README.md

metadata

title: GenAI Toolkit
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

🤖 GenAI Toolkit

A powerful, all-in-one Generative AI application combining multiple NLP features into a single, clean interface — powered by LangChain, LangGraph, and Groq LLaMA 3.

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🌟 Features

Feature	Description
🌐 Language Translation	Translate text across 20+ languages instantly
📝 Text Summarization	Condense long documents using map-reduce chunking
🔍 Keyword Extraction	Extract key topics and phrases from any text
✉️ Email Writer	Generate professional emails and cover letters
💬 Chat Assistant	Multi-turn AI conversations with memory
📄 PDF Q&A	Upload PDFs and ask questions using RAG

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🛠️ Tech Stack

Layer	Technology
LLM	Groq LLaMA 3.3 70B + LLaMA 3.1 8B
Orchestration	LangChain + LangGraph
Vector Store	FAISS + HuggingFace Embeddings
UI	Gradio
Deployment	Docker + Hugging Face Spaces

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🏗️ Architecture

GenAI-Toolkit/ ├── src/ │ ├── features/ │ │ ├── translation.py # LangChain PromptTemplate │ │ ├── summarization.py # Map-Reduce summarization │ │ ├── keyword_extraction.py # Structured output extraction │ │ ├── email_writer.py # Email + Cover letter generation │ │ └── pdf_qa.py # RAG pipeline with FAISS │ ├── graphs/ │ │ └── chat_graph.py # LangGraph conversation graph │ ├── utils/ │ │ ├── llm.py # Groq LLM setup │ │ └── vector_store.py # FAISS vector store │ └── app.py # Gradio UI ├── Dockerfile └── requirements.txt

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🚀 How It Works

Language Translation

Uses LangChain PromptTemplate with Groq LLaMA 3.1 to translate text between 20+ languages while preserving tone and context.

Text Summarization

Implements map-reduce chunking — splits long documents into chunks, summarizes each chunk, then combines into a final summary. Handles documents of any length.

Keyword Extraction

Uses structured LLM output to extract the most relevant keywords with context and relevance explanations.

Email & Cover Letter Writer

Template-driven generation with customizable tone, email type, and recipient details. Cover letter writer tailors content to specific job roles and companies.

Chat Assistant

Built with LangGraph StateGraph — maintains full conversation history across turns using a stateful graph architecture.

PDF Q&A

Implements RAG (Retrieval Augmented Generation):

1. Upload PDF → extract text with PyMuPDF
2. Chunk text with RecursiveCharacterTextSplitter
3. Embed chunks with HuggingFace sentence-transformers
4. Store in FAISS vector store
5. Retrieve top-k relevant chunks per question
6. Generate answer with LangGraph agent

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🔧 Local Setup

# Clone the repo
git clone https://github.com/shashankheg/GenAI-Toolkit.git
cd GenAI-Toolkit

# Create virtual environment
uv venv
.venv\Scripts\activate

# Install dependencies
uv pip install -r requirements.txt

# Add API key to .env
echo "GROQ_API_KEY=your-key-here" > .env

# Run the app
python -m src.app

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🐳 Docker

docker build -t genai-toolkit .
docker run -p 7860:7860 -e GROQ_API_KEY=your-key genai-toolkit

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🔑 API Keys

• Groq API (free) → https://console.groq.com
• No OpenAI key required — fully powered by Groq

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📊 Model Details

Task	Model	Speed
Translation, Keywords, Email	LLaMA 3.1 8B Instant	~0.5s
Summarization, Chat, PDF Q&A	LLaMA 3.3 70B Versatile	~1-2s

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👨‍💻 Author

• HuggingFace: @shashankheg