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import streamlit as st |
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import os |
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import re |
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import json |
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from dotenv import load_dotenv |
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from haystack.nodes.prompt import PromptNode, PromptTemplate |
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from haystack.nodes import EmbeddingRetriever |
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from haystack import Pipeline |
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import numpy as np |
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import pandas as pd |
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from haystack.document_stores import FAISSDocumentStore |
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from haystack.nodes import EmbeddingRetriever |
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from haystack.schema import Document |
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from huggingface_hub import login, HfApi, hf_hub_download, InferenceClient |
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hf_token = os.environ["HF_TOKEN"] |
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login(token=hf_token, add_to_git_credential=True) |
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openai_key = os.environ["OPENAI_API_KEY"] |
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template = PromptTemplate( |
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prompt=""" |
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Answer the given question using the following documents. \ |
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Formulate your answer in the style of an academic report. \ |
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Provide example quotes and citations using extracted text from the documents. \ |
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Use facts and numbers from the documents in your answer. \ |
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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 '&&&:')'. \ |
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If no relevant information to answer the question is present in the documents, just say you don't have enough information to answer. \ |
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Context: {' - '.join(['&&& '+d.meta['document_name']+' ref. '+str(d.meta['ref_id'])+' &&&: '+d.content for d in documents])}; Question: {query}; Answer:""", |
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) |
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model_options = ['chatGPT','Llama2'] |
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country_options = ['All Countries','Angola','Botswana','Lesotho','Kenya','Malawi','Mozambique','Namibia','Rwanda','South Africa','Zambia','Zimbabwe'] |
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examples = [ |
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"-", |
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"What specific initiatives are presented in the context to address the needs of groups such women and children to the effects climate change?", |
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"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?" |
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] |
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def get_docs(input_query, country = None): |
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''' |
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Construct a hacky query to focus the retriever on the target country (see notes below) |
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We take the top 150 k because we want to make sure we have 10 pertaining to the selected country |
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''' |
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if country == 'ALL': |
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query = input_query |
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else: |
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query = "For the country of "+country+", "+input_query |
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docs = retriever.retrieve(query=query,top_k = 150) |
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docs = [{**x.meta,"score":x.score,"content":x.content} for x in docs] |
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df_docs = pd.DataFrame(docs) |
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if country != 'All Countries': |
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df_docs = df_docs.query('country in @country') |
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df_docs = df_docs.head(10) |
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df_docs = df_docs.reset_index() |
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df_docs['ref_id'] = df_docs.index + 1 |
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ls_dict = [] |
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for index, row in df_docs.iterrows(): |
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doc = Document( |
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row['content'], |
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meta={'country': row['country'],'document_name': row['document'], 'ref_id': row['ref_id'], 'score': row['score']} |
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) |
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ls_dict.append(doc) |
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return(ls_dict) |
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def get_refs(docs, res): |
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''' |
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Parse response for engineered reference ids (refer to prompt template) |
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Extract documents using reference ids |
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''' |
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res = res.lower() |
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pattern = r'ref\. (\d+)' |
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ref_ids = [int(match) for match in re.findall(pattern, res)] |
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result_str = "" |
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for i in range(len(docs)): |
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doc = docs[i].to_dict() |
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ref_id = doc['meta']['ref_id'] |
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if ref_id in ref_ids: |
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result_str += "**Ref. " + str(ref_id) + " [" + doc['meta']['country'] + " " + doc['meta']['document_name'] + "]:** " + "*'" + doc['content'] + "'*<br> <br>" |
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return result_str |
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def get_prompt_llama2(docs, query): |
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base_prompt="Answer the given question using the following documents. \ |
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Formulate your answer in the style of an academic report. \ |
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Provide example quotes and citations using extracted text from the documents. \ |
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Use facts and numbers from the documents in your answer. \ |
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ALWAYS include references for information used from documents at the end of each applicable sentence using the format: '[ref. #]', where '[ref. #]' is included in the text provided at the start of each document (demarcated by the pattern '- &&& [ref. #] document_name &&&:')'. \ |
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Do not include page numbers in the references. \ |
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If no relevant information to answer the question is present in the documents, just say you don't have enough information to answer." |
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context = ' - '.join(['&&& [ref. '+str(d.meta['ref_id'])+'] '+d.meta['document_name']+' &&&: '+d.content for d in docs]) |
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prompt = base_prompt+"; Context: "+context+"; Question: "+query+"; Answer:" |
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return(prompt) |
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def run_query(input_text, country, model_sel): |
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docs = get_docs(input_text, country) |
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if model_sel == "chatGPT": |
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res = pipe.run(query=input_text, documents=docs) |
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output = res["results"][0] |
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references = get_refs(docs, res["results"][0]) |
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else: |
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res = client.text_generation(get_prompt_llama2(docs, query=input_text), max_new_tokens=4000, temperature=0.01, model=model) |
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output = res |
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references = get_refs(docs, res) |
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st.write('Response') |
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st.success(output) |
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st.write('References') |
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st.markdown('References are based on text automatically extracted from climate policy documents. These extracts may contain non-legible characters or disjointed text as an artifact of the extraction procedure') |
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st.markdown(references, unsafe_allow_html=True) |
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retriever = EmbeddingRetriever( |
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document_store=FAISSDocumentStore.load( |
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index_path="./cpv_full_southern_africa_kenya.faiss", |
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config_path="./cpv_full_southern_africa_kenya.json", |
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), |
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embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", |
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model_format="sentence_transformers", |
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progress_bar=False, |
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) |
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with st.sidebar: |
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country = st.sidebar.selectbox('Select a country:', country_options) |
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with st.container(): |
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st.markdown("<h2 style='text-align: center;'> Climate Policy Documents: Vulnerabilities Analysis Q&A </h2>", unsafe_allow_html=True) |
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st.write(' ') |
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with st.expander("ℹ️ - About this app", expanded=False): |
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st.write( |
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""" |
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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). The document store is limited to 10 Southern African countries (Angola, Botswana, Eswatini, Lesotho, Malawi, Mozambique, Namibia, South Africa, Zambia, Zimbabwe), as well as Kenya and Rwanda. 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. |
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**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.* |
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""") |
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selected_example = st.radio("Example questions", examples) |
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model_sel = "chatGPT" |
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if model_sel == "chatGPT": |
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model_name = "gpt-3.5-turbo" |
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pn = PromptNode(model_name_or_path=model_name, default_prompt_template=template, api_key=openai_key, max_length=2000, model_kwargs={"generation_kwargs": {"do_sample": False, "temperature": 0}}) |
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pipe = Pipeline() |
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pipe.add_node(component=pn, name="prompt_node", inputs=["Query"]) |
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else: |
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model = "meta-llama/Llama-2-70b-chat-hf" |
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client = InferenceClient() |
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if selected_example == "-": |
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text = st.text_area('Enter your question in the text box below using natural language or select an example from above:') |
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else: |
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text = st.text_area('Enter your question in the text box below using natural language or select an example from above:', value=selected_example) |
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if st.button('Submit'): |
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run_query(text, country=country, model_sel=model_sel) |
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