How can I pass in filters dynamically for Qdrant Vector DB used in Vector Toolkit

[chantlong]

I am currently using an Agent with initialize_agent for the Vector Store Toolkit.
I am using Qdrant as my Vector store and I would like to pass in id or some kind metadata dynamically to narrow the filter the search when prompted. Is that possible?
If I use the as_retriever method I am able to pass in search_kwargs, but I'm not sure how to do it when it comes to Agents using Vector Store Toolkit.

[dosubot[bot]]

🤖

Hello,

To dynamically pass in 'id' or other metadata as filters when using an Agent with initialize_agent for the Vector Store Toolkit in Qdrant Vector DB, you can use the filter parameter in the similarity_search_by_vector, asimilarity_search_by_vector, amax_marginal_relevance_search_by_vector, or amax_marginal_relevance_search_with_score_by_vector methods. This parameter accepts an instance of MetadataFilter which can be used to filter the results based on metadata.

Here is an example of how you can use it:

from qdrant_openapi_client.models.metadata_filter import MetadataFilter

# Create a MetadataFilter instance
metadata_filter = MetadataFilter(
    must: {"id": "your_id"},  # Replace 'your_id' with the actual id
    should: {},
    must_not: {}
)

# Use the filter in the similarity_search_by_vector method
documents = agent.similarity_search_by_vector(
    embedding=your_embedding,  # Replace 'your_embedding' with the actual embedding
    k=4,
    filter=metadata_filter
)

In this example, must is a dictionary where the key is the metadata field name and the value is the value to filter by. You can also use should and must_not for more complex filtering. Replace 'your_id' with the actual id and 'your_embedding' with the actual embedding.

As for the initialize_agent method, I wasn't able to find any information in the LangChain repository about whether it supports the use of MetadataFilter for filtering. It's possible that this feature is not currently supported or that the information is available elsewhere.

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

Best,
Dosu

Sources

• libs/langchain/langchain/vectorstores/qdrant.py

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