validation error for multiquery retriever

[sampratha28]

i am passing FAISS db as an input to the multi query retriever. I am getting the below error

ERROR - base - 1 validation error for MultiQueryRetriever base.py:329 retriever
value is not a valid dict (type=type_error.dict)

[dosubot[bot]]

🤖

Hi @sampratha28! 👋

I'm Dosu, your friendly neighborhood bot here to lend a hand with bugs, answer any questions, and help you become an awesome contributor. While we're waiting for a human maintainer to jump in, know that I'm here for you. So, let's crack this code together, shall we? 😉

The error you're encountering, "value is not a valid dict", typically arises when the input data type doesn't match the expected data type. In your case, it seems like you're trying to pass a FAISS db as an input to the MultiQueryRetriever in langflow. However, the build method in the VectaraSelfQueryRetriverComponent class expects a VectorStore object as an input, not a FAISS db.

The VectorStore object is a type of object defined in the langchain.schema.vectorstore module. If you're trying to use a FAISS db, you need to ensure that it is wrapped in a VectorStore object before passing it to the build method.

Here's how the build method in the VectaraSelfQueryRetriverComponent class is implemented:

def build(
    self,
    vectorstore: VectorStore,
    document_content_description: str,
    llm: BaseLanguageModel,
    metadata_field_info: List[str],
) -> BaseRetriever:
    
    metadata_field_obj = []
    for meta in metadata_field_info:
        meta_obj = json.loads(meta)
        if 'name' not in meta_obj or 'description' not in meta_obj or 'type' not in meta_obj :
            raise Exception('Incorrect metadata field info format.')
        attribute_info = AttributeInfo(
            name = meta_obj['name'],
            description = meta_obj['description'],
            type = meta_obj['type'],
        )
        metadata_field_obj.append(attribute_info)

    return SelfQueryRetriever.from_llm(
        llm,
        vectorstore, 
        document_content_description, 
        metadata_field_obj, 
        verbose=True
    )

This method expects four inputs:

1. vectorstore: An instance of VectorStore which is used to store and retrieve vectors.
2. document_content_description: A string that describes the content of the document.
3. llm: An instance of BaseLanguageModel which is used for language modeling.
4. metadata_field_info: A list of strings where each string is a JSON representation of a dictionary containing additional search metadata. Each dictionary should have the keys 'name', 'description', and 'type'.

If you're still facing issues, please ensure that the FAISS db is correctly formatted and compatible with the VectorStore object. If the issue persists, it might be due to an incompatibility between the FAISS db and the VectorStore object, and you might need to use a different type of vector store or modify the FAISS db to make it compatible.

You can find more details in the VectaraSelfQueryRetriver.py file.

If you need further clarification, please provide more details about how you're using the FAISS db and the MultiQueryRetriever.

Sources

• src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py

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[sampratha28]

@dosu-bot. my bad, i am passing FAISS vector store as an input to the multiquery retriever. But i am getting the above mentioned error

[dosubot[bot]]

🤖

Based on the context provided, it seems like the validation error you're experiencing is due to the fact that the VectaraSelfQueryRetriverComponent class in Langflow is designed to work with a VectorStore object, not specifically a FAISS vector store.

The build method of VectaraSelfQueryRetriverComponent class expects a VectorStore object as its first argument. If you're passing a FAISS vector store that doesn't inherit from or isn't an instance of VectorStore, it could lead to a validation error.

Here's the relevant code from the Langflow repository:

def build(
    self,
    vectorstore: VectorStore,
    document_content_description: str,
    llm: BaseLanguageModel,
    metadata_field_info: List[str],
) -> BaseRetriever:
    ...
    return SelfQueryRetriever.from_llm(
        llm,
        vectorstore, 
        document_content_description, 
        metadata_field_obj, 
        verbose=True
    )

In this code, vectorstore is expected to be an instance of VectorStore. If the FAISS vector store you're passing doesn't meet this requirement, it could lead to a validation error.

To resolve this issue, ensure that the FAISS vector store you're using is compatible with the VectorStore class in LangChain. If it's not, you might need to create a wrapper class for your FAISS vector store that inherits from VectorStore or modify your FAISS vector store to ensure it meets the requirements of the VectorStore class.

You can find more details about the VectorStore class and its requirements in the Weaviate.py file in the Langflow repository.

As for the MultiQueryRetriever class, it is designed to automate the process of generating multiple queries, retrieving relevant documents for each query, and combining the results to provide a more extensive and diverse set of potentially relevant documents. You can find more details about this class in the retrievers.mdx documentation.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py
• src/backend/langflow/components/vectorstores/Weaviate.py
• docs/docs/components/retrievers.mdx

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.