langchain-embeddings-vectorstores

What problem does it solve?

Embeddings and vector stores enable semantic search over text, turning raw data into searchable representations.

Core Features & Use Cases

• Embeddings creation: convert text into dense vectors for similarity search.
• Vector store management: store, persist, and retrieve embeddings across providers.
• Retrieval patterns: perform similarity, MMR, and filtered searches across large corpora.
• Use Case: Build RAG-enabled search for docs, code repos, and chat contexts.

Quick Start

Initialize an embedding model, index a set of documents into a vector store, and run a similarity search.