Welcome to LangChain Mastery – a curated, hands-on repository to help you learn LangChain from scratch to advanced use cases. Whether you're just starting out or looking to deepen your understanding of advanced LLM workflows, this repo provides structured examples, explanations, and projects to guide your journey.
LangChain is a powerful framework designed to simplify the development of applications using large language models (LLMs). It provides tools to chain together components like models, memory, agents, tools, and data sources into powerful AI-driven applications.
Explore more of my work:
- GenAI-Learnings: A deep dive into Generative AI concepts, tools, and projects.
- MCP-YFinance-Server: A backend service for financial analytics and modeling.
- Reinforcement-Learning: Hands-on experiments and theory in Reinforcement Learning.
- Complete Data Science: A deep dive into Data Science AI concepts, tools, and projects and all the material for learning and interview preparation.
- CompleteRAG: End-to-end implementation of Retrieval-Augmented Generation (RAG) systems.
LangChain is modular by design. These components can be combined in different ways to build sophisticated LLM-powered workflows. Here's a breakdown of the key components you'll explore:
| Module | Topic | Description |
|---|---|---|
| 01 | Introduction to LangChain | What LangChain is, how it works, and how to set it up. |
| 02 | Using Models | Use OpenAI and Hugging Face models with LangChain. |
| 03 | Prompts | Design and use prompts effectively in LangChain. |
| 04 | Output Parsers | Parse and structure LLM outputs for better usability. |
| 05 | Chains | Build multi-step workflows by chaining LLM calls. |
| 06 | Document Loaders | Load and preprocess documents from various formats. |
| 07 | Text Splitters | Split large text into manageable chunks for processing. |
| 08 | Vector Stores | Store embeddings and retrieve relevant documents using semantic search. |
| 09 | Retrievers | Combine retrieval and generation to answer questions from documents. |
| 10 | Rag | Use agents to choose tools or documents based on user input. |
| 11 | Final Projects | Build real-world apps like chatbots, PDF Q&A bots, and AI assistants. |
Start your LangChain journey with these foundational topics, designed to help you understand core concepts through hands-on examples.
- 📖 Intro to LangChain
A beginner-friendly introduction explaining:- What LangChain is
- Why it’s useful for building with LLMs
- How to set up and run your first LangChain application
🤖 Build Your First Chatbot
- LangChain + HuggingFace to build a basic conversational agent.
- Basic logic to handle input and LLM response.
- 📝 Basic Chatbot (chatbot.py)
🧠 Enhance Chatbot with Prompt Engineering
- Controlling LLM behavior via carefully designed prompts.
- How different phrasing influences the model’s responses.
- ✨ Chatbot with Prompt (prompt.py)
Learn how to connect LangChain with popular model providers and use different types of models in your applications:
Use text generation models to complete tasks like answering questions, summarizing, or generating content.
- Providers: OpenAI, Hugging Face, Cohere, and more
- Learn how to integrate and configure these models in LangChain
🔗 Simple LLM with OpenAI
A practical example showing how to use OpenAI's models with LangChain.
📄 View Code:llm_model.py
Designed for conversational use cases (like ChatGPT), these models allow message-based interactions.
- Handle structured dialogues with roles (
user,assistant, etc.) - Use tools like
ChatOpenAIfor better control and reliability
Convert text into high-dimensional vectors for tasks like:
- Semantic search
- Similarity comparison
- Retrieval-augmented generation (RAG)
🔍 Power your retrievers with vector embeddings for smarter, context-aware applications.
- 📝 Prompts Learn how to design effective prompts for different tasks and control LLM behavior. This section covers:
- Basic prompt construction principles
- Techniques to guide model outputs
- How to optimize prompts for specific applications
✨ Prompt Design Strategies How to make LLMs respond to user inputs more effectively Using different prompt patterns to influence model responses.
Learn how to extract structured data from raw LLM outputs using output parsers. This module covers:
- Why output parsing is important
- Different parser types in LangChain
- How to convert free-form text into usable formats like JSON or lists
🧪 Practical Parsing Examples See how to use built-in parsers to format and structure model outputs effectively: 📄 Explore Output Parsers
Chains let you build workflows by combining prompts, models, retrievers, and logic steps — allowing for more advanced LLM applications.
This module covers:
- How to structure multi-step workflows using LangChain’s
Chainclasses - Key patterns like
SequentialChain,ParallelChain,ConditionalChain, andRetrievalQA - Using memory and external data with
ConversationalRetrievalChain
Document Loaders allow you to ingest and preprocess content from a variety of file formats and sources into LangChain.
This module includes:
- Loading data from PDFs, CSVs, HTML, Notion, APIs, and more
- Chunking content for better downstream processing
- Attaching metadata to maintain document traceability
📚 Load Real-World Data Efficiently
Prepare diverse documents for downstream NLP workflows.
Learn how to split large texts into smaller, manageable chunks for better model performance and accuracy.
Key topics covered:
- Different splitter types like character, recursive, token, and semantic splitters
- Setting chunk size and chunk overlap parameters
- Combining splitters and evaluating their effectiveness with tools
🧩 Optimize Your Text Preprocessing
Improve retrieval, summarization, and Q&A with smart chunking.
Explore vector databases that power semantic search and document retrieval workflows.
Module highlights:
- Embedding models and converting text to vectors
- Popular vector stores: FAISS, Pinecone, Weaviate, Milvus, Chroma
🚀 Build scalable, fast semantic search applications
Combine retrieval with LLMs for powerful question answering.
Understand retrieval mechanisms with generation models to create knowledge-augmented applications.
This module covers:
- Integrating vector stores with generative LLMs
- RetrievalQA chains for context-aware answers
- Use cases like chatbots, summarizers, and research assistants
🎯 Generate answers grounded in relevant documents
Enhance LLMs with up-to-date and accurate knowledge.
Understand how to combine retrieval mechanisms with generation models to create knowledge-augmented applications.
This module covers:
- Integrating vector stores with generative LLMs
- RetrievalQA chains for context-aware answers
- Use cases like chatbots, summarizers, and research assistants
🎯 Generate answers grounded in relevant documents
Enhance LLMs with up-to-date and accurate knowledge.