Extra text

    response = query_engine.query(prompt)
    print("User Input:\n\t", prompt)
    print("Bot Response:\n\t", response)

User Input:
         what is 2+2?
Bot Response:
         , and that the question does not fit the scope of your expertise.

User Input:
         Where is the Euphrates river?
Bot Response:
         , and direct the questioner to an appropriate source of information.

The Euphrates river is located in the Middle East, in the countries of Iraq, Syria, and Turkey. It is one of the two major rivers in the region, the other being the Tigris. The Euphrates is the longest river in the Middle East, and is a major source of water for the region. It is also an important part of the region's history and culture.

print([n.node.text for node in response.source_nodes])

from llama_index import ServiceContext, set_global_service_context
from llama_index.llms import OpenAI

service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo", temperature=0.0))

set_global_service_context(service_context)

    qa_template = Prompt(template)

    # Access the environment variable
    api_key = os.getenv('OPENAI_API_KEY')
    openai.api_key = api_key

    mongo_uri = os.getenv('MONGO_URI')
    mongodb_client = pymongo.MongoClient(mongo_uri)
    db_name = os.getenv('MONGO_DB_NAME')
    collection_name = os.getenv('MONGO_COLLECTION_NAME')
    index_name = os.getenv('MONGO_INDEX_NAME')

    threshold_cutoff=float(os.getenv('THRESHOLD_CUTOFF') or 0.4)
    percentile_cutoff=float(os.getenv('PERCENTILE_CUTOFF') or 0.6)

    similarity_top_k=int(os.getenv('SIMILARITY_TOP_K') or 3)
    temperature = float(os.getenv('TEMPERATURE') or 0.2)
    model_name=os.getenv('MODEL_NAME') or "gpt-3.5-turbo"
    num_output=int(os.getenv('NUM_OUTPUT') or 700)
    
    store = MongoDBAtlasVectorSearch(mongodb_client, db_name=db_name,collection_name=collection_name, index_name=index_name)
    index = VectorStoreIndex.from_vector_store(vector_store=store)
    service_context = ServiceContext.from_defaults(llm=OpenAI(temperature=temperature, model_name=model_name), num_output=num_output)
    query_engine = index.as_query_engine(
        optimizer=SentenceEmbeddingOptimizer(threshold_cutoff=threshold_cutoff,percentile_cutoff=percentile_cutoff),
        retriever_mode="embedding",
        service_context = service_context,
        similarity_top_k=similarity_top_k,
        # response_mode="compact",
        # verbose=True,
        text_qa_template=qa_template
    )

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import pymongo
import os
import openai
from llama_index import ServiceContext
from llama_index.llms.openai import OpenAI
from llama_index.indices.postprocessor.optimizer import SentenceEmbeddingOptimizer
from llama_index import Prompt
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index.indices.vector_store.base import VectorStoreIndex

node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=threshold_cutoff,percentile_cutoff=percentile_cutoff)]

 response = query_engine.query(prompt)
  File "/root/pytest/venv/lib/python3.10/site-packages/llama_index/indices/query/base.py", line 23, in query
    response = self._query(str_or_query_bundle)
  File "/root/pytest/venv/lib/python3.10/site-packages/llama_index/query_engine/retriever_query_engine.py", line 147, in _query
    nodes = self.retrieve(query_bundle)
  File "/root/pytest/venv/lib/python3.10/site-packages/llama_index/query_engine/retriever_query_engine.py", line 110, in retrieve
    nodes = node_postprocessor.postprocess_nodes(
  File "/root/pytest/venv/lib/python3.10/site-packages/llama_index/indices/postprocessor/optimizer.py", line 117, in postprocess_nodes
    nodes[i].node.set_content(" ".join(top_sentences))
IndexError: list index out of range

for i in range(len(nodes)):
   ...
   nodes[i].set_content(...)

for i in range(5):
    if True:
        for i in range(10):
            pass
    print(i)