vector-database

Redis Vector Library (RedisVL) -- the AI-native Python client for Redis.

Java 8+ agentic SDK: unified LLM access (OpenAI/Anthropic/DashScope/Doubao/DeepSeek...), Tool Calling, MCP, RAG, Agent Runtime, and a built-in Coding Agent CLI/TUI/ACP.

The codebase for the book "AI-Powered Search" (Manning Publications, 2025) and associated "AI-Powered Search: Modern Retrieval for Humans & Agents" Maven course

📚 从零开始的向量数据库原理与实践教程，在线阅读地址：https://easy-vecdb.datawhale.cc/

Super performant RAG pipelines for AI apps. Summarization, Retrieve/Rerank and Code Interpreters in one simple API.

Cut LLM costs by up to 80% and unlock sub-millisecond responses with intelligent semantic caching.A drop-in, provider-agnostic LLM proxy written in Go with sub-millisecond response

Quickly and easily build AI website or application by using embeddings!

Embedding Studio is a framework which allows you transform your Vector Database into a feature-rich Search Engine.

Optimized local inference for LLMs with HuggingFace-like APIs for quantization, vision/language models, multimodal agents, speech, vector DB, and RAG.

Vector search engine inside Milvus, integrating FAISS, HNSW, DiskANN.

An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.

In-memory vector store with efficient read and write performance for semantic caching and retrieval system. Redis for Semantic Caching.

Program that lets you ask questions about your documents, audio, and video files.

Framework for benchmarking vector search engines

Fast, SQL powered, in-process vector search for any language with an SQLite driver

A curated list of awesome works related to high dimensional structure/vector search & database

Lightweight Nearest Neighbors with Flexible Backends

.NET client for Meilisearch

Self-hosted, OpenAI-compatible AI gateway for private RAG, natural-language data access, and tool-calling agents.

Weaviate vector database – examples