Get started with LLMs, FTS and vector search, RAG, and more, in Go!

Made with ✨sparkles✨ by maragu.

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Overview

This is a template application for developers interested in building Go web applications with:

• Large Language Models (LLMs) and foundation models integration
• Document search capabilities using both full-text search (BM25) and vector search (embeddings)
• A flexible architecture supporting RAG (Retrieval Augmented Generation) and tool use

Key features:

• Local database (SQLite) for document storage and retrieval
• Local LLM support (Llama 3) for text generation
• Local embeddings model (mxbai-embed-large-v1) for vector generation
• Document CRUD endpoints with automatic chunking
• Simple and extensible Go architecture

Roadmap

• Local SQLite database with full-text search (FTS5)
• Local LLM integration (Llama 3)
• Local embeddings model (mxbai-embed-large-v1)
• Document CRUD API with automatic chunking
• Vector search implementation
• Prompt endpoint with LLM tool use capabilities
• RAG implementation for improved LLM responses
• Advanced chunking strategies
• Multi-model support

Evals