更换provider，修复语法错误

deepsearcher/config.yaml

@@ -2,16 +2,16 @@ provide_settings:
   llm:
     provider: "OpenAILLM"
     config:
-      model: "Qwen/Qwen3-8B-FP8"
-      api_key: "empty"
-      base_url: "http://localhost:8000/v1"
+      model: "Qwen/Qwen3-30B-A3B"
+      api_key: "sk-fpzwvagjkhwysjsozfybvtjzongatcwqdihdxzuijnfdrjzt"
+      base_url: "https://api.siliconflow.cn/v1"
 
   embedding:
     provider: "OpenAIEmbedding"
-    config:
+    config: 
       model: "Qwen/Qwen3-Embedding-0.6B"
-      api_key: "empty"
-      base_url: "http://localhost:8001/v1"
+      api_key: "sk-fpzwvagjkhwysjsozfybvtjzongatcwqdihdxzuijnfdrjzt"
+      base_url: "https://api.siliconflow.cn/v1"
       dimension: 1024
       dim_change: false
 

deepsearcher/embedding/base.py

@@ -1,5 +1,3 @@
-from typing import List
-
 from tqdm import tqdm
 
 from deepsearcher.loader.splitter import Chunk
@@ -14,7 +12,7 @@ class BaseEmbedding:
     for the dimensionality of the embeddings.
     """
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """
         Embed a single query text.
 
@@ -26,7 +24,7 @@ class BaseEmbedding:
         """
         pass
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """
         Embed a list of document texts.
 
@@ -41,7 +39,7 @@ class BaseEmbedding:
         """
         return [self.embed_query(text) for text in texts]
 
-    def embed_chunks(self, chunks: List[Chunk], batch_size: int = 256) -> List[Chunk]:
+    def embed_chunks(self, chunks: list[Chunk], batch_size: int = 256) -> list[Chunk]:
         """
         Embed a list of Chunk objects.
 

deepsearcher/embedding/openai_embedding.py

@@ -1,5 +1,4 @@
 import os
-from typing import List
 
 from openai import OpenAI
 from openai._types import NOT_GIVEN
@@ -45,20 +44,20 @@ class OpenAIEmbedding(BaseEmbedding):
             model = kwargs.pop("model_name")
 
         if "dimension" in kwargs:
-            dimension = kwargs.pop("dimension") 
+            dimension = kwargs.pop("dimension")
         else:
             dimension = NOT_GIVEN
-        
+
         if "dim_change" in kwargs:
             dim_change = kwargs.pop("dim_change")
-        
+
         self.dim = dimension
         self.dim_change = dim_change
         self.model = model
 
         self.client = OpenAI(api_key=api_key, base_url=base_url, **kwargs)
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """
         Embed a single query text.
 
@@ -75,7 +74,7 @@ class OpenAIEmbedding(BaseEmbedding):
 
         return response.data[0].embedding
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """
         Embed a list of document texts.
 

deepsearcher/llm/openai_llm.py

@@ -59,7 +59,9 @@ class OpenAILLM(BaseLLM):
         for chunk in completion:
             stream_response = chunk.choices[0].delta.content
             if stream_response:
+                print(stream_response, end="", flush=True)
                 response += stream_response
                 if stream_callback:
                     stream_callback(stream_response)
+        print("\n")
         return response

deepsearcher/offline_loading.py

@@ -1,6 +1,5 @@
 import hashlib
 import os
-from typing import List, Union
 
 from tqdm import tqdm
 
@@ -10,7 +9,7 @@ from deepsearcher.loader.splitter import split_docs_to_chunks
 
 
 def load_from_local_files(
-    paths_or_directory: Union[str, List[str]],
+    paths_or_directory: str | list[str],
     collection_name: str = None,
     collection_description: str = None,
     force_new_collection: bool = False,
@@ -50,7 +49,7 @@ def load_from_local_files(
         description=collection_description,
         force_new_collection=force_new_collection,
     )
-    
+
     # 如果force_rebuild为True，则强制重建集合
     if force_rebuild:
         vector_db.init_collection(
@@ -59,7 +58,7 @@ def load_from_local_files(
             description=collection_description,
             force_new_collection=True,
         )
-    
+
     if isinstance(paths_or_directory, str):
         paths_or_directory = [paths_or_directory]
     all_docs = []
@@ -92,7 +91,7 @@ def load_from_local_files(
 
 
 def load_from_website(
-    urls: Union[str, List[str]],
+    urls: str | list[str],
     collection_name: str = None,
     collection_description: str = None,
     force_new_collection: bool = False,

deepsearcher/online_query.py

@@ -1,11 +1,9 @@
-from typing import List, Tuple
-
 # from deepsearcher.configuration import vector_db, embedding_model, llm
 from deepsearcher import configuration
 from deepsearcher.vector_db.base import RetrievalResult
 
 
-def query(original_query: str, max_iter: int = 3) -> Tuple[str, List[RetrievalResult]]:
+def query(original_query: str, max_iter: int = 3) -> tuple[str, list[RetrievalResult]]:
     """
     Query the knowledge base with a question and get an answer.
 
@@ -27,7 +25,7 @@ def query(original_query: str, max_iter: int = 3) -> Tuple[str, List[RetrievalRe
 
 def retrieve(
     original_query: str, max_iter: int = 3
-) -> Tuple[List[RetrievalResult], List[str]]:
+) -> tuple[list[RetrievalResult], list[str]]:
     """
     Retrieve relevant information from the knowledge base without generating an answer.
 
@@ -48,47 +46,3 @@ def retrieve(
         original_query, max_iter=max_iter
     )
     return retrieved_results, []
-
-
-def naive_retrieve(query: str, collection: str = None, top_k=10) -> List[RetrievalResult]:
-    """
-    Perform a simple retrieval from the knowledge base using the naive RAG approach.
-
-    This function uses the naive RAG agent to retrieve information from the knowledge base
-    without any advanced techniques like iterative refinement.
-
-    Args:
-        query: The question or query to search for.
-        collection: The name of the collection to search in. If None, searches in all collections.
-        top_k: The maximum number of results to return.
-
-    Returns:
-        A list of retrieval results.
-    """
-    naive_rag = configuration.naive_rag
-    all_retrieved_results, _ = naive_rag.retrieve(query)
-    return all_retrieved_results
-
-
-def naive_rag_query(
-    query: str, collection: str = None, top_k=10
-) -> Tuple[str, List[RetrievalResult]]:
-    """
-    Query the knowledge base using the naive RAG approach and get an answer.
-
-    This function uses the naive RAG agent to query the knowledge base and generate
-    an answer based on the retrieved information, without any advanced techniques.
-
-    Args:
-        query: The question or query to search for.
-        collection: The name of the collection to search in. If None, searches in all collections.
-        top_k: The maximum number of results to consider.
-
-    Returns:
-        A tuple containing:
-            - The generated answer as a string
-            - A list of retrieval results that were used to generate the answer
-    """
-    naive_rag = configuration.naive_rag
-    answer, retrieved_results = naive_rag.query(query)
-    return answer, retrieved_results

main.py

@@ -1,5 +1,4 @@
 import argparse
-from typing import Dict, List, Union
 
 import uvicorn
 from fastapi import Body, FastAPI, HTTPException, Query
@@ -31,7 +30,7 @@ class ProviderConfigRequest(BaseModel):
 
     feature: str
     provider: str
-    config: Dict
+    config: dict
 
 
 @app.get("/", response_class=HTMLResponse)
@@ -43,15 +42,15 @@ async def read_root():
     current_dir = os.path.dirname(os.path.abspath(__file__))
     # 构建模板文件路径 - 修复路径问题
     template_path = os.path.join(current_dir, "deepsearcher", "backend", "templates", "index.html")
-    
+
     # 读取HTML文件内容
     try:
-        with open(template_path, "r", encoding="utf-8") as file:
+        with open(template_path, encoding="utf-8") as file:
             html_content = file.read()
         return HTMLResponse(content=html_content, status_code=200)
     except FileNotFoundError:
         # 如果找不到文件，提供一个简单的默认页面
-        default_html = """
+        default_html = f"""
         <!DOCTYPE html>
         <html>
         <head>
@@ -71,7 +70,7 @@ async def read_root():
                 <div class="info">
                     <p>欢迎使用 DeepSearcher 智能搜索系统!</p>
                     <p>系统正在运行，但未找到前端模板文件。</p>
-                    <p>请确认文件是否存在: {}</p>
+                    <p>请确认文件是否存在: {template_path}</p>
                 </div>
                 <div class="info">
                     <h2>API 接口</h2>
@@ -86,7 +85,7 @@ async def read_root():
             </div>
         </body>
         </html>
-        """.format(template_path)
+        """
         return HTMLResponse(content=default_html, status_code=200)
 
 
@@ -118,7 +117,7 @@ def set_provider_config(request: ProviderConfigRequest):
 
 @app.post("/load-files/")
 def load_files(
-    paths: Union[str, List[str]] = Body(
+    paths: str | list[str] = Body(
         ...,
         description="A list of file paths to be loaded.",
         examples=["/path/to/file1", "/path/to/file2", "/path/to/dir1"],
@@ -160,7 +159,7 @@ def load_files(
             paths_or_directory=paths,
             collection_name=collection_name,
             collection_description=collection_description,
-            batch_size=batch_size if batch_size is not None else 256,  # 提供默认值
+            batch_size=batch_size if batch_size is not None else 8,  # 提供默认值
         )
         return {"message": "Files loaded successfully."}
     except Exception as e:
@@ -169,7 +168,7 @@ def load_files(
 
 @app.post("/load-website/")
 def load_website(
-    urls: Union[str, List[str]] = Body(
+    urls: str | list[str] = Body(
         ...,
         description="A list of URLs of websites to be loaded.",
         examples=["https://milvus.io/docs/overview.md"],
@@ -249,15 +248,15 @@ def perform_query(
         # 清除之前的进度消息
         from deepsearcher.utils.log import clear_progress_messages
         clear_progress_messages()
-        
+
         result_text, _, consume_token = query(original_query, max_iter)
-        
+
         # 获取进度消息
         from deepsearcher.utils.log import get_progress_messages
         progress_messages = get_progress_messages()
-        
+
         return {
-            "result": result_text, 
+            "result": result_text,
             "consume_token": consume_token,
             "progress_messages": progress_messages
         }
@@ -271,7 +270,7 @@ if __name__ == "__main__":
     parser.add_argument("--port", type=int, default=8000, help="Port to bind the server to")
     parser.add_argument("--enable-cors", action="store_true", default=False, help="Enable CORS support")
     args = parser.parse_args()
-    
+
     if args.enable_cors:
         app.add_middleware(
             CORSMiddleware,
@@ -283,6 +282,6 @@ if __name__ == "__main__":
         print("CORS is enabled.")
     else:
         print("CORS is disabled.")
-        
+
     print(f"Starting server on {args.host}:{args.port}")
-    uvicorn.run(app, host=args.host, port=args.port)
+    uvicorn.run(app, host=args.host, port=args.port)

test.py

@@ -11,7 +11,7 @@ config.load_config_from_yaml("deepsearcher/config.yaml")
 init_config(config = config)
 
 # Load your local data
-load_from_local_files(paths_or_directory="examples/data", force_rebuild=True)
+load_from_local_files(paths_or_directory="examples/data", force_rebuild=True, batch_size=8)
 
 # (Optional) Load from web crawling (`FIRECRAWL_API_KEY` env variable required)
 # from deepsearcher.offline_loading import load_from_website