [_res for _res in db_result if _res["rerank_score"] > rerank_threshold] + db_result_filtered = [r for r in all_db_result_dicts if r["distance"] > distance_threshold] + + if config.enable_reranker and len(db_result_filtered) > 0 and self.reranker: + texts_for_rerank = [r["entity"]["text"] for r in db_result_filtered if r.get("entity") and r["entity"].get("text")] + if texts_for_rerank: # Ensure there are texts to rerank + rerank_scores = self.reranker.compute_score([query_text, texts_for_rerank], normalize=False) # Use query_text + for i, r_filtered in enumerate(db_result_filtered): + if i < len(rerank_scores): # Check bounds + r_filtered["rerank_score"] = rerank_scores[i] + db_result_filtered.sort(key=lambda x: x.get("rerank_score", -1), reverse=True) # Handle missing rerank_score + db_result_filtered = [_res for _res in db_result_filtered if _res.get("rerank_score", -1) > rerank_threshold] if kwargs.get("top_k", None): - db_result = db_result[:kwargs["top_k"]] + db_result_filtered = db_result_filtered[:kwargs["top_k"]] return { - "results": db_result, - "all_results": all_db_result, + "results": db_result_filtered, + "all_results": all_db_result_dicts, # Return the full list before filtering for analysis } def get_retriever_by_db_id(self, db_id): @@ -602,27 +617,28 @@ class KnowledgeBase: "top_k": 10, } - def retriever(query): - response = self.query(query, db_id, **retriever_params) + def retriever(query_text): # Renamed query to query_text + response = self.query(query_text, db_id, **retriever_params) # Use query_text return response["results"] return retriever def get_retrievers(self): retrievers = {} - for db in self.get_all_databases(): - if self.check_embed_model(db["db_id"]): - retrievers[db["db_id"]] = { - "name": db["name"], - "description": db["description"], - "retriever": self.get_retriever_by_db_id(db["db_id"]), - "embed_model": db["embed_model"], + all_dbs = self.get_all_databases() # Returns list of dicts + for db_data in all_dbs: + if self.check_embed_model(db_data["db_id"]): + retrievers[db_data["db_id"]] = { + "name": db_data["name"], + "description": db_data["description"], + "retriever": self.get_retriever_by_db_id(db_data["db_id"]), + "embed_model": db_data["embed_model"], } else: logger.warning( - f"无法将知识库 {db['name']} 转换为 Tools, 因为向量模型不匹配," + f"无法将知识库 {db_data['name']} 转换为 Tools, 因为向量模型不匹配," f"当前向量模型: {self.embed_model.embed_model_fullname}," - f"知识库向量模型: {db['embed_model']}。" + f"知识库向量模型: {db_data['embed_model']}。" ) return retrievers @@ -630,21 +646,20 @@ class KnowledgeBase: #* Below is the code for milvus # ################################ def connect_to_milvus(self): - """ - 连接到 Milvus 服务。 - 使用配置中的 URI,如果没有配置,则使用默认值。 - """ try: uri = os.getenv('MILVUS_URI', config.get('milvus_uri', "http://milvus:19530")) self.client = MilvusClient(uri=uri) - # 可以添加一个简单的测试来确保连接成功 - self.client.list_collections() + self.client.list_collections() # Test connection logger.info(f"Successfully connected to Milvus at {uri}") return True except MilvusException as e: logger.error(f"Failed to connect to Milvus: {e},请检查 milvus 的容器是否正常运行。") logger.error("如果已退出,请重新启动 `docker restart milvus-standalone-dev`。") return False + except Exception as e: # Catch other potential errors like requests.exceptions.ConnectionError + logger.error(f"An unexpected error occurred while connecting to Milvus at {uri}: {e}") + return False + def get_collection_names(self): return self.client.list_collections() @@ -653,127 +668,180 @@ class KnowledgeBase: collections_name = self.client.list_collections() collections = [] for collection_name in collections_name: - collection = self.get_collection_info(collection_name) - collections.append(collection) - + collection_info = self.get_collection_info(collection_name) + collections.append(collection_info) return collections def get_collection_info(self, collection_name): - """获取Milvus集合信息,处理可能的错误""" try: - collection = self.client.describe_collection(collection_name) - collection.update(self.client.get_collection_stats(collection_name)) - return collection + collection_desc = self.client.describe_collection(collection_name) + collection_stats = self.client.get_collection_stats(collection_name) + # Combine description and stats + # Ensure all keys from description are present, then update/add stats + # Milvus describe_collection returns a dict like: + # {'collection_name': ..., 'description': ..., 'fields': [...], ...} + # Milvus get_collection_stats returns a dict like: {'row_count': ...} + combined_info = collection_desc.copy() # Start with description + combined_info.update(collection_stats) # Add/overwrite with stats + return combined_info except MilvusException as e: logger.warning(f"获取集合 {collection_name} 信息失败: {e}") - # 返回一个带有错误信息的基本结构 - return { - "name": collection_name, - "row_count": 0, - "status": "错误", - "error_message": str(e) - } + return {"name": collection_name, "row_count": 0, "status": "错误", "error_message": str(e)} def add_collection(self, collection_name, dimension=None): if self.client.has_collection(collection_name=collection_name): - logger.warning(f"Collection {collection_name} already exists, drop it") - self.client.drop_collection(collection_name=collection_name) + logger.warning(f"Collection {collection_name} already exists. It will be used as is or needs manual deletion if schema change is required.") + # Not dropping by default to avoid data loss. + # self.client.drop_collection(collection_name=collection_name) + # self.client.create_collection(collection_name=collection_name, dimension=dimension) + return self.client.create_collection( collection_name=collection_name, - dimension= dimension, # The vectors we will use in this demo has 768 dimensions + dimension=dimension or self.embed_model.get_dimension(), + # Define primary key field, default is 'id' of type Int64. + # If using KnowledgeNode.id (which is int), this is fine. + # Example for explicit PK: + # primary_field_name="node_id", + # id_type="Int64" + # Ensure vector field is also defined if not using defaults. ) + logger.info(f"Milvus collection {collection_name} created with dimension {dimension or self.embed_model.get_dimension()}.") - async def add_documents(self, docs, collection_name, chunk_infos=None, **kwargs): - """添加已经分块之后的文本""" - # 检查 collection 是否存在 - import random - if not self.client.has_collection(collection_name=collection_name): - logger.error(f"Collection {collection_name} not found, create it") - # self.add_collection(collection_name) - chunk_infos = chunk_infos or [{}] * len(docs) - vectors = await self.embed_model.abatch_encode(docs) - data = [{ - "id": int(random.random() * 1e12), - "vector": vectors[i], - "text": docs[i], - "hash": hashstr(docs[i], with_salt=True), - **kwargs, - **chunk_infos[i] - } for i in range(len(vectors))] - - res = self.client.insert(collection_name=collection_name, data=data) - return res - - def search(self, query, collection_name, limit=3): - """搜索数据库""" - query_vectors = self.embed_model.batch_encode([query]) + def search(self, query_text, collection_name, limit=3): # Renamed query to query_text + query_vectors = self.embed_model.batch_encode([query_text]) # Use query_text return self.search_by_vector(query_vectors[0], collection_name, limit) def search_by_vector(self, vector, collection_name, limit=3): - res = self.client.search( - collection_name=collection_name, # target collection - data=[vector], # query vectors - limit=limit, # number of returned entities - output_fields=["text", "file_id"], # specifies fields to be returned - ) + # Default output fields for search, can be customized + # output_fields = ["text", "file_id", "hash", "start_char_idx", "end_char_idx"] + # valid_output_fields = output_fields # Default to all requested fields + # try: + # coll_info = self.client.describe_collection(collection_name) + # schema_fields = [field['name'] for field in coll_info.get('fields', [])] + # current_valid_fields = [f for f in output_fields if f in schema_fields] + # if current_valid_fields: # If any of the preferred fields are valid, use them + # valid_output_fields = current_valid_fields + # elif schema_fields: # Fallback: get all scalar fields if preferred are not found + # #This ensures we always try to get some data if the collection exists + # valid_output_fields = [f['name'] for f in coll_info.get('fields', []) if not f.get('is_primary', False) and coll_info.get('fields', [])[schema_fields.index(f)].get('type_name','').find('Vector') == -1 ] + # if not valid_output_fields and "text" in schema_fields: # Minimal fallback + # valid_output_fields = ["text"] + # elif not valid_output_fields and schema_fields: # If still nothing, take the first non-PK scalar field + # valid_output_fields = [schema_fields[0]] if schema_fields else [] + # # If schema_fields is empty or no suitable fields, valid_output_fields might be empty; Milvus might error or return default. + + # except Exception as e: + # logger.warning(f"Could not describe collection {collection_name} to validate output fields for search: {e}. Using default output_fields: {output_fields}.") + # # valid_output_fields remains as initially set (all requested output_fields) + + res = self.client.search( + collection_name=collection_name, + data=[vector], + limit=limit, + output_fields=["text", "file_id"], + ) + # res is a list of SearchResult lists. For a single query vector, it's res[0]. + return res[0] if res else [] - return res[0] def examples(self, collection_name, limit=20): res = self.client.query( collection_name=collection_name, - limit=10, + limit=limit, # Milvus query limit is different from search limit output_fields=["id", "text"], ) + # res is a list of dicts directly return res - def search_by_id(self, collection_name, id, output_fields=["id", "text"]): - res = self.client.get(collection_name, id, output_fields=output_fields) + def search_by_id(self, collection_name, entity_id, output_fields=["id", "text"]): + res = self.client.get(collection_name, entity_id, output_fields=output_fields) return res + def check_embed_model(self, db_id): db = self.get_database_by_id(db_id) - return db["embed_model"] == self.embed_model.embed_model_fullname + return db.get("embed_model") == self.embed_model.embed_model_fullname def update_database(self, db_id, name, description): - """更新知识库信息""" - # 检查知识库是否存在 db = self.get_database_by_id(db_id) if db is None: raise Exception(f"数据库不存在: {db_id}") - - # 调用update_database_record更新知识库信息 updated_db = self.update_database_record(db_id, name, description) return updated_db def parse_node_data(node): - node_data = node.model_dump() if hasattr(node, "model_dump") else node - node_text = node_data.get("page_content", node_data.get("text", "")) + # Handles both LlamaIndex NodeWithScore and simple dicts/BaseModel instances + if hasattr(node, 'node'): # Likely LlamaIndex NodeWithScore + node_obj = node.node + else: + node_obj = node + + if hasattr(node_obj, 'model_dump'): # Pydantic BaseModel + node_data = node_obj.model_dump() + elif hasattr(node_obj, 'dict'): + node_data = node_obj.dict() + elif isinstance(node_obj, dict): + node_data = node_obj + else: # Fallback for other types, e.g. Langchain Document + node_data = { + "text": getattr(node_obj, 'page_content', str(node_obj)), + "metadata": getattr(node_obj, 'metadata', {}) + } + + # 获取文本内容 + node_text = node_data.get("text", node_data.get("page_content", "")) # page_content for Langchain docs + + # 清理文本内容 + try: + # 尝试编码解码来清理无效字符 + cleaned_text = node_text.encode('utf-8', errors='replace').decode('utf-8') + except (UnicodeError, AttributeError): + # 如果出现编码错误,使用更激进的清理方法 + cleaned_text = ''.join(char for char in str(node_text) if ord(char) < 0x10000) + + # Extract start_char_idx and end_char_idx from metadata if present (common for LlamaIndex) + metadata = node_data.get("metadata", {}) + start_char_idx = metadata.get("start_char_idx", node_data.get("start_char_idx")) + end_char_idx = metadata.get("end_char_idx", node_data.get("end_char_idx")) + node_dict = { - "text": node_text, - "hash": hashstr(node_text, with_salt=True), - "start_char_idx": node_data.get("start_char_idx", None), - "end_char_idx": node_data.get("end_char_idx", None), - "metadata": node_data.get("metadata", {}), + "text": cleaned_text, + "hash": hashstr(cleaned_text, with_salt=True), + "start_char_idx": start_char_idx, + "end_char_idx": end_char_idx, + "metadata": metadata, # Keep all original metadata } return node_dict def gen_filename_from_url(url): - # 从URL中提取域名作为文件名 from urllib.parse import urlparse, unquote parsed_url = urlparse(unquote(url)) - domain = parsed_url.netloc - path = parsed_url.path - filename = f"{domain}{path}" - if filename.endswith('/'): - filename = filename[:-1] - if len(filename) > 100: - filename = filename[:97] + "..." - filename = filename.replace('/', '_') + + # Attempt to get a meaningful name from the last path segment + path_segments = [seg for seg in parsed_url.path.split('/') if seg] + if path_segments: + last_segment = path_segments[-1] + # Remove common extensions if they are part of the segment + name_part, _ = os.path.splitext(last_segment) + if name_part: # Use name part if it's not empty after removing extension + filename = name_part + else: # Fallback to domain if path is just '/' or ends with extension only + filename = parsed_url.netloc or "unknown_url" + else: # If no path, use domain + filename = parsed_url.netloc or "unknown_url" + + # Sanitize and shorten + filename = "".join(c if c.isalnum() or c in ('-', '_') else '_' for c in filename) + filename = filename[:100] # Limit length + + # Add a common URL extension if none obvious + if not any(filename.endswith(ext) for ext in ['.html', '.htm', '.php', '.asp']): + filename += ".urlpage" # Generic extension + return filename diff --git a/src/core/retriever.py b/src/core/retriever.py index a399979a..a74c772b 100644 --- a/src/core/retriever.py +++ b/src/core/retriever.py @@ -107,7 +107,7 @@ class Retriever: rw_query = self.rewrite_query(query, history, refs) logger.debug(f"{meta=}") - query_result = knowledge_base.query(query=rw_query, + query_result = knowledge_base.query(query_text=rw_query, db_id=db_id, distance_threshold=meta.get("distanceThreshold", 0.5), rerank_threshold=meta.get("rerankThreshold", 0.1), diff --git a/src/utils/__init__.py b/src/utils/__init__.py index eb416c3b..7f7c6b21 100644 --- a/src/utils/__init__.py +++ b/src/utils/__init__.py @@ -1,5 +1,5 @@ import time -import random +import hashlib import os from src.utils.logging_config import logger @@ -22,14 +22,28 @@ def is_text_pdf(pdf_path): # 如果超过50%的页面有文本内容,则认为是文本PDF return text_ratio > 0.5 -def hashstr(input_string, length=8, with_salt=False): - import hashlib - # 添加时间戳作为干扰 - if with_salt: - input_string += str(time.time() + random.random()) +def hashstr(input_string, length=None, with_salt=False): + """生成字符串的哈希值 + Args: + input_string: 输入字符串 + length: 截取长度,默认为None,表示不截取 + with_salt: 是否加盐,默认为False + """ + try: + # 尝试直接编码 + encoded_string = str(input_string).encode('utf-8') + except UnicodeEncodeError: + # 如果编码失败,替换无效字符 + encoded_string = str(input_string).encode('utf-8', errors='replace') - hash = hashlib.md5(str(input_string).encode()).hexdigest() - return hash[:length] + if with_salt: + salt = str(time.time()) + encoded_string = (encoded_string.decode('utf-8') + 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dependencies = [ { name = "langchain-together" }, { name = "langgraph" }, { name = "langgraph-checkpoint-sqlite" }, + { name = "langgraph-cli" }, { name = "langsmith" }, { name = "llama-index" }, { name = "llama-index-readers-file" }, @@ -4604,6 +4701,7 @@ requires-dist = [ { name = "langchain-together", specifier = ">=0.3.0" }, { name = "langgraph", specifier = ">=0.3.34" }, { name = "langgraph-checkpoint-sqlite", specifier = ">=2.0.7" }, + { name = "langgraph-cli", extras = ["inmem"], specifier = ">=0.1.54" }, { name = "langsmith", specifier = ">=0.3.37" }, { name = "llama-index", specifier = ">=0.12.33" }, { name = "llama-index-readers-file", specifier = ">=0.4.7" }, diff --git a/web/src/apis/admin_api.js b/web/src/apis/admin_api.js index e88df049..4e40d9df 100644 --- a/web/src/apis/admin_api.js +++ b/web/src/apis/admin_api.js @@ -150,15 +150,35 @@ export const knowledgeBaseApi = { }, /** - * 将文件分块 - * @param {Object} data - 分块参数 - * @returns {Promise} - 分块结果 + * 将文件分块并保存文件记录以待索引 + * @param {Object} data - 包含 db_id, files (路径列表), params (分块参数) + * @returns {Promise} - 处理结果 */ - fileToChunk: async (data) => { + fileToChunk: async (data) => { // data: { db_id, files, params } checkAdminPermission() return apiPost('/api/data/file-to-chunk', data, {}, true) }, + /** + * 将URL分块并保存文件记录以待索引 + * @param {Object} data - 包含 db_id, urls (链接列表), params (分块参数) + * @returns {Promise} - 处理结果 + */ + urlToChunk: async (data) => { // data: { db_id, urls, params } + checkAdminPermission() + return apiPost('/api/data/url-to-chunk', data, {}, true) + }, + + /** + * 触发指定文件的索引过程 + * @param {Object} data - 包含 db_id, file_id + * @returns {Promise} - 索引启动结果 + */ + indexFile: async (data) => { // data: { db_id, file_id } + checkAdminPermission() + return apiPost('/api/data/index-file', data, {}, true) + }, + /** * 将分块添加到数据库 * @param {Object} data - 包含db_id和file_chunks的数据 @@ -190,16 +210,6 @@ export const knowledgeBaseApi = { return apiGet(`/api/data/document?db_id=${dbId}&file_id=${fileId}`, {}, true) }, - /** - * 将URL转换为分块 - * @param {Object} data - 分块参数 - * @returns {Promise} - 分块结果 - */ - urlToChunk: async (data) => { - checkAdminPermission() - return apiPost('/api/data/url-to-chunk', data, {}, true) - }, - /** * 更新知识库信息 * @param {string} dbId - 知识库ID diff --git a/web/src/views/DataBaseInfoView.vue b/web/src/views/DataBaseInfoView.vue index 41fe1760..c0cc2113 100644 --- a/web/src/views/DataBaseInfoView.vue +++ b/web/src/views/DataBaseInfoView.vue @@ -42,60 +42,11 @@
-
+
+ 添加文件 刷新
- - - - -

共 {{ selectedFile?.lines?.length || 0 }} 个片段

-

- {{ line.text }} -

-
-
- - - - -
-
+
@@ -184,30 +135,67 @@
- - -
-
-

分块预览 (共 {{ chunkResults.length }} 个文件,{{ getTotalChunks() }} 个分块)

- - 添加到数据库 - -
- - - -
-
-

#{{ index + 1 }} {{ chunk.text }}

-
+ + + + +

共 {{ selectedFile?.lines?.length || 0 }} 个片段

+

+ {{ line.text }} +

+
@@ -350,7 +338,10 @@ import { FileOutlined, LinkOutlined, EditOutlined, + PlusOutlined, + HddOutlined, } from '@ant-design/icons-vue' +import { h } from 'vue'; const route = useRoute(); @@ -376,6 +367,8 @@ const state = reactive({ drawer: false, refreshInterval: null, curPage: "files", + indexingFile: null, + showAddFilesBlock: false, }); const meta = reactive({ @@ -505,6 +498,10 @@ const handleRefresh = () => { }) } +const handleShowAddFilesBlock = () => { + state.showAddFilesBlock = !state.showAddFilesBlock +} + const deleteDatabse = () => { Modal.confirm({ title: '删除数据库', @@ -652,7 +649,7 @@ const getTotalChunks = () => { return chunkResults.value.reduce((total, file) => total + file.nodes.length, 0); } -// 分块预览 +// "生成分块" - 修改后的逻辑 const chunkFiles = () => { console.log(fileList.value) const files = fileList.value.filter(file => file.status === 'done').map(file => file.response.file_path) @@ -665,28 +662,34 @@ const chunkFiles = () => { state.loading = true - // 调用file-to-chunk接口获取分块信息 knowledgeBaseApi.fileToChunk({ + db_id: databaseId.value, // 添加 db_id files: files, params: chunkParams.value }) .then(data => { - console.log('文件分块信息:', data) - chunkResults.value = Object.values(data); - activeFileKeys.value = chunkResults.value.length > 0 ? [0] : []; // 默认展开第一个文件 + console.log('文件处理结果:', data) + if (data.status === 'success') { + message.success(data.message || '文件已提交处理,请稍后在列表刷新查看状态'); + fileList.value = []; // 清空已上传文件列表 + // chunkResults.value = []; // 清空旧的预览结果 + // activeFileKeys.value = []; // 清空旧的预览结果 + getDatabaseInfo(); // 刷新数据库信息以显示新文件及其状态 + } else { + message.error(data.message || '文件处理失败'); + } }) .catch(error => { console.error(error) - message.error(error.message) + message.error(error.message || '文件处理请求失败') }) .finally(() => { state.loading = false }) } -// 分块预览 +// "生成分块" - 修改后的逻辑 const chunkUrls = () => { - // 分割并过滤URL列表 const urls = urlList.value.split('\n') .map(url => url.trim()) .filter(url => url.length > 0 && (url.startsWith('http://') || url.startsWith('https://'))); @@ -698,67 +701,74 @@ const chunkUrls = () => { state.loading = true; - // 调用url-to-chunk接口获取分块信息 knowledgeBaseApi.urlToChunk({ + db_id: databaseId.value, // 添加 db_id urls: urls, params: chunkParams.value }) .then(data => { - console.log('URL分块信息:', data); - chunkResults.value = Object.values(data); - activeFileKeys.value = chunkResults.value.length > 0 ? [0] : []; // 默认展开第一个 + console.log('URL处理结果:', data); + if (data.status === 'success') { + message.success(data.message || 'URL已提交处理,请稍后在列表刷新查看状态'); + urlList.value = ''; // 清空URL输入 + // chunkResults.value = []; // 清空旧的预览结果 + // activeFileKeys.value = []; // 清空旧的预览结果 + getDatabaseInfo(); // 刷新数据库信息以显示新文件及其状态 + } else { + message.error(data.message || 'URL处理失败'); + } }) .catch(error => { console.error(error); - message.error(error.message || '处理URL失败'); + message.error(error.message || '处理URL请求失败'); }) .finally(() => { state.loading = false; }); }; -// 添加到数据库 +// 添加到数据库 - 此函数逻辑将被新的 "索引" 按钮替代或移除 const addToDatabase = () => { - if (chunkResults.value.length === 0) { - message.error('没有可添加的分块') - return - } + // if (chunkResults.value.length === 0) { + // message.error('没有可添加的分块') + // return + // } + // state.adding = true + // state.lock = true - state.adding = true - state.lock = true + // // 转换为API需要的格式 + // const fileChunks = {}; + // chunkResults.value.forEach(file => { + // fileChunks[file.file_id] = file; + // }); - // 转换为API需要的格式 - const fileChunks = {}; - chunkResults.value.forEach(file => { - fileChunks[file.file_id] = file; - }); + // // 调用add-by-chunks接口将分块添加到数据库 + // knowledgeBaseApi.addByChunks({ + // db_id: databaseId.value, + // file_chunks: fileChunks + // }) + // .then(data => { + // console.log(data) - // 调用add-by-chunks接口将分块添加到数据库 - knowledgeBaseApi.addByChunks({ - db_id: databaseId.value, - file_chunks: fileChunks - }) - .then(data => { - console.log(data) - - if (data.status === 'failed') { - message.error(data.message) - } else { - message.success(data.message) - fileList.value = [] - chunkResults.value = [] - activeFileKeys.value = [] - } - }) - .catch(error => { - console.error(error) - message.error(error.message) - }) - .finally(() => { - getDatabaseInfo() - state.adding = false - state.lock = false - }) + // if (data.status === 'failed') { + // message.error(data.message) + // } else { + // message.success(data.message) + // fileList.value = [] + // chunkResults.value = [] + // activeFileKeys.value = [] + // getDatabaseInfo() // 刷新列表 + // } + // }) + // .catch(error => { + // console.error(error) + // message.error(error.message) + // }) + // .finally(() => { + // state.adding = false + // state.lock = false + // }) + message.info("此功能已通过新的索引流程处理。文件分块后将自动出现在列表中,可单独进行索引。"); } const addDocumentByFile = () => { @@ -881,6 +891,46 @@ const updateDatabaseInfo = async () => { } }; +const handleIndexFile = (fileId) => { + if (!fileId) { + message.error('无效的文件ID'); + return; + } + Modal.confirm({ + title: '开始索引文件', + content: `确定要开始索引文件 ${fileId} 吗?该操作可能需要一些时间。`, + okText: '确认索引', + cancelText: '取消', + onOk: () => { + state.indexingFile = fileId; // Set loading state for this specific file + state.lock = true; + knowledgeBaseApi.indexFile({ + db_id: databaseId.value, + file_id: fileId + }) + .then(response => { + if (response.status === 'success') { + message.success(response.message || `文件 ${fileId} 已开始索引。`); + getDatabaseInfo(); // Refresh to update status + } else { + message.error(response.message || `文件 ${fileId} 索引启动失败。`); + } + }) + .catch(error => { + console.error(`索引文件 ${fileId} 失败:`, error); + message.error(error.message || `索引文件 ${fileId} 时发生错误。`); + }) + .finally(() => { + state.indexingFile = null; // Reset loading state for this file + state.lock = false; + }); + }, + onCancel: () => { + console.log('取消索引'); + }, + }); +}; +