Upload app.py

app.py

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