From 64e42705bfed4177b5afe8511b6e5895ede34596 Mon Sep 17 00:00:00 2001 From: wangmeihua <13383952685@163.com> Date: Wed, 6 Aug 2025 10:52:40 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E6=B7=BB=E5=8A=A0/=E5=88=A0?= =?UTF-8?q?=E9=99=A4=E6=96=87=E6=A1=A3=E5=8A=9F=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- rag/api_service.py | 282 ++++++++++++++++++++++++++++++++++++++++++++ rag/file.py | 283 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 565 insertions(+) create mode 100644 rag/api_service.py create mode 100644 rag/file.py diff --git a/rag/api_service.py b/rag/api_service.py new file mode 100644 index 0000000..e1f8cf5 --- /dev/null +++ b/rag/api_service.py @@ -0,0 +1,282 @@ +from appPublic.log import debug, error +from typing import Dict, Any, List +import aiohttp +from aiohttp import ClientSession, ClientTimeout +from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type +import traceback +import uuid +import json + +class APIService: + """处理 API 请求的服务类""" + + @retry( + stop=stop_after_attempt(3), + wait=wait_exponential(multiplier=1, min=1, max=10), + retry=retry_if_exception_type((aiohttp.ClientError, RuntimeError)), + before_sleep=lambda retry_state: debug(f"重试 API 请求，第 {retry_state.attempt_number} 次") + ) + async def _make_request(self, url: str, action: str, params: Dict[str, Any]) -> Dict[str, Any]: + """通用 API 请求函数""" + debug(f"开始 API 请求: action={action}, params={params}, url={url}") + try: + async with ClientSession(timeout=ClientTimeout(total=300)) as session: + async with session.post( + url, + headers={"Content-Type": "application/json"}, + json=params + ) as response: + debug(f"收到响应: status={response.status}, headers={response.headers}") + response_text = await response.text() + debug(f"响应内容: {response_text}") + result = await response.json() + debug(f"API 响应内容: {result}") + if response.status == 400: + debug(f"客户端错误，状态码: {response.status}, 返回响应: {result}") + return result + if response.status != 200: + error(f"API 调用失败，动作: {action}, 状态码: {response.status}, 响应: {response_text}") + raise RuntimeError(f"API 调用失败: {response.status}") + return result + except Exception as e: + error(f"API 调用失败: {action}, 错误: {str(e)}, 堆栈: {traceback.format_exc()}") + raise RuntimeError(f"API 调用失败: {str(e)}") + + # 嵌入服务 (BAAI/bge-m3) + async def get_embeddings(self, texts: list) -> list: + """调用嵌入服务获取文本向量""" + try: + async with ClientSession() as session: + async with session.post( + "http://localhost:9998/v1/embeddings", + headers={"Content-Type": "application/json"}, + json={"input": texts if isinstance(texts, list) else [texts]} + ) as response: + if response.status != 200: + error(f"嵌入服务调用失败，状态码: {response.status}") + raise RuntimeError(f"嵌入服务调用失败: {response.status}") + result = await response.json() + if result.get("object") != "list" or not result.get("data"): + error(f"嵌入服务响应格式错误: {result}") + raise RuntimeError("嵌入服务响应格式错误") + embeddings = [item["embedding"] for item in result["data"]] + debug(f"成功获取 {len(embeddings)} 个嵌入向量") + return embeddings + except Exception as e: + error(f"嵌入服务调用失败: {str(e)}") + raise RuntimeError(f"嵌入服务调用失败: {str(e)}") + + # 实体提取服务 (LTP/small) + async def extract_entities(self, query: str) -> list: + """调用实体识别服务""" + try: + if not query: + raise ValueError("查询文本不能为空") + async with ClientSession() as session: + async with session.post( + "http://localhost:9990/v1/entities", + headers={"Content-Type": "application/json"}, + json={"query": query} + ) as response: + if response.status != 200: + error(f"实体识别服务调用失败，状态码: {response.status}") + raise RuntimeError(f"实体识别服务调用失败: {response.status}") + result = await response.json() + if result.get("object") != "list" or not result.get("data"): + error(f"实体识别服务响应格式错误: {result}") + raise RuntimeError("实体识别服务响应格式错误") + entities = result["data"] + unique_entities = list(dict.fromkeys(entities)) + debug(f"成功提取 {len(unique_entities)} 个唯一实体") + return unique_entities + except Exception as e: + error(f"实体识别服务调用失败: {str(e)}") + return [] + + # 三元组抽取服务 (Babelscape/mrebel-large) + async def extract_triples(self, text: str) -> list: + """调用三元组抽取服务""" + request_id = str(uuid.uuid4()) + debug(f"Request #{request_id} started for triples extraction") + try: + async with ClientSession( + connector=aiohttp.TCPConnector(limit=30), + timeout=ClientTimeout(total=None) + ) as session: + async with session.post( + "http://localhost:9991/v1/triples", + headers={"Content-Type": "application/json; charset=utf-8"}, + json={"text": text} + ) as response: + if response.status != 200: + error_text = await response.text() + error(f"Request #{request_id} failed, status: {response.status}, response: {error_text}") + raise RuntimeError(f"三元组抽取服务调用失败: {response.status}, {error_text}") + result = await response.json() + if result.get("object") != "list" or not result.get("data"): + error(f"Request #{request_id} invalid response format: {result}") + raise RuntimeError("三元组抽取服务响应格式错误") + triples = result["data"] + debug(f"Request #{request_id} extracted {len(triples)} triples") + return triples + except Exception as e: + error(f"Request #{request_id} failed to extract triples: {str(e)}") + raise RuntimeError(f"三元组抽取服务调用失败: {str(e)}") + + # 重排序服务 (BAAI/bge-reranker-v2-m3) + async def rerank_results(self, query: str, results: list, top_n: int) -> list: + """调用重排序服务""" + try: + if not results: + debug("无结果需要重排序") + return results + + if not isinstance(top_n, int) or top_n < 1: + debug(f"无效的 top_n 参数: {top_n}, 使用 len(results)={len(results)}") + top_n = len(results) + else: + top_n = min(top_n, len(results)) + + documents = [result.get("text", str(result)) for result in results] + async with ClientSession() as session: + async with session.post( + "http://localhost:9997/v1/rerank", + headers={"Content-Type": "application/json"}, + json={ + "model": "rerank-001", + "query": query, + "documents": documents, + "top_n": top_n + } + ) as response: + if response.status != 200: + error(f"重排序服务调用失败，状态码: {response.status}") + raise RuntimeError(f"重排序服务调用失败: {response.status}") + result = await response.json() + if result.get("object") != "rerank.result" or not result.get("data"): + error(f"重排序服务响应格式错误: {result}") + raise RuntimeError("重排序服务响应格式错误") + rerank_data = result["data"] + reranked_results = [] + for item in rerank_data: + index = item["index"] + if index < len(results): + results[index]["rerank_score"] = item["relevance_score"] + reranked_results.append(results[index]) + debug(f"成功重排序 {len(reranked_results)} 条结果") + return reranked_results[:top_n] + except Exception as e: + error(f"重排序服务调用失败: {str(e)}") + return results + + # Neo4j 服务 + async def neo4j_docs(self) -> Dict[str, Any]: + """获取 Neo4j 文档""" + return await self._make_request("http://localhost:8885/docs", "docs", {}) + + async def neo4j_initialize(self) -> Dict[str, Any]: + """初始化 Neo4j 服务""" + return await self._make_request("http://localhost:8885/v1/initialize", "initialize", {}) + + async def neo4j_insert_triples(self, triples: list, document_id: str, knowledge_base_id: str, userid: str) -> Dict[str, Any]: + """插入三元组到 Neo4j""" + params = { + "triples": triples, + "document_id": document_id, + "knowledge_base_id": knowledge_base_id, + "userid": userid + } + return await self._make_request("http://localhost:8885/v1/inserttriples", "inserttriples", params) + + async def neo4j_delete_document(self, document_id: str) -> Dict[str, Any]: + """删除指定文档""" + return await self._make_request("http://localhost:8885/v1/deletedocument", "deletedocument", {"document_id": document_id}) + + async def neo4j_delete_knowledgebase(self, userid: str, knowledge_base_id: str) -> Dict[str, Any]: + """删除用户知识库""" + return await self._make_request("http://localhost:8885/v1/deleteknowledgebase", "deleteknowledgebase", + {"userid": userid, "knowledge_base_id": knowledge_base_id}) + + async def neo4j_match_triplets(self, query: str, query_entities: list, userid: str, knowledge_base_id: str) -> Dict[str, Any]: + """根据实体匹配相关三元组""" + params = { + "query": query, + "query_entities": query_entities, + "userid": userid, + "knowledge_base_id": knowledge_base_id + } + return await self._make_request("http://localhost:8885/v1/matchtriplets", "matchtriplets", params) + + # RAG 服务 + async def rag_create_collection(self, db_type: str = "") -> Dict[str, Any]: + """创建集合""" + params = {"db_type": db_type} if db_type else {} + return await self._make_request("http://localhost:8888/v1/createcollection", "createcollection", params) + + async def rag_delete_collection(self, db_type: str = "") -> Dict[str, Any]: + """删除集合""" + params = {"db_type": db_type} if db_type else {} + return await self._make_request("http://localhost:8888/v1/deletecollection", "deletecollection", params) + + async def rag_insert_file(self, file_path: str, userid: str, knowledge_base_id: str, document_id: str) -> Dict[str, Any]: + """添加记录""" + params = { + "file_path": file_path, + "userid": userid, + "knowledge_base_id": knowledge_base_id, + "document_id": document_id + } + return await self._make_request("http://localhost:8888/v1/insertfile", "insertfile", params) + + async def rag_delete_file(self, userid: str, file_path: str, knowledge_base_id: str, document_id: str) -> Dict[str, Any]: + """删除记录""" + params = { + "userid": userid, + "file_path": file_path, + "knowledge_base_id": knowledge_base_id, + "document_id": document_id + } + return await self._make_request("http://localhost:8888/v1/deletefile", "deletefile", params) + + async def rag_delete_knowledgebase(self, userid: str, knowledge_base_id: str) -> Dict[str, Any]: + """删除知识库""" + return await self._make_request("http://localhost:8888/v1/deleteknowledgebase", "deleteknowledgebase", + {"userid": userid, "knowledge_base_id": knowledge_base_id}) + + async def rag_search_query(self, query: str, userid: str, knowledge_base_ids: list, limit: int, offset: int, + use_rerank: bool) -> Dict[str, Any]: + """根据用户知识库检索""" + params = { + "query": query, + "userid": userid, + "knowledge_base_ids": knowledge_base_ids, + "limit": limit, + "offset": offset, + "use_rerank": use_rerank + } + return await self._make_request("http://localhost:8888/v1/searchquery", "searchquery", params) + + async def rag_fused_search_query(self, query: str, userid: str, knowledge_base_ids: list, limit: int, offset: int, + use_rerank: bool) -> Dict[str, Any]: + """根据用户知识库+知识图谱检索""" + params = { + "query": query, + "userid": userid, + "knowledge_base_ids": knowledge_base_ids, + "limit": limit, + "offset": offset, + "use_rerank": use_rerank + } + return await self._make_request("http://localhost:8888/v1/fusedsearchquery", "fusedsearchquery", params) + + async def rag_list_user_files(self, userid: str) -> Dict[str, Any]: + """列出用户知识库列表""" + return await self._make_request("http://localhost:8888/v1/listuserfiles", "listuserfiles", {"userid": userid}) + + async def rag_list_all_knowledgebases(self) -> Dict[str, Any]: + """列出数据库中所有数据""" + return await self._make_request("http://localhost:8888/v1/listallknowledgebases", "listallknowledgebases", {}) + + async def rag_docs(self) -> Dict[str, Any]: + """获取 RAG 帮助文档""" + return await self._make_request("http://localhost:8888/v1/docs", "docs", {}) \ No newline at end of file diff --git a/rag/file.py b/rag/file.py new file mode 100644 index 0000000..bd92832 --- /dev/null +++ b/rag/file.py @@ -0,0 +1,283 @@ +from api_service import APIService +from appPublic.registerfunction import RegisterFunction +from appPublic.log import debug, error, info +from sqlor.dbpools import DBPools +import asyncio +import aiohttp +from langchain_core.documents import Document +from langchain_text_splitters import RecursiveCharacterTextSplitter +import os +import re +import time +import uuid +from datetime import datetime +import traceback +from filetxt.loader import fileloader + +def init(): + rf = RegisterFunction() + rf.register('fileuploaded', file_uploaded) + rf.register('filedeleted', file_deleted) + +async def get_orgid_by_id(kdb_id): + """ + 根据 kdb 的 id 查询对应的 orgid。 + """ + dbs = { + "cfae": { + "driver": "mysql.connector", + "coding": "utf8", + "dbname": "cfae", + "kwargs": { + "user": "test", + "db": "cfae", + "password": "test123", + "host": "localhost" + } + } + } + loop = asyncio.get_event_loop() + pool = DBPools(dbs, loop) + db = DBPools() + dbname = get_module_dbname('rag') + sql = "SELECT orgid FROM kdb WHERE id = %s" + try: + async with db.sqlorContext(dbname) as sor: + result = await sor.sql(sql, (kdb_id,)) + if result and result.rows: + return result.rows[0][0] + return None + except Exception as e: + error(f"查询 orgid 失败: {str(e)}, 堆栈: {traceback.format_exc()}") + return None + +async def file_uploaded(params_kw): + """将文档插入 Milvus 并抽取三元组到 Neo4j""" + debug(f'Received params: {params_kw=}') + api_service = APIService() + realpath = params_kw.get('realpath', '') + fiid = params_kw.get('fiid', '') + id = params_kw.get('id', '') + orgid = await get_orgid_by_id(id) + db_type = '' + debug(f'Inserting document: file_path={realpath}, userid={orgid}, db_type={db_type}, knowledge_base_id={fiid}, document_id={id}') + + timings = {} + start_total = time.time() + + try: + if not orgid or not fiid or not id: + raise ValueError("orgid、fiid 和 id 不能为空") + if len(orgid) > 32 or len(fiid) > 255: + raise ValueError("orgid 或 fiid 的长度超出限制") + if not os.path.exists(realpath): + raise ValueError(f"文件 {realpath} 不存在") + + supported_formats = {'pdf', 'docx', 'xlsx', 'pptx', 'csv', 'txt'} + ext = realpath.rsplit('.', 1)[1].lower() if '.' in realpath else '' + if ext not in supported_formats: + raise ValueError(f"不支持的文件格式: {ext}, 支持的格式: {', '.join(supported_formats)}") + + debug(f"加载文件: {realpath}") + start_load = time.time() + text = fileloader(realpath) + text = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s.;,\n]', '', text) + timings["load_file"] = time.time() - start_load + debug(f"加载文件耗时: {timings['load_file']:.2f} 秒, 文本长度: {len(text)}") + if not text or not text.strip(): + raise ValueError(f"文件 {realpath} 加载为空") + + document = Document(page_content=text) + text_splitter = RecursiveCharacterTextSplitter( + chunk_size=500, + chunk_overlap=100, + length_function=len) + debug("开始分片文件内容") + start_split = time.time() + chunks = text_splitter.split_documents([document]) + timings["split_text"] = time.time() - start_split + debug(f"文本分片耗时: {timings['split_text']:.2f} 秒, 分片数量: {len(chunks)}, 分片内容: {[chunk.page_content[:50] for chunk in chunks[:5]]}") + if not chunks: + raise ValueError(f"文件 {realpath} 未生成任何文档块") + + filename = os.path.basename(realpath).rsplit('.', 1)[0] + upload_time = datetime.now().isoformat() + + debug("调用嵌入服务生成向量") + start_embedding = time.time() + texts = [chunk.page_content for chunk in chunks] + embeddings = await api_service.get_embeddings(texts) + if not embeddings or not all(len(vec) == 1024 for vec in embeddings): + raise ValueError("所有嵌入向量必须是长度为 1024 的浮点数列表") + timings["generate_embeddings"] = time.time() - start_embedding + debug(f"生成嵌入向量耗时: {timings['generate_embeddings']:.2f} 秒, 嵌入数量: {len(embeddings)}") + + chunks_data = [] + for i, chunk in enumerate(chunks): + chunks_data.append({ + "userid": orgid, + "knowledge_base_id": fiid, + "text": chunk.page_content, + "vector": embeddings[i], + "document_id": id, + "filename": filename + '.' + ext, + "file_path": realpath, + "upload_time": upload_time, + "file_type": ext, + }) + + debug(f"调用插入文件端点: {realpath}") + start_milvus = time.time() + result = await api_service.make_milvus_request("insertdocument", {"chunks": chunks_data, "db_type": db_type}) + timings["insert_milvus"] = time.time() - start_milvus + debug(f"Milvus 插入耗时: {timings['insert_milvus']:.2f} 秒") + + if result.get("status") != "success": + timings["total"] = time.time() - start_total + return {"status": "error", "document_id": id, "timings": timings, "message": result.get("message", "未知错误"), "status_code": 400} + + debug("调用三元组抽取服务") + start_triples = time.time() + try: + chunk_texts = [doc.page_content for doc in chunks] + debug(f"处理 {len(chunk_texts)} 个分片进行三元组抽取") + tasks = [api_service.extract_triples(chunk) for chunk in chunk_texts] + results = await asyncio.gather(*tasks, return_exceptions=True) + + triples = [] + for i, result in enumerate(results): + if isinstance(result, list): + triples.extend(result) + debug(f"分片 {i + 1} 抽取到 {len(result)} 个三元组: {result[:5]}") + else: + error(f"分片 {i + 1} 处理失败: {str(result)}") + + unique_triples = [] + seen = set() + for t in triples: + identifier = (t['head'].lower(), t['tail'].lower(), t['type'].lower()) + if identifier not in seen: + seen.add(identifier) + unique_triples.append(t) + else: + for existing in unique_triples: + if (existing['head'].lower() == t['head'].lower() and + existing['tail'].lower() == t['tail'].lower() and + len(t['type']) > len(existing['type'])): + unique_triples.remove(existing) + unique_triples.append(t) + debug(f"替换三元组为更具体类型: {t}") + break + + timings["extract_triples"] = time.time() - start_triples + debug(f"三元组抽取耗时: {timings['extract_triples']:.2f} 秒, 抽取到 {len(unique_triples)} 个三元组: {unique_triples[:5]}") + + debug(f"抽取到 {len(unique_triples)} 个三元组，调用 Neo4j 服务插入") + start_neo4j = time.time() + if unique_triples: + neo4j_result = await api_service.make_neo4j_request("inserttriples", { + "triples": unique_triples, "document_id": id, "knowledge_base_id": fiid, "userid": orgid + }) + debug(f"Neo4j 服务响应: {neo4j_result}") + if neo4j_result.get("status") != "success": + timings["insert_neo4j"] = time.time() - start_neo4j + timings["total"] = time.time() - start_total + return {"status": "error", "document_id": id, "collection_name": "ragdb", "timings": timings, + "message": f"Neo4j 三元组插入失败: {neo4j_result.get('message', '未知错误')}", "status_code": 400} + info(f"文件 {realpath} 三元组成功插入 Neo4j: {neo4j_result.get('message')}") + else: + debug(f"文件 {realpath} 未抽取到三元组") + timings["insert_neo4j"] = time.time() - start_neo4j + debug(f"Neo4j 插入耗时: {timings['insert_neo4j']:.2f} 秒") + + except Exception as e: + timings["extract_triples"] = time.time() - start_triples if "extract_triples" not in timings else timings["extract_triples"] + timings["insert_neo4j"] = time.time() - start_neo4j + debug(f"处理三元组或 Neo4j 插入失败: {str(e)}, 堆栈: {traceback.format_exc()}") + timings["total"] = time.time() - start_total + return {"status": "success", "document_id": id, "collection_name": "ragdb", "timings": timings, + "unique_triples": unique_triples, + "message": f"文件 {realpath} 成功嵌入，但三元组处理或 Neo4j 插入失败: {str(e)}", "status_code": 200} + + timings["total"] = time.time() - start_total + debug(f"总耗时: {timings['total']:.2f} 秒") + return {"status": "success", "document_id": id, "collection_name": "ragdb", "timings": timings, + "unique_triples": unique_triples, "message": f"文件 {realpath} 成功嵌入并处理三元组", "status_code": 200} + + except Exception as e: + error(f"插入文档失败: {str(e)}, 堆栈: {traceback.format_exc()}") + timings["total"] = time.time() - start_total + return {"status": "error", "document_id": id, "collection_name": "ragdb", "timings": timings, + "message": f"插入文档失败: {str(e)}", "status_code": 400} + +async def file_deleted(params_kw): + """删除用户指定文件数据，包括 Milvus 和 Neo4j 中的记录""" + api_service = APIService() + id = params_kw.get('id', '') + realpath = params_kw.get('realpath', '') + fiid = params_kw.get('fiid', '') + orgid = await get_orgid_by_id(id) + db_type = '' + collection_name = "ragdb" if not db_type else f"ragdb_{db_type}" + try: + required_fields = ['id', 'fiid', 'realpath'] + missing_fields = [field for field in required_fields if field not in params_kw or not params_kw[field]] + if missing_fields: + raise ValueError(f"缺少必填字段: {', '.join(missing_fields)}") + + debug(f"调用删除文件端点: userid={orgid}, file_path={realpath}, knowledge_base_id={fiid}") + document_id = id # 备用，使用 id 作为 document_id + milvus_result = await api_service.make_milvus_request("deletedocument", { + "userid": orgid, + "file_path": realpath, + "knowledge_base_id": fiid, + "document_id": document_id, + "db_type": db_type + }) + + if milvus_result.get("status") != "success": + raise ValueError(milvus_result.get("message", "Milvus 删除失败")) + + neo4j_deleted_nodes = 0 + neo4j_deleted_rels = 0 + try: + debug(f"调用 Neo4j 删除文档端点: document_id={document_id}") + neo4j_result = await api_service.make_neo4j_request("deletedocument", {"document_id": document_id}) + if neo4j_result.get("status") != "success": + raise ValueError(neo4j_result.get("message", "Neo4j 删除失败")) + nodes_deleted = neo4j_result.get("nodes_deleted", 0) + rels_deleted = neo4j_result.get("rels_deleted", 0) + neo4j_deleted_nodes += nodes_deleted + neo4j_deleted_rels += rels_deleted + info(f"成功删除 document_id={document_id} 的 {nodes_deleted} 个 Neo4j 节点和 {rels_deleted} 个关系") + except Exception as e: + error(f"删除 document_id={document_id} 的 Neo4j 数据失败: {str(e)}") + + return { + "status": "success", + "collection_name": collection_name, + "document_id": document_id, + "message": f"成功删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系", + "status_code": 200 + } + + except Exception as e: + error(f"删除文档失败: {str(e)}, 堆栈: {traceback.format_exc()}") + return { + "status": "error", + "collection_name": collection_name, + "document_id": document_id, + "message": f"删除文档失败: {str(e)}", + "status_code": 400 + } + +async def main(): + kdb_id = "textdb" + orgid = await get_orgid_by_id(kdb_id) + if orgid: + print(f"找到的 orgid: {orgid}") + else: + print("未找到对应的 orgid") + +if __name__ == "__main__": + asyncio.run(main())