<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This starter project provides a template for building Retrieval-Augmented Generation (RAG) chatbots using the Vercel AI SDK. It enables chatbots to store and retrieve information, ensuring responses are strictly confined to a defined knowledge base, suitable for applications like customer support or personal knowledge management.

How It Works

The project employs a stack comprising Next.js 14 (App Router), Vercel AI SDK, OpenAI for language model capabilities, and Drizzle ORM with Postgres and pgvector for data persistence and vector search. This architecture facilitates the creation of grounded chatbots that can query and utilize internal knowledge bases.

Quick Start & Requirements

Specific installation commands, non-default prerequisites (beyond the stack components like OpenAI API keys and a Postgres database with pgvector), or setup time estimates are not detailed in the provided description. It is presented as a starter for a guide, implying setup instructions will be found within that guide.

Highlighted Details

• Implements Retrieval-Augmented Generation (RAG) to ensure chatbot responses are based solely on provided knowledge.
• Integrates Vercel AI SDK with OpenAI for LLM functionality.
• Utilizes Postgres with pgvector for efficient vector storage and retrieval.
• Built on Next.js 14 (App Router) for modern web development.

Maintenance & Community

Information regarding notable contributors, sponsorships, community channels (e.g., Discord, Slack), or a project roadmap is not present in the provided text.

Licensing & Compatibility

The license type and any compatibility notes for commercial use or closed-source linking are not specified in the provided description.

Limitations & Caveats

As a starter project for a guide, it may represent a foundational implementation rather than a complete, production-ready solution. Specific limitations, unsupported platforms, or known issues are not detailed.