A comprehensive roadmap and resource collection for learning Generative AI with practical implementation using LangChain. This repository serves as a guided journey from basic concepts to advanced applications in the generative AI space.

• Overview
• GenAI Roadmap
• LangChain Integration
• Top Resources
• Getting Started
• Project Structure
• Examples
• Contributing
• License

This repository provides a structured learning path for developers interested in Generative AI with a focus on practical implementation using LangChain. It contains curated notes, code examples, and implementation guides to help you progress from foundational concepts to building sophisticated GenAI applications.

• Machine Learning Basics

  • Supervised vs. Unsupervised Learning
  • Neural Networks Fundamentals
  • Training and Evaluation Metrics

• NLP Fundamentals

  • Text Processing Techniques
  • Word Embeddings
  • Language Models Basics

• Deep Learning for NLP

  • RNNs, LSTMs, and GRUs
  • Attention Mechanisms
  • Transformers Architecture

• Transformer-Based Models

  • BERT, GPT Family (GPT-2, GPT-3, GPT-4)
  • T5, BART

• Multimodal Models

  • CLIP, DALL-E
  • Stable Diffusion
  • Multimodal Transformers

• Fine-tuning Strategies

  • Transfer Learning
  • Prompt Engineering
  • PEFT (Parameter-Efficient Fine-Tuning)
  • RLHF (Reinforcement Learning from Human Feedback)

• LangChain Basics

  • Components and Architecture
  • Chains and Agents
  • Memory Types

• Prompt Engineering with LangChain

  • Template Creation
  • Few-shot Learning
  • Chain of Thought Prompting

• Advanced LangChain Features

  • Document Loading and Splitting
  • Vector Stores and Embeddings
  • Retrieval Augmented Generation (RAG)
  • Tool and API Integration

• Building Conversational Agents

  • Chatbots and Virtual Assistants
  • Task-specific Agents

• Content Generation Systems

  • Text Summarization
  • Creative Writing Assistants
  • Code Generation

• Information Retrieval & Knowledge Systems

  • Question Answering
  • Knowledge Base Construction
  • Document Analysis

• Model Optimization

  • Quantization
  • Distillation
  • Inference Optimization

• Deployment Strategies

  • API Development
  • Containerization
  • Serverless Deployment

• Monitoring and Maintenance

  • Performance Metrics
  • Drift Detection
  • Continuous Improvement

LangChain provides a framework for developing applications powered by language models. This repository demonstrates how to leverage LangChain for:

• Building Complex Reasoning Chains
• Creating Domain-Specific Chatbots
• Implementing Retrieval-Augmented Generation (RAG)
• Developing Autonomous Agents
• Connecting LLMs to External Tools and APIs

• LangChain Documentation
• LangChain GitHub Repository
• LangChain Python API Reference

• "Building LLM Powered Applications" by Simon Willison
• "Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf
• "Generative Deep Learning" by David Foster
• "Transformers for Natural Language Processing" by Denis Rothman

• DeepLearning.AI - LangChain for LLM Application Development
• DeepLearning.AI - Building Systems with the ChatGPT API
• Coursera - Generative AI with Large Language Models
• Udemy - LangChain: Create LLM-Powered Applications

• LangChain Cookbook
• Building LLM Applications for Production by Chip Huyen
• Prompt Engineering Guide
• LangChain for Beginners
• The Illustrated Transformer

• LangChain
• DeepLearning.AI
• Weights & Biases
• AI Coffee Break with Letitia

• Attention Is All You Need - Transformer architecture
• Language Models are Few-Shot Learners - GPT-3 paper
• Training Language Models to Follow Instructions with Human Feedback - InstructGPT/RLHF
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - RAG paper

• Python 3.8 or higher
• pip (Python package manager)

1. Clone this repository:

git clone https://github.com/AdilShamim8/GenAI-Roadmap-with-Notes-Using-LangChain.git cd GenAI-Roadmap-with-Notes-Using-LangChain

2. Create and activate a virtual environment:

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install required packages:

pip install -r requirements.txt

4. Set up environment variables:

# Create a .env file with your API keys echo "OPENAI_API_KEY=your_openai_api_key" > .env

GenAI-Roadmap-with-Notes-Using-LangChain/ ├── foundations/ # Basic concepts and foundational knowledge │ ├── nlp_basics/ # NLP fundamentals │ ├── transformers/ # Transformer architecture notes │ └── llm_concepts/ # LLM theory and concepts ├── langchain_basics/ # Introduction to LangChain │ ├── components/ # Core components of LangChain │ ├── chains/ # Building and using chains │ └── memory/ # Working with different memory types ├── advanced_techniques/ # Advanced LangChain implementations │ ├── rag/ # Retrieval Augmented Generation │ ├── agents/ # Building autonomous agents │ └── fine_tuning/ # Fine-tuning techniques ├── projects/ # Complete project implementations │ ├── chatbot/ # Conversational agent examples │ ├── document_qa/ # Document Q&A system │ └── content_generator/ # Text generation applications ├── deployment/ # Deployment guides and examples │ ├── api_setup/ # Setting up APIs │ ├── optimization/ # Model optimization techniques │ └── monitoring/ # System monitoring ├── resources/ # Additional learning resources ├── notebooks/ # Jupyter notebooks with examples ├── requirements.txt # Project dependencies ├── .env.example # Example environment variables └── README.md # This documentation 

from langchain.llms import OpenAI from langchain.chains import LLMChain from langchain.prompts import PromptTemplate # Initialize the LLM llm = OpenAI(temperature=0.7) # Create a prompt template prompt = PromptTemplate( input_variables=["topic"], template="Write a short paragraph about {topic}." ) # Create a chain chain = LLMChain(llm=llm, prompt=prompt) # Run the chain result = chain.run("artificial intelligence") print(result)

from langchain.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA from langchain.llms import OpenAI # Load document loader = TextLoader("path/to/document.txt") documents = loader.load() # Split text into chunks text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) # Create embeddings and vector store embeddings = OpenAIEmbeddings() db = Chroma.from_documents(texts, embeddings) # Create a retrieval chain qa_chain = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=db.as_retriever() ) # Query the system query = "What are the key points in this document?" response = qa_chain.run(query) print(response)

Check the notebooks/ directory for more complete examples and tutorials.

Contributions are welcome! If you'd like to add to this roadmap, improve existing content, or share your implementations:

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a Pull Request

Please see CONTRIBUTING.md for detailed guidelines.

This project is licensed under the MIT License - see the LICENSE file for details.

• Website: Adil Shamim
• GitHub: Adil Shamim
• Create an issue in this repository for questions or suggestions