dotnet user-secrets initdotnet user-secrets set Azure:OpenAI:Endpoint [YOUR_AZURE_OPENAI_ENDPOINT]dotnet user-secrets set Azure:OpenAI:ApiKey [YOUR_AZURE_OPENAI_APIKEY]dotnet user-secrets set Azure:OpenAI:ModelName [YOUR_MODEL_DEPLOYMENT] 实现聊天功能有两种方式。一种是非流式响应,即一次性返回所有文...
嵌入模型通过 "embed_model "参数传递给评估函数,对于 OpenAI 模型来说,"embed_model "参数是一个用模型名称和模型维度初始化的 OpenAIEmbedding 对象。 from llama_index.embeddings.openai import OpenAIEmbedding embed_model = OpenAIEmbedding(model=model_spec['model_name'],dimensions=model_spec['dimensions'])...
llm = OpenAI(model_name=modal,openai_api_key=api_key,openai_api_base=api_url) chat_model = ChatOpenAI(model_name=modal,openai_api_key=api_key,openai_api_base=api_url) text = "What would be a good company name for a company that makes colorful socks?" messages = [HumanMessage(content...
response = client.chat.completions.create( model="gpt-3.5-turbo-1106", messages=messages, tools=tools, tool_choice="auto", # auto isdefault, but we'll be explicit ) response_message = response.choices[0].message tool_calls = response_message.tool_calls # Step 2: check if the model want...
需要更多时间进行过渡的开发者可以通过在他们的API请求的‘model’参数中指定gpt-3.5-turbo-0301，gpt-4-0314，或gpt-4-32k-0314来继续使用旧模型。这些旧模型将在9月13日之后仍然可以访问，之后指定这些模型名称的请求将失败。你可以通过我们的模型弃用页面来了解模型弃用的最新信息。这是对这些模型的第一次更新；...
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[1].embedding = {str(result.data[1].embedding...
但是用java代码调用api就出现上面的错误。 Sort by:Most helpful Guan007 Follow 0Reputation points Sep 17, 2023, 5:36 PM 解决了,我的 DockerfileCopy deploymentOrModelId参数传错了,我一直传的是ModelName,没有改成DeploymentName。后来尝试修改了才调用成功。
model_name = 'resnet50' image_embedding = 2048 text_encoder_model = "distilbert-base-uncased" text_embedding = 768 text_tokenizer = "distilbert-base-uncased" max_length = 200 pretrained = True # for both image encoder and text encoder ...
// The OpenAI API model name. internal const val OPENAI_MODEL_COMPLETIONS = "text-davinci-003" // The OpenAI API endpoint. internal const val API_ENDPOINT_COMPLETIONS = "https://api.openai.com/v1/completions" // ChatGPT prompts.
if model_name == "openai-api": model_name = config.get("model_name") model = AzureChatOpenAI( streaming=streaming, verbose=verbose, callbacks=callbacks, openai_api_key=config.get("api_key"), openai_api_base=config.get("api_base_url"), ...