langflow-ai / langflow

Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
http://www.langflow.org
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ChromaDB #2575

Closed AtmehEsraa closed 3 months ago

AtmehEsraa commented 4 months ago

I try to link chroma DB, to the vector store and it doesnt work, could you help me in that

image

dosubot[bot] commented 4 months ago

To link ChromaDB to the vector store and ensure it works correctly, follow these steps:

  1. Configuration Settings: Ensure that the configuration settings for ChromaDB are correctly set. This includes the collection name, persist directory, and any server settings if applicable.

  2. ChromaComponent Setup: Verify that the ChromaComponent is properly configured. Here is an example configuration:

    from langchain_community.vectorstores.chroma import Chroma
    from langflow.interface.custom.custom_component import CustomComponent
    
    class ChromaComponent(CustomComponent):
       display_name: str = "Chroma"
       description: str = "Implementation of Vector Store using Chroma"
    
       def build_config(self):
           return {
               "collection_name": {"display_name": "Collection Name", "value": "UAE_Schools"},
               "index_directory": {"display_name": "Persist Directory", "value": "test"},
               "embedding": {"display_name": "Embedding"},
               "chroma_server_host": {"display_name": "Server Host", "advanced": True},
               "chroma_server_port": {"display_name": "Server Port", "advanced": True},
               "chroma_server_ssl_enabled": {"display_name": "Server SSL Enabled", "advanced": True},
           }
    
       def build(self, collection_name, embedding, index_directory, chroma_server_host=None, chroma_server_port=None, chroma_server_ssl_enabled=False):
           chroma_settings = None
           if chroma_server_host:
               chroma_settings = chromadb.config.Settings(
                   chroma_server_host=chroma_server_host,
                   chroma_server_port=chroma_server_port,
                   chroma_server_ssl_enabled=chroma_server_ssl_enabled,
               )
           return Chroma(
               collection_name=collection_name,
               persist_directory=index_directory,
               embedding=embedding,
               client_settings=chroma_settings,
           )
  3. VectorStore Retriever Configuration: Ensure that the VectorStore Retriever is correctly set up to use the Chroma vector store. Here is an example:

    from langchain_core.vectorstores import VectorStoreRetriever
    
    class VectorStoreRetrieverComponent(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):
           return vectorstore.as_retriever()
  4. Check Connections: Verify that all components are correctly interconnected in your workflow. Ensure that the VectorStore Retriever is linked to the ChromaComponent and that the RetrieverTool is correctly configured to use the retriever.

  5. Troubleshooting:

    • Server Host and Port: Ensure the Chroma server host and port are correctly set.
    • SSL Configuration: If SSL is enabled, ensure the server is configured to handle SSL connections.
    • CORS Configuration: If accessing the server from a different origin, ensure CORS settings are correctly configured.
    • API Key and Authentication: Ensure necessary authentication credentials are provided.
    • Document and Embedding Checks: Ensure documents and embeddings are correctly provided and not empty.

By following these steps and ensuring all configurations are correctly set, you should be able to link ChromaDB to the vector store successfully [1][2].

To continue talking to Dosu, mention @dosu.

carlosrcoelho commented 3 months ago

@hakeemsyd the VectorStore Retriever is outdated, thanks for sharing this. I'll let the team know about this and make an update soon.

anovazzi1 commented 3 months ago

@AtmehEsraa I believe there's a misunderstanding about the retriever. One of the outputs is the retriever itself, and the other is the search result. Therefore, you don't need a separate vectorStore Retriever, as the retriever is already included as one of the outputs.