LangChain Tutorial
This project is a comprehensive tutorial on LangChain, a framework for developing applications powered by language models. The notebook demonstrates how to build LLM applications using LangChain, integrate with OpenAI's GPT models, and use LangSmith for tracing and monitoring. The tutorial covers fundamental concepts including prompt templates, chains, output parsers, and LangSmith integration for testing and debugging. It provides hands-on examples of chain composition, prompt engineering, and observability setup across different platforms.
Overview
This project is a comprehensive tutorial on LangChain, a framework for developing applications powered by language models. The notebook demonstrates how to build LLM applications using LangChain, integrate with OpenAI's GPT models, and use LangSmith for tracing and monitoring. The tutorial covers fundamental concepts including prompt templates, chains, output parsers, and LangSmith integration for testing and debugging. It provides hands-on examples of chain composition, prompt engineering, and observability setup across different platforms.
Key Features
LangChain framework integration for LLM applications
OpenAI GPT-3.5-turbo integration
Prompt templates and chain composition
Output parsers for structured responses
LangSmith tracing and monitoring
Jupyter Notebook tutorial format
Hands-on examples and exercises
Observability setup across platforms
Technical Highlights
Created comprehensive LangChain tutorial with practical examples
Integrated OpenAI API for LLM interactions
Demonstrated prompt engineering and chain composition techniques
Set up LangSmith for application tracing and debugging
Provided hands-on examples for building LLM applications
Challenges and Solutions
Prompt Engineering
Designed effective prompt templates and chain compositions for reliable LLM responses
Observability
Integrated LangSmith for tracing and monitoring LLM application behavior
Chain Composition
Built complex chains with proper error handling and output parsing
Technologies
Framework
LLM
Tools
Language
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Tutorial/Educational Project
- Category
- Machine Learning