137 lines
4.0 KiB
TypeScript
137 lines
4.0 KiB
TypeScript
/**
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* Code Interpreter Assistant
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*
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* Demonstrates:
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* - Python code execution
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* - File uploads for data analysis
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* - Retrieving generated files (charts, CSVs)
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* - Data visualization
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*/
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import OpenAI from 'openai';
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import fs from 'fs';
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const openai = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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});
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async function main() {
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console.log('📊 Creating Data Analyst Assistant...\n');
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// 1. Create assistant with code interpreter
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const assistant = await openai.beta.assistants.create({
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name: "Data Analyst",
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instructions: "You are a data analyst. Analyze data and create visualizations. Always explain your approach and findings.",
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tools: [{ type: "code_interpreter" }],
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model: "gpt-4o",
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});
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console.log(`✅ Assistant created: ${assistant.id}\n`);
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// 2. Upload a data file (CSV example)
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// For this example, create a sample CSV
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const csvData = `date,revenue,expenses
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2025-01-01,10000,4000
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2025-01-02,12000,4500
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2025-01-03,9500,4200
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2025-01-04,15000,5000
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2025-01-05,13500,4800`;
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fs.writeFileSync('sample_data.csv', csvData);
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const file = await openai.files.create({
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file: fs.createReadStream('sample_data.csv'),
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purpose: 'assistants',
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});
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console.log(`✅ File uploaded: ${file.id}\n`);
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// 3. Create thread with file attachment
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const thread = await openai.beta.threads.create({
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messages: [{
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role: "user",
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content: "Analyze this revenue data. Calculate total revenue, average daily revenue, and create a visualization showing revenue and expenses over time.",
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attachments: [{
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file_id: file.id,
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tools: [{ type: "code_interpreter" }],
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}],
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}],
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});
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console.log(`✅ Thread created: ${thread.id}\n`);
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// 4. Run the assistant
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console.log('🏃 Running analysis...\n');
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const run = await openai.beta.threads.runs.create(thread.id, {
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assistant_id: assistant.id,
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});
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// 5. Poll for completion
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let runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);
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while (!['completed', 'failed', 'cancelled'].includes(runStatus.status)) {
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await new Promise(resolve => setTimeout(resolve, 1000));
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runStatus = await openai.beta.threads.runs.retrieve(thread.id, run.id);
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console.log(` Status: ${runStatus.status}`);
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}
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if (runStatus.status !== 'completed') {
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console.error(`❌ Run ${runStatus.status}:`, runStatus.last_error);
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process.exit(1);
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}
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console.log('\n✅ Analysis completed!\n');
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// 6. Retrieve the response
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const messages = await openai.beta.threads.messages.list(thread.id);
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const responseMessage = messages.data[0];
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console.log('💬 Analysis Results:\n');
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for (const content of responseMessage.content) {
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if (content.type === 'text') {
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console.log(content.text.value);
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console.log('\n---\n');
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}
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// Download generated image files (charts)
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if (content.type === 'image_file') {
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const imageFileId = content.image_file.file_id;
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console.log(`📈 Chart generated: ${imageFileId}`);
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// Download the image
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const imageData = await openai.files.content(imageFileId);
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const imageBuffer = Buffer.from(await imageData.arrayBuffer());
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fs.writeFileSync(`chart_${imageFileId}.png`, imageBuffer);
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console.log(` Saved as: chart_${imageFileId}.png\n`);
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}
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}
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// 7. Check run steps to see code that was executed
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const runSteps = await openai.beta.threads.runs.steps.list(thread.id, run.id);
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console.log('🔍 Execution Steps:\n');
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for (const step of runSteps.data) {
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if (step.step_details.type === 'tool_calls') {
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for (const toolCall of step.step_details.tool_calls) {
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if (toolCall.type === 'code_interpreter') {
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console.log('Python code executed:');
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console.log(toolCall.code_interpreter.input);
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console.log('\nOutput:');
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console.log(toolCall.code_interpreter.outputs);
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console.log('\n---\n');
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}
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}
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}
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}
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// Cleanup
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fs.unlinkSync('sample_data.csv');
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console.log('\n📊 Usage:');
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console.log(` Total tokens: ${runStatus.usage?.total_tokens}`);
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}
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main().catch(console.error);
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