Is there a way to convert haystack document store into langchain vector database?

[Yen444]

Hi,
I built a FAISS document store with haystack and now for some reason I am moving to langchain. Is there anyway to convert that document store to be used in langchain?

[dosubot[bot]]

🤖

Based on the information provided in the LangChain repository, it appears that while LangChain does support the use of FAISS document stores, it does not provide a direct utility or method to import data from an existing FAISS document store created with Haystack.

However, LangChain's FAISS class does have a method to directly import or read a FAISS index created by another library such as Haystack. The method is called load_local. This method loads a FAISS index, docstore, and index_to_docstore_id from disk.

Here is the method:

@classmethod
def load_local(
    cls,
    folder_path: str,
    embeddings: Embeddings,
    index_name: str = "index",
    **kwargs: Any,
) -> FAISS:
    """Load FAISS index, docstore, and index_to_docstore_id from disk.

    Args:
        folder_path: folder path to load index, docstore,
            and index_to_docstore_id from.
        embeddings: Embeddings to use when generating queries
        index_name: for saving with a specific index file name
        asynchronous: whether to use async version or not
    """
    path = Path(folder_path)
    # load index separately since it is not picklable
    faiss = dependable_faiss_import()
    index = faiss.read_index(
        str(path / "{index_name}.faiss".format(index_name=index_name))
    )

    # load docstore and index_to_docstore_id
    with open(path / "{index_name}.pkl".format(index_name=index_name), "rb") as f:
        docstore, index_to_docstore_id = pickle.load(f)
    return cls(embeddings, index, docstore, index_to_docstore_id, **kwargs)

This method takes the folder path where the index, docstore, and index_to_docstore_id are stored, the embeddings to use when generating queries, and the index name as arguments. It returns an instance of the FAISS class with the loaded index, docstore, and index_to_docstore_id.

Please note that this method is for loading data that was previously saved using LangChain's own methods, not for importing data from an external source like a Haystack FAISS document store. Therefore, you might need to do some additional work to convert the data from the Haystack FAISS document store to a format that can be loaded by LangChain's load_local method.

You can find more information about this in the LangChain repository.

Sources

• libs/langchain/langchain/vectorstores/faiss.py
• libs/langchain/tests/unit_tests/vectorstores/test_faiss.py

---

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[Yen444]

Thank you!
Actually, the final sentence is my question: how to convert the data from the Haystack FAISS document store to a format that can be loaded by LangChain's load_local method.
What I have from haystack document store is an sql db, an index, and a json file, which points to the sql db.
Meanwhile, what langchain needs to load a vector database is an .index and .pickle
And I can't see how to convert

[dmitrimahayana]

Hi @Yen444
Do you have any updates about this issue?
I have the same issue that required to load FAISS DB Haystack to my Langchain chatbot.