Similarity search Filter

[BajrangWappnet]

vectordb.similarity_search(query_document, k=n_results, filter = {...})
I have checked through documentation of chroma but didnt get any solution.
Can you please help me out filer Like what i need to pass in filter section.

This is code which i am using.
`def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Chroma.

    Args:
        query (str): Query text to search for.
        k (int): Number of results to return. Defaults to 4.
        filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.

    Returns:
        List[Document]: List of documents most similar to the query text.
    """
    docs_and_scores = self.similarity_search_with_score(query, k, filter=filter)
    return [doc for doc, _ in docs_and_scores]

`

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're having trouble figuring out what to pass in the filter parameter of the similarity_search function in the LangChain framework. This parameter is designed to allow you to refine your search results based on specific metadata fields.

When I use the similarity_search function, I use the filter parameter as a dictionary where the keys are the metadata fields I want to filter by, and the values are the specific values I'm interested in. For instance, if I have a collection of documents with a 'category' metadata field and I want to find documents similar to my query but only within the 'science' category, I would use filter={'category': 'science'}.

Here's how I would use it in code:

vectordb.similarity_search(query_document, k=n_results, filter={'category': 'science'})

This would return the n_results most similar documents to query_document that also have 'science' as their 'category' metadata.

Please keep in mind that this is just one way to use the filter parameter. The actual metadata fields you can filter by will depend on the metadata of the documents in your collection.

If you find this explanation helpful and feel that it could benefit others, I encourage you to make a pull request to update the documentation. This way, other users who might have the same question can benefit from your contribution.

Sources

• langchain/vectorstores/chroma.py

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[guidorietbroek]

Above answer should be correct, I tested filtering on metadata and had good results. You have to use a dictionary. But it only works if you have metadata in your vector...