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LangChain Projects

Welcome to the LangChain Projects repository! 🎉

This repository is dedicated to tutorials and practical examples for working with LangChain, Retrieval-Augmented Generation (RAG), and Vector Databases. Whether you're a beginner or an experienced practitioner, you'll find valuable resources to enhance your understanding of these powerful tools for building advanced AI applications.

📚 Tutorials Covered

1. LangChain Introduction

  • Overview of LangChain and its core concepts.
  • Understanding the building blocks like chains, agents, and tools.
  • Getting started with your first LangChain project.

2. Data Ingestion through LangChain Document Loaders

  • How to load and process data using LangChain's document loaders.
  • Supported formats and integration with various data sources.
  • Practical examples for loading PDFs, text files, and more.

3. Data Transformation Using Text Splitting

  • Techniques for efficient data transformation.
  • Splitting large documents into manageable chunks for downstream processing.
  • Practical examples using LangChain's text splitting utilities.

4. Embeddings Generation

  • Generating embeddings for your data using:
    • OpenAI models
    • Ollama models
    • Hugging Face models
  • Understanding embedding quality and applications.

5. Vector Stores: FAISS and ChromaDB

  • Storing and querying embeddings efficiently.
  • Hands-on tutorials for:
    • FAISS: Scalable similarity search.
    • ChromaDB: Flexible vector database.
  • Building RAG workflows using these vector stores.

🚀 Why LangChain, RAG, and Vector Databases?

  • LangChain: A framework to build powerful AI applications by chaining together LLMs, tools, and APIs.
  • RAG (Retrieval-Augmented Generation): Combines retrieval systems with language models for grounded and accurate AI outputs.
  • Vector Databases: Enable fast and scalable similarity search for embeddings, empowering applications like semantic search and recommendation systems.

🛠 Prerequisites

Before diving into the tutorials, ensure you have the following installed:

  • Python 3.8 or higher
  • Required libraries (install via requirements.txt): 
    pip install -r requirements.txt

🚀 Clone the Repostiroy:

  • Clone the repository using following command:
git clone https://github.com/SurajBhar/langchain_projects.git
  • Navigate to the desired tutorial folder.
  • Follow the instructions in each tutorial to get started.

🚀 Repostiroy Structure:

langchain_projects/
│
├── README.md                   # Project documentation
├── requirements.txt            # Python dependencies
├── L1_Langchain/
│   ├── 1_1_openai/GettingStarted.ipynb # Introduction to LangChain
│   ├── 1_2_Data_ingestion/langchain_data_ingestion.ipynb         # Document loaders tutorial
│   ├── 1_3_Text_Splitting/       # Data transformation examples
│   │        ├── text_splitting.ipynb # Introduction to text splitting
│   │        ├── html_header_text_splitter.ipynb # HTML Text Splitting
│   │        ├── json_data_splitting.ipynb # Json data Splitting
├── L2_Embeddings/
│   ├── L2_1_openai/       # Openai for embeddings
│   │        ├── openai_embeddings.ipynb
│   ├── L2_2_Ollama/       # Ollama for embeddings
│   │        ├── ollama_embeddings.ipynb
│   ├── L2_3_Hugging_Face/       # Hugging Face for Embeddings
│   │        ├── embed_hugging_face.ipynb
├── L3_VectorStore/
│   ├── L3_1_FAISS/       # FAISS Library for Vector Stores
│   │        ├── faiss.ipynb
│   ├── L3_2_ChromaDB/       # ChromaDB VectorStore
│   │        ├── chroma.ipynb

🤝 Contributing

Contributions are welcome! If you have ideas, bug fixes, or enhancements, feel free to open an issue or submit a pull request.

💡 Resources