Issue: how to merge two vector dbs?

[poojitharamachandra]

Issue you'd like to raise.

hi,

how do I merge 2 vector dbs? I am trying to update an existing db with new information

vectorstore = FAISS.from_documents(docs_chunked, Embeddings())
vectorstore.save_local("faiss_index_table_string")
vector_db = FAISS.load_local("faiss_index_table_string", Embeddings())

I want to do something like

vectorstore2 = FAISS.from_documents(docs_chunked2, Embeddings())
vectorstore2.update_local("faiss_index_table_string")
vector_db_updated = FAISS.load_local("faiss_index_table_string", Embeddings())

Suggestion:

No response

[dosubot[bot]]

🤖

Hi,

To merge two vector databases in LangChain, you can use the add_texts method of the Cassandra class. This method allows you to add new texts (and their corresponding embeddings) to an existing database. If the drop_old parameter is set to False (which is the default), the new texts will be added to the existing collection, effectively merging the old and new data.

Here is an example of how you can use this method:

from langchain.vectorstores import Cassandra
from langchain.embeddings.openai import OpenAIEmbeddings
from cassandra.cluster import Session

# Initialize the OpenAI embeddings and Cassandra session
embeddings = OpenAIEmbeddings()
session = Session()

# Initialize the Cassandra vector store
vectorstore = Cassandra(embeddings, session, 'my_keyspace', 'my_doc_archive')

# Add new texts to the existing database
new_texts = ["new text 1", "new text 2", "new text 3"]
vectorstore.add_texts(new_texts)

In this example, new_texts is a list of new texts that you want to add to the existing database. The add_texts method will compute the embeddings for these texts and add them to the database. If the database already contains other texts, they will not be removed, effectively merging the old and new data.

Please note that this method assumes that you have a function to compute the embeddings for the texts. In this example, OpenAIEmbeddings() is used as a placeholder for this function. You should replace it with the actual function you are using to compute the embeddings.

Regarding your question about the FAISS.from_documents method, I wasn't able to find specific information about this method in the LangChain repository. It would be helpful if you could provide more context or details about this method.

I hope this helps! Let me know if you have any other questions.

Sources

• does merging index1.merge_from(index2) dump index2?
• libs/langchain/langchain/vectorstores/epsilla.py
• libs/langchain/langchain/vectorstores/cassandra.py

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[poojitharamachandra]

I used the code from here :https://python.langchain.com/docs/integrations/vectorstores/faiss#merging and it works fine. Thanks How do I make sure that there are no duplicates in the vectordb?

[dosubot[bot]]

Hi, @poojitharamachandra! I'm Dosu, and I'm here to help the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale.

From what I understand, the issue is about merging two vector databases using the FAISS library. You were looking for a way to update an existing database with new information and you provided code snippets for reference. In the comments, I suggested using the add_texts method of the Cassandra class to merge the databases. You confirmed that you used the code from the documentation and it worked fine. Additionally, you asked for suggestions on how to ensure there are no duplicates in the vectordb.

Before we close this issue, we wanted to check if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days.

Thank you for your contribution to the LangChain repository! If you have any further questions or need assistance with anything else, feel free to ask.