QRetrievalAugmentedGeneration

Userinterface for LLM question-answering on PDF documents

Overview

QRetrievalAugmentedGeneration is a Qt/C++ project designed to facilitate question-answering tasks on PDF documents. Leveraging the power of language models, particularly Mistral, and retrieval augmented generation techniques, it provides a interface for querying PDF content and references the used sources including page numbers.

Features

• PDF Parsing: The project includes functionality to parse PDF documents stored in the designated data folder.
• Question Generation: Users can prompt questions through the interface.
• Retrieval Augmented Generation: The system utilizes retrieval augmented generation techniques with embedding database to provide context-aware prompts to the language model.
• Language Model Integration: LLM, powered by Ollama, serves as the language model for generating responses with context from the PDFs.

Dependencies

• Qt: The project is built using the Qt framework.
• Ollama: The language model integration is facilitated by Ollama.
• LLMs: Mistral: Run ollama pull mistral and nomic-embed-text: Run ollama pull nomic-embed-text in the console

Contributing

Contributions to QRetrievalAugmentedGeneration are welcome! If you have ideas for new features, improvements, or bug fixes, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Acknowledgments

The code in this project is inspired by the youtube video: Python RAG Tutorial (with Local LLMs): AI For Your PDFs from pixegami