Llama_index unexpected keyword argument error on ChatGPT Model Python

I'm testing a couple of the widely published GPT models just trying to get my feet wet and I am running into an error that I cannot solve.

I am running this code:

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"] = 'MYKEY'

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="Enter your text"),
                     outputs="text",
                     title="Custom-trained AI Chatbot")

index = construct_index("salesdocs")
iface.launch(share=False)

And I keep getting this error

  File "C:\Users\Anonymous\anaconda3\lib\site-packages\llama_index\indices\vector_store\base.py", line 58, in __init__
    super().__init__(
TypeError: __init__() got an unexpected keyword argument 'llm_predictor'

Having a hard time finding much documentation on llamma index errors, hoping someone can point me in the right direction.

You need to change the code according to this example: LlamaIndex usage pattern

Basically, you need to send that information as a ServiceContext:

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)

But the majority of the tutorials online are older versions. So, you were misguided, and me as well.

To complete even more the answer, if you later need to load the created index you must send the service_context as well:

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

Otherwise, the code will break while loading the index file.

I got the same error When tried that example too. A simplest fix is to use from_document using defaults:

    index = GPTSimpleVectorIndex.from_documents(documents)

But if you want to set your own prompt context and llm_predictor, then create a context and pass it to GPTSimpleVectorIndex.from_document() call:

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)

    service_context = ServiceContext.from_defaults(
        llm_predictor=llm_predictor, prompt_helper=prompt_helper
    )

    index = GPTSimpleVectorIndex.from_documents(documents,service_context=service_context)

    index.save_to_disk('index.json')

    return index

Consider using model_name="gpt-3.5-turbo" that is faster and costs less.

llm_predictor_gpt = LLMPredictor(llm=ChatOpenAI(temperature=0.7,
   model_name="gpt-3.5-turbo", max_tokens=num_outputs))