This repository provides simple, educational examples for building local AI assistants, primarily focusing on Retrieval-Augmented Generation (RAG). It targets newcomers to LLM application development, offering a straightforward way to understand and implement local LLM functionalities like ChatPDF.

How It Works

The project utilizes a RAG approach, enabling LLMs to access and process external documents. This allows for more contextually relevant and accurate responses, as demonstrated by the ChatPDF example. The architecture is designed for simplicity, making it accessible for learning.

Quick Start & Requirements

• Primary install / run command: Not specified in README.
• Non-default prerequisites and dependencies: Not specified in README.
• Estimated setup time or resource footprint: Not specified in README.
• Links: Blog post: Build your own RAG and run it locally: Langchain + Ollama + Streamlit

Highlighted Details

• Focus on local LLM execution.
• Includes a "Simple RAG" example for Retrieval-Augmented Generation.
• Designed for educational purposes to simplify LLM concepts.

Maintenance & Community

• Created by vndee.
• No specific community channels or roadmap mentioned.

Licensing & Compatibility

• License: Not specified in README.
• Compatibility: Not specified in README.

Limitations & Caveats

This repository is explicitly not intended for production use and is designed for educational purposes only. Further examples are planned but not yet available.