Is `MongoDBAtlasVectorSearch` built upon `FAISS` algorithm?

Question

I am using langchain for building softwares for pdf document reading and question-answering. While working on it, I have to build vector embeddings for the texts in the document. But while working on it, I have come across multiple platforms such as MongoDB, Pinecone, etc. and libraries such as FAISS that considered to be the best for similarity search among the vector embeddings. On further research, I came across that langchain.vectorstores.FAISS stores the vector embeddings on cloud memory or RAM (I am not sure which one) and cannot be used later once the code block is terminated and Pinecone is built upon FAISS algorithm. But due to costing in Pinecone, I am thinking not to move forward with it, instead go forward with MongoDB. LangChain has got a function, langchain.vectorstores.MongoDBAtlasVectorSearch which saves the vector embeddings in MongoDB platform. I wanted to know is MongoDBAtlasVectorSearch built upon FAISS. Also, any other recommendations for saving vector embedding platforms for longer period of time with multiple index values.

Answer 1

(MongoDB Employee here)

MongoDB's Atlas Vector Search is not built using FAISS, but it does utilize a HNSW Graph to provide fast and efficient Vector Search over your MongoDB Collection data.