This repository provides a tutorial series for learning the Langchain framework, aimed at developers and researchers familiar with Python. It offers a structured approach to understanding Langchain's core components and building LLM applications, with a focus on practical examples.

How It Works

The tutorial series guides users through Langchain's fundamental building blocks, including models, data connectors, prompt management, output parsing, chains, memory, and agents. It emphasizes a practical, hands-on learning experience by demonstrating how to integrate these components into complete applications.

Quick Start & Requirements

• Installation: No specific installation command is provided, but the tutorial assumes a Python environment.
• Prerequisites:
  • Python
  • OpenAI models for LLM usage.
  • Langchain version 0.0.235 (specified for example code stability).
• Resources: Links to individual tutorial modules are provided within the README.

Highlighted Details

• Covers core Langchain components: Models, Data Connectors, Prompts, Output Parsers, Chains, Memory, Agents.
• Includes a full example application to demonstrate integration.
• Focuses on practical implementation for learning.

Maintenance & Community

• The project is a personal learning project by a single contributor.
• No community channels or roadmap are indicated.

Licensing & Compatibility

• The license is not specified in the README.
• Compatibility for commercial use or closed-source linking is not addressed.

Limitations & Caveats

The tutorial uses a specific, potentially outdated version of Langchain (0.0.235), which may lead to compatibility issues with newer versions of the framework due to its rapid development. The project's personal nature suggests limited ongoing maintenance or community support.