Replace WatsonX TypeScript Jupyter notebook with a Node.js module

Author: jamesmurdza

examples/watsonx-ai-code-interpreter-js/.env.template

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+# Get your credentials at dataplatform.cloud.ibm.com:
+# - Under developer access, select the default project
+# - Get the project ID
+# - Get the wastonxai URL
+# - Create an API key
+WATSONX_PROJECT_ID = "project_id"
+WATSONX_URL = "https://region.ml.cloud.ibm.com"
+WATSONX_API_KEY = "your_api_key"
+
+# Get your API key at e2b.dev:
+E2B_API_KEY = "your_api_key"

examples/watsonx-ai-code-interpreter-js/app.ts

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+// Code interpreter with IBM WatsonX AI Graphite
+// This example uses the [E2B SDK](https://github.com/e2b-dev/code-interpreter) as a code interpreter for [IBM Granite Code](https://www.ibm.com/granite/docs/models/code/) on [IBM WatsonX](https://www.ibm.com/watsonx).
+// The code generated by the LLM runs in a [E2B secure cloud sandbox](https://e2b.dev/docs/sandbox/overview).
+
+// 1. Imports and API keys
+// You need to get your API keys and save them in .env file. You can copy and rename the .env.template file. We import all necessary libraries.
+
+import { config } from "dotenv";
+import { WatsonXAI } from "@ibm-cloud/watsonx-ai";
+import { IamAuthenticator } from "ibm-cloud-sdk-core";
+import { Sandbox } from "@e2b/code-interpreter";
+import fs from "fs";
+
+// Load environment variables from .env file
+config({ path: "./.env" });
+
+// 2. Initialize WatsonX AI Client
+// We initialize the WatsonX AI client using the credentials stored in the environment variables. This client will be used to interact with the IBM WatsonX Granite model.
+
+// Environment variables
+const WATSONX_PROJECT_ID = process.env.WATSONX_PROJECT_ID;
+const WATSONX_URL = process.env.WATSONX_URL || "https://us-south.ml.cloud.ibm.com";
+const WATSONX_API_KEY = process.env.WATSONX_API_KEY;
+
+// Initialize WatsonX client
+const watsonxAIService = WatsonXAI.newInstance({
+version: "2024-05-31",
+serviceUrl: WATSONX_URL,
+authenticator: new IamAuthenticator({ apikey: WATSONX_API_KEY || "" }),
+});
+
+// Parameters for the text generation request
+const params = {
+modelId: "ibm/granite-34b-code-instruct",
+projectId: WATSONX_PROJECT_ID!,
+parameters: {
+    max_new_tokens: 1024,
+},
+};
+
+console.log("WatsonX service initialized");
+
+// 3. Configure System Prompt and Generate a Response
+// We define a system prompt that instructs the model on how to behave. Then, we send a user query to the model and retrieve a response.
+
+// System prompt configuration
+// Because this is Markdown, the LLM will respond in Markdown
+const SYSTEM_PROMPT = `
+## Your job & context
+You are a python data scientist. You are given tasks to complete and you run Python code to solve them.
+- The Python code runs in Jupyter notebook.
+- You have access to the internet and can make API requests.
+- You also have access to the filesystem and can read/write files.
+- You can install any pip package but the usual packages for data analysis are already preinstalled.
+`;
+
+// Specify the prompt
+const userMessage = "Plot a 3D chart of sin x cos y.";
+console.log(`User Message: ${userMessage}`);
+
+const prompt = SYSTEM_PROMPT + userMessage + "\n";
+
+// Generate response using the Watson SDK
+console.log("Generating response from WatsonX...");
+const response = await watsonxAIService.generateText({ input: prompt, ...params });
+const content = response.result.results[0].generated_text;
+
+console.log(`Model response:`);
+console.log(`${"=".repeat(50)}\n${content}\n${"=".repeat(50)}`);
+
+// 4. Extract and Display AI-Generated Code
+// The AI-generated response may include Markdown formatting. We extract the code block from the response using regular expressions.
+
+// Extract code blocks
+const pattern = /```(?:python)?[\n\r](.*?)```/gs;
+const results = Array.from(content.matchAll(pattern));
+const codeBlock = results.length ? results.map(match => match[1]).join("\n") : content;
+
+// Print the code block
+console.log(`AI-generated code:`);
+console.log(`${"=".repeat(50)}\n${codeBlock}\n${"=".repeat(50)}`);
+
+// 5. Execute Code in an E2B Code Interpreter
+// We run the AI-generated code in a secure E2B sandbox environment, capturing its output, errors, and runtime exceptions.
+
+// Run the code in an E2B code interpreter
+const codeInterpreter = await Sandbox.create();
+const execution = await codeInterpreter.runCode(codeBlock);
+
+console.log("Stdout:", execution.logs.stdout);
+console.log("Stderr:", execution.logs.stderr);
+console.log("Python runtime error:", execution.error ?? "None");
+
+// 6. Display Generated Figures
+// If the AI-generated code produces visualizations, we retrieve and display them.
+
+// Print the first figure
+const figures = execution.results.map(result => {
+// In a real script, you'd want to save this to a file or display it another way
+const binaryData = Buffer.from(result.png, 'base64');
+return binaryData; // In a script, you'd likely save this to a file
+});
+
+console.log(`Generated ${figures.length} figures`);
+fs.writeFileSync('figure.png', figures[0]);
+
+// 7. Terminate the Code Interpreter
+// Finally, we safely terminate the E2B code interpreter to free up resources.
+
+// Kill the code interpreter
+await codeInterpreter.kill();