openai-node

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import 'openai/shims/node.mjs';
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import OpenAI from 'openai';
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import { distance } from 'fastest-levenshtein';
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import { ChatCompletion } from 'openai/resources/chat/completions';
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import * as shims from 'openai/_shims/index';
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// The tests in this file don't typecheck with "moduleResolution": "node"
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function typeTests(x: shims.Request) {
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  const url: string = x.url;
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}
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const client = new OpenAI();
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declare global {
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  namespace jest {
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    interface Matchers<R> {
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      toBeSimilarTo(comparedTo: string, expectedDistance: number): R;
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    }
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  }
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}
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expect.extend({
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  toBeSimilarTo(received, comparedTo: string, expectedDistance: number) {
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    const message = () =>
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      [
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        `Received: ${JSON.stringify(received)}`,
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        `Expected: ${JSON.stringify(comparedTo)}`,
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        `Expected distance: ${expectedDistance}`,
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        `Received distance: ${actualDistance}`,
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      ].join('\n');
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    const actualDistance = distance(received, comparedTo);
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    if (actualDistance < expectedDistance) {
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      return {
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        message,
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        pass: true,
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      };
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    }
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    return {
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      message,
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      pass: false,
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    };
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  },
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});
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it(`raw response`, async function () {
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  const response = await client.chat.completions
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    .create({
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      model: 'gpt-4',
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      messages: [{ role: 'user', content: 'Say this is a test' }],
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    })
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    .asResponse();
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  // test that we can use node-fetch Response API
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  const chunks: string[] = [];
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  const { body } = response;
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  if (!body) throw new Error(`expected response.body to be defined`);
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  body.on('data', (chunk) => chunks.push(chunk));
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  await new Promise<void>((resolve, reject) => {
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    body.once('end', resolve);
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    body.once('error', reject);
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  });
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  const json: ChatCompletion = JSON.parse(chunks.join(''));
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  expect(json.choices[0]?.message.content || '').toBeSimilarTo('This is a test', 10);
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});
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