🚀 Quick Start | 🎯 What is GenAI? | 🔧 Types of GenAI | 👨💻 Builder's Perspective | 👤 User's Perspective | 📚 Projects | ⚡ Installation
This repository demonstrates the implementation of Generative AI systems using LangChain for workflow orchestration and HuggingFace for state-of-the-art models. Unlike traditional AI approaches, this framework enables scalable, modular, and production-ready AI applications capable of complex text generation, multimodal processing, and seamless model integration.
🔄 Complete Generative AI Pipeline Architecture
- 🤖 GenAI with Langchain and Huggingface
Get up and running in less than 5 minutes:
# 1. Clone the repository
git clone https://github.com/mohd-faizy/GenAI-with-Langchain-and-Huggingface.git
cd GenAI-with-Langchain-and-Huggingface
# 2. Set up environment
uv venv && source .venv/bin/activate # Linux/Mac
# OR
uv venv && .venv\Scripts\activate # Windows
# 3. Install dependencies
uv add -r requirements.txt
# 4. Run your first GenAI app
python examples/basic_text_generation.py🧠 Generative AI is a revolutionary branch of artificial intelligence that creates entirely new content —
text,images,audio,code, andvideo— by learning intricate patterns and relationships from vast datasets. It doesn't just analyze; it creates, innovates, and imagines.
Generative AI learns the distribution of data to generate new, original samples that maintain the essence of the training data while being completely novel.
| 🎨 Domain | 🔧 Technology | 🌟 Examples |
|---|---|---|
| 💬 Text | Large Language Models | ChatGPT, Claude, Gemini |
| 🖼️ Images | Diffusion Models | DALL-E, Midjourney, Stable Diffusion |
| 💻 Code | Code Generation LLMs | GitHub Copilot, CodeLlama |
| 🎵 Audio | Neural Audio Synthesis | ElevenLabs, Mubert |
| 🎬 Video | Video Generation Models | Sora, RunwayML |
🎨 Comprehensive Overview of Generative AI Model Categories
📝 Text Generation Models
- 🤖 GPT Family: GPT-3.5, GPT-4, GPT-4 Turbo
- 🔄 T5 Variants: T5-Small, T5-Base, T5-Large, Flan-T5
- 🧠 BERT Derivatives: RoBERTa, DeBERTa, ALBERT
- 🦙 Open Source: Llama 2, Mistral, Falcon
🖼️ Image Generation
- 🎨 Stable Diffusion: SD 1.5, SD 2.1, SDXL
- 🎭 DALL-E Integration: DALL-E 2, DALL-E 3
- 🖌️ Custom Models: ControlNet, LoRA fine-tuning
- ⚡ Real-time Generation: LCM, Turbo models
🎵 Audio Processing
- 🎤 Speech-to-Text: Whisper, Wav2Vec2
- 🗣️ Text-to-Speech: Bark, Tortoise TTS
- 🎼 Music Generation: MusicLM, Jukebox
- 🔊 Audio Enhancement: Real-ESRGAN Audio
Understanding the technical foundations that power modern AI systems
🧱 Core architectural components of foundation models
Key Components:
- 🔤 Tokenization Layer: Converting raw text to numerical representations
- 🧠 Transformer Blocks: Self-attention mechanisms for context understanding
- 📊 Embedding Layers: Dense vector representations of tokens
- 🎯 Output Heads: Task-specific prediction layers
⚙️ End-to-end model training workflow
Training Stages:
- 📚 Pre-training: Learning from massive text corpora
- 🎯 Fine-tuning: Task-specific adaptation
- 🔧 RLHF: Reinforcement Learning from Human Feedback
- ✅ Evaluation: Comprehensive model assessment
🔄 Data preprocessing and augmentation pipeline
Processing Steps:
- 🧹 Data Cleaning: Removing noise and inconsistencies
- 🔀 Augmentation: Expanding dataset diversity
- ⚖️ Balancing: Ensuring representative samples
- 🔒 Privacy: Implementing data protection measures
🏛️ Detailed neural network architecture design
Architecture Elements:
- 🔗 Layer Connections: Skip connections and residual blocks
- ⚡ Activation Functions: ReLU, GELU, Swish optimizations
- 📏 Normalization: Layer norm and batch norm strategies
- 🎛️ Hyperparameters: Learning rates, batch sizes, regularization
☁️ Scalable cloud infrastructure for model training
Infrastructure Components:
- 💻 Compute Resources: GPUs, TPUs, distributed training
- 💾 Storage Systems: High-performance data storage
- 🌐 Networking: High-bandwidth interconnects
- 📊 Monitoring: Real-time training metrics
🌍 Production deployment and scaling strategies
Deployment Options:
- ☁️ Cloud Deployment: AWS, GCP, Azure integration
- 🏠 On-Premise: Local server deployment
- 📱 Edge Computing: Mobile and IoT deployment
- 🔄 Auto-scaling: Dynamic resource allocation
🖼️ Modern, intuitive user interface design principles
Design Principles:
- 🎯 User-Centric: Intuitive navigation and clear workflows
- 📱 Responsive: Works seamlessly across all devices
- ♿ Accessible: WCAG compliant for all users
- 🎨 Beautiful: Modern aesthetics with purposeful design
💬 Natural and engaging user interaction patterns
Interaction Features:
- 💬 Chat Interface: Natural language conversations
- 🎛️ Parameter Controls: Fine-tune model behavior
- 📁 File Upload: Multi-format document processing
- 🔄 Real-time Updates: Live generation feedback
🚀 Lightning-fast response generation pipeline
Generation Process:
- ⚡ Streaming: Real-time token generation
- 🎯 Context Awareness: Maintaining conversation history
- 🔧 Customization: User-defined parameters
- ✅ Quality Control: Output validation and filtering
🌐 Seamless integration with existing systems
Integration Capabilities:
- 🔌 API Endpoints: RESTful and GraphQL APIs
- 🔗 Webhooks: Event-driven integrations
- 📊 Database: Persistent data storage
- 🔐 Authentication: Secure user management
📊 Comprehensive performance monitoring and analytics
Key Metrics:
- ⚡ Response Time: Sub-second generation speeds
- 🎯 Accuracy: High-quality output consistency
- 👥 User Satisfaction: Feedback and rating systems
- 📈 Usage Analytics: Detailed usage insights
# 📥 Clone the repository
git clone https://github.com/mohd-faizy/GenAI-with-Langchain-and-Huggingface.git
cd GenAI-with-Langchain-and-Huggingface
# 🏗️ Initialize UV project
uv init
# 🌐 Create virtual environment
uv venv
# 🔌 Activate environment
# Linux/Mac:
source .venv/bin/activate
# Windows:
.venv\Scripts\activate
# 📦 Install dependencies
uv add -r requirements.txt🐍 Using pip
# 📥 Clone repository
git clone https://github.com/mohd-faizy/GenAI-with-Langchain-and-Huggingface.git
cd GenAI-with-Langchain-and-Huggingface
# 🌐 Create virtual environment
python -m venv genai_env
# 🔌 Activate environment
# Linux/Mac:
source genai_env/bin/activate
# Windows:
genai_env\Scripts\activate
# 📦 Install dependencies
pip install -r requirements.txt🐍 Using conda
# 📥 Clone repository
git clone https://github.com/mohd-faizy/GenAI-with-Langchain-and-Huggingface.git
cd GenAI-with-Langchain-and-Huggingface
# 🌐 Create conda environment
conda create -n genai_env python=3.9
conda activate genai_env
# 📦 Install dependencies
pip install -r requirements.txt💬 Basic Text Generation
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
# 🤖 Initialize model
generator = pipeline("text-generation",
model="microsoft/DialoGPT-medium")
llm = HuggingFacePipeline(pipeline=generator)
# 💬 Generate response
response = llm("Hello, how are you?")
print(response)📄 Document Q&A
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
# 📄 Load document
loader = PyPDFLoader("document.pdf")
documents = loader.load()
# 🔍 Create vector store
vectorstore = FAISS.from_documents(documents, embeddings)
# ❓ Setup Q&A chain
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever()
)
# 💬 Ask question
answer = qa_chain.run("What is the main topic?")- 🍴 Fork the repository
- 🌿 Create your feature branch (
git checkout -b feature/AmazingFeature) - 💾 Commit your changes (
git commit -m 'Add some AmazingFeature') - 📤 Push to the branch (
git push origin feature/AmazingFeature) - 🔄 Open a Pull Request
| 🎯 Type | 📝 Description | 🔗 How to Help |
|---|---|---|
| 🐛 Bug Reports | Found an issue? | Open an Issue |
| 📝 Documentation | Improve docs | Edit Documentation |
| 💻 Code | Add features | Submit Pull Request |
This project is licensed under the MIT License - see the LICENSE file for details.
If this repo helped you, please consider:
| 🎯 Action | 📝 Description |
|---|---|
| ⭐ Star this repo | Show appreciation |
| 🤝 Contribute | Make it better |
This repository draws inspiration from the exceptional educational content developed by Nitish, Krish Naik, and the DataCamp course Developing LLMs with LangChain. The implementations and examples provided here are grounded in their comprehensive tutorials on Generative AI, with a particular focus on LangChain and Hugging Face.
➤ If you have questions or feedback, feel free to reach out!!!
