Embeddings

App: Exa

A search engine built for AI — embeddings-based retrieval that finds pages by meaning, not keyword overlap.

App: Weaviate

An open-source AI-native database combining vector search, structured filtering, and built-in model integrations.

App: Chroma

The embedding database that starts as a Python import and grows into a distributed cloud service.

App: Qdrant

An open-source vector search engine written in Rust — fast, memory-efficient, and self-hostable.

App: Pinecone

A fully managed vector database for semantic search and retrieval at billion-scale, with no infrastructure to run.

Skill: OpenSearch Launchpad

by OpenSearch Project

Featured

Take an OpenSearch search application from requirements to a running cluster — BM25, dense and sparse vectors, hybrid retrieval, agentic search and RAG, with relevance evaluation built in.

Skill: Sentence Transformers Trainer

by Hugging Face

New

Train bi-encoders, rerankers, SPLADE sparse encoders and ColBERT multi-vector models with the loss, evaluator and template each type actually needs.

Skill: Weaviate Database Operations

by Weaviate

Weaviate's official skill for searching and managing vector collections — hybrid, semantic and keyword search, schema inspection, filtered fetches and bulk imports.

MCP: Docs MCP Server

by arabold

Self-hosted documentation retrieval for coding agents — index any library's docs from the web, GitHub, npm, PyPI or local files and query them by exact version. An open-source Context7 alternative.

MCP: Chroma

by Chroma

Chroma's official MCP server — build and query vector collections directly from an AI client.