Llama_index issue behind HTTP request

I'm having an issue using Llama_Index to use an index previously generated for custom content ChatGPT queries.

I generated the index with the following code:

from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper, ServiceContext
from langchain import OpenAI
import sys
import os

def construct_index():
    # set maximum input size
    max_input_size = 40960
    # set number of output tokens
    num_outputs = 20000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600

    # define prompt helper
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))

    documents = SimpleDirectoryReader("./data").load_data()

    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

To use query something the code I use is the following:

def ask_ai(query):
    index = GPTSimpleVectorIndex.load_from_disk('./index.json')
    response = index.query(query)

    return response.response

This is a popular code that in fact works when I run it in a virtual environment (py ./index.py) adding a line to call by default the construct_index or ask_ai methods.

However, when I try to put it behind an HTTP request, like in an AWS Lambda or Flask API, the ask_ai fails in the load_from_disk method for both approaches with the same error:

ERROR:app:Exception on /api/query [POST]
Traceback (most recent call last):
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\flask\app.py", line 2190, in wsgi_app
    response = self.full_dispatch_request()
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\flask\app.py", line 1486, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\flask\app.py", line 1484, in full_dispatch_request
    rv = self.dispatch_request()
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\flask\app.py", line 1469, in dispatch_request
    return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
  File "C:\pmi\python-flask-restapi\app.py", line 17, in qyery_chatgpt
    return appService.ask_ai(query)
  File "C:\pmi\python-flask-restapi\app_service.py", line 10, in ask_ai
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\llama_index\indices\base.py", line 352, in load_from_disk
    return cls.load_from_string(file_contents, **kwargs)
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\llama_index\indices\base.py", line 328, in load_from_string
    return cls.load_from_dict(result_dict, **kwargs)
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\llama_index\indices\vector_store\base.py", line 260, in load_from_dict
    return super().load_from_dict(result_dict, **config_dict, **kwargs)
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\llama_index\indices\base.py", line 304, in load_from_dict
    docstore = DocumentStore.load_from_dict(result_dict[DOCSTORE_KEY])
  File "C:\pmi\python-flask-restapi\flaskapi\lib\site-packages\llama_index\docstore.py", line 72, in load_from_dict
    for doc_id, doc_dict in docs_dict["docs"].items():
KeyError: 'docs'

For the implementation of Flask I took it from here:

https://github.com/bbachi/python-flask-restapi

Just by leaving a single method in the AppService (the ask_ai) and invoking it from a single route in the controller. The docker file for the AWS container-based lambda is:

FROM public.ecr.aws/lambda/python:3.10

COPY requirements.txt ./
RUN pip3 install -r requirements.txt
COPY myfunction.py ./
COPY index.json ./

CMD ["myfunction.lambda_handler"]

In both cases the index.json holds the index generated with the above-provided code and for the lambda the myfunction.py contains the ask_ai method implementation behind the lambda_handler method.

Has someone faced such issue? any help is much appreciated in advance!

I used Chat GPT to examine this issue and it led me to find out the cause, actually it was a big mistake on my side because I dumbly generated the index with a different version of Llama_Index than the one I was later using to load it, I haven't noticed that.

If someone's facing the same issue try to stick to version 0.5.6.