get retriever response

[sampratha28]

i am using a vector store agent to answer from a set of documents. I want to print the document and its metadata fetched by the retriever in answering the question

[dosubot[bot]]

🤖

Hey there, @sampratha28! I'm Dosu, an AI bot here to help you while we wait for a human maintainer. I can assist you in debugging, answering questions, and getting you up to speed on becoming a contributor. Just know, we're in this together!

Based on the information provided, it appears that the langflow codebase does contain functionality related to a "vector store agent" and a retriever class that interacts with the VectorStore class to fetch documents and their metadata. However, there doesn't seem to be a feature that directly allows you to print the document and its metadata fetched by the retriever.

The VectaraSelfQueryRetriverComponent class in the src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py file is responsible for creating a SelfQueryRetriever instance that interacts with the VectorStore to fetch documents and their metadata. Here is the relevant code:

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 first converts the metadata field information from JSON format to AttributeInfo objects. Then, it uses the from_llm method of the SelfQueryRetriever class to create a SelfQueryRetriever instance with the VectorStore instance, the BaseLanguageModel instance, the document content description, and the AttributeInfo objects. This SelfQueryRetriever instance is then returned by the build method.

However, it's important to note that this does not directly print the document and its metadata. You would likely need to implement additional functionality to achieve this. For example, you could modify the SelfQueryRetriever class to include a method that prints the document and its metadata after they are retrieved.

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

Best, Dosu

Sources

• src/backend/langflow/interface/vector_store/base.py
• src/backend/langflow/template/frontend_node/vectorstores.py
• src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py

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