This project provides a minimal, functional implementation of a "chat with PDF" application, targeting developers and researchers interested in understanding the core mechanics of such systems. It enables users to upload documents and query their content conversationally.

How It Works

The system processes documents by splitting them into paragraph-sized chunks. Each chunk is then converted into a vector embedding using OpenAI's embedding API. User questions are similarly embedded, and a similarity search is performed against the document chunk embeddings to find the most relevant context. This context is then passed to OpenAI's chat API to generate a relevant answer.

Quick Start & Requirements

• Primary install / run command: docker compose up
• Non-default prerequisites: Docker, OpenAI API key (set as OPENAI_API_KEY environment variable).
• Links: Project Repository

Highlighted Details

• Demonstrates the fundamental pipeline for semantic search and retrieval-augmented generation (RAG).
• Utilizes OpenAI's embedding and chat completion APIs for core functionality.
• Employs a simple chunking strategy for document processing.

Maintenance & Community

No specific community channels or maintenance activity are detailed in the README.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

This is a minimal demo project and may lack robust error handling, advanced chunking strategies, or support for various document formats beyond what's implicitly handled by the underlying libraries. The reliance on OpenAI APIs means costs are associated with usage.