{"library":"llama-index-vector-stores-faiss","type":"library","category":null,"description":"llama-index-vector-stores-faiss provides an integration for LlamaIndex to use FAISS (Facebook AI Similarity Search) as a high-performance vector store. It allows users to store and retrieve document embeddings efficiently for Retrieval-Augmented Generation (RAG) applications, leveraging FAISS's capabilities for fast similarity search. The current version is 0.6.0, and it generally follows the rapid release cadence of the broader LlamaIndex ecosystem.","language":"python","status":"active","version":"0.6.0","tags":["llama-index","faiss","vector-store","embeddings","rag","similarity-search"],"last_verified":"Tue May 26","install":[{"cmd":"pip install llama-index-vector-stores-faiss faiss-cpu","imports":["from llama_index.vector_stores.faiss import FAISSVectorStore","from llama_index.core import VectorStoreIndex","from llama_index.core import StorageContext","from llama_index.core import SimpleDirectoryReader","from llama_index.core import load_index_from_storage","import faiss","from llama_index.core.embeddings import resolve_embed_model"]},{"cmd":"pip install llama-index-vector-stores-faiss faiss-gpu","imports":[]}],"homepage":"https://www.llamaindex.ai","github":null,"docs":null,"changelog":null,"pypi":"https://pypi.org/project/llama-index-vector-stores-faiss/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":60,"avg_install_s":20.3,"avg_import_s":null,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/llama-index-vector-stores-faiss/compatibility"}}