“vector search and databases”

Implement efficient similarity search with vector databases.

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).

Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

Elasticsearch as a vector database, combining vector search with hybrid lexical search, filters and reranking.

Vector and hybrid search built into MongoDB, running on the same operational data as the database.

Build vector databases on Redis, with vector search combined with exact-match and full-text search.

Store and retrieve text in a Qdrant vector database by similarity search.

Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools

In-memory vector search with TF-IDF and cosine similarity.

Python library that builds a tree index of long documents and has an LLM reason through it to retrieve answers, without a vector database or chunking.

All-in-one Python AI framework for embeddings-based semantic search, LLM pipelines, workflows and agents.

Minimal Python graph framework of about 100 lines for LLM agents, workflows, RAG and map-reduce pipelines.