how the query engine decides if it find

It's up to the LLM to decide that 🙂 We just show the model some text, and it's up to it to decide how best to respond

If you want, you could use a chat engine or data agent, which may help with this use-case

i want something to use as a flag or something to do if condition if the response is from the document (index) or not

Not really possible at the moment, at least with just a query engine. The chat engine and data agents give you more control in how to instruct the model to respond though, by setting a system prompt

https://gpt-index.readthedocs.io/en/latest/examples/chat_engine/chat_engine_context.html

https://gpt-index.readthedocs.io/en/latest/examples/agent/openai_agent_with_query_engine.html

do they have the same accuracy and functionalty of the basic query engine ?

It uses a query engine under the hood yes, but also an extra "chat" layer on top, allowing for more normal interaction and conversation history

okay could you guide me how to use the chatengine in the code above ?

and how to add the condition to instruct

The notebook is pretty thorough, but

The system prompt is where you put instructions on how to act

okay thankyou

ValueError: Unknown chat mode: context i am getting these always why do you think that ?

def create_index(path):
max_input = 4096
tokens = 512
chunk_size = 600
max_chunk_overlap = 0.2
promptHelper = PromptHelper(max_input, tokens, max_chunk_overlap, chunk_size_limit=chunk_size)

# Define LLm
llmPredictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003", max_tokens=tokens))

# Load data
docs = SimpleDirectoryReader(path).load_data()

# Create vector index
service_context = ServiceContext.from_defaults(llm_predictor=llmPredictor, prompt_helper=promptHelper)

vectorIndex = GPTVectorStoreIndex.from_documents(documents=docs, service_context=service_context)
vectorIndex.storage_context.persist(persist_dir='index_store')
return vectorIndex


def answerMe(question):
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.memory import ChatMemoryBuffer
data = SimpleDirectoryReader('uploads').load_data()
index = VectorStoreIndex.from_documents(data)
memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
chat_engine = index.as_chat_engine(
chat_mode="context",
memory=memory,
system_prompt="You are a chatbot, able to have normal interactions, as well as talk the index,if the response is negative return none.",
)
response = chat_engine.chat(question)
return response
here is my two methods

Try updating your llama index version

pip install --upgrade llama_index