Tutorial 3: Document Processing with LangChain

Welcome to the third tutorial in our LangChain and LangGraph series! In this tutorial, we'll explore document processing techniques using LangChain, focusing on loading, parsing, and analyzing text documents.

What you'll learn

1. Loading and parsing different document types
2. Text splitting and chunking strategies
3. Building a simple question-answering system
4. Implementing semantic search

Prerequisites

• Completion of Tutorial 1 and 2
• Basic understanding of Python and Jupyter Notebooks
• A Groq API key (sign up at https://console.groq.com)

Getting Started

1. Ensure Virtual Environment is Activated

For Linux/macOS:

cd langchain-langgraph-tutorial
source venv/bin/activate
cd Tutorial03

For Windows:

cd langchain-langgraph-tutorial
.\venv\Scripts\activate
cd Tutorial03

Install Ollama for Embedding Generation

From the website https://ollama.com/download - download the Ollama CLI and install it. Then run the following command to pull the minilm model.

Pull Ollama Models

ollama pull all-minilm

2. Launch Jupyter Notebook

jupyter notebook Tutorial_3_Document_Processing.ipynb

What's Included

Core Components

• Tutorial_3_Document_Processing.ipynb: Main tutorial notebook
• sample_documents/: Example documents for processing
  • Text files (.txt)
  • PDF documents (.pdf)
  • Word documents (.docx)
  • Markdown files (.md)
• utils/: Helper functions for document processing
• README.md: Documentation file

Key Topics

Document Loading

• Different document formats support
• Metadata extraction
• Error handling strategies
• Batch processing

Text Processing

• Chunking algorithms
• Splitting strategies
• Token management
• Content preservation

Search Implementation

• Vector store setup
• Embedding generation
• Query processing
• Result ranking

Troubleshooting

Common Issues

1. Document Loading Errors

   • File format compatibility
   • Encoding issues
   • Memory constraints
   • Permission problems

2. Processing Challenges

   • Large document handling
   • Special character management
   • Language detection
   • Metadata preservation

Next Steps

After completing this tutorial:

1. Experiment with different document types
2. Optimize chunking strategies
3. Build custom document processors
4. Prepare for Tutorial 4: Agents in LangChain

Stay tuned for Tutorial 4 where we'll explore:

• Agent architectures
• Tool integration
• Planning strategies
• Multi-agent systems

Additional Resources

• LangChain Document Loaders Guide
• Text Splitting Best Practices
• Vector Store Documentation
• Embedding Models Overview

Happy learning!