appStore/rag.py

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-0125"
model_select ="gpt-4-0125-preview"

# define a special function for putting the prompt together (as we can't use haystack)
def get_prompt(context, label):
  base_prompt="Summarize the following context efficiently in bullet points, the less the better - but keep concrete goals. \
  Summarize only elements of the context that address vulnerability of "+label+" to climate change. \
  If there is no mention of "+label+" in the context, return nothing. \
  Formatting example: \
    - Bullet point 1 \
    - Bullet point 2 \
"

  # Add the meta data for references
  # context = ' - '.join([d.content for d in docs])
  prompt = base_prompt+"; Context: "+context+"; Answer:"
  
  return prompt

# def get_prompt(context, label):
#   base_prompt="Summarize the following context efficiently in bullet points, the less the better - but keep concrete goals. \
#   Summarize only elements of the context that address vulnerability to climate change. \
#   Formatting example: \
#     - Bullet point 1 \
#     - Bullet point 2 \
# "

#   # Add the meta data for references
#   # context = ' - '.join([d.content for d in docs])
#   prompt = base_prompt+"; Context: "+context+"; Answer:"
  
#   return prompt

#   base_prompt="Summarize the following context efficiently in bullet points, the less the better- but keep concrete goals. \
#   Summarize only activities that address the vulnerability of "+label+" to climate change. \
#   Formatting example: \
#     - Collect and utilize gender-disaggregated data to inform and improve climate change adaptation efforts. \
#     - Prioritize gender sensitivity in adaptation options, ensuring participation and benefits for women, who are more vulnerable to climate impacts. \
# "
# # 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={
#             'label': row['Vulnerability Label']}
#         )
#         # 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(context, label):
    '''
    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(context, label)}], 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)