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28 lines
1.4 KiB
Python
28 lines
1.4 KiB
Python
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import streamlit as st
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from utils import retrieve_doc
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from annotated_text import annotated_text
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def annotate_answer(answer,context):
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start_idx = context.find(answer)
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end_idx = start_idx+len(answer)
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annotated_text(context[:start_idx],(answer,"ANSWER","#8ef"),context[end_idx:])
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st.write("# Haystack Demo")
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st.sidebar.header("Options")
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top_k_reader = st.sidebar.slider("Number of answers",min_value=1,max_value=10,value=5,step=1)
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top_k_retriever = st.sidebar.slider("Number of documents from retriever",min_value=1,max_value=10,value=3,step=1)
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question = st.text_input("Please provide your query:",value="Who is the father of Arya Starck?")
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run_query = st.button("Run")
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debug = st.sidebar.checkbox("Show debug info")
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if run_query:
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with st.spinner("Performing neural search on documents... 🧠 \n "
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"Do you want to optimize speed or accuracy? \n"
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"Check out the docs: https://haystack.deepset.ai/docs/latest/optimizationmd "):
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results,raw_json = retrieve_doc(question,top_k_reader=top_k_reader,top_k_retriever=top_k_retriever)
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st.write("## Retrieved answers:")
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for result in results:
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annotate_answer(result['answer'],result['context'])
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'**Relevance:** ', result['relevance'] , '**source:** ' , result['source']
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if debug:
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st.subheader('REST API JSON response')
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st.write(raw_json)
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