An InvalidRequestError indicates that your request was malformed or missing some required parameters, such as a token or an input. This could be due to a typo, a formatting error, or a logic error in your code. If you encounter an InvalidRequestError, please try the following steps: - Re...
针对你遇到的openai.error.invalidrequesterror: must provide an 'engine' or 'deployment_id'错误,这通常是因为在调用OpenAI API时,请求参数中缺少了必要的engine或deployment_id字段。以下是一些可能的解决步骤: 1. 确认错误类型 你遇到的错误是openai.error.invalidrequesterror,这表示你的请求参数不完整或格式不正...
import openai openai_api_key = '■■■j5LuDiSb' openai.api_key = openai_api_key def ask_openai(message): response = openai.Completion.create( model="text-davinci-003", prompt = message, max_tokens=150, n=1, stop=None, tempe...
Issue: openai.error.InvalidRequestError: This model’s maximum context length is 4097 tokens. However, your messages resulted in 4275 tokens. Please reduce the length of the messages. Hello Team, I am across the error above, and have tried all possible solutions but could resolve the issue. ...
前言OpenAI差不多都写烂了，我这边就简单写个如何调用接口的吧，大家在自己玩玩如何调用？两种方法1 ...
Describe the bug Traceback (most recent call last): File "", line 1, in File "/Users/ruili/miniforge3/envs/gpt/lib/python3.9/site-packages/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs...
HI, Is that azure Openai embedding had limitation ? Traceback (most recent call last): File "D:\Corent\AI\LangChain\azure\azure_connection.py", line 45, in VectorStore = Milvus.from_texts( ^^^ File "D:\Corent\AI\LangChain\...
openai_api_version="2023-03-15-preview", ) docsearch = Chroma.from_documents(split_docs, embeddings) qa = RetrievalQA.from_chain_type(llm=OpenAI(engine="t-ada"), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True) ...
I am trying to replicate the the add your own data feature for Azure Open AI following the instruction found here: Quickstart: Chat with Azure OpenAI models using your own data import os import openai import dotenv dotenv.load_dotenv() endpoint =…
response = openai.ChatCompletion.create(model=engine, messages=message, temperature=0.5, max_tokens=16385) return response['choices'][0]['message']['content'] @app.route('/process') def process_csv(): # Open or create the output CSV file ...