FAISS
FAISS is an open-source software library for efficient similarity search and clustering of dense vectors.
Grokking AI Applications
FAISS is a software library for efficient similarity search and clustering of dense vectors.
- How it is used
- Supports RAG applications by storing embedded chunks for similarity-based retrieval.
- How deep it goes
- Explains chunking, embedding connections, index naming, execution, and one deserialization setting, but not search internals.
- Examples
- Uses Italian recipe chunks, an OpenAI Embeddings component, and a cookbook index in a HealthyChef workflow.
- What you can do after
- Operate/configure FAISS in Langflow to create a searchable vector index from embedded document chunks.
Generative AI in Action
FAISS is a library for efficient similarity search and clustering of dense vectors on CPUs and GPUs.
- How it is used
- Indexes document embeddings for similarity search in a PDF question-answering workflow.
- How deep it goes
- Defines FAISS capabilities and shows index creation through LangChain, but does not work through FAISS index configuration or internals.
- Examples
- Uses confidential dog-related PDFs, text chunks, OpenAI embeddings, and `FAISS.from_documents(chunks, embeddings)`.
- What you can do after
- Apply it via LangChain to index embedded PDF chunks, while considering index speed, accuracy, and memory trade-offs.
Machine Learning Platform Engineering
FAISS is a software library for efficient similarity search and clustering of dense numerical vectors.
- How it is used
- Indexes document embeddings to retrieve semantically similar chunks as context for RAG generation.
- How deep it goes
- Shows index creation, local persistence, and a retrieval check through LangChain, but does not detail FAISS indexing algorithms.
- Examples
- Builds a FAISS index from Markdown chunks and tests similarity_search with an encryption-key rotation question.
- What you can do after
- Apply it via LangChain to build, save, reload, and test a document-retrieval index.
Read how Machine Learning Platform Engineering explains FAISS →
Effective Conversational AI
FAISS is a software library for efficient similarity search and clustering of dense vectors.
- How it is used
- Supports retrieval-augmented generation by searching similar embeddings quickly to reduce response latency.
- How deep it goes
- Defines FAISS as an efficient similarity-search option, then stops short of implementation details and emphasizes selection and benchmarking.
- Examples
- Uses PharmaBot’s medical-question scenario to compare FAISS and Elasticsearch with different embedding models.
- What you can do after
- Evaluate FAISS-based indexing with embeddings against accuracy, relevance, latency, throughput, and resource objectives.
Related concepts
- similarity searchuses · Similarity search is a retrieval technique that finds or ranks items by comparing their representations with a query according to a similarity or distance measure.
- semantic cachingused by · Semantic caching is a technique that stores and reuses results for requests judged sufficiently similar in meaning, rather than requiring identical text or keys.
- GPTCachealternative to · GPTCache is an open-source library that caches large-language-model responses and can reuse semantically similar results.
- embeddinguses · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
- vector databaseuses · A vector database is a data-management system that stores and indexes numerical vector representations and supports retrieval through similarity or distance searches.
- Chroma DBalternative to · Chroma is an open-source vector database for storing and querying embeddings and associated data, such as documents and metadata.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.