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import os
# import json
import numpy as np
import pandas as pd
import openai
from haystack.schema import Document
import streamlit as st
from tenacity import retry, stop_after_attempt, wait_random_exponential
# Get openai API key
openai.api_key = os.environ["OPENAI_API_KEY"]
model_select = "gpt-3.5-turbo-1106"
# define a special function for putting the prompt together (as we can't use haystack)
def get_prompt(docs):
base_prompt="Provide a single paragraph summary of the documents provided below. \
Formulate your answer in the style of an academic report."
# Add the meta data for references
context = ' - '.join([d.content for d in docs])
prompt = base_prompt+"; Context: "+context+"; Answer:"
return prompt
# convert df rows to Document object so we can feed it into the summarizer easily
def get_document(df):
# we take a list of each extract
ls_dict = []
for index, row in df.iterrows():
# Create a Document object for each row (we only need the text)
doc = Document(
row['text'],
meta={
'filename': row['filename']}
)
# Append the Document object to the documents list
ls_dict.append(doc)
return ls_dict
# exception handling for issuing multiple API calls to openai (exponential backoff)
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def completion_with_backoff(**kwargs):
return openai.ChatCompletion.create(**kwargs)
# construct RAG query, send to openai and process response
def run_query(df):
docs = get_document(df)
'''
For non-streamed completion, enable the following 2 lines and comment out the code below
'''
# res = openai.ChatCompletion.create(model=model_select, messages=[{"role": "user", "content": get_prompt(docs)}])
# result = res.choices[0].message.content
# instantiate ChatCompletion as a generator object (stream is set to True)
response = completion_with_backoff(model=model_select, messages=[{"role": "user", "content": get_prompt(docs)}], stream=True)
# iterate through the streamed output
report = []
res_box = st.empty()
for chunk in response:
# extract the object containing the text (totally different structure when streaming)
chunk_message = chunk['choices'][0]['delta']
# test to make sure there is text in the object (some don't have)
if 'content' in chunk_message:
report.append(chunk_message.content) # extract the message
# add the latest text and merge it with all previous
result = "".join(report).strip()
# res_box.success(result) # output to response text box
res_box.success(result)
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