https://github.com/abdelrahman-amen/rag_groq_integration

In this project, we implement a Retrieval-Augmented Generation (RAG) system using the Groq API. The application dynamically retrieves and processes context from documents, enabling accurate and context-aware question-answering powered by advanced AI embeddings and a language model.
https://github.com/abdelrahman-amen/rag_groq_integration

api chain dotenv embeddings faiss genai generative-ai groq-api langchain promptengineering python retreival-augmented-generation streamlit

Last synced: about 15 hours ago
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In this project, we implement a Retrieval-Augmented Generation (RAG) system using the Groq API. The application dynamically retrieves and processes context from documents, enabling accurate and context-aware question-answering powered by advanced AI embeddings and a language model.

• Host: GitHub
• URL: https://github.com/abdelrahman-amen/rag_groq_integration
• Owner: Abdelrahman-Amen
• Created: 2025-02-02T13:05:19.000Z (1 day ago)
• Default Branch: main
• Last Pushed: 2025-02-02T13:06:08.000Z (1 day ago)
• Last Synced: 2025-02-02T14:19:35.187Z (1 day ago)
• Topics: api, chain, dotenv, embeddings, faiss, genai, generative-ai, groq-api, langchain, promptengineering, python, retreival-augmented-generation, streamlit
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🌐 ChatGroq: A RAG-Based Streamlit Application

This repository contains a Streamlit web application that implements a Retrieval-Augmented Generation (RAG) pipeline using Groq LLM, Google Generative AI embeddings, and FAISS. The app allows users to load content from a URL, generate embeddings, and perform context-aware question answering.

![Image](https://github.com/user-attachments/assets/98d982a1-806d-42fd-8538-bc46dc89196c)

# 📖 What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) combines information retrieval with language generation. It uses a retriever to fetch relevant documents and augments the input for a language model to generate responses based on the retrieved context.

In this project, we:

1. Use FAISS to manage embeddings for document chunks.

2. Retrieve the most relevant context for a query.

3. Pass the context to Groq LLM for precise, context-driven answers.

# ⚙️ What is Groq?

Groq LLM is a powerful, large language model that specializes in advanced reasoning and natural language processing tasks. This application leverages the Groq API for accurate question-answering.

# ✍️ What is a Prompt Template?

A Prompt Template defines the structure and language of queries sent to the language model. It ensures the LLM receives context and questions in a consistent, optimized format for better results.

# 🔗 What is a Chain?

In LangChain, a chain connects various components like retrievers, prompt templates, and language models. This project creates:

1. A document chain for combining documents with a prompt. 

2. A retrieval chain that integrates a retriever and the document chain to handle context-aware queries.

# 🖥️ What Does the Application Do?

## 🚀 Features

1. Load Documents: Input a URL, and the app fetches and processes the content.

2. Embed Documents: Splits content into chunks and generates embeddings using Google Generative AI.

3. Query Answering: Ask questions about the content, and the app retrieves relevant context to provide precise answers.

4. Similarity Search: View the relevant chunks used to generate the response.

# 🛠️ How It Works

### 1. Session State Management

• Embeddings: Initializes and stores embeddings in the session.

• Vectors: Manages FAISS-based document embeddings dynamically.

### 2. Document Processing

• URL Loader: Fetches content from a provided URL.

• Text Splitter: Breaks content into manageable chunks for efficient embedding.

### 3. Question Answering

• Combines a retriever (FAISS) with Groq LLM via a retrieval chain.

• Provides context-aware answers to user queries.

# 🏗️ Tech Stack

1. Streamlit: Interactive front-end for user input and visualization.

2. Groq LLM: Core language model for question answering.

3. LangChain: Framework for chaining together document loaders, retrievers, and LLMs.

4. Google Generative AI: For embeddings and similarity search.

5. FAISS: Efficient management of vector embeddings for document retrieval.

# Demo 📽

Below is a demonstration of how the application works:

![Demo of the Application](https://github.com/Abdelrahman-Amen/RAG_Groq_Integration/blob/main/Demo.gif)