Merge remote-tracking branch 'origin/dev' into zustand/io/migration

src/backend/langflow/api/v1/login.py

@@ -89,7 +89,9 @@ async def auto_login(
 
 
 @router.post("/refresh")
-async def refresh_token(request: Request, response: Response, settings_service=Depends(get_settings_service)):
+async def refresh_token(
+    request: Request, response: Response, settings_service=Depends(get_settings_service)
+):
     auth_settings = settings_service.auth_settings
 
     token = request.cookies.get("refresh_token_lf")

src/backend/langflow/components/retrievers/VectorStoreRetriever.py

@@ -0,0 +1,17 @@
+from langchain_core.vectorstores import VectorStoreRetriever
+
+from langflow import CustomComponent
+from langflow.field_typing import VectorStore
+
+
+class VectoStoreRetrieverComponent(CustomComponent):
+    display_name = "VectorStore Retriever"
+    description = "A vector store retriever"
+
+    def build_config(self):
+        return {
+            "vectorstore": {"display_name": "Vector Store", "type": VectorStore},
+        }
+
+    def build(self, vectorstore: VectorStore) -> VectorStoreRetriever:
+        return vectorstore.as_retriever()

src/backend/langflow/components/tools/RetrieverTool.py

@@ -0,0 +1,32 @@
+from langchain.tools.retriever import create_retriever_tool
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseRetriever, Tool
+
+
+class RetrieverToolComponent(CustomComponent):
+    display_name = "RetrieverTool"
+    description = "Tool for interacting with retriever"
+
+    def build_config(self):
+        return {
+            "retriever": {
+                "display_name": "Retriever",
+                "info": "Retriever to interact with",
+                "type": BaseRetriever,
+            },
+            "name": {"display_name": "Name", "info": "Name of the tool"},
+            "description": {"display_name": "Description", "info": "Description of the tool"},
+        }
+
+    def build(
+        self,
+        retriever: BaseRetriever,
+        name: str,
+        description: str,
+    ) -> Tool:
+        return create_retriever_tool(
+            retriever=retriever,
+            name=name,
+            description=description,
+        )

src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py

@@ -1,7 +1,8 @@
 from typing import List, Optional
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent
+from langflow.components.vectorstores.MongoDBAtlasVector import \
+    MongoDBAtlasComponent
 from langflow.field_typing import Embeddings, NestedDict, Text
 from langflow.schema import Record
 
@@ -36,13 +37,13 @@ class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent)
         mongodb_atlas_cluster_uri: str = "",
         search_kwargs: Optional[NestedDict] = None,
     ) -> List[Record]:
-        vector_store = super().build(
+
+        search_kwargs = search_kwargs or {}
+        vector_store =  super().build(
+            connection_string=mongodb_atlas_cluster_uri,
+            namespace=f"{db_name}.{collection_name}",
             embedding=embedding,
-            collection_name=collection_name,
-            db_name=db_name,
             index_name=index_name,
-            mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
-            search_kwargs=search_kwargs,
         )
         if not vector_store:
             raise ValueError("Failed to create MongoDB Atlas Vector Store")