Turn any website into clean markdown or structured JSON — crawling, JavaScript rendering, and extraction in one API.
RAG
Retrieval-augmented generation — tools that ground model answers in your own documents and data.
14 apps, 7 skills and 6 MCP servers tagged RAG.
Apps
Instantly provide support with your custom AI agent
NotebookLM helps you learn from your sources with AI.
A self-hosted, feature-rich chat interface that puts a polished front end on Ollama and any OpenAI-compatible API.
An all-in-one private AI workspace: chat with your documents, run agents, and keep everything on your device.
A coding agent built for very large codebases, with a context engine that indexes millions of lines.
An open-source platform for building production LLM applications — visual workflows, RAG, and agents in one place.
The framework and observability platform most teams use to build, debug, and ship LLM agents.
Document parsing and retrieval built for agents — turn messy PDFs into structured, queryable context.
A fully managed vector database for semantic search and retrieval at billion-scale, with no infrastructure to run.
An open-source AI-native database combining vector search, structured filtering, and built-in model integrations.
ETL for unstructured data — turn PDFs, slides, emails, and images into clean, AI-ready structured output.
A search API purpose-built for RAG and agents — returns synthesised, cited content instead of a list of links.
Enterprise Work AI platform — permission-aware search across every company app, plus an assistant and agents that act on the results.
Skills
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.
Redis' own guidance for FT.CREATE schema design, FT.SEARCH / FT.AGGREGATE / FT.HYBRID, HNSW vector similarity and RAG retrieval pipelines.
Parse, convert, chunk, and structurally analyze PDFs, DOCX, PPTX, HTML, and images with IBM's open-source Docling toolkit.
Brave's official search skill: one CLI that searches, scrapes and extracts in a single call, returning token-budgeted web content instead of a list of links.
Deploy, configure and troubleshoot the NVIDIA RAG Blueprint from your agent — Docker Compose, Helm or library, with feature toggles for VLM, guardrails and agentic RAG.
Skill: Qdrant Advisor
by Qdrant
Qdrant's meta-skill: instead of shipping static docs, it loads the current official Qdrant skill tree live from skills.qdrant.tech and diagnoses from that.
Skill: MongoDB Search & AI
by MongoDB
MongoDB's official skill for choosing between Atlas Search, Vector Search and Hybrid Search, then building the right indexes and queries for the use case.
MCP servers
Weaviate's MCP server, built into the database itself — hybrid search, schema inspection, tenant listing and batch upserts at /v1/mcp, scoped by RBAC.
MCP: Milvus
by Zilliz
Search and manage Milvus vector collections from an AI client — vector, full-text, and hybrid queries.
MCP: Pinecone
by Pinecone
Pinecone's official MCP server — search and manage vector indexes, and query Pinecone's docs while you build.
MCP: Chroma
by Chroma
Chroma's official MCP server — build and query vector collections directly from an AI client.
MCP: Qdrant
by Qdrant
Qdrant's official MCP server — semantic memory for AI assistants backed by a real vector database.
MCP: Dense-Mem
by markhuangai
Self-hosted agent memory server on Postgres/pgvector that tracks evidence, flags contradictions, and never silently rewrites facts.
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