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🚀 feat(loading.py): add support for MongoDBAtlasVectorSearch vector store

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The `instantiate_vectorstore` function now supports the `MongoDBAtlasVectorSearch` vector store. This allows for the use of MongoDB Atlas as a vector store for Langflow. The `search_kwargs` parameter is now supported for all vector stores that have a `as_retriever` method. This allows for the configuration of the vector store's search parameters.
This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-06-23 13:20:02 -03:00
commit fb7bcb50e5
1 changed files with 17 additions and 8 deletions
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25
src/backend/langflow/interface/initialize/loading.py
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@ -9,6 +9,7 @@ from langchain.agents.tools import BaseTool
from langflow.interface.initialize.vector_store import (
initialize_chroma,
initialize_faiss,
initialize_mongodb,
initialize_pinecone,
initialize_qdrant,
initialize_supabase,
@ -149,29 +150,37 @@ def instantiate_embedding(class_object, params):
def instantiate_vectorstore(class_object, params):
search_kwargs = params.pop("search_kwargs", {})
# could be documents or texts
if class_object.__name__ == "Pinecone":
return initialize_pinecone(class_object, params)
vecstore = initialize_pinecone(class_object, params)
# Chroma requires all metadata values to not be None
elif class_object.__name__ == "Chroma":
return initialize_chroma(class_object, params)
vecstore = initialize_chroma(class_object, params)
elif class_object.__name__ == "Qdrant":
return initialize_qdrant(class_object, params)
vecstore = initialize_qdrant(class_object, params)
elif class_object.__name__ == "Weaviate":
return initialize_weaviate(class_object, params)
vecstore = initialize_weaviate(class_object, params)
elif class_object.__name__ == "FAISS":
return initialize_faiss(class_object, params)
vecstore = initialize_faiss(class_object, params)
elif class_object.__name__ == "SupabaseVectorStore":
return initialize_supabase(class_object, params)
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")
vector_store = class_object.from_documents(**params)
return vector_store
vecstore = class_object.from_documents(**params)
# ! This might not work. Need to test
if search_kwargs and hasattr(vecstore, "as_retriever"):
vecstore = vecstore.as_retriever(search_kwargs=search_kwargs)
return vecstore
def instantiate_documentloader(class_object, params):