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🔍 RAG App
🔍 RAG / Retrieval
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RAG App with OpenAI & Pinecone

Build a production RAG application using OpenAI for embeddings and generation, Pinecone for vector storage, and Supabase for user data.

Setup: 1 week
6 tools
3 use cases
Best For
Document Q&A
Knowledge bases
Enterprise search

Stack Components

Getting Started
  1. 1
    Supabase

    Postgres development platform with instant APIs, auth, realtime sync, storage, pgvector support, and an MCP experience that fits modern AI app teams.

  2. 2
    Vercel

    AI cloud for shipping web products with Git-based deployment, previews, global edge delivery, agent tooling, fluid compute, and integrated AI app infrastructure.

  3. 3
    Pinecone

    Managed vector database for semantic search and hybrid retrieval with serverless operations, metadata filters, and production-ready indexing for AI workloads.

  4. 4
    OpenAI API

    OpenAI's platform API for chat, tool-calling agents, realtime voice, structured outputs, image generation, and production AI product backends.

  5. 5
    Pinecone

    Purpose-built vector database for production retrieval with hybrid search, reranking, tenant isolation, and managed serverless or BYOC deployment.

  6. 6
    Clerk

    Developer-focused auth platform for shipping user accounts, orgs, sessions, and admin controls into AI SaaS products, copilots, and internal tools.

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