'commit'

dsRag/Milvus/Utils/MilvusCollectionManager.py

@@ -17,7 +17,7 @@ class MilvusCollectionManager:
         """
         if utility.has_collection(self.collection_name):
             self.collection = Collection(name=self.collection_name)
-            #print(f"集合 '{self.collection_name}' 已加载。")
+            # print(f"集合 '{self.collection_name}' 已加载。")
         else:
             print(f"集合 '{self.collection_name}' 不存在。")
 
@@ -52,6 +52,7 @@ class MilvusCollectionManager:
         if self.collection is None:
             raise Exception("集合未加载，请检查集合是否存在。")
         self.collection.insert(entities)
+
     def load_collection(self):
         """
         加载集合到内存
@@ -70,7 +71,7 @@ class MilvusCollectionManager:
             # 使用 Milvus 的 query 方法查询指定 ID 的记录
             results = self.collection.query(
                 expr=f"id == {id}",  # 查询条件
-                output_fields=["id", "person_id", "user_input", "model_response", "timestamp"]  # 返回的字段
+                output_fields=["id", "document_id", "user_input", "timestamp"]  # 返回的字段
             )
             if results:
                 return results[0]  # 返回第一条记录
@@ -79,6 +80,7 @@ class MilvusCollectionManager:
         except Exception as e:
             print(f"查询失败: {e}")
             return None
+
     def search(self, data, search_params, expr=None, limit=5):
         """
         在集合中搜索与输入向量最相似的数据
@@ -121,4 +123,4 @@ class MilvusCollectionManager:
         if result:
             return result[0]["text"]
         else:
-            return None
+            return None

dsRag/Milvus/X1_create_collection.py

@@ -27,13 +27,12 @@ if utility.has_collection(collection_name):
 # 5. 定义集合的字段和模式
 fields = [
     FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),  # 主键字段，自动生成 ID
-    FieldSchema(name="person_id", dtype=DataType.VARCHAR, max_length=64),  # 会话 ID
+    FieldSchema(name="document_id", dtype=DataType.VARCHAR, max_length=64),  # 文档 ID
     FieldSchema(name="user_input", dtype=DataType.VARCHAR, max_length=65535),  # 用户问题
-    FieldSchema(name="model_response", dtype=DataType.VARCHAR, max_length=65535),  # 大模型反馈结果
     FieldSchema(name="timestamp", dtype=DataType.VARCHAR, max_length=32),  # 时间
     FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=MS_DIMENSION)  # 向量字段，维度为 200
 ]
-schema_description = "Chat records collection with person_id , user input, model response, and timestamp"
+schema_description = "Chat records collection with document_id , user_input, and timestamp"
 
 # 6. 创建集合
 print(f"正在创建集合 '{collection_name}'...")

dsRag/Milvus/X4_InsertMathData.py

@@ -46,16 +46,15 @@ for filename in os.listdir(txt_dir):
 
             # 5. 获取当前时间和会话ID
             timestamp = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
-            person_id = "MATH_DATA_" + str(hash(filename))
+            document_id = "MATH_DATA_1" # 史校长的这本书定义为 MATH_DATA_1
 
             # 6. 将文本转换为嵌入向量
             embedding = text_to_embedding(content)
 
             # 7. 插入数据
             entities = [
-                [person_id],  # person_id
+                [document_id],  # document_id
                 [content],  # user_input
-                [""],  # model_response (留空)
                 [timestamp],  # timestamp
                 [embedding]  # embedding
             ]

dsRag/Milvus/X5_select_all_data.py

@@ -21,7 +21,7 @@ try:
     # 使用 Milvus 的 query 方法查询所有数据
     results = collection_manager.collection.query(
         expr="",  # 空表达式表示查询所有数据
-        output_fields=["id", "person_id", "user_input", "model_response", "timestamp", "embedding"],  # 指定返回的字段
+        output_fields=["id", "document_id", "user_input",  "timestamp", "embedding"],  # 指定返回的字段
         limit=1000  # 设置最大返回记录数
     )
     print("查询结果：")
@@ -29,16 +29,14 @@ try:
         for result in results:
             try:
                 # 获取字段值
-                person_id = result["person_id"]
+                document_id = result["document_id"]
                 user_input = result["user_input"]
-                model_response = result["model_response"]
                 timestamp = result["timestamp"]
                 embedding = result["embedding"]
                 # 打印结果
                 print(f"ID: {result['id']}")
-                print(f"会话 ID: {person_id}")
+                print(f"文档 ID: {document_id}")
                 print(f"用户问题: {user_input}")
-                print(f"大模型回复: {model_response}")
                 print(f"时间: {timestamp}")
                 print(f"向量: {embedding[:5]}...")  # 只打印前 5 维向量
                 print("-" * 40)  # 分隔线

dsRag/Milvus/X6_search_near_data.py

@@ -62,9 +62,8 @@ if results:
                 # 查询非向量字段
                 record = collection_manager.query_by_id(hit.id)
                 print(f"ID: {hit.id}")
-                print(f"会话 ID: {record['person_id']}")
+                print(f"文档 ID: {record['document_id']}")
                 print(f"用户问题: {record['user_input']}")
-                print(f"大模型回复: {record['model_response']}")
                 print(f"时间: {record['timestamp']}")
                 print(f"距离: {hit.distance}")
                 print("-" * 40)  # 分隔线

dsRag/Start.py

@@ -32,11 +32,11 @@ print(f"模型加载成功，词向量维度: {model.vector_size}")
 async def lifespan(app: FastAPI):
     # 初始化Milvus连接池
     app.state.milvus_pool = MilvusConnectionPool(host=MS_HOST, port=MS_PORT, max_connections=MS_MAX_CONNECTIONS)
-    
+
     # 初始化集合管理器
     app.state.collection_manager = MilvusCollectionManager(MS_COLLECTION_NAME)
     app.state.collection_manager.load_collection()
-    
+
     # 初始化DeepSeek客户端
     app.state.deepseek_client = OpenAI(
         api_key=Config.DEEPSEEK_API_KEY,
@@ -47,8 +47,10 @@ async def lifespan(app: FastAPI):
     # 关闭Milvus连接池
     app.state.milvus_pool.close()
 
+
 app = FastAPI(lifespan=lifespan)
 
+
 # 将文本转换为嵌入向量
 def text_to_embedding(text):
     words = jieba.lcut(text)  # 使用 jieba 分词
@@ -63,11 +65,12 @@ def text_to_embedding(text):
         print("未找到有效词，返回零向量")
         return [0.0] * model.vector_size
 
+
 async def generate_stream(client, milvus_pool, collection_manager, query):
     """生成SSE流"""
     # 从连接池获取连接
     connection = milvus_pool.get_connection()
-    
+
     try:
         # 1. 将查询文本转换为向量
         current_embedding = text_to_embedding(query)
@@ -79,10 +82,10 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
         }
         # 7. 将文本转换为嵌入向量
         results = collection_manager.search(current_embedding, search_params, limit=5)  # 返回 2 条结果
-        
+
         # 3. 处理搜索结果
         print("最相关的历史对话:")
-        context=""
+        context = ""
         if results:
             for hits in results:
                 for hit in hits:
@@ -90,10 +93,9 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
                         # 查询非向量字段
                         record = collection_manager.query_by_id(hit.id)
                         print(f"ID: {hit.id}")
-                        print(f"会话 ID: {record['person_id']}")
+                        print(f"文档 ID: {record['document_id']}")
                         print(f"用户问题: {record['user_input']}")
-                        context=context+record['user_input']
+                        context = context + record['user_input']
-                        print(f"大模型回复: {record['model_response']}")
                         print(f"时间: {record['timestamp']}")
                         print(f"距离: {hit.distance}")
                         print("-" * 40)  # 分隔线
@@ -101,7 +103,7 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
                         print(f"查询失败: {e}")
         else:
             print("未找到相关历史对话，请检查查询参数或数据。")
-        
+
         prompt = f"""根据以下关于'{query}'的相关信息，# Role: 信息检索与回答助手
 
 ## Profile
@@ -154,7 +156,7 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
 
 相关信息：
 {context}"""
-        
+
         response = client.chat.completions.create(
             model="deepseek-chat",
             messages=[
@@ -164,7 +166,7 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
             temperature=0.3,
             stream=True
         )
-        
+
         for chunk in response:
             if chunk.choices[0].delta.content:
                 yield {"data": chunk.choices[0].delta.content}
@@ -174,9 +176,13 @@ async def generate_stream(client, milvus_pool, collection_manager, query):
     finally:
         # 释放连接
         milvus_pool.release_connection(connection)
+
+
 """
 http://10.10.21.22:8000/api/rag?query=小学数学中有哪些模型
 """
+
+
 @app.post("/api/rag")
 async def rag_stream(request: Request, query: str = Body(...)):
     """RAG+DeepSeek流式接口"""
@@ -189,7 +195,8 @@ async def rag_stream(request: Request, query: str = Body(...)):
         )
     )
 
+
 app.mount("/static", StaticFiles(directory="Static"), name="static")
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    uvicorn.run(app, host="0.0.0.0", port=8000)