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

The video introduces a tutorial on constructing a custom AI agent using Python, Lang chain, and retrieval augmented generation. The AI agent is designed to interact with GitHub repositories, summarizing and responding to issues. It showcases the agent's capabilities, such as querying a GitHub repository for issues related to 'flashing messages', summarizing them, and even saving notes to a local computer. The process involves setting up a virtual environment, installing necessary packages, and using a Vector store database to store and retrieve issue data efficiently.

This segment focuses on setting up the development environment by creating a virtual environment and installing required packages like `python-dotenv`, `requests`, and `langchain`. It also covers the creation of an `.env` file for storing sensitive credentials, such as GitHub tokens and API keys for Data Stacks' Astra DB, which serves as the vector store database. The video provides a step-by-step guide to obtaining a GitHub token and setting up the Astra DB, including generating an API token and identifying the necessary endpoints and keys.

The tutorial continues with the configuration of connections to both GitHub and Astra DB. It demonstrates how to use the GitHub API to fetch repository issues and how to set up the Astra DB by creating a serverless vector database. The process includes initializing the database, generating an application token, and retrieving the API endpoint. The video also guides viewers on how to insert the obtained credentials into the `.env` file for later use in the project.

The speaker writes code to fetch GitHub issues and store them in a vector store database. The process involves defining functions to fetch issues from GitHub using the GitHub API and to load these issues into the database as documents. The video explains how to extract relevant metadata from the issues, such as author, comments, and creation date, and how to combine the issue title and body to create a searchable document for the vector store.

This part of the video demonstrates how to implement a similarity search within the vector store database. It shows testing the database connection and the ability to search for issues similar to a given query, such as 'flash messages'. The video also highlights the process of debugging and fixing errors that arise during the implementation, ensuring that the vector store can accurately retrieve and display relevant GitHub issues.

The video moves on to creating tools for the AI agent, starting with a retriever tool that interfaces with the vector store database. It details the process of defining the tool, describing its purpose, and setting up the necessary configurations. The tool is designed to allow the AI agent to search the vector store for information related to GitHub issues. The segment also covers how to utilize Lang chain Hub to download a pre-configured prompt for the AI agent.

The tutorial concludes with the assembly of the AI agent using the Lang chain library. It involves creating the agent with the specified LLM (Language Model), tools, and prompt. The agent is then equipped with an executor that allows it to be queried interactively. The video demonstrates how to write a loop for continuous agent interaction, showing the agent's ability to use the vector store tool to answer questions about GitHub issues and to utilize a note-taking tool to save information locally.

The final segment wraps up the tutorial by importing and adding a note-taking tool to the agent's capabilities. It shows how to create a simple Python function wrapped as a tool that the agent can use to append notes to a local file. The video then demonstrates the agent's functionality by asking it to perform a series of tasks, such as summarizing GitHub issues and saving notes. The successful execution of these tasks highlights the agent's ability to utilize multiple tools effectively.