fix: 修复max_iter迭代失效的问题

fix: 修复default_collection参数缺失的问题

deepsearcher/agent/deep_search.py

@@ -323,6 +323,9 @@ class DeepSearch(BaseAgent):
                 - A list of retrieved document results
                 - Additional information about the retrieval process
         """
+        # Get max_iter from kwargs or use default
+        max_iter = kwargs.get('max_iter', self.max_iter)
+        
         ### SUB QUERIES ###
         send_think(f"<query> {original_query} </query>")
         all_search_results = []
@@ -336,7 +339,7 @@ class DeepSearch(BaseAgent):
             send_think(f"Break down the original query into new sub queries: {sub_queries}")
         all_sub_queries.extend(sub_queries)
 
-        for it in range(self.max_iter):
+        for it in range(max_iter):
             send_think(f">> Iteration: {it + 1}")
 
             # Execute all search tasks sequentially
@@ -353,7 +356,7 @@ class DeepSearch(BaseAgent):
             
             ### REFLECTION & GET MORE SUB QUERIES ###
             # Only generate more queries if we haven't reached the maximum iterations
-            if it + 1 < self.max_iter:
+            if it + 1 < max_iter:
                 send_think("Reflecting on the search results...")
                 sub_queries = self._generate_more_sub_queries(
                     original_query, all_sub_queries, all_search_results

deepsearcher/config.yaml

@@ -80,7 +80,7 @@ provide_settings:
   #     port: 6333
 
 query_settings:
-  max_iter: 1
+  max_iter: 3
 
 load_settings:
   chunk_size: 1024

deepsearcher/offline_loading.py

@@ -43,14 +43,13 @@ def load_from_local_files(
     embedding_model = configuration.embedding_model
     file_loader = configuration.file_loader
 
-    # 如果force_rebuild为True，则强制重建集合
-    if force_rebuild:
-        vector_db.init_collection(
-            dim=embedding_model.dimension,
-            collection=collection_name,
-            description=collection_description,
-            force_rebuild=True,
-        )
+    # 初始化集合（如果不存在则创建，如果force_rebuild为True则重建）
+    vector_db.init_collection(
+        dim=embedding_model.dimension,
+        collection=collection_name,
+        description=collection_description,
+        force_rebuild=force_rebuild,
+    )
 
     if isinstance(paths_or_directory, str):
         paths_or_directory = [paths_or_directory]

deepsearcher/vector_db/milvus.py

@@ -18,6 +18,7 @@ class Milvus(BaseVectorDB):
         user: str = "",
         password: str = "",
         db: str = "default",
+        default_collection: str = "deepsearcher",
         **kwargs,
     ):
         """
@@ -29,9 +30,11 @@ class Milvus(BaseVectorDB):
             user (str, optional): Username for authentication. Defaults to "".
             password (str, optional): Password for authentication. Defaults to "".
             db (str, optional): Database name. Defaults to "default".
+            default_collection (str, optional): Default collection name. Defaults to "deepsearcher".
             **kwargs: Additional keyword arguments to pass to the MilvusClient.
         """
         super().__init__()
+        self.default_collection = default_collection
         self.client = MilvusClient(
             uri=uri, user=user, password=password, token=token, db_name=db, timeout=30, **kwargs
         )

main.py

@@ -98,6 +98,11 @@ def load_files(
         description="Optional batch size for the collection.",
         examples=[256],
     ),
+    force_rebuild: bool = Body(
+        False,
+        description="Whether to force rebuild the collection if it already exists.",
+        examples=[False],
+    ),
 ):
     """
     Load files into the vector database.
@@ -107,6 +112,7 @@ def load_files(
         collection_name (str, optional): Name for the collection. Defaults to None.
         collection_description (str, optional): Description for the collection. Defaults to None.
         batch_size (int, optional): Batch size for processing. Defaults to None.
+        force_rebuild (bool, optional): Whether to force rebuild the collection. Defaults to False.
 
     Returns:
         dict: A dictionary containing a success message.
@@ -120,6 +126,7 @@ def load_files(
             collection_name=collection_name,
             collection_description=collection_description,
             batch_size=batch_size if batch_size is not None else 8,
+            force_rebuild=force_rebuild,
         )
         return {"message": "Files loaded successfully."}
     except Exception as e:
@@ -148,6 +155,11 @@ def load_website(
         description="Optional batch size for the collection.",
         examples=[256],
     ),
+    force_rebuild: bool = Body(
+        False,
+        description="Whether to force rebuild the collection if it already exists.",
+        examples=[False],
+    ),
 ):
     """
     Load website content into the vector database.
@@ -157,6 +169,7 @@ def load_website(
         collection_name (str, optional): Name for the collection. Defaults to None.
         collection_description (str, optional): Description for the collection. Defaults to None.
         batch_size (int, optional): Batch size for processing. Defaults to None.
+        force_rebuild (bool, optional): Whether to force rebuild the collection. Defaults to False.
 
     Returns:
         dict: A dictionary containing a success message.
@@ -170,6 +183,7 @@ def load_website(
             collection_name=collection_name,
             collection_description=collection_description,
             batch_size=batch_size if batch_size is not None else 8,
+            force_rebuild=force_rebuild,
         )
         return {"message": "Website loaded successfully."}
     except Exception as e: