Llama_index ChatGPT 모델 Python에서 예기치 않은 키워드 인수 오류

from llama_index import SimpleDirectoryReader, GPTListIndex, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import gradio as gr
import sys
import os
os.environ[OPENAI_API_KEY] = '내키'
def construct_index(directory_path):
    max_input_size = 4096
    num_outputs = 512
    max_chunk_overlap = 20
    chunk_size_limit = 600
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
    llm_predictor_gpt = LLMPredictor(llm=OpenAI(temperature=0.7, model_name=text-davinci-003, max_tokens=num_outputs))
    documents = SimpleDirectoryReader(directory_path).load_data()
    index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor_gpt, prompt_helper=prompt_helper)
    index.save_to_disk('index.json')
    return index
def chatbot(input_text):
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    response = index.query(input_text, response_mode=compact)
    return response.response
iface = gr.Interface(fn=chatbot,
                     inputs=gr.inputs.Textbox(lines=7, label=텍스트를 입력하세요),
                     outputs=text,
                     title=사용자 정의 AI 챗봇)
index = construct_index(salesdocs)
iface.launch(share=False)

그리고 계속해서 이 오류를 받고 있습니다.

  File C:\Users\Anonymous\anaconda3\lib\site-packages\llama_index\indices\vector_store\base.py, line 58, in __init__
    super().__init__(
TypeError: __init__()에 예상하지 못한 키워드 인수 'llm_predictor'가 있습니다.

llama index 오류에 대해 많은 문서를 찾기 어려워서 누군가가 제게 올바른 방향을 가르쳐줄 수 있으면 좋겠습니다.

답변 1

이 예시에 따라 코드를 변경해야 합니다: LlamaIndex 사용 패턴

기본적으로, 그 정보를 ServiceContext로 보내야 합니다:

from llama_index import ServiceContext
service_context = ServiceContext.from_defaults(
  llm_predictor=llm_predictor, 
  prompt_helper=prompt_helper,
  embed_model=embedding_llm,
  )
    
index = GPTSimpleVectorIndex(nodes, service_context=service_context)

하지만 대부분의 온라인 튜토리얼은 오래된 버전입니다. 그래서 당신과 나도 잘못된 정보를 받았습니다.

더욱 완전한 답변을 위해, 나중에 생성된 인덱스를 로드해야 한다면 service_context도 보내야 합니다:

index = GPTSimpleVectorIndex.load_from_disk(
           filename, service_context=service_context
        )

그렇지 않으면, 인덱스 파일을 로드하는 동안 코드가 깨질 수 있습니다.

답변 2

Title: Understanding the Llama Index in Python: Troubleshooting Unexpected Keyword Argument Errors
Introduction:
In recent years, Python has emerged as one of the most popular programming languages due to its simplicity, versatility, and expansive community support. Among its various applications, Python is extensively used in Natural Language Processing (NLP) tasks, including language modeling. ChatGPT, a language model developed by OpenAI, is widely employed for generating conversational responses. However, while working with ChatGPT models, it is not uncommon to encounter unexpected keyword argument errors, such as the Llama_index unexpected keyword argument error. In this essay, we will delve into the concept of the Llama Index error and provide troubleshooting steps to overcome it, helping Python developers improve their programming skills and resolve such issues more effectively.
Understanding the Llama Index Error:
The Llama Index error is a type of unexpected keyword argument error that can occur when working with ChatGPT models in Python. Most commonly, it arises when calling the model's generate function while passing an invalid or unknown keyword argument. This error occurs due to a mismatch of arguments between the model's function call and the defined function parameters.
Troubleshooting Steps:
While encountering the Llama Index unexpected keyword argument error might seem daunting at first glance, it can be resolved effectively by following these troubleshooting steps:
1. Review the Model's Documentation and Parameters:
Carefully consult the documentation provided by OpenAI or the specific model's API documentation. These resources act as a blueprint, providing vital details about the model's functions, input requirements, and supported keyword arguments. Make sure to cross-verify the function call against the accepted parameters to identify any discrepancies.
2. Check for Typos or Aliases:
Double-check your code for any spelling errors, whitespace issues, or incorrect capitalization when specifying the keyword arguments. Even a minor mistake can result in unexpected keyword argument errors. Additionally, confirm that the arguments you provided are within the model's defined scope and are not aliases that differ from the allowed parameters.
3. Verify the Model Version:
Different versions of ChatGPT models might have variations in their function signatures or supported keyword arguments. Ensure that the function calls and the arguments you pass are compatible with the specific model version you are using. This step is particularly crucial when working with the GPT-3 models, as they have evolved over time.
4. Update the Python Packages:
Sometimes, unexpected keyword argument errors can occur due to outdated Python packages. Ensure that all the relevant libraries, including the OpenAI library, are updated to their latest versions. Outdated packages may lack necessary patch fixes, resulting in compatibility issues that trigger unexpected errors.
5. Seek Community Support:
If you have diligently followed the aforementioned troubleshooting steps but have been unable to resolve the Llama Index error, it is beneficial to reach out to the Python community or the OpenAI support channels. There, experienced developers and experts can provide valuable insights, debugging tips, or even identify potential bugs in the model's implementation.
Conclusion:
As Python continues to empower developers in the domain of NLP and language modeling, encountering unexpected keyword argument errors like the Llama Index error can sometimes hinder progress. However, with the troubleshooting steps outlined in this essay, Python developers can efficiently tackle such issues and gain a deeper understanding of effective debugging techniques. By adhering to proper documentation, reviewing parameters, maintaining updated packages, and leveraging community support, programmers can overcome these challenges and enhance their Python programming skills, ensuring smoother experiences with ChatGPT models in the future.