https://github.com/guhan-tofu/rag

FREE Implementation of a RAG pipeline using nvidia llama-3.1 nemo model and pinecone
https://github.com/guhan-tofu/rag

ai llama3 nvidia pinecone python rag retrieval-augmented-generation

Last synced: about 1 month ago
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FREE Implementation of a RAG pipeline using nvidia llama-3.1 nemo model and pinecone

• Host: GitHub
• URL: https://github.com/guhan-tofu/rag
• Owner: guhan-tofu
• License: mit
• Created: 2024-10-29T17:58:18.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-06T19:01:34.000Z (3 months ago)
• Last Synced: 2024-12-17T10:45:08.423Z (about 2 months ago)
• Topics: ai, llama3, nvidia, pinecone, python, rag, retrieval-augmented-generation
• Language: Python
• Homepage:
• Size: 78.1 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# RAG Application: Retrieval-Augmented Generation with NVIDIA LLaMA 3.1, Pinecone, and MPNet

This project is a Retrieval-Augmented Generation (RAG) pipeline that leverages NVIDIA's **LLaMA 3.1** language model for natural language interaction, **Pinecone** for vector storage and retrieval, and the **all-mpnet-base-v2** model to create text embeddings. The application allows users to upload documents, process them into embeddings, store them in a vector database, and perform contextualized Q&A based on relevant retrieved documents.

![image](https://github.com/user-attachments/assets/a6aacad3-4aef-43bd-bb63-c50dc860b01d)

## Contributors 

Sri Guhanathan

Ernest Bozjigtov

## Features

- **Upload Document**: Supports .txt file uploads for document embedding and storage in Pinecone.

- **Conversational AI**: Uses NVIDIA's LLaMA 3.1 model to handle user queries with relevant context retrieval.

- **Vector Database Integration**: Efficient vector storage and retrieval using Pinecone.

- **Embedding Model**: Employs the `all-mpnet-base-v2` embedding model for creating high-quality vector representations.

## Tech Stack

- **Frontend**: HTML, CSS, JavaScript (React)  

- **Backend**: Python (Flask)

- **Model Integration**: NVIDIA LLaMA 3.1 and Sentence Transformers (all-mpnet-base-v2)

- **Vector Database**: Pinecone

## Embedding Model

- **all-mpnet-base-v2**: 768 dimensions and uses dot product scoring instead of cosine

## Nvidia Model

- **nvidia / llama-3.1-nemotron-70b-instruct**: [Llama-3.1-Nemotron-70B-Instruct](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct) is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.

### Model Architecture:

- Architecture Type: Transformer

- Network Architecture: Llama 3.1

### Input:

- Input Type(s): Text

- Input Format: String

- Input Parameters: One Dimensional (1D)

- Other Properties Related to Input: Max of 128k tokens

### Output:

- Output Type(s): Text

- Output Format: String

- Output Parameters: One Dimensional (1D)

- Other Properties Related to Output: Max of 4k tokens

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