{ "model": "gpt-4o-2024-08-06", "messages": [ { "role": "system", "content": "You are a helpful math tutor." }, { "role": "user", "content": "solve 8x + 31 = 2" } ], "response_format": { "type": "json_schema", # 规范输出 "...
模型返回3张相似的随机变体图片response=openai.Image.create_variation(image=image_data,n=3,size="256...
response = client.fine_tuning.jobs.create( training_file=training_file_id, validation_file=validation_file_id, model=MODEL, suffix="recipe-ner", ) job_id = response.id print("Job ID:", response.id) print("Status:", response.status) 检查任务状态 你可以发送一个GET请求到https://api.opena...
response = openai.Completion.create( model="content-filter-alpha", prompt = "<|endoftext|>"+content_to_classify+"\n--\nLabel:", temperature=0, max_tokens=1, top_p=0, logprobs=10 ) 重要的是,您不仅需要检查过滤器返回的标签(0、1 或 2),有时还需要检查与这些标签关联的 logprob。 如果...
client=OpenAI(api_key=api_key)defrecognize_image():response=client.chat.completions.create(model="gpt-4-vision-preview",messages=[{"role":"user","content":[{"type":"text","text":"这个图片里面有什么"},{"type":"image_url","image_url":"https://upload.wikimedia.org/wikipedia/commons/th...
await openai.createEmbedding({ model: "text-embedding-ada-002", input: fs.readFileSync('./document.md', 'utf-8').toString(), }).then((response) => response.data.data[0].embedding); 存储 保存Embedding数据。(对于大型数据集,可以使用矢量数据库) ...
print(response.choices[0].text.strip()) 代码详解 导入库:首先导入openai库。 设置API密钥:使用您的API密钥进行身份验证。 调用API:使用openai.Completion.create方法调用GPT-4o模型,传入相应的参数: model:指定使用的模型为“gpt-4o”。 prompt:输入的提示文本,用于引导模型生成内容。
response=client.audio.speech.create(model="tts-1",voice="alloy",input=text_to_speech # 使用读取的文本作为输入)# 将响应流式传输到文件 response.stream_to_file(speech_file_path)print(f"语音文件已生成在:{speech_file_path}") 注意为了让上面你的代码能够成功运行,你需要将你先前准备好的openai密钥粘...
{ model: OPENAI_MODEL,// gpt-4o, gpt-3.5-turbo, etc. Pulled from .env filemax_tokens:1024, temperature, response_format: {type:"json_object", }, messages: [ { role:'system', content: systemPrompt }, { role:'user', content: userPrompt } ],// @ts-expect-error data_sources is...
model="gpt-3.5-turbo", response_format={ "type": "json_object" }, messages=[ {"role": "system", "content": "You are a helpful assistant designed to output JSON.."}, {"role": "user", "content": "Generate me 3 Jargons that I can use for my Social Media content as a Data ...