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.

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📚 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.

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🚀 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.

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🛠 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

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🚀 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.

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🚀 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

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🤝 Contributing

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

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💡 Resources

• LangChain Documentation : https://python.langchain.com/docs/integrations/document_loaders/
• OpenAI : https://platform.openai.com/docs/overview
• Hugging Face : https://huggingface.co/
• FAISS : https://faiss.ai/index.html
• ChromaDB : https://docs.trychroma.com/
• Ollama : https://ollama.com/blog

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