Files
gh-jezweb-claude-skills-ski…/templates/code-interpreter-assistant.ts
2025-11-30 08:25:15 +08:00

137 lines
4.0 KiB
TypeScript

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