How can I merge multiple ChromaDB collections for a joint similarity search?

Below is an example of how you can merge two FAISS vectorstores and then perform a joint similarity search. You can also merge more than two collections by sequentially calling the merge method on your chosen primary vectorstore.

Merging Two Collections

If you have two FAISS vectorstores—one for project documentation (db_1) and another for project code (db_2)—you can merge them as follows:

PYTHONCopied
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

# Initialize your embeddings
embeddings = OpenAIEmbeddings()

# Create two FAISS instances from your documents
db_1 = FAISS.from_texts(["Project documentation"], embeddings)
db_2 = FAISS.from_texts(["Project code"], embeddings)

# Merge db_2 into db_1 so that db_1 now holds data from both sources
db_1.merge_from(db_2)

# Now you can perform a similarity search on the merged vectorstore
results = db_1.similarity_search("your query", k=top_k)

Merging More Than Two Collections

The same principle applies for merging more than two vectorstores. Just choose one of them as the primary store and use the merge_from method on it with the other collections, one after the other. Here’s an example with three collections:

PYTHONCopied
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

# Create FAISS instances for each collection
db_1 = FAISS.from_texts(["Project documentation"], embeddings)
db_2 = FAISS.from_texts(["Project code"], embeddings)
db_3 = FAISS.from_texts(["Additional data"], embeddings)

# Merge db_2 and db_3 into db_1 sequentially
db_1.merge_from(db_2)
db_1.merge_from(db_3)

# Now perform a similarity search on the merged vectorstore
results = db_1.similarity_search("your query", k=top_k)

Summary

• Merging Collections: Use the merge_from method to combine vectorstores. This allows you to consolidate documents from different sources into one vectorstore.
• Joint Similarity Search: After merging, performing a similarity search on the resulting vectorstore will consider all documents from the multiple collections.
• Multiple Merges: You can sequentially merge as many collections as needed by repeatedly calling merge_from on the primary vectorstore.

This approach streamlines your document retrieval process by enabling a unified search over documents stored across various FAISS collections.