Python AI Agent Tutorial - Build a Coding Assistant w/ RAG & LangChain

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AI Summary

Video Summary: Build a Custom AI Agent with Langchain and RAG

Introduction

• Topic: Creating a custom AI agent using Langchain and Retrieval Augmented Generation (RAG).
• Tools: Python, GitHub API.
• Audience: Intermediate Python users.
• Objective: Query GitHub repository issues and create an AI coding/GitHub assistant.

Demonstration

• Update Issues: Option to update issues from GitHub.
• Functionality: Summarize issues, respond to them, and use various tools.
• Example: Summarizing issues related to “flashing messages” and saving the summary as a note.

Tutorial Overview

• Environment Setup: Using VS Code, creating a virtual environment, and installing packages.
• GitHub Token: Generating a personal access token for GitHub API access.
• Astra DB: Setting up a vector store database with DataStacks Astra DB.
• OpenAI API Key: Obtaining an API key from OpenAI for using ChatGPT.

Coding the Project

• Fetching GitHub Issues: Writing Python code to retrieve issues from a GitHub repository.
• Vector Store Database: Storing issues in Astra DB for fast retrieval based on similarity.
• Building the AI Agent: Creating an agent with access to the database for querying specific issues.

Connecting to GitHub and Astra DB

• GitHub Connection: Writing a function to fetch issues using the GitHub API.
• Astra DB Connection: Writing a function to connect to Astra DB and store issues as vectors.

Executing the Agent

• Agent Tools: Creating tools for the agent to use, such as a retriever for GitHub issues.
• Agent Prompt: Using a pre-configured prompt from Langchain Hub for the agent.
• Agent Execution: Writing a loop to ask questions and get responses from the agent.

Additional Tool: Note Saving

• Note Tool: Writing a Python function to save notes as a tool for the agent.

Testing the Agent

• Running the Code: Executing the agent to summarize GitHub issues and save notes.
• Agent Interaction: Interacting with the agent through the command line to demonstrate its capabilities.

Conclusion

• Potential: The simplicity of the agent built in the video can be extended for more complex and effective applications.
• Acknowledgment: Thanks to DataStacks for sponsoring the video.

Commands to Run the Project

• python3 main.py: To start the agent and interact with it.

Files Created

• main.py: Main file to run the agent.
• note.py: Contains the note-saving tool.
• notes.txt: File where notes are saved.
• requirements.txt: Lists the packages required for the project.

Final Thoughts

• The video showcases the process of building an AI agent that can act as a coding assistant, demonstrating the power and flexibility of combining different tools and APIs.