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Browse files- public_app.py +0 -83
public_app.py
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import logging
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from langchain import PromptTemplate, LLMChain
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from langchain.chains.question_answering import load_qa_chain
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from langchain.vectorstores import FAISS
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.chat_models import ChatOpenAI
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import gradio as gr
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import json
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from prompts import PROMPT_EXTRACT_DATE, PROMPT_FED_ANALYST
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from filterminutes import search_with_filter
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# --------------------------Load the sentence transformer and the vector store--------------------------#
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model_name = 'sentence-transformers/all-mpnet-base-v2'
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model_kwargs = {'device': 'cpu'}
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encode_kwargs = {'normalize_embeddings': False}
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embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs)
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vs = FAISS.load_local("MINUTES_FOMC_HISTORY", embeddings)
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# --------------------------Import the prompts------------------#
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PROMPT_DATE = PromptTemplate.from_template(PROMPT_EXTRACT_DATE)
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PROMPT_ANALYST = PromptTemplate.from_template(PROMPT_FED_ANALYST)
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# --------------------------define the qa chain for answering queries--------------------------#
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def load_chains(open_ai_key):
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date_extractor = LLMChain(llm=ChatOpenAI(temperature=0, model_name='gpt-3.5-turbo', openai_api_key=open_ai_key),
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prompt=PROMPT_DATE)
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fed_chain = load_qa_chain(llm=ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, openai_api_key=open_ai_key),
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chain_type='stuff', prompt=PROMPT_ANALYST)
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return date_extractor, fed_chain
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def get_chain(query, api_key):
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"""
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Detects the date, computes similarity, and answers the query using
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only documents corresponding to the date requested.
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The query is first passed to the date extractor to extract the date
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and then to the qa chain to answer the query.
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Parameters
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----------
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query : str
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Query to be answered.
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api_key : str
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OpenAI API key.
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Returns
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Answer to the query.
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"""
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date_extractor, fed_chain = load_chains(api_key)
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logging.info('Extracting the date in numeric format..')
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date_response = date_extractor.run(query)
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if date_response != 'False':
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filter_date = json.loads(date_response)
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logging.info(f'Date parameters retrieved: {filter_date}')
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logging.info('Running the qa with filtered context..')
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filtered_context = search_with_filter(vs, query, init_k=200, step=300, target_k=7, filter_dict=filter_date)
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logging.info(20 * '-' + 'Metadata for the documents to be used' + 20 * '-')
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for doc in filtered_context:
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logging.info(doc.metadata)
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else:
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logging.info('No date elements found. Running the qa without filtering can output incorrect results.')
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filtered_context = vs.similarity_search(query, k=7)
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return fed_chain({'input_documents': filtered_context[:7], 'question': query})['output_text']
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if __name__ == '__main__':
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app = gr.Interface(fn=get_chain,
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inputs=[gr.Textbox(lines=2, placeholder="Enter your query", label='Your query'),
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gr.Textbox(lines=1, placeholder="Your OpenAI API key here", label='OpenAI Key')],
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description='Query the public database in FRED from 1936-2023',
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outputs=gr.Textbox(lines=1, label='Answer'),
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title='Chat with the FOMC meeting minutes',
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examples=[['What was the economic outlook from the staff presented in the meeting '
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'of April 2009 with respect to labour market developments and industrial production?'],
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['Who were the voting members present in the meeting on March 2010?'],
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['How important was the pandemic of Covid-19 in the discussions during 2020?'],
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['What was the impact of the oil crisis for the economic outlook during 1973?']],
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)
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app.launch()
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