client = openai.OpenAI(api_key=OPENAI_API_KEY) embedding_model_name = "text-embedding-ada-002" result = client.embeddings.create( input=[ "This is a sentence", "A second sentence" ], model=embedding_model_name, ) print(f"len(result.data) = {len(result.data)}") print(f"result.data...
response = await openai_async_client.embeddings.create( File "/home/ws_0802/anaconda3/envs/lightrag/lib/python3.10/site-packages/openai/resources/embeddings.py", line 236, in create return await self._post( File "/home/ws_0802/anaconda3/envs/lightrag/lib/python3.10/site-packages/openai/_ba...
public Mono getEmbeddings(String deploymentOrModelName, EmbeddingsOptions embeddingsOptions) 返回给定提示的嵌入。 Parameters: deploymentOrModelName - 指定使用 Azure OpenAI) 时 (模型部署名称,或使用非 Azure OpenAI) 用于此请求时 (模型名称。 embeddingsOptions - 嵌入请求的配置信息。 嵌入度量文本字符串的相...
openai.ChatCompletion.create()client.chat.completions.create() openai.Completion.create()client.completions.create() openai.Edit.create()client.edits.create() openai.Embedding.create()client.embeddings.create() openai.File.create()client.files.create() ...
client.create( input=texts[i : i + chunk_size_], **self._invocation_params ) if not isinstance(response, dict): response = response.dict() embeddings.extend(r["embedding"] for r in response["data"]) at line 576 you should use chunk_size_ rather than self.chunk_size....
open_ai_client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY")) response = open_ai_client.embeddings.create( input=[query], model="text-embedding-3-small" ) return response.data[0].embedding def generate_result(query: str, entities, relationships): open_ai_client = openai.OpenAI(api...
see:https://platform.openai.com/docs/api-reference/chat/create#chat/create-stream Code import{CreateChatCompletionStreamResponse,}from'openai-api-client'// Return nothing when the 'stream' option is truevoidopenAI.createChatCompletion({requestBody:{model:'gpt-3.5-turbo-0613',stream:true,messages:...
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from openai import OpenAI import os client = OpenAI( # 替换为您需要调用的模型服务Base Url base_url="<BASE_URL>", # 环境变量中配置您的API Key api_key=os.environ.get("ARK_API_KEY") ) print("--- embeddings request ---") resp = client.embeddings.create( model="<Model>", input=[...
Create the client by passing in the token provider: import{AzureOpenAI}from"openai";constdeployment="Your deployment name";constapiVersion="2024-10-21";constclient=newAzureOpenAI({azureADTokenProvider,deployment,apiVersion}); Grant access to your Azure OpenAI resource to your trusted entities by ...