Single-Node RAG Integration with Llama 3 for European AI Act PDF Q&A
## Description
We aim to integrate a single-node Retrieval-Augmented Generation (RAG) system with Llama 3 to facilitate Q&A capabilities for a combination of various sources of the European AI Act documents. The objective is to enable efficient and accurate question-answering from these documents.
Additionally, we seek to evaluate the performance and understand the answers provided by various open-source vector stores and embeddings for the different PDF files involved.
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#### Interesting Library - RAGxplorer
[https://github.com/gabrielchua/RAGxplorer?tab=readme-ov-file](url)
`pip install ragxplorer`
## Tasks
### Done
- [x] Built a simple single-node RAG-System
- [x] Answers from FAISS and Chrome
### To-do
- [x] Connect it with LLM-hosted Llama-3
- [x] Containerize and enhance UI
#### Improvizations
- [ ] Enhance RAG system training with additional PDF documents
- [ ] Dynamic chunking of PDF data to ensure information consistency
- [ ] Benchmark the performance of FAISS and Chrome
- [ ] Draw inspiration from RAGxplorer
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