app.py

# inspiration from Ekimetrics climate qa

import streamlit as st
import os
import json
from dotenv import load_dotenv
from haystack.nodes.prompt import PromptNode, PromptTemplate
from haystack.nodes import EmbeddingRetriever
from haystack import Pipeline
import numpy as np
import pandas as pd
from haystack.document_stores import FAISSDocumentStore
from haystack.nodes import EmbeddingRetriever
from haystack.schema import Document


# Enter openai API key
openai_key = os.environ["OPENAI_API_KEY"]

# Select model
model_name = "gpt-3.5-turbo"

# Define the template
template = PromptTemplate(
    prompt="""
Answer the given question using the following documents \
Formulate your answer in the style of an academic report \
Provide example quotes and citations using extracted text from the documents. \
Use facts and numbers from the documents in your answer. \
Reference information used from documents at the end of each applicable sentence (ex: [source: document_name]), where 'document_name' is the text provided at the start of each document (demarcated by '- &&&' and '&&&:')'. \
If no relevant information to answer the question is present in the documents, just say you don't have enough information to answer. \
Format your response as a JSON object with "answer" and "sources" as the keys. \
The "answer" key is the response to the query and "sources" key is the reference information used from the documents. \

Context: {' - '.join(['&&& '+d.meta['document_name']+' ref. '+str(d.meta['ref_id'])+' &&&: '+d.content for d in documents])}; Question: {query}; Answer:""",
)

# Create a list of options for the dropdown
country_options = ['Angola','Botswana','Lesotho','Kenya','Malawi','Mozambique','Namibia','South Africa','Zambia','Zimbabwe']

# List of examples
examples = [
    "-",
    "What specific initiatives are presented in the context to address the needs of groups such women and children to the effects climate change?",
    "In addition to gender, children, and youth, is there any mention of other groups facing disproportional impacts from climate change due to their geographic location, socio-economic status, age, gender, health, and occupation?"
]

def get_docs(input_query, country = None):
    # Construct a hacky query to focus the retriever on the target country (see notes below)
    if country:
        query = "For the country of "+country+", "+input_query
    else:
        query = input_query
    # Get top 150 because we want to make sure we have 10 pertaining to the selected country
    # TEMP SOLUTION: not ideal, but FAISS document store doesnt allow metadata filtering. Needs to be tested with the full dataset
    docs = retriever.retrieve(query=query,top_k = 150)
    # Break out the key fields and convert to pandas for filtering
    docs = [{**x.meta,"score":x.score,"content":x.content} for x in docs]
    df_docs = pd.DataFrame(docs)
    if country:
      df_docs = df_docs.query('country in @country')
    # Take the top 10
    df_docs = df_docs.head(10)
    df_docs = df_docs.reset_index()
    df_docs['ref_id'] = df_docs.index + 1
    # Convert back to Document format
    ls_dict = []
    # Iterate over df and add relevant fields to the dict object
    for index, row in df_docs.iterrows():
        # Create a Document object for each row
        doc = Document(
            row['content'],
            meta={'country': row['country'],'document_name': row['document'], 'ref_id': row['ref_id'], 'score': row['score']}
        )
        
        # Append the Document object to the documents list
        ls_dict.append(doc)
    return(ls_dict)

def get_refs(res):
  '''
  Parse response for engineered reference ids (refer to prompt template)
  Extract documents using reference ids
  '''
  import re
  text = res["results"][0]
  # This pattern should be returned by gpt3.5
  # pattern = r'ref\. (\d+)\]\.'
  pattern = r'ref\. (\d+)'
  ref_ids = [int(match) for match in re.findall(pattern, text)]
  # extract
  for i in range(len(res['documents'])):
    doc = res['documents'][i].to_dict()
    ref_id = doc['meta']['ref_id']
    if ref_id in ref_ids:
      print("Ref. "+str(ref_id)+" ["+doc['meta']['country']+" "+doc['meta']['document_name']+"]: "+doc['content'])

def run_query(input_text):
    docs = get_docs(input_text)
    res = pipe.run(query=input_text, documents=docs)
    output = res["results"][0]
    st.write('Response')
    st.success(output)
    st.write('References')
    st.success(get_refs(res))


# Setup retriever, pulling from local faiss datastore
retriever = EmbeddingRetriever(
    document_store=FAISSDocumentStore.load(
        index_path="./cpv_southern_africa_kenya.faiss",
        config_path="./cpv_southern_africa_kenya.json",
    ),
    embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1",
    model_format="sentence_transformers",
    progress_bar=False,
)

# Initialize the PromptNode
pn = PromptNode(model_name_or_path=model_name, default_prompt_template=template, api_key=openai_key, max_length=700, model_kwargs={"generation_kwargs": {"do_sample": False, "temperature": 0}})

# Initialize the pipeline
pipe = Pipeline()
pipe.add_node(component=pn, name="prompt_node", inputs=["Query"])


# Guiding text             
st.title('Climate Policy Documents: Vulnerabilities Analysis Q&A (test)')
st.markdown('This tool seeks to provide an interface for quering national climate policy documents (NDCs, LTS etc.). The current version is powered by chatGPT (3.5) and limited to 9 Southern African countries (Angola, Botswana, Eswatini, Lesotho, Malawi, Mozambique, Namibia, South Africa, Zambia, Zimbabwe). The intended use case is to allow users to interact with the documents and obtain valuable insights on various vulnerable groups affected by climate change.')
st.markdown('**DISCLAIMER:** This prototype tool based on LLMs (Language Models) is provided "as is" for experimental and exploratory purposes only, and should not be used for critical or production applications. Users are advised that the tool may contain errors, bugs, or limitations and should be used with caution and awareness of potential risks, and the developers make no warranties or guarantees regarding its performance, reliability, or suitability for any specific purpose.')
    

# Dropdown selectbox
country = st.selectbox('Select a country:', country_options) 

# Display the text passages as radio buttons
selected_example = st.radio("Example questions", examples)

if selected_example == "-":
    text = st.text_area('Enter your question in the text box below using natural language or select an example from above:')
else:
    text = st.text_area('Enter your question in the text box below using natural language or select an example from above:', value=selected_example)


if st.button('Submit'):
    run_query(text)