Hi everyone,
thank you in advance to those who are checking my thread.
any kind of help or guidance is greatly appreciated.
I have a CSV file with two columns, one for questions and another for answers:
something like this:
| Question | Answer |
|---|---|
| How many times you should wash your teeth per day? | it is advisable to wash it three times per day after each meal. |
| how many times should I use dental floss per day? | The American Dental Association suggest that everyone should floss their teeth once a day |
[What I want to do]
I want the program to receive the user question, find the most similar question from the CSV and output the answer exactly the same as the one in the answer column.
Because these questions are related to health, I do not want the program to Hallucinate or give answers based on its own knowledge. using temperature 0 only is not enough because I did some tests and sometimes it changed the nuance of the answer or combined the reply from two answers into one.
To prevent this, I want the program to output the exact same answer than the answer column.
my current code is as follows. also, I created the index by loading it with pandasCSVReader and using GPTVectorStoreIndex function
Index creation:
loader = PandasCSVReader()
documents = loader.load_data(file=Path('./health.csv'))
index = GPTVectorStoreIndex.from_documents(documents,llm_predictor=llm_predictor,prompt_helper=prompt_helper)
and the actual program is as follows:
Query engine
import os
os.environ["OPENAI_API_KEY"] = 'XXXXX'
from pathlib import Path
from llama_index import LLMPredictor, GPTVectorStoreIndex, PromptHelper, ServiceContext
from llama_index import StorageContext, load_index_from_storage
from langchain.chat_models import ChatOpenAI
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import gradio as gr
def load_index():
global index
llm_predictor = LLMPredictor(
llm=ChatOpenAI(
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
temperature=0,
model_name="gpt-3.5-turbo",
max_tokens=2000
)
)
max_input_size = 4096
max_chunk_overlap = 0.2
chunk_size_limit = 60
num_outputs = 2000
prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context, service_context=service_context)
def chat(chat_history, user_input):
query_engine = index.as_query_engine()
question = user_input
bot_response = query_engine.query(question)
#print("Q:",user_input)
response = ""
for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:
response += letter + ""
yield chat_history + [(user_input, response)]
#print("A:",response)
with gr.Blocks(css="footer {visibility: hidden}") as demo:
gr.Markdown('health advice database)')
load_index()
with gr.Tab("chatbot"):
chatbot = gr.Chatbot()
message = gr.Textbox ()
message.submit(chat, [chatbot, message], chatbot)
demo.queue().launch(share= False)
is vectorizing the CSV file and then query it the right approach? or is there another way ?
any kind of example or help os really appreciated.
specially if there is an example. I have almost 0 coding experience and most what I have done is by learning from everyone here and selfstudy.
Kind regards and wish you a great day