rag
This commit is contained in:
parent
043fb80ed4
commit
68a9c43390
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from appPublic.log import debug, error
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from typing import Dict, Any, List
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import aiohttp
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from aiohttp import ClientSession, ClientTimeout
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from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
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import traceback
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import uuid
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import json
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class APIService:
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"""处理 API 请求的服务类"""
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=1, max=10),
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retry=retry_if_exception_type((aiohttp.ClientError, RuntimeError)),
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before_sleep=lambda retry_state: debug(f"重试 API 请求，第 {retry_state.attempt_number} 次")
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)
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async def _make_request(self, url: str, action: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""通用 API 请求函数"""
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debug(f"开始 API 请求: action={action}, params={params}, url={url}")
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try:
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async with ClientSession(timeout=ClientTimeout(total=300)) as session:
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async with session.post(
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url,
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headers={"Content-Type": "application/json"},
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json=params
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) as response:
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debug(f"收到响应: status={response.status}, headers={response.headers}")
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response_text = await response.text()
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debug(f"响应内容: {response_text}")
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result = await response.json()
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debug(f"API 响应内容: {result}")
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if response.status == 400:
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debug(f"客户端错误，状态码: {response.status}, 返回响应: {result}")
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return result
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if response.status != 200:
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error(f"API 调用失败，动作: {action}, 状态码: {response.status}, 响应: {response_text}")
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raise RuntimeError(f"API 调用失败: {response.status}")
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return result
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except Exception as e:
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error(f"API 调用失败: {action}, 错误: {str(e)}, 堆栈: {traceback.format_exc()}")
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raise RuntimeError(f"API 调用失败: {str(e)}")
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# 嵌入服务 (BAAI/bge-m3)
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async def get_embeddings(self, texts: list) -> list:
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"""调用嵌入服务获取文本向量"""
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try:
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async with ClientSession() as session:
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async with session.post(
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"https://embedding.opencomputing.net:10443/v1/embeddings", # 使用外网地址
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headers={"Content-Type": "application/json"},
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json={"input": texts if isinstance(texts, list) else [texts]}
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) as response:
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if response.status != 200:
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error(f"嵌入服务调用失败，状态码: {response.status}")
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raise RuntimeError(f"嵌入服务调用失败: {response.status}")
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result = await response.json()
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if result.get("object") != "list" or not result.get("data"):
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error(f"嵌入服务响应格式错误: {result}")
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raise RuntimeError("嵌入服务响应格式错误")
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embeddings = [item["embedding"] for item in result["data"]]
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debug(f"成功获取 {len(embeddings)} 个嵌入向量")
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return embeddings
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except Exception as e:
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error(f"嵌入服务调用失败: {str(e)}")
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raise RuntimeError(f"嵌入服务调用失败: {str(e)}")
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# 实体提取服务 (LTP/small)
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async def extract_entities(self, query: str) -> list:
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"""调用实体识别服务"""
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try:
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if not query:
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raise ValueError("查询文本不能为空")
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async with ClientSession() as session:
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async with session.post(
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"https://entities.opencomputing.net:10443/v1/entities", # 使用外网地址
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headers={"Content-Type": "application/json"},
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json={"query": query}
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) as response:
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if response.status != 200:
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error(f"实体识别服务调用失败，状态码: {response.status}")
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raise RuntimeError(f"实体识别服务调用失败: {response.status}")
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result = await response.json()
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if result.get("object") != "list" or not result.get("data"):
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error(f"实体识别服务响应格式错误: {result}")
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raise RuntimeError("实体识别服务响应格式错误")
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entities = result["data"]
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unique_entities = list(dict.fromkeys(entities))
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debug(f"成功提取 {len(unique_entities)} 个唯一实体")
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return unique_entities
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except Exception as e:
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error(f"实体识别服务调用失败: {str(e)}")
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return []
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# 三元组抽取服务 (Babelscape/mrebel-large)
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async def extract_triples(self, text: str) -> list:
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"""调用三元组抽取服务"""
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request_id = str(uuid.uuid4())
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debug(f"Request #{request_id} started for triples extraction")
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try:
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async with ClientSession(
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connector=aiohttp.TCPConnector(limit=30),
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timeout=ClientTimeout(total=None)
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) as session:
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async with session.post(
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"https://triples.opencomputing.net:10443/v1/triples", # 使用外网地址
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headers={"Content-Type": "application/json; charset=utf-8"},
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json={"text": text}
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) as response:
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if response.status != 200:
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error_text = await response.text()
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error(f"Request #{request_id} failed, status: {response.status}, response: {error_text}")
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raise RuntimeError(f"三元组抽取服务调用失败: {response.status}, {error_text}")
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result = await response.json()
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if result.get("object") != "list":
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error(f"Request #{request_id} invalid response format: {result}")
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raise RuntimeError("三元组抽取服务响应格式错误")
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triples = result["data"]
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debug(f"Request #{request_id} extracted {len(triples)} triples")
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return triples
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except Exception as e:
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error(f"Request #{request_id} failed to extract triples: {str(e)}")
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raise RuntimeError(f"三元组抽取服务调用失败: {str(e)}")
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# 重排序服务 (BAAI/bge-reranker-v2-m3)
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async def rerank_results(self, query: str, results: list, top_n: int) -> list:
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"""调用重排序服务"""
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try:
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if not results:
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debug("无结果需要重排序")
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return results
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if not isinstance(top_n, int) or top_n < 1:
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debug(f"无效的 top_n 参数: {top_n}, 使用 len(results)={len(results)}")
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top_n = len(results)
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else:
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top_n = min(top_n, len(results))
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documents = [result.get("text", str(result)) for result in results]
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async with ClientSession() as session:
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async with session.post(
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"https://reranker.opencomputing.net:10443/v1/rerank", # 使用外网地址
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headers={"Content-Type": "application/json"},
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json={
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"model": "rerank-001",
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"query": query,
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"documents": documents,
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"top_n": top_n
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}
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) as response:
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if response.status != 200:
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error(f"重排序服务调用失败，状态码: {response.status}")
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raise RuntimeError(f"重排序服务调用失败: {response.status}")
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result = await response.json()
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if result.get("object") != "rerank.result" or not result.get("data"):
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error(f"重排序服务响应格式错误: {result}")
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raise RuntimeError("重排序服务响应格式错误")
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rerank_data = result["data"]
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reranked_results = []
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for item in rerank_data:
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index = item["index"]
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if index < len(results):
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results[index]["rerank_score"] = item["relevance_score"]
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reranked_results.append(results[index])
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debug(f"成功重排序 {len(reranked_results)} 条结果")
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return reranked_results[:top_n]
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except Exception as e:
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error(f"重排序服务调用失败: {str(e)}")
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return results
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# Neo4j 服务
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async def neo4j_docs(self) -> str:
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"""获取 Neo4j 文档（返回文本格式）"""
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async with ClientSession(timeout=ClientTimeout(total=300)) as session:
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async with session.get("https://graphdb.opencomputing.net:10443/docs") as response:
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if response.status != 200:
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error(f"Neo4j 文档请求失败，状态码: {response.status}")
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raise RuntimeError(f"Neo4j 文档请求失败: {response.status}")
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text = await response.text() # 获取纯文本内容
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debug(f"Neo4j 文档内容: {text}")
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return text
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async def neo4j_initialize(self) -> Dict[str, Any]:
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"""初始化 Neo4j 服务"""
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return await self._make_request("https://graphdb.opencomputing.net:10443/v1/initialize", "initialize", {})
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async def neo4j_insert_triples(self, triples: list, document_id: str, knowledge_base_id: str, userid: str) -> Dict[str, Any]:
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"""插入三元组到 Neo4j"""
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params = {
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"triples": triples,
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"document_id": document_id,
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"knowledge_base_id": knowledge_base_id,
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"userid": userid
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}
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return await self._make_request("https://graphdb.opencomputing.net:10443/v1/inserttriples", "inserttriples", params)
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async def neo4j_delete_document(self, document_id: str) -> Dict[str, Any]:
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"""删除指定文档"""
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return await self._make_request("https://graphdb.opencomputing.net:10443/v1/deletedocument", "deletedocument", {"document_id": document_id})
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async def neo4j_delete_knowledgebase(self, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
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"""删除用户知识库"""
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return await self._make_request("https://graphdb.opencomputing.net:10443/v1/deleteknowledgebase", "deleteknowledgebase",
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{"userid": userid, "knowledge_base_id": knowledge_base_id})
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async def neo4j_match_triplets(self, query: str, query_entities: list, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
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"""根据实体匹配相关三元组"""
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params = {
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"query": query,
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"query_entities": query_entities,
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"userid": userid,
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"knowledge_base_id": knowledge_base_id
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}
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return await self._make_request("https://graphdb.opencomputing.net:10443/v1/matchtriplets", "matchtriplets", params)
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# Milvus 服务
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async def milvus_create_collection(self, db_type: str = "") -> Dict[str, Any]:
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"""创建 Milvus 集合"""
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params = {"db_type": db_type} if db_type else {}
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/createcollection", "createcollection", params)
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async def milvus_delete_collection(self, db_type: str = "") -> Dict[str, Any]:
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"""删除 Milvus 集合"""
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params = {"db_type": db_type} if db_type else {}
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/deletecollection", "deletecollection", params)
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async def milvus_insert_document(self, chunks: List[Dict], db_type: str = "") -> Dict[str, Any]:
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"""添加 Milvus 记录"""
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params = {
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"chunks": chunks,
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"dbtype": db_type
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}
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# 计算请求体大小
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payload = json.dumps(params) # 转换为 JSON 字符串
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payload_bytes = payload.encode() # 编码为字节
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payload_size = len(payload_bytes) # 获取字节数
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debug(f"Request payload size for insertdocument: {payload_size} bytes")
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/insertdocument", "insertdocument", params)
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async def milvus_delete_document(self, userid: str, file_path: str, knowledge_base_id: str, document_id:str, db_type: str = "") -> Dict[str, Any]:
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"""删除 Milvus 记录"""
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params = {
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"userid": userid,
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"file_path": file_path,
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"knowledge_base_id": knowledge_base_id,
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"document_id": document_id,
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"dbtype": db_type
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}
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/deletedocument", "deletedocument", params)
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async def milvus_delete_knowledgebase(self, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
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"""删除 Milvus 知识库"""
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/deleteknowledgebase", "deleteknowledgebase",
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{"userid": userid, "knowledge_base_id": knowledge_base_id})
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async def milvus_search_query(self, query_vector: List[float], userid: str, knowledge_base_ids: list, limit: int, offset: int) -> Dict[str, Any]:
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"""根据用户知识库检索 Milvus"""
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params = {
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"query_vector": query_vector,
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"userid": userid,
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"knowledge_base_ids": knowledge_base_ids,
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"limit": limit,
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"offset": offset
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}
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/searchquery", "searchquery", params)
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async def milvus_list_user_files(self, userid: str) -> Dict[str, Any]:
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"""列出 Milvus 用户知识库列表"""
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/listuserfiles", "listuserfiles", {"userid": userid})
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async def milvus_list_all_knowledgebases(self) -> Dict[str, Any]:
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"""列出 Milvus 数据库中所有数据"""
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return await self._make_request("https://vectordb.opencomputing.net:10443/v1/listallknowledgebases", "listallknowledgebases", {})
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# RAG 服务
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async def rag_create_collection(self, db_type: str = "") -> Dict[str, Any]:
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"""创建 RAG 集合"""
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params = {"db_type": db_type} if db_type else {}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/createcollection", "createcollection", params)
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async def rag_delete_collection(self, db_type: str = "") -> Dict[str, Any]:
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"""删除 RAG 集合"""
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params = {"db_type": db_type} if db_type else {}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/deletecollection", "deletecollection", params)
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async def rag_insert_file(self, file_path: str, userid: str, knowledge_base_id: str, document_id: str) -> Dict[str, Any]:
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"""添加 RAG 记录"""
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params = {
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"file_path": file_path,
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"userid": userid,
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"knowledge_base_id": knowledge_base_id,
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"document_id": document_id
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}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/insertfile", "insertfile", params)
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async def rag_delete_file(self, userid: str, file_path: str, knowledge_base_id: str, document_id: str) -> Dict[str, Any]:
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"""删除 RAG 记录"""
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params = {
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"userid": userid,
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"file_path": file_path,
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"knowledge_base_id": knowledge_base_id,
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"document_id": document_id
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}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/deletefile", "deletefile", params)
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async def rag_delete_knowledgebase(self, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
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"""删除 RAG 知识库"""
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return await self._make_request("https://rag.opencomputing.net:10443/v1/deleteknowledgebase", "deleteknowledgebase",
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{"userid": userid, "knowledge_base_id": knowledge_base_id})
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async def rag_search_query(self, query: str, userid: str, knowledge_base_ids: list, limit: int, offset: int,
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use_rerank: bool) -> Dict[str, Any]:
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"""根据用户知识库检索 RAG"""
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params = {
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"query": query,
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"userid": userid,
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"knowledge_base_ids": knowledge_base_ids,
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"limit": limit,
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"offset": offset,
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"use_rerank": use_rerank
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}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/searchquery", "searchquery", params)
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async def rag_fused_search_query(self, query: str, userid: str, knowledge_base_ids: list, limit: int, offset: int,
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use_rerank: bool) -> Dict[str, Any]:
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"""根据用户知识库+知识图谱检索 RAG"""
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params = {
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"query": query,
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"userid": userid,
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"knowledge_base_ids": knowledge_base_ids,
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"limit": limit,
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"offset": offset,
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"use_rerank": use_rerank
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}
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return await self._make_request("https://rag.opencomputing.net:10443/v1/fusedsearchquery", "fusedsearchquery", params)
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async def rag_list_user_files(self, userid: str) -> Dict[str, Any]:
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"""列出 RAG 用户知识库列表"""
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return await self._make_request("https://rag.opencomputing.net:10443/v1/listuserfiles", "listuserfiles", {"userid": userid})
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async def rag_list_all_knowledgebases(self) -> Dict[str, Any]:
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"""列出 RAG 数据库中所有数据"""
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return await self._make_request("https://rag.opencomputing.net:10443/v1/listallknowledgebases", "listallknowledgebases", {})
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async def rag_docs(self) -> Dict[str, Any]:
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"""获取 RAG 帮助文档"""
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return await self._make_request("https://rag.opencomputing.net:10443/v1/docs", "docs", {})
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@ -1,27 +0,0 @@
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from abc import ABC, abstractmethod
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from typing import Dict
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from appPublic.log import debug, error, info, exception
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connection_pathMap = {}
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def connection_register(connection_key, Klass):
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"""为给定的连接键注册一个连接类"""
|
||||
global connection_pathMap
|
||||
connection_pathMap[connection_key] = Klass
|
||||
info(f"Registered {connection_key} with class {Klass}")
|
||||
|
||||
def get_connection_class(connection_path):
|
||||
"""根据连接路径查找对应的连接类"""
|
||||
global connection_pathMap
|
||||
debug(f"connection_pathMap: {connection_pathMap}")
|
||||
klass = connection_pathMap.get(connection_path)
|
||||
if klass is None:
|
||||
error(f"{connection_path} has not mapping to a connection class")
|
||||
raise Exception(f"{connection_path} has not mapping to a connection class")
|
||||
return klass
|
||||
|
||||
class BaseConnection(ABC):
|
||||
@abstractmethod
|
||||
async def handle_connection(self, action: str, params: Dict = None) -> Dict:
|
||||
"""处理数据库操作，根据 action 执行创建集合等"""
|
||||
pass
|
||||
@ -1,653 +0,0 @@
|
||||
import llmengine.milvus_connection
|
||||
from traceback import format_exc
|
||||
import argparse
|
||||
from aiohttp import web
|
||||
from llmengine.base_connection import get_connection_class
|
||||
from appPublic.registerfunction import RegisterFunction
|
||||
from appPublic.log import debug, error, info
|
||||
from ahserver.serverenv import ServerEnv
|
||||
from ahserver.webapp import webserver
|
||||
import os
|
||||
import json
|
||||
|
||||
helptext = """Milvus Connection Service API (using pymilvus Collection API):
|
||||
|
||||
1. Create Collection Endpoint:
|
||||
path: /v1/createcollection
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"db_type": "textdb" // 可选，若不提供则使用默认集合 ragdb
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", "collection_name": "ragdb" or "ragdb_textdb", "message": "集合 ragdb 或 ragdb_textdb 创建成功"}
|
||||
- Error: HTTP 400, {"status": "error", "collection_name": "ragdb" or "ragdb_textdb", "message": "<error message>"}
|
||||
|
||||
2. Delete Collection Endpoint:
|
||||
path: /v1/deletecollection
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"db_type": "textdb" // 可选，若不提供则删除默认集合 ragdb
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", "collection_name": "ragdb" or "ragdb_textdb", "message": "集合 ragdb 或 ragdb_textdb 删除成功"}
|
||||
- Success (collection does not exist): HTTP 200, {"status": "success", "collection_name": "ragdb" or "ragdb_textdb", "message": "集合 ragdb 或 ragdb_textdb 不存在，无需删除"}
|
||||
- Error: HTTP 400, {"status": "error", "collection_name": "ragdb" or "ragdb_textdb", "message": "<error message>"}
|
||||
|
||||
3. Insert File Endpoint:
|
||||
path: /v1/insertfile
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"file_path": "/path/to/file.txt", // 必填，文件路径
|
||||
"userid": "user123", // 必填，用户 ID
|
||||
"db_type": "textdb", // 可选，若不提供则使用默认集合 ragdb
|
||||
"knowledge_base_id": "kb123" // 必填，知识库 ID
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", "document_id": "<uuid>", "collection_name": "ragdb" or "ragdb_textdb", "message": "文件 <file_path> 成功嵌入并处理三元组", "status_code": 200}
|
||||
- Success (triples failed): HTTP 200, {"status": "success", "document_id": "<uuid>", "collection_name": "ragdb" or "ragdb_textdb", "message": "文件 <file_path> 成功嵌入，但三元组处理失败: <error>", "status_code": 200}
|
||||
- Error: HTTP 400, {"status": "error", "document_id": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "<error message>", "status_code": 400}
|
||||
|
||||
4. Delete Document Endpoint:
|
||||
path: /v1/deletefile
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"userid": "user123", // 必填，用户 ID
|
||||
"filename": "file.txt", // 必填，文件名
|
||||
"db_type": "textdb", // 可选，若不提供则使用默认集合 ragdb
|
||||
"knowledge_base_id": "kb123" // 必填，知识库 ID
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", "document_id": "<uuid1,uuid2>", "collection_name": "ragdb" or "ragdb_textdb", "message": "成功删除 <count> 条 Milvus 记录，<nodes> 个 Neo4j 节点，<rels> 个 Neo4j 关系，userid=<userid>, filename=<filename>", "status_code": 200}
|
||||
- Success (no records): HTTP 200, {"status": "success", "document_id": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "没有找到 userid=<userid>, filename=<filename>, knowledge_base_id=<knowledge_base_id> 的记录，无需删除", "status_code": 200}
|
||||
- Success (collection missing): HTTP 200, {"status": "success", "document_id": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "集合 <collection_name> 不存在，无需删除", "status_code": 200}
|
||||
- Error: HTTP 400, {"status": "error", "document_id": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "<error message>", "status_code": 400}
|
||||
|
||||
5. Fused Search Query Endpoint:
|
||||
path: /v1/fusedsearchquery
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"query": "苹果公司在北京开设新店",
|
||||
"userid": "user1",
|
||||
"db_type": "textdb", // 可选，若不提供则使用默认集合 ragdb
|
||||
"knowledge_base_ids": ["kb123"],
|
||||
"limit": 5,
|
||||
"offset": 0,
|
||||
"use_rerank": true
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {
|
||||
"status": "success",
|
||||
"results": [
|
||||
{
|
||||
"text": "<完整文本内容>",
|
||||
"distance": 0.95,
|
||||
"source": "fused_query_with_triplets",
|
||||
"rerank_score": 0.92, // 若 use_rerank=true
|
||||
"metadata": {
|
||||
"userid": "user1",
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file.txt",
|
||||
"file_path": "/path/to/file.txt",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "txt"
|
||||
}
|
||||
},
|
||||
...
|
||||
],
|
||||
"timing": {
|
||||
"collection_load": <float>, // 集合加载耗时（秒）
|
||||
"entity_extraction": <float>, // 实体提取耗时（秒）
|
||||
"triplet_matching": <float>, // 三元组匹配耗时（秒）
|
||||
"triplet_text_combine": <float>, // 拼接三元组文本耗时（秒）
|
||||
"embedding_generation": <float>, // 嵌入向量生成耗时（秒）
|
||||
"vector_search": <float>, // 向量搜索耗时（秒）
|
||||
"deduplication": <float>, // 去重耗时（秒）
|
||||
"reranking": <float>, // 重排序耗时（秒，若 use_rerank=true）
|
||||
"total_time": <float> // 总耗时（秒）
|
||||
},
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
- Error: HTTP 400, {
|
||||
"status": "error",
|
||||
"message": "<error message>",
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
6. Search Query Endpoint:
|
||||
path: /v1/searchquery
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"query": "知识图谱的知识融合是什么？",
|
||||
"userid": "user1",
|
||||
"db_type": "textdb", // 可选，若不提供则使用默认集合 ragdb
|
||||
"knowledge_base_ids": ["kb123"],
|
||||
"limit": 5,
|
||||
"offset": 0,
|
||||
"use_rerank": true
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {
|
||||
"status": "success",
|
||||
"results": [
|
||||
{
|
||||
"text": "<完整文本内容>",
|
||||
"distance": 0.95,
|
||||
"source": "vector_query",
|
||||
"rerank_score": 0.92, // 若 use_rerank=true
|
||||
"metadata": {
|
||||
"userid": "user1",
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file.txt",
|
||||
"file_path": "/path/to/file.txt",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "txt"
|
||||
}
|
||||
},
|
||||
...
|
||||
],
|
||||
"timing": {
|
||||
"collection_load": <float>, // 集合加载耗时（秒）
|
||||
"embedding_generation": <float>, // 嵌入向量生成耗时（秒）
|
||||
"vector_search": <float>, // 向量搜索耗时（秒）
|
||||
"deduplication": <float>, // 去重耗时（秒）
|
||||
"reranking": <float>, // 重排序耗时（秒，若 use_rerank=true）
|
||||
"total_time": <float> // 总耗时（秒）
|
||||
},
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
- Error: HTTP 400, {
|
||||
"status": "error",
|
||||
"message": "<error message>",
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
|
||||
7. List User Files Endpoint:
|
||||
path: /v1/listuserfiles
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"userid": "user1",
|
||||
"db_type": "textdb" // 可选，若不提供则使用默认集合 ragdb
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {
|
||||
"status": "success",
|
||||
"files_by_knowledge_base": {
|
||||
"kb123": [
|
||||
{
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file1.txt",
|
||||
"file_path": "/path/to/file1.txt",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "txt",
|
||||
"knowledge_base_id": "kb123"
|
||||
},
|
||||
...
|
||||
],
|
||||
"kb456": [
|
||||
{
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file2.pdf",
|
||||
"file_path": "/path/to/file2.pdf",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "pdf",
|
||||
"knowledge_base_id": "kb456"
|
||||
},
|
||||
...
|
||||
]
|
||||
},
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
- Error: HTTP 400, {
|
||||
"status": "error",
|
||||
"message": "<error message>",
|
||||
"collection_name": "ragdb" or "ragdb_textdb"
|
||||
}
|
||||
8. Connection Endpoint (for compatibility):
|
||||
path: /v1/connection
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"action": "<initialize|get_params|create_collection|delete_collection|insert_document|delete_document|fused_search|search_query|list_user_files>",
|
||||
"params": {...}
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", ...}
|
||||
- Error: HTTP 400, {"status": "error", "message": "<error message>"}
|
||||
|
||||
9. Docs Endpoint:
|
||||
path: /docs
|
||||
method: GET
|
||||
response: This help text
|
||||
|
||||
10. Delete Knowledge Base Endpoint:
|
||||
path: /v1/deleteknowledgebase
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"userid": "user123", // 必填，用户 ID
|
||||
"knowledge_base_id": "kb123",// 必填，知识库 ID
|
||||
"db_type": "textdb" // 可选，若不提供则使用默认集合 ragdb
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {"status": "success", "document_id": "<uuid1,uuid2>", "filename": "<filename1,filename2>", "collection_name": "ragdb" or "ragdb_textdb", "message": "成功删除 <count> 条 Milvus 记录，<nodes> 个 Neo4j 节点，<rels> 个 Neo4j 关系，userid=<userid>, knowledge_base_id=<knowledge_base_id>", "status_code": 200}
|
||||
- Success (no records): HTTP 200, {"status": "success", "document_id": "", "filename": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "没有找到 userid=<userid>, knowledge_base_id=<knowledge_base_id> 的记录，无需删除", "status_code": 200}
|
||||
- Success (collection missing): HTTP 200, {"status": "success", "document_id": "", "filename": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "集合 <collection_name> 不存在，无需删除", "status_code": 200}
|
||||
- Error: HTTP 400, {"status": "error", "document_id": "", "filename": "", "collection_name": "ragdb" or "ragdb_textdb", "message": "<error message>", "status_code": 400}
|
||||
|
||||
10. List All Knowledge Bases Endpoint:
|
||||
path: /v1/listallknowledgebases
|
||||
method: POST
|
||||
headers: {"Content-Type": "application/json"}
|
||||
data: {
|
||||
"db_type": "textdb" // 可选，若不提供则使用默认集合 ragdb
|
||||
}
|
||||
response:
|
||||
- Success: HTTP 200, {
|
||||
"status": "success",
|
||||
"users_knowledge_bases": {
|
||||
"user1": {
|
||||
"kb123": [
|
||||
{
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file1.txt",
|
||||
"file_path": "/path/to/file1.txt",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "txt",
|
||||
"knowledge_base_id": "kb123"
|
||||
},
|
||||
...
|
||||
],
|
||||
"kb456": [
|
||||
{
|
||||
"document_id": "<uuid>",
|
||||
"filename": "file2.pdf",
|
||||
"file_path": "/path/to/file2.pdf",
|
||||
"upload_time": "<iso_timestamp>",
|
||||
"file_type": "pdf",
|
||||
"knowledge_base_id": "kb456"
|
||||
},
|
||||
...
|
||||
]
|
||||
},
|
||||
"user2": {...}
|
||||
},
|
||||
"collection_name": "ragdb" or "ragdb_textdb",
|
||||
"message": "成功列出 <count> 个用户的知识库和文件",
|
||||
"status_code": 200
|
||||
}
|
||||
- Error: HTTP 400, {
|
||||
"status": "error",
|
||||
"users_knowledge_bases": {},
|
||||
"collection_name": "ragdb" or "ragdb_textdb",
|
||||
"message": "<error message>",
|
||||
"status_code": 400
|
||||
}
|
||||
"""
|
||||
|
||||
def init():
|
||||
rf = RegisterFunction()
|
||||
rf.register('createcollection', create_collection)
|
||||
rf.register('deletecollection', delete_collection)
|
||||
rf.register('insertfile', insert_file)
|
||||
rf.register('deletefile', delete_file)
|
||||
rf.register('deleteknowledgebase', delete_knowledge_base)
|
||||
rf.register('fusedsearchquery', fused_search_query)
|
||||
rf.register('searchquery', search_query)
|
||||
rf.register('listuserfiles', list_user_files)
|
||||
rf.register('listallknowledgebases', list_all_knowledge_bases)
|
||||
rf.register('connection', handle_connection)
|
||||
rf.register('docs', docs)
|
||||
|
||||
async def docs(request, params_kw, *params, **kw):
|
||||
return web.Response(text=helptext, content_type='text/plain')
|
||||
|
||||
async def not_implemented(request, params_kw, *params, **kw):
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": "功能尚未实现"
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=501)
|
||||
|
||||
async def create_collection(request, params_kw, *params, **kw):
|
||||
debug(f'{params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
db_type = params_kw.get('db_type', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
result = await engine.handle_connection("create_collection", {"db_type": db_type})
|
||||
debug(f'{result=}')
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'创建集合失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"message": str(e)
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def delete_collection(request, params_kw, *params, **kw):
|
||||
debug(f'{params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
db_type = params_kw.get('db_type', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
result = await engine.handle_connection("delete_collection", {"db_type": db_type})
|
||||
debug(f'{result=}')
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'删除集合失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"message": str(e)
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def insert_file(request, params_kw, *params, **kw):
|
||||
debug(f'Received params: {params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
file_path = params_kw.get('file_path', '')
|
||||
userid = params_kw.get('userid', '')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
knowledge_base_id = params_kw.get('knowledge_base_id', '')
|
||||
document_id = params_kw.get('document_id', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
required_fields = ['file_path', 'userid', 'knowledge_base_id', 'document_id']
|
||||
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'Calling insert_document with: file_path={file_path}, userid={userid}, db_type={db_type}, knowledge_base_id={knowledge_base_id}, document_id={document_id}')
|
||||
result = await engine.handle_connection("insert_document", {
|
||||
"file_path": file_path,
|
||||
"userid": userid,
|
||||
"db_type": db_type,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"document_id": document_id
|
||||
})
|
||||
debug(f'Insert result: {result=}')
|
||||
status = 200 if result.get("status") == "success" else 400
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=status)
|
||||
except Exception as e:
|
||||
error(f'插入文件失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"document_id": document_id,
|
||||
"message": str(e)
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def delete_file(request, params_kw, *params, **kw):
|
||||
debug(f'Received delete_file params: {params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
userid = params_kw.get('userid', '')
|
||||
file_path = params_kw.get('file_path', '')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
knowledge_base_id = params_kw.get('knowledge_base_id', '')
|
||||
document_id = params_kw.get('document_id', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
required_fields = ['userid', 'file_path', 'knowledge_base_id', 'document_id']
|
||||
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'Calling delete_document with: userid={userid}, file_path={file_path}, db_type={db_type}, knowledge_base_id={knowledge_base_id}, document_id={document_id}')
|
||||
result = await engine.handle_connection("delete_document", {
|
||||
"userid": userid,
|
||||
"file_path": file_path,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"document_id": document_id,
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'Delete result: {result=}')
|
||||
status = 200 if result.get("status") == "success" else 400
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=status)
|
||||
except Exception as e:
|
||||
error(f'删除文件失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"document_id": document_id,
|
||||
"message": str(e),
|
||||
"status_code": 400
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def delete_knowledge_base(request, params_kw, *params, **kw):
|
||||
debug(f'Received delete_knowledge_base params: {params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
userid = params_kw.get('userid', '')
|
||||
knowledge_base_id = params_kw.get('knowledge_base_id', '')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
required_fields = ['userid', 'knowledge_base_id']
|
||||
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'Calling delete_knowledge_base with: userid={userid}, knowledge_base_id={knowledge_base_id}, db_type={db_type}')
|
||||
result = await engine.handle_connection("delete_knowledge_base", {
|
||||
"userid": userid,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'Delete knowledge base result: {result=}')
|
||||
status = 200 if result.get("status") == "success" else 400
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=status)
|
||||
except Exception as e:
|
||||
error(f'删除知识库失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"document_id": "",
|
||||
"filename": "",
|
||||
"message": str(e),
|
||||
"status_code": 400
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def search_query(request, params_kw, *params, **kw):
|
||||
debug(f'Received search_query params: {params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
query = params_kw.get('query')
|
||||
userid = params_kw.get('userid')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
knowledge_base_ids = params_kw.get('knowledge_base_ids')
|
||||
limit = params_kw.get('limit', 5)
|
||||
offset = params_kw.get('offset', 0)
|
||||
use_rerank = params_kw.get('use_rerank', True)
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
if not all([query, userid, knowledge_base_ids]):
|
||||
debug(f'query, userid 或 knowledge_base_ids 未提供')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": "query, userid 或 knowledge_base_ids 未提供",
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
result = await engine.handle_connection("search_query", {
|
||||
"query": query,
|
||||
"userid": userid,
|
||||
"knowledge_base_ids": knowledge_base_ids,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
"use_rerank": use_rerank,
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'Search result: {result=}')
|
||||
response = {
|
||||
"status": "success",
|
||||
"results": result.get("results", []),
|
||||
"timing": result.get("timing", {}),
|
||||
"collection_name": collection_name
|
||||
}
|
||||
return web.json_response(response, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'纯向量搜索失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def fused_search_query(request, params_kw, *params, **kw):
|
||||
debug(f'Received fused_search_query params: {params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
query = params_kw.get('query')
|
||||
userid = params_kw.get('userid')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
knowledge_base_ids = params_kw.get('knowledge_base_ids')
|
||||
limit = params_kw.get('limit', 5)
|
||||
offset = params_kw.get('offset', 0)
|
||||
use_rerank = params_kw.get('use_rerank', True)
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
if not all([query, userid, knowledge_base_ids]):
|
||||
debug(f'query, userid 或 knowledge_base_ids 未提供')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": "query, userid 或 knowledge_base_ids 未提供",
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
result = await engine.handle_connection("fused_search", {
|
||||
"query": query,
|
||||
"userid": userid,
|
||||
"knowledge_base_ids": knowledge_base_ids,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
"use_rerank": use_rerank,
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'Fused search result: {result=}')
|
||||
response = {
|
||||
"status": "success",
|
||||
"results": result.get("results", []),
|
||||
"timing": result.get("timing", {}),
|
||||
"collection_name": collection_name
|
||||
}
|
||||
return web.json_response(response, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'融合搜索失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def list_user_files(request, params_kw, *params, **kw):
|
||||
debug(f'{params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
userid = params_kw.get('userid')
|
||||
db_type = params_kw.get('db_type', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
if not userid:
|
||||
debug(f'userid 未提供')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": "userid 未提供",
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
result = await engine.handle_connection("list_user_files", {
|
||||
"userid": userid,
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'{result=}')
|
||||
response = {
|
||||
"status": "success",
|
||||
"files_by_knowledge_base": result,
|
||||
"collection_name": collection_name
|
||||
}
|
||||
return web.json_response(response, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'列出用户文件失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": str(e),
|
||||
"collection_name": collection_name
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def list_all_knowledge_bases(request, params_kw, *params, **kw):
|
||||
debug(f'{params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
db_type = params_kw.get('db_type', '')
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
result = await engine.handle_connection("list_all_knowledge_bases", {
|
||||
"db_type": db_type
|
||||
})
|
||||
debug(f'{result=}')
|
||||
response = {
|
||||
"status": result.get("status", "success"),
|
||||
"users_knowledge_bases": result.get("users_knowledge_bases", {}),
|
||||
"collection_name": collection_name,
|
||||
"message": result.get("message", ""),
|
||||
"status_code": result.get("status_code", 200)
|
||||
}
|
||||
return web.json_response(response, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=response["status_code"])
|
||||
except Exception as e:
|
||||
error(f'列出所有用户知识库失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"users_knowledge_bases": {},
|
||||
"collection_name": collection_name,
|
||||
"message": str(e),
|
||||
"status_code": 400
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
async def handle_connection(request, params_kw, *params, **kw):
|
||||
debug(f'{params_kw=}')
|
||||
se = ServerEnv()
|
||||
engine = se.engine
|
||||
try:
|
||||
data = await request.json()
|
||||
action = data.get('action')
|
||||
if not action:
|
||||
debug(f'action 未提供')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": "action 参数未提供"
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
result = await engine.handle_connection(action, data.get('params', {}))
|
||||
debug(f'{result=}')
|
||||
return web.json_response(result, dumps=lambda obj: json.dumps(obj, ensure_ascii=False))
|
||||
except Exception as e:
|
||||
error(f'处理连接操作失败: {str(e)}')
|
||||
return web.json_response({
|
||||
"status": "error",
|
||||
"message": str(e)
|
||||
}, dumps=lambda obj: json.dumps(obj, ensure_ascii=False), status=400)
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(prog="Milvus Connection Service")
|
||||
parser.add_argument('-w', '--workdir')
|
||||
parser.add_argument('-p', '--port', default='8888')
|
||||
parser.add_argument('connection_path')
|
||||
args = parser.parse_args()
|
||||
debug(f"Arguments: {args}")
|
||||
Klass = get_connection_class(args.connection_path)
|
||||
se = ServerEnv()
|
||||
se.engine = Klass()
|
||||
workdir = args.workdir or os.getcwd()
|
||||
port = args.port
|
||||
debug(f'{args=}')
|
||||
webserver(init, workdir, port)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
380
rag/file.py
380
rag/file.py
@ -1,380 +0,0 @@
|
||||
from rag.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
|
||||
from ahserver.serverenv import get_serverenv
|
||||
from typing import List, Dict, Any
|
||||
|
||||
api_service = APIService()
|
||||
|
||||
async def get_orgid_by_id(kdb_id):
|
||||
"""
|
||||
根据 kdb 的 id 查询对应的 orgid。
|
||||
"""
|
||||
db = DBPools()
|
||||
# f = get_serverenv("get_module_dbname")
|
||||
# dbname = f("rag")
|
||||
dbname = "kyrag"
|
||||
sql = "SELECT orgid FROM kdb WHERE id = ${id}$"
|
||||
try:
|
||||
async with db.sqlorContext(dbname) as sor:
|
||||
result = await sor.sqlExe(sql,{"id":kdb_id})
|
||||
print(result)
|
||||
if result and len(result) > 0:
|
||||
return result[0].get('orgid')
|
||||
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=}')
|
||||
realpath = params_kw.get('realpath', '')
|
||||
fiid = params_kw.get('fiid', '')
|
||||
id = params_kw.get('id', '')
|
||||
orgid = await get_orgid_by_id(fiid)
|
||||
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")
|
||||
debug(f'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 = []
|
||||
for i in range(0, len(texts), 10): # 每次处理 10 个文本块
|
||||
batch_texts = texts[i:i + 10]
|
||||
batch_embeddings = await api_service.get_embeddings(batch_texts)
|
||||
embeddings.extend(batch_embeddings)
|
||||
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()
|
||||
for i in range(0, len(chunks_data), 10): # 每次处理 10 条数据
|
||||
batch_chunks = chunks_data[i:i + 10]
|
||||
result = await api_service.milvus_insert_document(batch_chunks, db_type)
|
||||
if result.get("status") != "success":
|
||||
raise ValueError(result.get("message", "Milvus 插入失败"))
|
||||
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()
|
||||
for i in range(0, len(unique_triples), 30): # 每次插入 30 个三元组
|
||||
batch_triples = unique_triples[i:i + 30]
|
||||
neo4j_result = await api_service.neo4j_insert_triples(batch_triples, id, fiid, 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", "userid": orgid, "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"""
|
||||
id = params_kw.get('id', '')
|
||||
realpath = params_kw.get('realpath', '')
|
||||
fiid = params_kw.get('fiid', '')
|
||||
orgid = await get_orgid_by_id(fiid)
|
||||
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}")
|
||||
milvus_result = await api_service.milvus_delete_document(orgid, realpath, fiid, id, 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={id}")
|
||||
neo4j_result = await api_service.neo4j_delete_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={id} 的 {nodes_deleted} 个 Neo4j 节点和 {rels_deleted} 个关系")
|
||||
except Exception as e:
|
||||
error(f"删除 document_id={id} 的 Neo4j 数据失败: {str(e)}")
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"collection_name": collection_name,
|
||||
"document_id": 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": id,
|
||||
"message": f"删除文档失败: {str(e)}",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
async def _search_query(query: str, userid: str, knowledge_base_ids: List[str], limit: int = 5,
|
||||
offset: int = 0, use_rerank: bool = True, db_type: str = "") -> Dict[str, Any]:
|
||||
"""纯向量搜索，调用服务化端点"""
|
||||
start_time = time.time()
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
timing_stats = {}
|
||||
try:
|
||||
info(
|
||||
f"开始纯向量搜索: query={query}, userid={userid}, db_type={db_type}, knowledge_base_ids={knowledge_base_ids}, limit={limit}, offset={offset}, use_rerank={use_rerank}")
|
||||
|
||||
if not query:
|
||||
raise ValueError("查询文本不能为空")
|
||||
if not userid:
|
||||
raise ValueError("userid 不能为空")
|
||||
if limit <= 0 or limit > 16384:
|
||||
raise ValueError("limit 必须在 1 到 16384 之间")
|
||||
if offset < 0:
|
||||
raise ValueError("offset 不能为负数")
|
||||
if limit + offset > 16384:
|
||||
raise ValueError("limit + offset 不能超过 16384")
|
||||
if not knowledge_base_ids:
|
||||
raise ValueError("knowledge_base_ids 不能为空")
|
||||
for kb_id in knowledge_base_ids:
|
||||
if not isinstance(kb_id, str):
|
||||
raise ValueError(f"knowledge_base_id 必须是字符串: {kb_id}")
|
||||
if len(kb_id) > 100:
|
||||
raise ValueError(f"knowledge_base_id 长度超出 100 个字符: {kb_id}")
|
||||
|
||||
# 将查询文本转换为向量
|
||||
vector_start = time.time()
|
||||
query_vector = await api_service.get_embeddings([query])
|
||||
if not query_vector or not all(len(vec) == 1024 for vec in query_vector):
|
||||
raise ValueError("查询向量必须是长度为 1024 的浮点数列表")
|
||||
query_vector = query_vector[0] # 取第一个向量
|
||||
timing_stats["vector_generation"] = time.time() - vector_start
|
||||
debug(f"生成查询向量耗时: {timing_stats['vector_generation']:.3f} 秒")
|
||||
|
||||
# 调用纯向量搜索端点
|
||||
search_start = time.time()
|
||||
result = await api_service.milvus_search_query(query_vector, userid, knowledge_base_ids, limit, offset)
|
||||
timing_stats["vector_search"] = time.time() - search_start
|
||||
debug(f"向量搜索耗时: {timing_stats['vector_search']:.3f} 秒")
|
||||
|
||||
if result.get("status") != "success":
|
||||
error(f"纯向量搜索失败: {result.get('message', '未知错误')}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
unique_results = result.get("results", [])
|
||||
if use_rerank and unique_results:
|
||||
rerank_start = time.time()
|
||||
debug("开始重排序")
|
||||
unique_results = await api_service.rerank_results(query, unique_results, limit)
|
||||
unique_results = sorted(unique_results, key=lambda x: x.get('rerank_score', 0), reverse=True)
|
||||
timing_stats["reranking"] = time.time() - rerank_start
|
||||
debug(f"重排序耗时: {timing_stats['reranking']:.3f} 秒")
|
||||
debug(f"重排序分数分布: {[round(r.get('rerank_score', 0), 3) for r in unique_results]}")
|
||||
else:
|
||||
unique_results = [{k: v for k, v in r.items() if k != 'rerank_score'} for r in unique_results]
|
||||
|
||||
timing_stats["total_time"] = time.time() - start_time
|
||||
info(f"纯向量搜索完成，返回 {len(unique_results)} 条结果，总耗时: {timing_stats['total_time']:.3f} 秒")
|
||||
return {"results": unique_results[:limit], "timing": timing_stats}
|
||||
|
||||
except Exception as e:
|
||||
error(f"纯向量搜索失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
async def main():
|
||||
dbs = {
|
||||
"kyrag":{
|
||||
"driver":"aiomysql",
|
||||
"async_mode":True,
|
||||
"coding":"utf8",
|
||||
"maxconn":100,
|
||||
"dbname":"kyrag",
|
||||
"kwargs":{
|
||||
"user":"test",
|
||||
"db":"kyrag",
|
||||
"password":"QUZVcXg5V1p1STMybG5Ia6mX9D0v7+g=",
|
||||
"host":"db"
|
||||
}
|
||||
}
|
||||
}
|
||||
DBPools(dbs)
|
||||
# 测试 file_uploaded
|
||||
print("测试 file_uploaded...")
|
||||
test_file_path = "/home/wangmeihua/data/kg.txt"
|
||||
test_params_upload = {
|
||||
"realpath": test_file_path,
|
||||
"fiid": "1",
|
||||
"id": "doc1"
|
||||
}
|
||||
upload_result = await file_uploaded(test_params_upload)
|
||||
print(f"file_uploaded 结果: {upload_result}")
|
||||
|
||||
# # 测试 file_deleted
|
||||
# test_file_path = "/home/wangmeihua/data/kg.txt"
|
||||
# print("测试 file_deleted...")
|
||||
# test_params_delete = {
|
||||
# "realpath": test_file_path,
|
||||
# "fiid": "1",
|
||||
# "id": "doc1"
|
||||
# }
|
||||
# delete_result = await file_deleted(test_params_delete)
|
||||
# print(f"file_deleted 结果: {delete_result}")
|
||||
|
||||
# # 测试 _search_query
|
||||
# print("测试 _search_query...")
|
||||
# test_params_query = {
|
||||
# "query": "什么是关系抽取",
|
||||
# "userid": "04J6VbxLqB_9RPMcgOv_8",
|
||||
# "knowledge_base_ids": ["1"],
|
||||
# "limit": 5,
|
||||
# "offset": 0,
|
||||
# "use_rerank": True
|
||||
# }
|
||||
# query_result = await _search_query(query="什么是知识融合？", userid="testuser1", knowledge_base_ids=["kb1", "kb2"], limit=5, offset=0, use_rerank=True, db_type="")
|
||||
# print(f"file_uploaded 结果: {query_result}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@ -1,978 +0,0 @@
|
||||
import os
|
||||
from appPublic.log import debug, error, info
|
||||
from base_connection import connection_register
|
||||
from typing import Dict, List, Any
|
||||
import numpy as np
|
||||
import aiohttp
|
||||
from aiohttp import ClientSession, ClientTimeout
|
||||
from langchain_core.documents import Document
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from filetxt.loader import fileloader
|
||||
import time
|
||||
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type
|
||||
import traceback
|
||||
import asyncio
|
||||
import re
|
||||
# 嵌入缓存
|
||||
EMBED_CACHE = {}
|
||||
|
||||
class MilvusConnection:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
@retry(stop = stop_after_attempt(3))
|
||||
async def _make_neo4japi_request(self, action: str, params: Dict[str, Any]) -> Dict[str, Any]:
|
||||
debug(f"开始API请求：action={action}, params={params}")
|
||||
try:
|
||||
async with ClientSession(timeout=ClientTimeout(total=300)) as session:
|
||||
url = f"http://localhost:8885/v1/{action}"
|
||||
debug(f"发起POST请求：{url}")
|
||||
async with session.post(
|
||||
url,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
json=params
|
||||
) as response:
|
||||
debug(f"收到相应: status={response.status}, headers={response.headers}")
|
||||
respose_text = await response.text()
|
||||
debug(f"响应内容: {respose_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}")
|
||||
debug(f"API 调用成功: {action}, 响应: {result}")
|
||||
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)}")
|
||||
|
||||
@retry(stop=stop_after_attempt(3))
|
||||
async def _make_api_request(self, action: str, params: Dict[str, Any]) -> Dict[str, Any]:
|
||||
debug(f"开始 API 请求: action={action}, params={params}")
|
||||
try:
|
||||
async with ClientSession(timeout=ClientTimeout(total=300)) as session:
|
||||
url = f"http://localhost:8886/v1/{action}"
|
||||
debug(f"发起 POST 请求: {url}")
|
||||
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: # 客户端错误，直接返回
|
||||
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}")
|
||||
debug(f"API 调用成功: {action}, 响应: {result}")
|
||||
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)}")
|
||||
|
||||
async def handle_connection(self, action: str, params: Dict = None) -> Dict:
|
||||
"""处理数据库操作"""
|
||||
try:
|
||||
debug(f"处理操作: action={action}, params={params}")
|
||||
if not params:
|
||||
params = {}
|
||||
# 通用 db_type 验证
|
||||
db_type = params.get("db_type", "")
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
if db_type and "_" in db_type:
|
||||
return {"status": "error", "message": "db_type 不能包含下划线", "collection_name": collection_name,
|
||||
"document_id": "", "status_code": 400}
|
||||
if db_type and len(db_type) > 100:
|
||||
return {"status": "error", "message": "db_type 的长度应小于 100", "collection_name": collection_name,
|
||||
"document_id": "", "status_code": 400}
|
||||
|
||||
if action == "initialize":
|
||||
return {"status": "success", "message": "Milvus 服务已初始化"}
|
||||
elif action == "get_params":
|
||||
return {"status": "success", "params": {}}
|
||||
elif action == "create_collection":
|
||||
return await self._create_collection(db_type)
|
||||
elif action == "delete_collection":
|
||||
return await self._delete_collection(db_type)
|
||||
elif action == "insert_document":
|
||||
file_path = params.get("file_path", "")
|
||||
userid = params.get("userid", "")
|
||||
knowledge_base_id = params.get("knowledge_base_id", "")
|
||||
document_id = params.get("document_id", "")
|
||||
if not file_path or not userid or not knowledge_base_id or not document_id:
|
||||
return {"status": "error", "message": "file_path、userid document_id和 knowledge_base_id 不能为空",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
if "_" in userid or "_" in knowledge_base_id:
|
||||
return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
|
||||
"collection_name": collection_name, "document_id": document_id, "status_code": 400}
|
||||
if len(knowledge_base_id) > 100:
|
||||
return {"status": "error", "message": "knowledge_base_id 的长度应小于 100",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
return await self._insert_document(file_path, userid, knowledge_base_id, document_id, db_type)
|
||||
elif action == "delete_document":
|
||||
userid = params.get("userid", "")
|
||||
file_path = params.get("file_path", "")
|
||||
knowledge_base_id = params.get("knowledge_base_id", "")
|
||||
document_id = params.get("document_id", "")
|
||||
if not userid or not file_path or not knowledge_base_id or not document_id:
|
||||
return {"status": "error", "message": "userid、file_path document_id和 knowledge_base_id 不能为空",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
if "_" in userid or "_" in knowledge_base_id:
|
||||
return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
if len(userid) > 100 or len(file_path) > 255 or len(knowledge_base_id) > 100:
|
||||
return {"status": "error", "message": "userid、file_path 或 knowledge_base_id 的长度超出限制",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
return await self._delete_document(userid, file_path, knowledge_base_id, document_id, db_type)
|
||||
elif action == "delete_knowledge_base":
|
||||
userid = params.get("userid", "")
|
||||
knowledge_base_id = params.get("knowledge_base_id", "")
|
||||
if not userid or not knowledge_base_id:
|
||||
return {"status": "error", "message": "userid 和 knowledge_base_id 不能为空",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
if "_" in userid or "_" in knowledge_base_id:
|
||||
return {"status": "error", "message": "userid 和 knowledge_base_id 不能包含下划线",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
if len(userid) > 100 or len(knowledge_base_id) > 100:
|
||||
return {"status": "error", "message": "userid 或 knowledge_base_id 的长度超出限制",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
return await self._delete_knowledge_base(db_type, userid, knowledge_base_id)
|
||||
elif action == "search_query":
|
||||
query = params.get("query", "")
|
||||
userid = params.get("userid", "")
|
||||
knowledge_base_ids = params.get("knowledge_base_ids", [])
|
||||
limit = params.get("limit", 5)
|
||||
offset = params.get("offset", 0)
|
||||
db_type = params.get("db_type", "")
|
||||
use_rerank = params.get("use_rerank", True)
|
||||
if not query or not userid or not knowledge_base_ids:
|
||||
return {"status": "error", "message": "query、userid 或 knowledge_base_ids 不能为空",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
return await self._search_query(query, userid, knowledge_base_ids, limit, offset, use_rerank, db_type)
|
||||
elif action == "fused_search":
|
||||
query = params.get("query", "")
|
||||
userid = params.get("userid", "")
|
||||
knowledge_base_ids = params.get("knowledge_base_ids", [])
|
||||
limit = params.get("limit", 5)
|
||||
offset = params.get("offset", 0)
|
||||
db_type = params.get("db_type", "")
|
||||
use_rerank = params.get("use_rerank", True)
|
||||
if not query or not userid or not knowledge_base_ids:
|
||||
return {"status": "error", "message": "query、userid 或 knowledge_base_ids 不能为空",
|
||||
"collection_name": collection_name, "document_id": "", "status_code": 400}
|
||||
return await self._fused_search(query, userid, knowledge_base_ids, limit, offset, use_rerank, db_type)
|
||||
elif action == "list_user_files":
|
||||
userid = params.get("userid", "")
|
||||
if not userid:
|
||||
return {"status": "error", "message": "userid 不能为空", "collection_name": collection_name,
|
||||
"document_id": "", "status_code": 400}
|
||||
return await self._list_user_files(userid, db_type)
|
||||
elif action == "list_all_knowledge_bases":
|
||||
return await self._list_all_knowledge_bases(db_type)
|
||||
else:
|
||||
return {"status": "error", "message": f"未知的 action: {action}", "collection_name": collection_name,
|
||||
"document_id": "", "status_code": 400}
|
||||
except Exception as e:
|
||||
error(f"处理操作失败: action={action}, 错误: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"message": f"服务器错误: {str(e)}",
|
||||
"collection_name": collection_name,
|
||||
"document_id": "",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
async def _create_collection(self, db_type: str = "") -> Dict[str, Any]:
|
||||
"""创建 Milvus 集合"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
if len(collection_name) > 255:
|
||||
raise ValueError(f"集合名称 {collection_name} 超过 255 个字符")
|
||||
if len(db_type) > 100:
|
||||
raise ValueError("db_type 的长度应小于 100")
|
||||
debug(f"调用创建集合端点: {collection_name}, 参数: {{'db_type': '{db_type}'}}")
|
||||
result = await self._make_api_request("createcollection", {"db_type": db_type})
|
||||
return result
|
||||
except Exception as e:
|
||||
error(f"创建集合失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||||
return {
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"message": f"创建集合失败: {str(e)}",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
async def _delete_collection(self, db_type: str = "") -> Dict:
|
||||
"""删除 Milvus 集合通过服务化端点"""
|
||||
try:
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
if len(collection_name) > 255:
|
||||
raise ValueError(f"集合名称 {collection_name} 超过 255 个字符")
|
||||
if db_type and "_" in db_type:
|
||||
raise ValueError("db_type 不能包含下划线")
|
||||
if db_type and len(db_type) > 100:
|
||||
raise ValueError("db_type 的长度应小于 100")
|
||||
debug(f"调用删除集合端点: {collection_name}")
|
||||
|
||||
result = await self._make_api_request("deletecollection", {"db_type": db_type})
|
||||
return result
|
||||
except Exception as e:
|
||||
error(f"删除集合失败: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"message": str(e),
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
async def _insert_document(self, file_path: str, userid: str, knowledge_base_id: str, document_id:str, db_type: str = "") -> Dict[
|
||||
str, Any]:
|
||||
"""将文档插入 Milvus 并抽取三元组到 Neo4j"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
debug(
|
||||
f'Inserting document: file_path={file_path}, userid={userid}, db_type={db_type}, knowledge_base_id={knowledge_base_id}, document_id={document_id}')
|
||||
|
||||
timings = {}
|
||||
start_total = time.time()
|
||||
|
||||
try:
|
||||
# 验证参数
|
||||
if not userid or not knowledge_base_id:
|
||||
raise ValueError("userid 和 knowledge_base_id 不能为空")
|
||||
if "_" in userid or "_" in knowledge_base_id:
|
||||
raise ValueError("userid 和 knowledge_base_id 不能包含下划线")
|
||||
if len(userid) > 100 or len(knowledge_base_id) > 100:
|
||||
raise ValueError("userid 或 knowledge_base_id 的长度超出限制")
|
||||
if not os.path.exists(file_path):
|
||||
raise ValueError(f"文件 {file_path} 不存在")
|
||||
|
||||
supported_formats = {'pdf', 'docx', 'xlsx', 'pptx', 'csv', 'txt'}
|
||||
ext = file_path.rsplit('.', 1)[1].lower() if '.' in file_path 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)}")
|
||||
|
||||
info(f"生成 document_id: {document_id} for file: {file_path}")
|
||||
|
||||
# 文件加载
|
||||
debug(f"加载文件: {file_path}")
|
||||
start_load = time.time()
|
||||
text = fileloader(file_path)
|
||||
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 {file_path} 加载为空")
|
||||
|
||||
# 文本分片
|
||||
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 {file_path} 未生成任何文档块")
|
||||
|
||||
filename = os.path.basename(file_path).rsplit('.', 1)[0]
|
||||
upload_time = datetime.now().isoformat()
|
||||
|
||||
# 生成嵌入向量
|
||||
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 self._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)}")
|
||||
|
||||
# 构造 chunks 参数（展平结构）
|
||||
chunks_data = []
|
||||
for i, chunk in enumerate(chunks):
|
||||
chunks_data.append({
|
||||
"userid": userid,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"text": chunk.page_content,
|
||||
"vector": embeddings[i].tolist(),
|
||||
"document_id": document_id,
|
||||
"filename": filename + '.' + ext,
|
||||
"file_path": file_path,
|
||||
"upload_time": upload_time,
|
||||
"file_type": ext,
|
||||
})
|
||||
|
||||
# 调用 Milvus 插入端点
|
||||
debug(f"调用插入文件端点: {file_path}")
|
||||
start_milvus = time.time()
|
||||
result = await self._make_api_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": document_id,
|
||||
"collection_name": collection_name,
|
||||
"timings": timings,
|
||||
"message": result.get("message", "未知错误"),
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
# 三元组抽取
|
||||
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 = [self._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)}")
|
||||
|
||||
# 去重
|
||||
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]}")
|
||||
|
||||
# Neo4j 插入
|
||||
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 self._make_neo4japi_request("inserttriples", {
|
||||
"triples":unique_triples,
|
||||
"document_id": document_id,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"userid": userid
|
||||
})
|
||||
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": document_id,
|
||||
"collection_name": collection_name,
|
||||
"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 {file_path} 三元组成功插入 Neo4j: {neo4j_result.get('message')}")
|
||||
else:
|
||||
debug(f"文件 {file_path} 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": document_id,
|
||||
"collection_name": collection_name,
|
||||
"timings": timings,
|
||||
"unique_triples": unique_triples,
|
||||
"message": f"文件 {file_path} 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": document_id,
|
||||
"collection_name": collection_name,
|
||||
"timings": timings,
|
||||
"unique_triples": unique_triples,
|
||||
"message": f"文件 {file_path} 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": document_id,
|
||||
"collection_name": collection_name,
|
||||
"timings": timings,
|
||||
"message": f"插入文档失败: {str(e)}",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
@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"重试三元组抽取服务，第 {retry_state.attempt_number} 次")
|
||||
)
|
||||
async def _get_embeddings(self, texts: List[str]) -> List[List[float]]:
|
||||
"""调用嵌入服务获取文本的向量，带缓存"""
|
||||
try:
|
||||
# 检查缓存
|
||||
uncached_texts = [text for text in texts if text not in EMBED_CACHE]
|
||||
if uncached_texts:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.post(
|
||||
"http://localhost:9998/v1/embeddings",
|
||||
headers={"Content-Type": "application/json"},
|
||||
json={"input": uncached_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"]]
|
||||
for text, embedding in zip(uncached_texts, embeddings):
|
||||
EMBED_CACHE[text] = np.array(embedding) / np.linalg.norm(embedding)
|
||||
debug(f"成功获取 {len(embeddings)} 个新嵌入向量，缓存大小: {len(EMBED_CACHE)}")
|
||||
# 返回缓存中的嵌入
|
||||
return [EMBED_CACHE[text] for text in texts]
|
||||
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)}")
|
||||
|
||||
async def _extract_triples(self, text: str) -> List[Dict]:
|
||||
"""调用三元组抽取服务，无超时限制"""
|
||||
request_id = str(uuid.uuid4()) # 为每个请求生成唯一 ID
|
||||
start_time = time.time()
|
||||
debug(f"Request #{request_id} started for triples extraction")
|
||||
try:
|
||||
async with aiohttp.ClientSession(
|
||||
connector=aiohttp.TCPConnector(limit=30),
|
||||
timeout=aiohttp.ClientTimeout(total=None) # 无限等待
|
||||
) as session:
|
||||
async with session.post(
|
||||
"http://localhost:9991/v1/triples",
|
||||
headers={"Content-Type": "application/json; charset=utf-8"},
|
||||
json={"text": text}
|
||||
) as response:
|
||||
elapsed_time = time.time() - start_time
|
||||
debug(f"Request #{request_id} received response, status: {response.status}, took {elapsed_time:.2f} seconds")
|
||||
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, total time: {elapsed_time:.2f} seconds")
|
||||
return triples
|
||||
except Exception as e:
|
||||
elapsed_time = time.time() - start_time
|
||||
error(f"Request #{request_id} failed to extract triples: {str(e)}, took {elapsed_time:.2f} seconds")
|
||||
debug(f"Request #{request_id} traceback: {traceback.format_exc()}")
|
||||
raise RuntimeError(f"三元组抽取服务调用失败: {str(e)}")
|
||||
|
||||
async def _delete_document(self, userid: str, file_path: str, knowledge_base_id: str, document_id:str, db_type: str = "") -> Dict[str, Any]:
|
||||
"""删除用户指定文件数据，包括 Milvus 和 Neo4j 中的记录"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
# 调用 Milvus 删除文件端点
|
||||
debug(f"调用删除文件端点: userid={userid}, file_path={file_path}, knowledge_base_id={knowledge_base_id}, document_id={document_id}")
|
||||
milvus_result = await self._make_api_request("deletedocument", {
|
||||
"userid": userid,
|
||||
"file_path": file_path,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"document_id": document_id,
|
||||
"db_type": db_type
|
||||
})
|
||||
|
||||
if milvus_result.get("status") != "success":
|
||||
error(f"Milvus 删除文件失败: {milvus_result.get('message', '未知错误')}")
|
||||
return milvus_result
|
||||
|
||||
# 调用 Neo4j 删除端点
|
||||
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 self._make_neo4japi_request("deletedocument", {
|
||||
"document_id": document_id
|
||||
})
|
||||
if neo4j_result.get("status") != "success":
|
||||
error(
|
||||
f"Neo4j 删除文档失败: document_id={document_id}, 错误: {neo4j_result.get('message', '未知错误')}")
|
||||
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)}")
|
||||
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 _delete_knowledge_base(self, db_type: str, userid: str, knowledge_base_id: str) -> Dict[str, Any]:
|
||||
"""删除用户的整个知识库，包括 Milvus 和 Neo4j 中的记录"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
# 调用 Milvus 删除知识库端点
|
||||
debug(f"调用删除知识库端点: userid={userid}, knowledge_base_id={knowledge_base_id}")
|
||||
milvus_result = await self._make_api_request("deleteknowledgebase", {
|
||||
"userid": userid,
|
||||
"knowledge_base_id": knowledge_base_id,
|
||||
"db_type": db_type
|
||||
})
|
||||
|
||||
if milvus_result.get("status") != "success":
|
||||
error(f"Milvus 删除知识库失败: {milvus_result.get('message', '未知错误')}")
|
||||
return milvus_result
|
||||
|
||||
deleted_files = milvus_result.get("deleted_files", [])
|
||||
|
||||
# 新增：调用 Neo4j 删除知识库端点
|
||||
neo4j_deleted_nodes = 0
|
||||
neo4j_deleted_rels = 0
|
||||
try:
|
||||
debug(f"调用 Neo4j 删除知识库端点: userid={userid}, knowledge_base_id={knowledge_base_id}")
|
||||
neo4j_result = await self._make_neo4japi_request("deleteknowledgebase", {
|
||||
"userid": userid,
|
||||
"knowledge_base_id": knowledge_base_id
|
||||
})
|
||||
if neo4j_result.get("status") == "success":
|
||||
neo4j_deleted_nodes = neo4j_result.get("nodes_deleted", 0)
|
||||
neo4j_deleted_rels = neo4j_result.get("rels_deleted", 0)
|
||||
info(f"成功删除 {neo4j_deleted_nodes} 个 Neo4j 节点和 {neo4j_deleted_rels} 个关系")
|
||||
else:
|
||||
error(f"Neo4j 删除知识库失败: {neo4j_result.get('message', '未知错误')}")
|
||||
return {
|
||||
"status": "success",
|
||||
"collection_name": collection_name,
|
||||
"deleted_files": deleted_files,
|
||||
"message": f"成功删除 Milvus 知识库，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，但 Neo4j 删除失败: {neo4j_result.get('message')}",
|
||||
"status_code": 200
|
||||
}
|
||||
except Exception as e:
|
||||
error(f"Neo4j 删除知识库失败: {str(e)}")
|
||||
return {
|
||||
"status": "success",
|
||||
"collection_name": collection_name,
|
||||
"deleted_files": deleted_files,
|
||||
"message": f"成功删除 Milvus 知识库，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，但 Neo4j 删除失败: {str(e)}",
|
||||
"status_code": 200
|
||||
}
|
||||
|
||||
if not deleted_files and neo4j_deleted_nodes == 0 and neo4j_deleted_rels == 0:
|
||||
debug(f"没有删除任何记录，userid={userid}, knowledge_base_id={knowledge_base_id}")
|
||||
return {
|
||||
"status": "success",
|
||||
"collection_name": collection_name,
|
||||
"deleted_files": [],
|
||||
"message": f"没有找到 userid={userid}, knowledge_base_id={knowledge_base_id} 的记录，无需删除",
|
||||
"status_code": 200
|
||||
}
|
||||
|
||||
info(
|
||||
f"总计删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，删除文件: {deleted_files}")
|
||||
return {
|
||||
"status": "success",
|
||||
"collection_name": collection_name,
|
||||
"deleted_files": deleted_files,
|
||||
"message": f"成功删除 Milvus 记录，{neo4j_deleted_nodes} 个 Neo4j 节点，{neo4j_deleted_rels} 个 Neo4j 关系，删除文件: {deleted_files}",
|
||||
"status_code": 200
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error(f"删除知识库失败: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"collection_name": collection_name,
|
||||
"deleted_files": [],
|
||||
"message": f"删除知识库失败: {str(e)}",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
async def _extract_entities(self, query: str) -> List[str]:
|
||||
"""调用实体识别服务"""
|
||||
try:
|
||||
if not query:
|
||||
raise ValueError("查询文本不能为空")
|
||||
async with aiohttp.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)) # 去重
|
||||
debug(f"成功提取 {len(unique_entities)} 个唯一实体: {unique_entities}")
|
||||
return unique_entities
|
||||
except Exception as e:
|
||||
error(f"实体识别服务调用失败: {str(e)}")
|
||||
return []
|
||||
|
||||
async def _rerank_results(self, query: str, results: List[Dict], top_n: int) -> List[Dict]:
|
||||
"""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))
|
||||
debug(f"重排序 top_n={top_n}, 原始结果数={len(results)}")
|
||||
|
||||
documents = [result["text"] for result in results]
|
||||
async with aiohttp.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
|
||||
|
||||
async def _search_query(self, query: str, userid: str, knowledge_base_ids: List[str], limit: int = 5,
|
||||
offset: int = 0, use_rerank: bool = True, db_type: str = "") -> Dict[str, Any]:
|
||||
"""纯向量搜索，调用服务化端点"""
|
||||
start_time = time.time()
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
timing_stats = {}
|
||||
try:
|
||||
info(
|
||||
f"开始纯向量搜索: query={query}, userid={userid}, db_type={db_type}, knowledge_base_ids={knowledge_base_ids}, limit={limit}, offset={offset}, use_rerank={use_rerank}")
|
||||
|
||||
if not query:
|
||||
raise ValueError("查询文本不能为空")
|
||||
if not userid:
|
||||
raise ValueError("userid 不能为空")
|
||||
if "_" in userid or (db_type and "_" in db_type):
|
||||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||||
raise ValueError("userid 或 db_type 的长度超出限制")
|
||||
if limit <= 0 or limit > 16384:
|
||||
raise ValueError("limit 必须在 1 到 16384 之间")
|
||||
if offset < 0:
|
||||
raise ValueError("offset 不能为负数")
|
||||
if limit + offset > 16384:
|
||||
raise ValueError("limit + offset 不能超过 16384")
|
||||
if not knowledge_base_ids:
|
||||
raise ValueError("knowledge_base_ids 不能为空")
|
||||
for kb_id in knowledge_base_ids:
|
||||
if not isinstance(kb_id, str):
|
||||
raise ValueError(f"knowledge_base_id 必须是字符串: {kb_id}")
|
||||
if len(kb_id) > 100:
|
||||
raise ValueError(f"knowledge_base_id 长度超出 100 个字符: {kb_id}")
|
||||
if "_" in kb_id:
|
||||
raise ValueError(f"knowledge_base_id 不能包含下划线: {kb_id}")
|
||||
|
||||
# 将查询文本转换为向量
|
||||
vector_start = time.time()
|
||||
query_vector = await self._get_embeddings([query])
|
||||
if not query_vector or not all(len(vec) == 1024 for vec in query_vector):
|
||||
raise ValueError("查询向量必须是长度为 1024 的浮点数列表")
|
||||
query_vector = query_vector[0] # 取第一个向量
|
||||
timing_stats["vector_generation"] = time.time() - vector_start
|
||||
debug(f"生成查询向量耗时: {timing_stats['vector_generation']:.3f} 秒")
|
||||
|
||||
# 调用纯向量搜索端点
|
||||
search_start = time.time()
|
||||
result = await self._make_api_request("searchquery", {
|
||||
"query_vector": query_vector.tolist(),
|
||||
"userid": userid,
|
||||
"knowledge_base_ids": knowledge_base_ids,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
"db_type": db_type
|
||||
})
|
||||
timing_stats["vector_search"] = time.time() - search_start
|
||||
debug(f"向量搜索耗时: {timing_stats['vector_search']:.3f} 秒")
|
||||
|
||||
if result.get("status") != "success":
|
||||
error(f"纯向量搜索失败: {result.get('message', '未知错误')}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
unique_results = result.get("results", [])
|
||||
if use_rerank and unique_results:
|
||||
rerank_start = time.time()
|
||||
debug("开始重排序")
|
||||
unique_results = await self._rerank_results(query, unique_results, limit)
|
||||
unique_results = sorted(unique_results, key=lambda x: x.get('rerank_score', 0), reverse=True)
|
||||
timing_stats["reranking"] = time.time() - rerank_start
|
||||
debug(f"重排序耗时: {timing_stats['reranking']:.3f} 秒")
|
||||
debug(f"重排序分数分布: {[round(r.get('rerank_score', 0), 3) for r in unique_results]}")
|
||||
else:
|
||||
unique_results = [{k: v for k, v in r.items() if k != 'rerank_score'} for r in unique_results]
|
||||
|
||||
timing_stats["total_time"] = time.time() - start_time
|
||||
info(f"纯向量搜索完成，返回 {len(unique_results)} 条结果，总耗时: {timing_stats['total_time']:.3f} 秒")
|
||||
return {"results": unique_results[:limit], "timing": timing_stats}
|
||||
|
||||
except Exception as e:
|
||||
error(f"纯向量搜索失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
async def _fused_search(self, query: str, userid: str, knowledge_base_ids: List[str], limit: int = 5,
|
||||
offset: int = 0, use_rerank: bool = True, db_type: str = "") -> Dict[str, Any]:
|
||||
"""融合搜索，调用服务化端点"""
|
||||
start_time = time.time()
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
timing_stats = {}
|
||||
try:
|
||||
info(
|
||||
f"开始融合搜索: query={query}, userid={userid}, db_type={db_type}, knowledge_base_ids={knowledge_base_ids}, limit={limit}, offset={offset}, use_rerank={use_rerank}")
|
||||
|
||||
if not query or not userid or not knowledge_base_ids:
|
||||
raise ValueError("query、userid 和 knowledge_base_ids 不能为空")
|
||||
if "_" in userid or (db_type and "_" in db_type):
|
||||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||||
raise ValueError("db_type 或 userid 的长度超出限制")
|
||||
if limit < 1 or limit > 16384 or offset < 0:
|
||||
raise ValueError("limit 必须在 1 到 16384 之间，offset 必须大于或等于 0")
|
||||
|
||||
# 提取实体
|
||||
entity_extract_start = time.time()
|
||||
query_entities = await self._extract_entities(query)
|
||||
timing_stats["entity_extraction"] = time.time() - entity_extract_start
|
||||
debug(f"提取实体: {query_entities}, 耗时: {timing_stats['entity_extraction']:.3f} 秒")
|
||||
|
||||
# 调用 Neo4j 服务进行三元组匹配
|
||||
all_triplets = []
|
||||
triplet_match_start = time.time()
|
||||
for kb_id in knowledge_base_ids:
|
||||
debug(f"调用 Neo4j 三元组匹配: knowledge_base_id={kb_id}")
|
||||
try:
|
||||
neo4j_result = await self._make_neo4japi_request("matchtriplets", {
|
||||
"query": query,
|
||||
"query_entities": query_entities,
|
||||
"userid": userid,
|
||||
"knowledge_base_id": kb_id
|
||||
})
|
||||
if neo4j_result.get("status") == "success":
|
||||
triplets = neo4j_result.get("triplets", [])
|
||||
all_triplets.extend(triplets)
|
||||
debug(f"知识库 {kb_id} 匹配到 {len(triplets)} 个三元组: {triplets[:5]}")
|
||||
else:
|
||||
error(
|
||||
f"Neo4j 三元组匹配失败: knowledge_base_id={kb_id}, 错误: {neo4j_result.get('message', '未知错误')}")
|
||||
except Exception as e:
|
||||
error(f"Neo4j 三元组匹配失败: knowledge_base_id={kb_id}, 错误: {str(e)}")
|
||||
continue
|
||||
timing_stats["triplet_matching"] = time.time() - triplet_match_start
|
||||
debug(f"三元组匹配总耗时: {timing_stats['triplet_matching']:.3f} 秒")
|
||||
|
||||
# 拼接三元组文本
|
||||
triplet_text_start = time.time()
|
||||
triplet_texts = []
|
||||
for triplet in all_triplets:
|
||||
head = triplet.get('head', '')
|
||||
type_ = triplet.get('type', '')
|
||||
tail = triplet.get('tail', '')
|
||||
if head and type_ and tail:
|
||||
triplet_texts.append(f"{head} {type_} {tail}")
|
||||
else:
|
||||
debug(f"无效三元组: {triplet}")
|
||||
combined_text = query
|
||||
if triplet_texts:
|
||||
combined_text += " [三元组] " + "; ".join(triplet_texts)
|
||||
debug(
|
||||
f"拼接文本: {combined_text[:200]}... (总长度: {len(combined_text)}, 三元组数量: {len(triplet_texts)})")
|
||||
timing_stats["triplet_text_combine"] = time.time() - triplet_text_start
|
||||
debug(f"拼接三元组文本耗时: {timing_stats['triplet_text_combine']:.3f} 秒")
|
||||
|
||||
# 将拼接文本转换为向量
|
||||
vector_start = time.time()
|
||||
query_vector = await self._get_embeddings([combined_text])
|
||||
if not query_vector or not all(len(vec) == 1024 for vec in query_vector):
|
||||
raise ValueError("查询向量必须是长度为 1024 的浮点数列表")
|
||||
query_vector = query_vector[0] # 取第一个向量
|
||||
timing_stats["vector_generation"] = time.time() - vector_start
|
||||
debug(f"生成查询向量耗时: {timing_stats['vector_generation']:.3f} 秒")
|
||||
|
||||
# 调用融合搜索端点
|
||||
search_start = time.time()
|
||||
result = await self._make_api_request("searchquery", {
|
||||
"query_vector": query_vector.tolist(),
|
||||
"userid": userid,
|
||||
"knowledge_base_ids": knowledge_base_ids,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
"db_type": db_type
|
||||
})
|
||||
timing_stats["vector_search"] = time.time() - search_start
|
||||
debug(f"向量搜索耗时: {timing_stats['vector_search']:.3f} 秒")
|
||||
|
||||
if result.get("status") != "success":
|
||||
error(f"融合搜索失败: {result.get('message', '未知错误')}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
unique_results = result.get("results", [])
|
||||
if use_rerank and unique_results:
|
||||
rerank_start = time.time()
|
||||
debug("开始重排序")
|
||||
unique_results = await self._rerank_results(combined_text, unique_results, limit)
|
||||
unique_results = sorted(unique_results, key=lambda x: x.get('rerank_score', 0), reverse=True)
|
||||
timing_stats["reranking"] = time.time() - rerank_start
|
||||
debug(f"重排序耗时: {timing_stats['reranking']:.3f} 秒")
|
||||
debug(f"重排序分数分布: {[round(r.get('rerank_score', 0), 3) for r in unique_results]}")
|
||||
else:
|
||||
unique_results = [{k: v for k, v in r.items() if k != 'rerank_score'} for r in unique_results]
|
||||
|
||||
timing_stats["total_time"] = time.time() - start_time
|
||||
info(f"融合搜索完成，返回 {len(unique_results)} 条结果，总耗时: {timing_stats['total_time']:.3f} 秒")
|
||||
return {"results": unique_results[:limit], "timing": timing_stats}
|
||||
|
||||
except Exception as e:
|
||||
error(f"融合搜索失败: {str(e)}, 堆栈: {traceback.format_exc()}")
|
||||
return {"results": [], "timing": timing_stats}
|
||||
|
||||
async def _list_user_files(self, userid: str, db_type: str = "") -> Dict[str, List[Dict]]:
|
||||
"""列出用户的所有知识库及其文件，按 knowledge_base_id 分组"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
info(f"列出用户文件: userid={userid}, db_type={db_type}")
|
||||
|
||||
if not userid:
|
||||
raise ValueError("userid 不能为空")
|
||||
if "_" in userid or (db_type and "_" in db_type):
|
||||
raise ValueError("userid 和 db_type 不能包含下划线")
|
||||
if (db_type and len(db_type) > 100) or len(userid) > 100:
|
||||
raise ValueError("userid 或 db_type 的长度超出限制")
|
||||
|
||||
# 调用列出用户文件端点
|
||||
result = await self._make_api_request("listuserfiles", {
|
||||
"userid": userid,
|
||||
"db_type": db_type
|
||||
})
|
||||
|
||||
if result.get("status") != "success":
|
||||
error(f"列出用户文件失败: {result.get('message', '未知错误')}")
|
||||
return {}
|
||||
|
||||
return result.get("files_by_knowledge_base", {})
|
||||
|
||||
except Exception as e:
|
||||
error(f"列出用户文件失败: {str(e)}")
|
||||
return {}
|
||||
|
||||
async def _list_all_knowledge_bases(self, db_type: str = "") -> Dict[str, Any]:
|
||||
"""列出数据库中所有用户的知识库及其文件，按用户分组"""
|
||||
collection_name = "ragdb" if not db_type else f"ragdb_{db_type}"
|
||||
try:
|
||||
info(f"列出所有用户的知识库: db_type={db_type}")
|
||||
|
||||
if db_type and "_" in db_type:
|
||||
raise ValueError("db_type 不能包含下划线")
|
||||
if db_type and len(db_type) > 100:
|
||||
raise ValueError("db_type 的长度应小于 100")
|
||||
|
||||
# 调用列出所有知识库端点
|
||||
result = await self._make_api_request("listallknowledgebases", {
|
||||
"db_type": db_type
|
||||
})
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
error(f"列出所有用户知识库失败: {str(e)}")
|
||||
return {
|
||||
"status": "error",
|
||||
"users_knowledge_bases": {},
|
||||
"collection_name": collection_name,
|
||||
"message": f"列出所有用户知识库失败: {str(e)}",
|
||||
"status_code": 400
|
||||
}
|
||||
|
||||
connection_register('Rag', MilvusConnection)
|
||||
info("MilvusConnection registered")
|
||||
Loading…
x
Reference in New Issue
Block a user