人工智能应用开发_大语言模型Prompt工程与自然语义编程_基于OpenAI和Claude的AI代码助手与Chrome插件开发_...

# OpenAI Node API Library [](https://npmjs.org/package/openai)  [](https://jsr.io/@openai/openai) This library provides convenient access to the OpenAI REST API from TypeScript or JavaScript. It is generated from our [OpenAPI specification](https://github.com/openai/openai-openapi) with [Stainless](https://stainlessapi.com/). To learn how to use the OpenAI API, check out our [API Reference](https://platform.openai.com/docs/api-reference) and [Documentation](https://platform.openai.com/docs). ## Installation ```sh npm install openai ``` You can also import from jsr: <!-- x-release-please-start-version --> ```ts import OpenAI from 'jsr:@openai/openai'; ``` <!-- x-release-please-end --> ## Usage The full API of this library can be found in [api.md file](api.md) along with many [code examples](https://github.com/openai/openai-node/tree/master/examples). The code below shows how to get started using the chat completions API. <!-- prettier-ignore --> ```js import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted }); async function main() { const chatCompletion = await client.chat.completions.create({ messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo', }); } main(); ``` ## Streaming responses We provide support for streaming responses using Server Sent Events (SSE). ```ts import OpenAI from 'openai'; const client = new OpenAI(); async function main() { const stream = await client.chat.completions.create({ model: 'gpt-4', messages: [{ role: 'user', content: 'Say this is a test' }], stream: true, }); for await (const chunk of stream) { process.stdout.write(chunk.choices[0]?.delta?.content || ''); } } main(); ``` If you need to cancel a stream, you can `break` from the loop or call `stream.controller.abort()`. ### Request & Response types This library includes TypeScript definitions for all request params and response fields. You may import and use them like so: <!-- prettier-ignore --> ```ts import OpenAI from 'openai'; const client = new OpenAI({ apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted }); async function main() { const params: OpenAI.Chat.ChatCompletionCreateParams = { messages: [{ role: 'user', content: 'Say this is a test' }], model: 'gpt-3.5-turbo', }; const chatCompletion: OpenAI.Chat.ChatCompletion = await client.chat.completions.create(params); } main(); ``` Documentation for each method, request param, and response field are available in docstrings and will appear on hover in most modern editors. > [!IMPORTANT] > Previous versions of this SDK used a `Configuration` class. See the [v3 to v4 migration guide](https://github.com/openai/openai-node/discussions/217). ### Polling Helpers When interacting with the API some actions such as starting a Run and adding files to vector stores are asynchronous and take time to complete. The SDK includes helper functions which will poll the status until it reaches a terminal state and then return the resulting object. If an API method results in an action which could benefit from polling there will be a corresponding version of the method ending in 'AndPoll'. For instance to create a Run and poll until it reaches a terminal state you can run: ```ts const run = await openai.beta.threads.runs.createAndPoll(thread.id, { assistant_id: assistantId, }); ``` More information on the lifecycle of a Run can be found in the [Run Lifecycle Documentation](https://platform.openai.com/docs/assistants/deep-dive/run-lifecycle) ### Bulk Upload Helpers When creating and interacting with vector stores, you can use the polling helpers to monitor the status of operations. For convenience, we also provide a bulk upload helper to allow you to simultaneously upload several files at once. ```ts const fileList = [ createReadStream('/home/data/example.pdf'), ... ]; const batch = await openai.vectorStores.fileBatches.uploadAndPoll(vectorStore.id, fileList); ``` ### Streaming Helpers The SDK also includes helpers to process streams and handle the incoming events. ```ts const run = openai.beta.threads.runs .stream(thread.id, { assistant_id: assistant.id, }) .on('textCreated', (text) => process.stdout.write('\nassistant > ')) .on('textDelta', (textDelta, snapshot) => process.stdout.write(textDelta.value)) .on('toolCallCreated', (toolCall) => process.stdout.write(`\nassistant > ${toolCall.type}\n\n`)) .on('toolCallDelta', (toolCallDelta, snapshot) => { if (toolCallDelta.type === 'code_interpreter') { if (toolCallDelta.code_interpreter.input) { process.stdout.write(toolCallDelta.code_interpreter.input); } if (toolCallDelta.code_interpreter.outputs) { process.stdout.write('\noutput >\n'); toolCallDelta.code_interpreter.outputs.forEach((output) => { if (output.type === 'logs') { process.stdout.write(`\n${output.logs}\n`); } }); } } }); ``` More information on streaming helpers can be found in the dedicated documentation: [helpers.md](helpers.md) ### Streaming responses This library provides several conveniences for streaming chat completions, for example: ```ts import OpenAI from 'openai'; const openai = new OpenAI(); async function main() { const stream = await openai.beta.chat.completions.stream({ model: 'gpt-4', messages: [{ role: 'user', content: 'Say this is a test' }], stream: true, }); stream.on('content', (delta, snapshot) => { process.stdout.write(delta); }); // or, equivalently: for await (const chunk of stream) { process.stdout.write(chunk.choices[0]?.delta?.content || ''); } const chatCompletion = await stream.finalChatCompletion(); console.log(chatCompletion); // {id: "…", choices: […], …} } main(); ``` Streaming with `openai.beta.chat.completions.stream({…})` exposes [various helpers for your convenience](helpers.md#events) including event handlers and promises. Alternatively, you can use `openai.chat.completions.create({ stream: true, … })` which only returns an async iterable of the chunks in the stream and thus uses less memory (it does not build up a final chat completion object for you). If you need to cancel a stream, you can `break` from a `for await` loop or call `stream.abort()`. ### Automated function calls We provide the `openai.beta.chat.completions.runTools({…})` convenience helper for using function tool calls with the `/chat/completions` endpoint which automatically call the JavaScript functions you provide and sends their results back to the `/chat/completions` endpoint, looping as long as the model requests tool calls. If you pass a `parse` function, it will automatically parse the `arguments` for you and returns any parsing errors to the model to attempt auto-recovery. Otherwise, the args will be passed to the function you provide as a string. If you pass `tool_choice: {function: {name: …}}` instead of `auto`, it returns immediately after calling that function (and only loops to auto-recover parsing errors). ```ts import OpenAI from 'openai'; const client = new OpenAI(); async function main() { const runner = client.beta.chat.completions .runTools({ model: 'gpt-3.5-turbo', messages: [{ role: 'user', content: 'How is the weather this week?' }], tools: [ { type: 'function', function: { function: getCurrentLocation, parameters: { type: 'object', properties: {} }, }, }, { type: 'function', function: { function: getWeather, parse: JSON.parse, // or use a validation library