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https://github.com/abdelrahman-amen/multi_pdf_rag_with_groq
In this project, we developed a Streamlit app that uses Groq API and Google Generative AI for question-answering by embedding and retrieving content from multiple PDFs. The app leverages prompt engineering, RAG (Retrieval-Augmented Generation), and document chaining for accurate and context-based responses.
https://github.com/abdelrahman-amen/multi_pdf_rag_with_groq
api chain dotenv embeddings faiss genai genrative-ai groq-api langchain pdf prompt-engineering python retrieval-augmented-generation streamlit vectorstore
Last synced: about 4 hours ago
JSON representation
In this project, we developed a Streamlit app that uses Groq API and Google Generative AI for question-answering by embedding and retrieving content from multiple PDFs. The app leverages prompt engineering, RAG (Retrieval-Augmented Generation), and document chaining for accurate and context-based responses.
- Host: GitHub
- URL: https://github.com/abdelrahman-amen/multi_pdf_rag_with_groq
- Owner: Abdelrahman-Amen
- Created: 2025-02-03T15:08:37.000Z (1 day ago)
- Default Branch: main
- Last Pushed: 2025-02-03T15:32:00.000Z (1 day ago)
- Last Synced: 2025-02-03T16:24:49.210Z (about 23 hours ago)
- Topics: api, chain, dotenv, embeddings, faiss, genai, genrative-ai, groq-api, langchain, pdf, prompt-engineering, python, retrieval-augmented-generation, streamlit, vectorstore
- Language: Python
- Homepage:
- Size: 10.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📚 Chatgroq with Llama3 Demo 🚀
Welcome to the Chatgroq with Llama3 Demo! This interactive application allows you to query multiple PDF documents and get precise, context-aware answers powered by advanced AI models like Llama3, GroqAPI, and Google Generative AI. Here's an overview of how it all works and what exciting features it offers:
# 📄 What Does This Application Do?
📁 Multiple PDFs in a Folder: Upload a collection of PDF files into the ./PDFS folder, and the app will process them all! Whether you need to search one document or many, this app has you covered.
🔍 Ask Questions: Input your question, and the app retrieves the most relevant parts of the documents, providing an accurate answer based on context.
💡 Cutting-Edge Technology: The app leverages Retrieval-Augmented Generation (RAG), prompt engineering, and AI embeddings to deliver reliable and context-specific results.
# 🧠 What is RAG (Retrieval-Augmented Generation)?
RAG combines document retrieval and generative AI to produce precise, context-aware answers:
1. Retrieve: The most relevant document sections are identified using vector embeddings.
2. Generate: The generative AI model uses these retrieved sections to craft an accurate, context-specific response.
This approach ensures the answers are grounded in the provided documents.
# ✍️ What is Prompt Engineering?
Prompt Engineering is the art of crafting precise instructions (prompts) to guide the AI in producing optimal responses. Here, the prompts ensure that:
1. The AI uses only the provided context to answer.
2. It delivers clear and relevant answers to the questions.
# 🔗 What is a Chain?
Chains are logical workflows that combine various components like:
• Document Loaders: For loading data (e.g., PDFs).
• Text Splitters: To break content into manageable chunks.
• Vector Stores: To store embeddings for retrieval.
• LLMs: To generate responses.
In this app, we use the LangChain framework to create:
1. Document Combination Chain: For combining documents and prompting the AI.
2. Retrieval Chain: For retrieving the most relevant chunks from the vector store.
# 🌟 How GroqAPI Enhances This App
The GroqAPI connects to the Llama3 model, a high-performance large language model. Its integration ensures:
1. Efficient handling of large prompts and contexts.
2. Accurate and responsive answers.
3. Seamless integration with other AI components.
# ⚙️ How It Works
### 1. PDF Ingestion:
• PDFs are loaded using the PyPDFDirectoryLoader.
• Text from the PDFs is split into smaller chunks for embedding.
### 2. Vector Embeddings:
• Google's Generative AI creates embeddings, representing the document text in numerical form.
• These embeddings are stored in a vector database powered by FAISS.
### 3. Ask Your Question:
• Input your question in the text box.
• The app retrieves relevant document sections and generates a precise answer using Llama3.
### 4. Document Similarity Search:
• View the most relevant sections from the documents in the similarity search panel.
# 🛠️ Key Features
• 📂 Handles Multiple PDFs: Query across multiple files effortlessly.
• ⚡ RAG Framework: Combines retrieval and generation for accurate answers.
• 🎯 Precision with Prompt Engineering: Guides the AI for reliable responses.
• 🌍 Advanced AI Models: Uses GroqAPI, Llama3, and Google Generative AI.
# 🏁 Try It Now!
1. Place your PDFs in the ./PDFS folder.
2. Click "Documents Embedding" to initialize the vector store.
3. Enter your question and enjoy AI-powered insights!
🎉 Dive into the future of AI-enhanced document querying! 🧐
# Demo 📽
Below is a demonstration of how the application works:
