{"id":24953903,"url":"https://github.com/abdelrahman-amen/rag_groq_integration","last_synced_at":"2026-05-06T22:03:13.560Z","repository":{"id":275430572,"uuid":"926059656","full_name":"Abdelrahman-Amen/RAG_Groq_Integration","owner":"Abdelrahman-Amen","description":"In this project, we implement a Retrieval-Augmented Generation (RAG) system using the Groq API. 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The app allows users to load content from a URL, generate embeddings, and perform context-aware question answering.\n\n![Image](https://github.com/user-attachments/assets/98d982a1-806d-42fd-8538-bc46dc89196c)\n\n\n\n# 📖 What is Retrieval-Augmented Generation (RAG)?\nRetrieval-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.\n\nIn this project, we:\n\n1. Use FAISS to manage embeddings for document chunks.\n\n2. Retrieve the most relevant context for a query.\n\n3. Pass the context to Groq LLM for precise, context-driven answers.\n\n\n# ⚙️ What is Groq?\nGroq 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.\n\n\n\n# ✍️ What is a Prompt Template?\nA 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.\n\n\n# 🔗 What is a Chain?\nIn LangChain, a chain connects various components like retrievers, prompt templates, and language models. This project creates:\n\n1. A document chain for combining documents with a prompt. \n\n2. A retrieval chain that integrates a retriever and the document chain to handle context-aware queries.\n\n\n\n\n# 🖥️ What Does the Application Do?\n\n## 🚀 Features\n\n1. Load Documents: Input a URL, and the app fetches and processes the content.\n\n2. Embed Documents: Splits content into chunks and generates embeddings using Google Generative AI.\n\n3. Query Answering: Ask questions about the content, and the app retrieves relevant context to provide precise answers.\n\n4. Similarity Search: View the relevant chunks used to generate the response.\n\n# 🛠️ How It Works\n\n\n### 1. Session State Management\n\n• Embeddings: Initializes and stores embeddings in the session.\n\n• Vectors: Manages FAISS-based document embeddings dynamically.\n\n### 2. Document Processing\n\n• URL Loader: Fetches content from a provided URL.\n\n• Text Splitter: Breaks content into manageable chunks for efficient embedding.\n\n### 3. Question Answering\n\n• Combines a retriever (FAISS) with Groq LLM via a retrieval chain.\n\n• Provides context-aware answers to user queries.\n\n\n\n# 🏗️ Tech Stack\n\n1. Streamlit: Interactive front-end for user input and visualization.\n\n2. Groq LLM: Core language model for question answering.\n\n3. LangChain: Framework for chaining together document loaders, retrievers, and LLMs.\n\n4. Google Generative AI: For embeddings and similarity search.\n\n5. FAISS: Efficient management of vector embeddings for document retrieval.\n\n\n\n\n\n# Demo 📽\n\nBelow is a demonstration of how the application works:\n\n![Demo of the Application](https://github.com/Abdelrahman-Amen/RAG_Groq_Integration/blob/main/Demo.gif)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdelrahman-amen%2Frag_groq_integration","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabdelrahman-amen%2Frag_groq_integration","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdelrahman-amen%2Frag_groq_integration/lists"}