This project implements a user-friendly document Q&A chatbot built with cutting-edge technologies.
- User Upload: Empowers users to process and explore information from their own document sets (increased flexibility compared to pre-defined data).
- Llama 3 Integration: Leverages the power of the Llama 3 large language model (LLM) by Hugging Face for superior information retrieval and response generation.
- Streamlit Interface: Provides a user-friendly web interface built with Streamlit, fostering intuitive interaction with the chatbot.
- Groq Inferencing: Utilizes the efficient Groq inferencing engine with an LPU (Language Processing Unit) for optimized model execution.
- Large Language Model (LLM): Llama 3
- Inferencing Engine: Groq
- Web Framework: Streamlit
- Vector Embeddings: Google's Vector Embedding
Streamlining document analysis workflows in various fields like research, legal review, and customer support. Enabling efficient information retrieval from personal document collections.
QA.RAG.mp4
