'commit'

dsRag/Doc/8、Docling.txt

@@ -0,0 +1,5 @@
+# 官网
+https://github.com/DS4SD/docling
+
+# 文档解析神器 Docling 上线，快速转换文件格式
+https://blog.csdn.net/XLionXxxx/article/details/144915203

dsRag/Start.py

@@ -4,12 +4,16 @@ from contextlib import asynccontextmanager
 from logging.handlers import RotatingFileHandler
 
 import uvicorn
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, UploadFile, File, Request
+from sse_starlette.sse import EventSourceResponse
+from elasticsearch import Elasticsearch
+from openai import OpenAI
 
 from Dao.KbDao import KbDao
 from Util.MySQLUtil import init_mysql_pool
-
+from Config import Config
 from fastapi.staticfiles import StaticFiles
+import urllib3
 
 # 初始化日志
 logger = logging.getLogger(__name__)
@@ -23,6 +27,23 @@ async def lifespan(app: FastAPI):
     # 初始化数据库连接池
     app.state.kb_dao = KbDao(await init_mysql_pool())
     
+    # 初始化ES连接
+    import urllib3
+    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+    
+    # 初始化ES连接时添加verify_certs=False
+    app.state.es = Elasticsearch(
+        hosts=Config.ES_CONFIG['hosts'],
+        basic_auth=Config.ES_CONFIG['basic_auth'],
+        verify_certs=False  # 禁用证书验证
+    )
+    
+    # 初始化DeepSeek客户端
+    app.state.deepseek_client = OpenAI(
+        api_key=Config.DEEPSEEK_API_KEY,
+        base_url=Config.DEEPSEEK_URL
+    )
+    
     # 启动文档处理任务
     async def document_processor():
         while True:
@@ -97,6 +118,85 @@ async def upload_file(kb_id: int, file: UploadFile = File(...)):
     """文件上传接口"""
     return await app.state.kb_dao.handle_upload(kb_id, file)
 
+def search_related_data(es, query):
+    """搜索与查询相关的数据"""
+    # 向量搜索
+    vector_results = es.search(
+        index=Config.ES_CONFIG['default_index'],
+        body={
+            "query": {
+                "match": {
+                    "content": {
+                        "query": query,
+                        "analyzer": "ik_smart"
+                    }
+                }
+            },
+            "size": 5
+        }
+    )
+    
+    # 文本精确搜索
+    text_results = es.search(
+        index="raw_texts",
+        body={
+            "query": {
+                "match": {
+                    "text.keyword": query
+                }
+            },
+            "size": 5
+        }
+    )
+    
+    # 合并结果
+    context = ""
+    for hit in vector_results['hits']['hits']:
+        context += f"向量相似度结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
+    
+    for hit in text_results['hits']['hits']:
+        context += f"文本精确匹配结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
+    
+    return context
+
+async def generate_stream(client, es, query):
+    """生成SSE流"""
+    context = search_related_data(es, query)
+    
+    prompt = f"""根据以下关于'{query}'的相关信息，整理一份结构化的报告：
+要求：
+1. 分章节组织内容
+2. 包含关键数据和事实
+3. 语言简洁专业
+
+相关信息：
+{context}"""
+    
+    try:
+        response = client.chat.completions.create(
+            model="deepseek-chat",
+            messages=[
+                {"role": "system", "content": "你是一个专业的文档整理助手"},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=0.3,
+            stream=True
+        )
+        
+        for chunk in response:
+            if chunk.choices[0].delta.content:
+                yield {"data": chunk.choices[0].delta.content}
+            await asyncio.sleep(0.01)
+    except Exception as e:
+        yield {"data": f"生成报告时出错: {str(e)}"}
+
+@app.get("/api/rag")
+async def rag_stream(query: str, request: Request):
+    """RAG+DeepSeek流式接口"""
+    return EventSourceResponse(
+        generate_stream(request.app.state.deepseek_client, request.app.state.es, query)
+    )
+
 app.mount("/static", StaticFiles(directory="Static"), name="static")
 
 if __name__ == "__main__":

dsRag/StartSSE.py

@@ -1,112 +0,0 @@
-"""
-conda activate rag
-pip install fastapi uvicorn sse-starlette
-"""
-import asyncio
-
-from elasticsearch import Elasticsearch
-from fastapi import FastAPI
-from openai import OpenAI
-from sse_starlette.sse import EventSourceResponse
-
-from Config import Config
-
-app = FastAPI()
-
-# 初始化ES连接
-es = Elasticsearch(
-    hosts=Config.ES_CONFIG['hosts'],
-    basic_auth=Config.ES_CONFIG['basic_auth'],
-    verify_certs=Config.ES_CONFIG['verify_certs']
-)
-
-# 初始化DeepSeek客户端
-client = OpenAI(
-    api_key=Config.DEEPSEEK_API_KEY,
-    base_url=Config.DEEPSEEK_URL
-)
-
-def search_related_data(query):
-    """搜索与查询相关的数据"""
-    # 向量搜索
-    vector_results = es.search(
-        index=Config.ES_CONFIG['default_index'],
-        body={
-            "query": {
-                "match": {
-                    "content": {
-                        "query": query,
-                        "analyzer": "ik_smart"
-                    }
-                }
-            },
-            "size": 5
-        }
-    )
-    
-    # 文本精确搜索
-    text_results = es.search(
-        index="raw_texts",
-        body={
-            "query": {
-                "match": {
-                    "text.keyword": query
-                }
-            },
-            "size": 5
-        }
-    )
-    
-    # 合并结果
-    context = ""
-    for hit in vector_results['hits']['hits']:
-        context += f"向量相似度结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
-    
-    for hit in text_results['hits']['hits']:
-        context += f"文本精确匹配结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
-    
-    return context
-
-async def generate_stream(query):
-    """生成SSE流"""
-    context = search_related_data(query)
-    
-    prompt = f"""根据以下关于'{query}'的相关信息，整理一份结构化的报告：
-要求：
-1. 分章节组织内容
-2. 包含关键数据和事实
-3. 语言简洁专业
-
-相关信息：
-{context}"""
-    
-    try:
-        response = client.chat.completions.create(
-            model="deepseek-chat",
-            messages=[
-                {"role": "system", "content": "你是一个专业的文档整理助手"},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0.3,
-            stream=True
-        )
-        
-        for chunk in response:
-            if chunk.choices[0].delta.content:
-                yield {"data": chunk.choices[0].delta.content}
-            await asyncio.sleep(0.01)
-    except Exception as e:
-        yield {"data": f"生成报告时出错: {str(e)}"}
-
-@app.get("/api/rag")
-async def rag_stream(query: str):
-    """RAG+DeepSeek流式接口"""
-    return EventSourceResponse(generate_stream(query))
-
-"""
-http://localhost:8000/api/rag?query=整理云南省初中在校生情况文档
-http://10.10.21.20:8000/api/rag?query=整理云南省初中在校生情况文档
-"""
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)