Difference as_retriever metadata filters parameters 'metadata' and 'search_kwargs'

[guidorietbroek]

I was testing with a retriever and setting a metadata filter as follows:

vector_search_metadata.as_retriever(
    metadata={'spo_id':'1'},
    search_kwargs={'filter':{'spo_id':'1'}}
    )

Giving me the following:

VectorStoreRetriever(tags=['Pinecone'], metadata={'spo_id': '1'}, vectorstore=<langchain.vectorstores.pinecone.Pinecone object at 0x7fee884e4850>, search_type='similarity', search_kwargs={'filter': {'spo_id': '1'}})

But when testing, the 'metadata' parameter is not working, the 'search_kwargs' filter is.

When not setting the metadata parameter, the retriever has metadata=None.

Why is the metadata parameter not working?

[dosubot[bot]]

Answer generated by a 🤖

Answer

I understand that you're having trouble distinguishing between the 'metadata' and 'search_kwargs' parameters in the 'as_retriever' function of the LangChain framework. You've noticed that the 'metadata' parameter doesn't seem to affect the retrieval process, while the 'search_kwargs' parameter does.

Let me clarify this for you. The 'metadata' parameter in the 'as_retriever' function is not intended for filtering data. Instead, it's used to store additional information about the retriever. This information doesn't directly influence the retrieval process, but it's passed as arguments to the handlers defined in 'callbacks' during each call to the retriever. This can be useful for identifying a specific instance of a retriever or its use case.

On the other hand, the 'search_kwargs' parameter is used to pass arguments to the search function of the retriever. In your case, you're passing a 'filter' argument, which is used to filter the data based on the 'spo_id'.

In the code you've provided, the 'metadata' parameter doesn't appear to be used in the retrieval process. However, the 'search_kwargs' parameter is used in the '_get_relevant_documents' and '_aget_relevant_documents' methods to pass arguments to the search function of the vectorstore.

The 'as_retriever' function in the VectorStore class takes the "tags" from the kwargs and extends it with the retriever tags. The remaining kwargs are then passed to the VectorStoreRetriever. This is why the 'metadata' parameter appears in the VectorStoreRetriever, but it doesn't affect the retrieval process.

The VectorStoreRetriever class has a 'search_kwargs' field, which is used to store keyword arguments to pass to the search function. The 'search_type' field determines the type of search to perform. The allowed search types are "similarity", "similarity_score_threshold", and "mmr". If the 'search_type' is "similarity_score_threshold", a 'score_threshold' must be specified in 'search_kwargs'.

This explanation should help clarify the difference between the 'metadata' and 'search_kwargs' parameters. However, this is just one potential interpretation based on the information provided. If you find this explanation helpful and believe it could benefit other users, I encourage you to make a pull request to update the documentation. This way, you can contribute to the LangChain community and help other developers understand the framework better.

Sources

• libs/langchain/langchain/vectorstores/base.py
• libs/langchain/langchain/schema/retriever.py

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