from collections.abc import AsyncGenerator from typing import Any from langchain_core.documents import Document from langchain_core.messages import HumanMessage, SystemMessage from langchain_openai import ChatOpenAI, OpenAIEmbeddings from langchain_text_splitters import RecursiveCharacterTextSplitter from app.api.v1.module_system.auth.schema import AuthSchema from app.config.setting import settings from app.core.exceptions import CustomException from app.core.logger import log from .chroma import chroma_manager from .crud import AgentConfigCRUD, KnowledgeCRUD, KnowledgeDocumentCRUD from .schema import ( AgentConfigCreateSchema, AgentConfigOutSchema, AgentConfigSchema, AgentConfigUpdateSchema, ChatQuerySchema, KnowledgeCreateSchema, KnowledgeDocumentCreateSchema, KnowledgeDocumentOutSchema, KnowledgeDocumentUpdateSchema, KnowledgeOutSchema, KnowledgeQueryParam, KnowledgeUpdateSchema, ) class AgentConfigService: """智能体配置服务层""" @classmethod async def get_by_id_service( cls, auth: AuthSchema, id: int ) -> dict[str, Any]: """ 获取智能体配置详情 参数: - auth (AuthSchema): 认证信息模型 - id (int): 智能体配置ID 返回: - dict[str, Any]: 智能体配置详情字典 """ obj = await AgentConfigCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="智能体配置不存在") return AgentConfigOutSchema.model_validate(obj).model_dump() @classmethod async def get_default_service( cls, auth: AuthSchema ) -> dict[str, Any]: """ 获取默认智能体配置 参数: - auth (AuthSchema): 认证信息模型 返回: - dict[str, Any]: 智能体配置详情字典 """ obj = await AgentConfigCRUD(auth).get_default_crud() if not obj: raise CustomException(msg="默认智能体配置不存在") return AgentConfigOutSchema.model_validate(obj).model_dump() @classmethod async def get_list_service( cls, auth: AuthSchema, query_params: Any ) -> dict[str, Any]: """ 列表查询智能体配置 参数: - auth (AuthSchema): 认证信息模型 - query_params (Any): 查询参数 返回: - dict[str, Any]: 智能体配置列表字典 """ search = {} if query_params.name: search["name"] = query_params.name if query_params.provider: search["provider"] = query_params.provider if query_params.is_default is not None: search["is_default"] = query_params.is_default if query_params.is_active is not None: search["is_active"] = query_params.is_active if query_params.created_id: search["created_id"] = query_params.created_id if query_params.updated_id: search["updated_id"] = query_params.updated_id if hasattr(query_params, "created_time") and query_params.created_time: search["created_time"] = query_params.created_time if hasattr(query_params, "updated_time") and query_params.updated_time: search["updated_time"] = query_params.updated_time objs = await AgentConfigCRUD(auth).get_list_crud( search=search, order_by=[{"id": "desc"}] ) data = [AgentConfigOutSchema.model_validate(obj).model_dump() for obj in objs] return {"total": len(data), "data": data} @classmethod async def create_service( cls, auth: AuthSchema, data: AgentConfigCreateSchema ) -> dict[str, Any]: """ 创建智能体配置 参数: - auth (AuthSchema): 认证信息模型 - data (AgentConfigCreateSchema): 创建智能体配置模型 返回: - dict[str, Any]: 创建的智能体配置字典 """ if data.is_default: existing_default = await AgentConfigCRUD(auth).get_default_crud() if existing_default: update_data = AgentConfigUpdateSchema.model_construct(is_default=False) await AgentConfigCRUD(auth).update_crud(id=existing_default.id, data=update_data) obj = await AgentConfigCRUD(auth).create_crud(data=data) if not obj: raise CustomException(msg="创建智能体配置失败") return AgentConfigOutSchema.model_validate(obj).model_dump() @classmethod async def update_service( cls, auth: AuthSchema, id: int, data: AgentConfigUpdateSchema ) -> dict[str, Any]: """ 更新智能体配置 参数: - auth (AuthSchema): 认证信息模型 - id (int): 智能体配置ID - data (AgentConfigUpdateSchema): 更新智能体配置模型 返回: - dict[str, Any]: 更新的智能体配置字典 """ obj = await AgentConfigCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="智能体配置不存在") if data.is_default: existing_default = await AgentConfigCRUD(auth).get_default_crud() if existing_default and existing_default.id != id: update_data = AgentConfigUpdateSchema.model_construct(is_default=False) await AgentConfigCRUD(auth).update_crud(id=existing_default.id, data=update_data) obj = await AgentConfigCRUD(auth).update_crud(id=id, data=data) if not obj: raise CustomException(msg="更新智能体配置失败") return AgentConfigOutSchema.model_validate(obj).model_dump() @classmethod async def delete_service(cls, auth: AuthSchema, ids: list[int]) -> None: """ 批量删除智能体配置 参数: - auth (AuthSchema): 认证信息模型 - ids (list[int]): 智能体配置ID列表 返回: - None """ await AgentConfigCRUD(auth).delete_crud(ids=ids) class RAGService: """RAG 检索增强生成服务层""" @classmethod async def retrieve_documents( cls, query: str, knowledge_ids: list[int], top_k: int = 3, auth: AuthSchema | None = None, ) -> list[Document]: """ 从知识库中检索相关文档 参数: - query (str): 查询文本 - knowledge_ids (list[int]): 知识库ID列表 - top_k (int): 返回最相关的文档数量 - auth (AuthSchema | None): 认证信息模型 返回: - list[Document]: 相关文档列表 """ embeddings = OpenAIEmbeddings( api_key=lambda: settings.OPENAI_API_KEY, base_url=settings.OPENAI_BASE_URL, ) query_embedding = await embeddings.aembed_query(query) results = chroma_manager.query_documents( query_embeddings=[query_embedding], n_results=top_k, where={"knowledge_id": {"$in": knowledge_ids}}, ) documents = [] if results and "documents" in results and len(results["documents"]) > 0: for i, doc_content in enumerate(results["documents"][0]): metadata = results["metadatas"][0][i] if "metadatas" in results and len(results["metadatas"]) > 0 else {} documents.append( Document( page_content=doc_content, metadata=metadata, ) ) return documents @classmethod def format_context(cls, documents: list[Document]) -> str: """ 格式化检索到的文档为上下文 参数: - documents (list[Document]): 文档列表 返回: - str: 格式化的上下文文本 """ if not documents: return "没有找到相关的知识库内容。" context_parts = [] for i, doc in enumerate(documents, 1): title = doc.metadata.get("title", "未知文档") content = doc.page_content context_parts.append(f"[文档 {i}] {title}\n{content}") return "\n\n".join(context_parts) class AgentService: """智能体服务层""" @classmethod async def chat_query( cls, query: ChatQuerySchema, config: AgentConfigSchema | None = None ) -> AsyncGenerator[str, Any]: """ 处理聊天查询 参数: - query (ChatQuerySchema): 聊天查询模型 - config (AgentConfigSchema | None): 智能体配置模型 返回: - AsyncGenerator[str, None]: 异步生成器,每次返回一个聊天响应 """ if config is None: config = AgentConfigSchema( provider="openai", model=settings.OPENAI_MODEL, api_key=settings.OPENAI_API_KEY, base_url=settings.OPENAI_BASE_URL, temperature=0.7, system_prompt="你是一个有用的AI助手，可以帮助用户回答问题和提供帮助。请用中文回答用户的问题。", ) system_prompt = config.system_prompt if query.knowledge_ids: retrieved_docs = await RAGService.retrieve_documents( query=query.message, knowledge_ids=query.knowledge_ids, top_k=3, ) context = RAGService.format_context(retrieved_docs) system_prompt = f"""{config.system_prompt} 以下是从知识库中检索到的相关内容，请参考这些内容回答用户的问题： {context} 如果检索到的内容与问题无关，请忽略这些内容，直接回答用户的问题。""" llm = ChatOpenAI( api_key=lambda: config.api_key, model=config.model, base_url=config.base_url, temperature=config.temperature, streaming=True, ) messages = [ SystemMessage(content=system_prompt), HumanMessage(content=query.message), ] try: async for chunk in llm.astream(messages): yield chunk.text except Exception as e: log.debug(f"关闭 LLM 客户端时发生异常(预期行为，服务可能正在关闭): {e}") status_code = getattr(e, "status_code", None) body = getattr(e, "body", None) message = None error_type = None error_code = None try: if isinstance(body, dict) and "error" in body: err = body.get("error") or {} error_type = err.get("type") error_code = err.get("code") message = err.get("message") except Exception: raise CustomException(f"解析 OpenAI 错误失败: {e!s}") text = str(e) msg = message or text if ( (error_code == "Arrearage") or (error_type == "Arrearage") or ("in good standing" in (msg or "")) ): raise ValueError( "账户欠费或结算异常，访问被拒绝。请检查账号状态或更换有效的 API Key。" ) if status_code == 401 or "invalid api key" in msg.lower(): raise ValueError("鉴权失败，API Key 无效或已过期。请检查系统配置中的 API Key。") if status_code == 403 or error_type in { "PermissionDenied", "permission_denied", }: raise ValueError("访问被拒绝，权限不足或账号受限。请检查账户权限设置。") if status_code == 429 or error_type in { "insufficient_quota", "rate_limit_exceeded", }: raise ValueError("请求过于频繁或配额已用尽。请稍后重试或提升账户配额。") if status_code == 400: raise ValueError(f"请求参数错误或服务拒绝：{message or '请检查输入内容。'}") if status_code in {500, 502, 503, 504}: raise ValueError("服务暂时不可用，请稍后重试。") raise CustomException(f"处理您的请求时出现错误：{msg}") class KnowledgeService: """知识库服务层""" @classmethod async def detail_service(cls, auth: AuthSchema, id: int) -> dict[str, Any]: """ 获取知识库详情 参数: - auth (AuthSchema): 认证信息模型 - id (int): 知识库ID 返回: - dict[str, Any]: 知识库详情字典 """ obj = await KnowledgeCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="知识库不存在") return KnowledgeOutSchema.model_validate(obj).model_dump() @classmethod async def list_service( cls, auth: AuthSchema, search: KnowledgeQueryParam | None = None, order_by: list[dict[str, str]] | None = None, ) -> list[dict[str, Any]]: """ 列表查询知识库 参数: - auth (AuthSchema): 认证信息模型 - search (KnowledgeQueryParam | None): 查询参数模型 - order_by (list[dict[str, str]] | None): 排序参数列表 返回: - list[dict[str, Any]]: 知识库详情字典列表 """ search_dict = search.__dict__ if search else None obj_list = await KnowledgeCRUD(auth).get_list_crud(search=search_dict, order_by=order_by) return [KnowledgeOutSchema.model_validate(obj).model_dump() for obj in obj_list] @classmethod async def create_service(cls, auth: AuthSchema, data: KnowledgeCreateSchema) -> dict[str, Any]: """ 创建知识库 参数: - auth (AuthSchema): 认证信息模型 - data (KnowledgeCreateSchema): 创建知识库模型 返回: - dict[str, Any]: 创建的知识库详情字典 """ obj = await KnowledgeCRUD(auth).get_by_name_crud(name=data.name) if obj: raise CustomException(msg="创建失败，知识库已存在") obj = await KnowledgeCRUD(auth).create_crud(data=data) return KnowledgeOutSchema.model_validate(obj).model_dump() @classmethod async def update_service( cls, auth: AuthSchema, id: int, data: KnowledgeUpdateSchema ) -> dict[str, Any]: """ 更新知识库 参数: - auth (AuthSchema): 认证信息模型 - id (int): 知识库ID - data (KnowledgeUpdateSchema): 更新知识库模型 返回: - dict[str, Any]: 更新的知识库详情字典 """ obj = await KnowledgeCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="更新失败，该数据不存在") exist_obj = await KnowledgeCRUD(auth).get_by_name_crud(name=data.name) if exist_obj and exist_obj.id != id: raise CustomException(msg="更新失败，知识库名称重复") obj = await KnowledgeCRUD(auth).update_crud(id=id, data=data) return KnowledgeOutSchema.model_validate(obj).model_dump() @classmethod async def delete_service(cls, auth: AuthSchema, ids: list[int]) -> None: """ 批量删除知识库 参数: - auth (AuthSchema): 认证信息模型 - ids (list[int]): 知识库ID列表 返回: - None """ if len(ids) < 1: raise CustomException(msg="删除失败，删除对象不能为空") for id in ids: obj = await KnowledgeCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="删除失败，该数据不存在") await KnowledgeCRUD(auth).delete_crud(ids=ids) @classmethod async def document_detail_service(cls, auth: AuthSchema, id: int) -> dict[str, Any]: """ 获取知识库文档详情 参数: - auth (AuthSchema): 认证信息模型 - id (int): 文档ID 返回: - dict[str, Any]: 文档详情字典 """ obj = await KnowledgeDocumentCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="文档不存在") return KnowledgeDocumentOutSchema.model_validate(obj).model_dump() @classmethod async def document_list_service( cls, auth: AuthSchema, search: Any | None = None, order_by: list[dict[str, str]] | None = None, ) -> list[dict[str, Any]]: """ 列表查询知识库文档 参数: - auth (AuthSchema): 认证信息模型 - search (Any | None): 查询参数模型 - order_by (list[dict[str, str]] | None): 排序参数列表 返回: - list[dict[str, Any]]: 文档详情字典列表 """ search_dict = search.__dict__ if search else None obj_list = await KnowledgeDocumentCRUD(auth).get_list_crud( search=search_dict, order_by=order_by ) return [KnowledgeDocumentOutSchema.model_validate(obj).model_dump() for obj in obj_list] @classmethod async def document_create_service( cls, auth: AuthSchema, data: KnowledgeDocumentCreateSchema ) -> dict[str, Any]: """ 创建知识库文档 参数: - auth (AuthSchema): 认证信息模型 - data (KnowledgeDocumentCreateSchema): 创建文档模型 返回: - dict[str, Any]: 创建的文档详情字典 """ knowledge = await KnowledgeCRUD(auth).get_by_id_crud(id=data.knowledge_id) if not knowledge: raise CustomException(msg="创建失败，知识库不存在") obj = await KnowledgeDocumentCRUD(auth).create_crud(data=data) if not obj: raise CustomException(msg="创建文档失败") try: text_splitter = RecursiveCharacterTextSplitter( chunk_size=knowledge.chunk_size, chunk_overlap=knowledge.chunk_overlap, ) chunks = text_splitter.split_text(data.content) if settings.OPENAI_API_KEY and settings.OPENAI_BASE_URL: try: embeddings = OpenAIEmbeddings( model=settings.OPENAI_MODEL, ) chunk_embeddings = await embeddings.aembed_documents(chunks) ids = [] documents = [] metadatas = [] for i, chunk in enumerate(chunks): chunk_id = f"{obj.id}_chunk_{i}" ids.append(chunk_id) documents.append(chunk) metadatas.append( { "document_id": obj.id, "knowledge_id": obj.knowledge_id, "title": obj.title, "chunk_index": i, } ) chroma_manager.add_documents( ids=ids, embeddings=chunk_embeddings, documents=documents, metadatas=metadatas, ) update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=True ) except Exception as e: log.warning(f"嵌入生成失败，使用虚拟嵌入: {e!s}") chunk_embeddings = [[0.0] * 1536 for _ in chunks] ids = [] documents = [] metadatas = [] for i, chunk in enumerate(chunks): chunk_id = f"{obj.id}_chunk_{i}" ids.append(chunk_id) documents.append(chunk) metadatas.append( { "document_id": obj.id, "knowledge_id": obj.knowledge_id, "title": obj.title, "chunk_index": i, } ) chroma_manager.add_documents( ids=ids, embeddings=chunk_embeddings, documents=documents, metadatas=metadatas, ) update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=True ) else: log.info("未配置嵌入模型，跳过向量索引") update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=False ) await KnowledgeDocumentCRUD(auth).update_crud(id=obj.id, data=update_data) updated_obj = await KnowledgeDocumentCRUD(auth).get_by_id_crud(id=obj.id) return KnowledgeDocumentOutSchema.model_validate(updated_obj).model_dump() except CustomException: raise except Exception as e: log.error(f"创建知识库文档时发生错误: {e!s}") raise CustomException(msg=f"创建知识库文档失败: {e!s}") @classmethod async def document_update_service( cls, auth: AuthSchema, id: int, data: KnowledgeDocumentUpdateSchema ) -> dict[str, Any]: """ 更新知识库文档 参数: - auth (AuthSchema): 认证信息模型 - id (int): 文档ID - data (KnowledgeDocumentUpdateSchema): 更新文档模型 返回: - dict[str, Any]: 更新的文档详情字典 """ obj = await KnowledgeDocumentCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="更新失败，该数据不存在") content_changed = data.content is not None and data.content != obj.content if content_changed: chunk_ids = [f"{obj.id}_chunk_{i}" for i in range(obj.chunk_count or 0)] chroma_manager.delete_documents(ids=chunk_ids) obj = await KnowledgeDocumentCRUD(auth).update_crud(id=id, data=data) if not obj: raise CustomException(msg="更新文档失败") if content_changed and obj.content: knowledge = await KnowledgeCRUD(auth).get_by_id_crud(id=obj.knowledge_id) if knowledge: text_splitter = RecursiveCharacterTextSplitter( chunk_size=knowledge.chunk_size, chunk_overlap=knowledge.chunk_overlap, ) chunks = text_splitter.split_text(obj.content) if settings.OPENAI_API_KEY and settings.OPENAI_BASE_URL: try: embeddings = OpenAIEmbeddings( api_key=lambda: settings.OPENAI_API_KEY, base_url=settings.OPENAI_BASE_URL, ) chunk_embeddings = await embeddings.aembed_documents(chunks) ids = [] documents = [] metadatas = [] for i, chunk in enumerate(chunks): chunk_id = f"{obj.id}_chunk_{i}" ids.append(chunk_id) documents.append(chunk) metadatas.append( { "document_id": obj.id, "knowledge_id": obj.knowledge_id, "title": obj.title, "chunk_index": i, } ) chroma_manager.add_documents( ids=ids, embeddings=chunk_embeddings, documents=documents, metadatas=metadatas, ) update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=True ) except Exception as e: log.warning(f"嵌入生成失败，跳过向量索引: {e!s}") update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=False ) else: log.info("未配置嵌入模型，跳过向量索引") update_data = KnowledgeDocumentUpdateSchema.model_construct( chunk_count=len(chunks), is_indexed=False ) obj = await KnowledgeDocumentCRUD(auth).update_crud(id=obj.id, data=update_data) return KnowledgeDocumentOutSchema.model_validate(obj).model_dump() @classmethod async def document_delete_service(cls, auth: AuthSchema, ids: list[int]) -> None: """ 批量删除知识库文档 参数: - auth (AuthSchema): 认证信息模型 - ids (list[int]): 文档ID列表 返回: - None """ if len(ids) < 1: raise CustomException(msg="删除失败，删除对象不能为空") chunk_ids = [] for id in ids: obj = await KnowledgeDocumentCRUD(auth).get_by_id_crud(id=id) if not obj: raise CustomException(msg="删除失败，该数据不存在") if obj.chunk_count: chunk_ids.extend([f"{obj.id}_chunk_{i}" for i in range(obj.chunk_count)]) if chunk_ids: chroma_manager.delete_documents(ids=chunk_ids) await KnowledgeDocumentCRUD(auth).delete_crud(ids=ids)