'commit'

dsRag/Start.py

@@ -1,6 +1,21 @@
+import logging
+import os
+import subprocess
+import tempfile
 import urllib.parse
+import uuid
+import warnings
+from io import BytesIO
+from logging.handlers import RotatingFileHandler
 
+import fastapi
+import uvicorn
+from fastapi import FastAPI, HTTPException
+from starlette.staticfiles import StaticFiles
+
+from Util.ALiYunUtil import ALiYunUtil
 from Util.SearchUtil import *
+
 # 初始化日志
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
@@ -24,7 +39,6 @@ logger.addHandler(file_handler)
 logger.addHandler(console_handler)
 
 
-
 async def lifespan(app: FastAPI):
     # 初始化阿里云大模型工具
     app.state.aliyun_util = ALiYunUtil()
@@ -42,7 +56,7 @@ app.mount("/static", StaticFiles(directory="Static"), name="static")
 
 
 @app.post("/api/save-word")
-async def save_to_word(request: Request):
+async def save_to_word(request: fastapi.Request):
     output_file = None
     try:
         # Parse request data
@@ -91,18 +105,17 @@ async def save_to_word(request: Request):
             logger.warning(f"Failed to clean up temp files: {str(e)}")
 
 
-
+@app.post("/api/rag", response_model=None)
-@app.post("/api/rag")
+async def rag(request: fastapi.Request):
-async def rag(request: Request):
     data = await request.json()
     query = data.get('query', '')
     query_tags = data.get('tags', [])
 
     # 调用es进行混合搜索
-    search_results = queryByEs(query, query_tags)
+    search_results = queryByEs(query, query_tags, logger)
 
     # 调用大模型
-    markdown_content = callLLM(request, query, search_results)
+    markdown_content = callLLM(request, query, search_results, logger)
 
     # 如果有正确的结果
     if markdown_content:

dsRag/Util/ModelUtil.py

@@ -0,0 +1,4 @@
+from typing import List
+
+from pydantic import BaseModel, Field
+

dsRag/Util/SearchUtil.py

@@ -1,29 +1,10 @@
-import logging
-import os
-import subprocess
-import tempfile
-import urllib.parse
-import uuid
-from io import BytesIO
-from logging.handlers import RotatingFileHandler
-from typing import List
-
-import uvicorn
-from fastapi import FastAPI, Request, HTTPException
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel, Field
 from starlette.responses import StreamingResponse
 
 from Config.Config import ES_CONFIG
-import warnings
-from Util.ALiYunUtil import ALiYunUtil
 from Util.EsSearchUtil import EsSearchUtil
 
-# 初始化日志
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
 
-def queryByEs(query, query_tags):
+def queryByEs(query, query_tags,logger):
     # 获取EsSearchUtil实例
     es_search_util = EsSearchUtil(ES_CONFIG)
 
@@ -144,7 +125,7 @@ def queryByEs(query, query_tags):
         es_search_util.es_pool.release_connection(es_conn)
 
 
-def callLLM(request, query, search_results):
+def callLLM(request, query, search_results, logger,streamBack=False):
     # 调用阿里云大模型整合结果
     aliyun_util = request.app.state.aliyun_util
 
@@ -183,7 +164,16 @@ def callLLM(request, query, search_results):
     if len(context) > 0:
         # 调用大模型生成回答
         logger.info("正在调用阿里云大模型生成回答...")
-        markdown_content = aliyun_util.chat(prompt)
+        
-        logger.info(f"调用阿里云大模型生成回答成功完成!")
+        if streamBack:
-        return markdown_content
+            # SSE流式返回
+            async def generate():
+                async for chunk in aliyun_util.chat_stream(prompt):
+                    yield f"data: {chunk}\n\n"
+            return StreamingResponse(generate(), media_type="text/event-stream")
+        else:
+            # 一次性返回
+            markdown_content = aliyun_util.chat(prompt)
+            logger.info(f"调用阿里云大模型生成回答成功完成!")
+            return markdown_content
     return None