My main use case for FAISS is in a retrieval-augmented generation project using it with OpenAI, where we use FAISS to store our embeddings created by the Colbert model and for retrieval as well. In our mental generation project, FAISS fits into our workflow by providing an indexing DB that supports token-level embedding, which most classical or typically used vector DBs do not support. FAISS came into the scene because of that, and its retrieval speed is high and very scalable, making it good for our project.
I made some vectors for my data set. I added them and got the embeddings from Hugging Face. I made a query key-value pair for Faiss. When you want to do a query search on it, you call Faiss and send your query within it.
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My main use case for FAISS is in a retrieval-augmented generation project using it with OpenAI, where we use FAISS to store our embeddings created by the Colbert model and for retrieval as well. In our mental generation project, FAISS fits into our workflow by providing an indexing DB that supports token-level embedding, which most classical or typically used vector DBs do not support. FAISS came into the scene because of that, and its retrieval speed is high and very scalable, making it good for our project.
I made some vectors for my data set. I added them and got the embeddings from Hugging Face. I made a query key-value pair for Faiss. When you want to do a query search on it, you call Faiss and send your query within it.
We use Faiss for document-related tasks, particularly in the context of augmented generation per document.