🔀 refactor(loading.py): use dictionary to initialize vector stores

The `instantiate_vectorstore` function now uses a dictionary to initialize vector stores instead of a series of if-else statements. This improves the readability and maintainability of the code. A new dictionary `vecstore_initializer` is added to `vector_store.py` to map the class names of vector stores to their respective initialization functions.

src/backend/langflow/interface/initialize/loading.py

@@ -6,15 +6,8 @@ from langchain.agents import agent as agent_module
 from langchain.agents.agent import AgentExecutor
 from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.agents.tools import BaseTool
-from langflow.interface.initialize.vector_store import (
+from langflow.interface.initialize.vector_store import vecstore_initializer
-    initialize_chroma,
+
-    initialize_faiss,
-    initialize_mongodb,
-    initialize_pinecone,
-    initialize_qdrant,
-    initialize_supabase,
-    initialize_weaviate,
-)
 from pydantic import ValidationError
 
 from langflow.interface.custom_lists import CUSTOM_NODES
@@ -151,29 +144,11 @@ def instantiate_embedding(class_object, params):
 
 def instantiate_vectorstore(class_object, params):
     search_kwargs = params.pop("search_kwargs", {})
-    # could be documents or texts
+    if initializer := vecstore_initializer.get(class_object.__name__):
-    if class_object.__name__ == "Pinecone":
+        vecstore = initializer(class_object, params)
-        vecstore = initialize_pinecone(class_object, params)
-    # Chroma requires all metadata values to not be None
-    elif class_object.__name__ == "Chroma":
-        vecstore = initialize_chroma(class_object, params)
-
-    elif class_object.__name__ == "Qdrant":
-        vecstore = initialize_qdrant(class_object, params)
-
-    elif class_object.__name__ == "Weaviate":
-        vecstore = initialize_weaviate(class_object, params)
-    elif class_object.__name__ == "FAISS":
-        vecstore = initialize_faiss(class_object, params)
-    elif class_object.__name__ == "SupabaseVectorStore":
-        vecstore = initialize_supabase(class_object, params)
-    elif class_object.__name__ == "MongoDBAtlasVectorSearch":
-        vecstore = initialize_mongodb(class_object, params)
-
     else:
         if "texts" in params:
             params["documents"] = params.pop("texts")
-
         vecstore = class_object.from_documents(**params)
 
     # ! This might not work. Need to test

src/backend/langflow/interface/initialize/vector_store.py

@@ -1,5 +1,5 @@
 import json
-from typing import Type
+from typing import Any, Callable, Dict, Type
 from langchain.vectorstores import (
     Pinecone,
     Qdrant,
@@ -9,6 +9,7 @@ from langchain.vectorstores import (
     SupabaseVectorStore,
     MongoDBAtlasVectorSearch,
 )
+
 import os
 
 
@@ -29,7 +30,9 @@ def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dic
     from pymongo import MongoClient
     import certifi
 
-    client = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
+    client: MongoClient = MongoClient(
+        MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where()
+    )
     db_name = params.pop("db_name", None)
     collection_name = params.pop("collection_name", None)
     if not db_name or not collection_name:
@@ -207,3 +210,14 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict):
         return class_object(client=client, **lc_params)
 
     return class_object.from_documents(**params)
+
+
+vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
+    "Pinecone": initialize_pinecone,
+    "Chroma": initialize_chroma,
+    "Qdrant": initialize_qdrant,
+    "Weaviate": initialize_weaviate,
+    "FAISS": initialize_faiss,
+    "SupabaseVectorStore": initialize_supabase,
+    "MongoDBAtlasVectorSearch": initialize_mongodb,
+}