Build an OpenAI code interpreter for Python

Introduction

Welcome to our RAG Hack live stream! RAG Hack is a global hackathon occurring from September 3rd to 16th, focusing on retrieval-augmented generation (RAG) applications. We’re excited to guide you through the process of building an OpenAI code interpreter using LangChain and Azure technologies. This session is aimed at developers keen on enhancing their applications with intelligent capabilities by leveraging language models and cloud infrastructures.

Overview of the Application

In this article, we will create an application that integrates an OpenAI code interpreter along with Python. The application will utilize Azure Container Apps and Azure AI Search to facilitate the retrieval of information and the execution of Python code dynamically. We will cover the following main steps:

1. Setting Up Vector Store and Embeddings: The first part of our application involves loading PDFs, chunking them up, generating embeddings, and inserting this data into Azure AI Search's vector store. This establishes a functional backend from which our application can retrieve information.

2. Building the Front-End Application: We will build a front-end chat interface using LangChain and Chainlit. Users will be able to ask questions, and the application will determine if it can respond directly or needs to query the vector store for more complex information.

3. Adding a Code Interpreter: We will enhance the application by adding a Python code interpreter. This involves integrating LangChain's built-in tools, enabling the application to process and respond to queries that require computation.

4. Deploying to Azure: Finally, we will discuss how to deploy the application to Azure Container Apps, taking advantage of serverless architecture to scale effectively.

Step-by-Step Breakdown

Loading Data into Vector Store

We start with a simple Python script that loads a set of documents (PDFs). The script reads the files, chunk them into smaller parts, generates embeddings using Azure OpenAI, and then stores those embeddings into Azure AI Search. This setup allows the chat application to access relevant data when users ask questions.

Key Script Element: Data Indexer Job

An indexer job will be configured to repeatedly load new data into Azure AI Search, ensuring our vector store remains updated with the most relevant documents.

Creating the Front-End Chat Application

With the vector store established, the next task is to build the front-end using Chainlit for the chat interface. The application initializes an Azure OpenAI GPT-3.5 model, allowing users to interact with it. User queries are processed through the model, which can determine whether to search the vector store or provide answers directly.

Key Script Element: Chat Interaction

The interaction logic involves invoking the model with user input, and the response is displayed back, including intermediate steps for transparency.

Integrating the Code Interpreter

The highlight of our application is the integration of a Python code interpreter. By updating our chat application to include a code interpreter tool, we enable the model to execute Python code for more complex queries, including tasks like mathematical operations and data analysis. This is done through the use of dynamic sessions in Azure Container Apps, which guarantees safe and isolated execution.

Key Script Element: Dynamic Sessions

Dynamic sessions allow rapid access to isolated environments where Python code can be executed without jeopardizing the application’s security.

Deployment to Azure Container Apps

Finally, we discuss the deployment of the application. By leveraging Azure Container Apps, we can run our chat application serverlessly, scaling it without managing underlying infrastructure.

Conclusion

By following these steps, you can create a sophisticated application capable of performing computations and providing intelligent responses using a Python code interpreter within a secure and scalable environment. This application serves as a model for building intelligent, retrieval-augmented applications.

Keywords

• OpenAI
• Code Interpreter
• Python
• Azure Container Apps
• LangChain
• Vector Store
• Dynamic Sessions
• Retrieval-Augmented Generation

FAQ

Q1: What technologies do I need to follow along with the tutorial?
A1: You will need access to Azure services, Python development environment, and familiarity with LangChain and Chainlit.

Q2: What is the main purpose of the code interpreter in the application?
A2: The code interpreter allows the application to execute Python code dynamically, enabling it to handle complex queries that require computation.

Q3: How does the application ensure security when executing code?
A3: The application utilizes Azure Dynamic Sessions, which create isolated environments for executing code, preventing any malicious actions from affecting the main application.

Q4: Can I deploy the application locally instead of using Azure?
A4: While this tutorial focuses on deploying to Azure, you can adapt the code to run locally, but you'll miss out on benefits like automatic scaling and serverless execution.

Q5: What data formats can the application handle?
A5: The application can handle various document formats, including PDFs and CSV files, allowing for diverse data retrieval and processing capabilities.