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https://github.com/deepset-ai/haystack.git
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42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
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import requests
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import streamlit as st
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import os
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API_ENDPOINT = os.getenv("API_ENDPOINT", "http://localhost:8000")
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MODEL_ID = "1"
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DOC_REQUEST = "doc-qa"
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def format_request(question,filters=None,top_k_reader=5,top_k_retriever=5):
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if filters == None:
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return {
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"questions": [question],
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"top_k_retriever": top_k_retriever,
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"top_k_reader": top_k_reader
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}
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return {
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"questions": [question],
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"filters": {
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"option1":[filters]
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},
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"top_k_retriever": top_k_retriever,
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"top_k_reader": top_k_reader
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}
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@st.cache(show_spinner=False)
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def retrieve_doc(question,filters=None,top_k_reader=5,top_k_retriever=5):
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# Query Haystack API
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url = API_ENDPOINT +'/models/' + MODEL_ID + "/" + DOC_REQUEST
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req = format_request(question,filters,top_k_reader=top_k_reader,top_k_retriever=top_k_retriever)
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response_raw = requests.post(url,json=req).json()
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# Format response
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result = []
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answers = response_raw['results'][0]['answers']
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for i in range(top_k_reader):
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answer = answers[i]['answer']
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if answer:
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context = '...' + answers[i]['context'] + '...'
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meta_name = answers[i]['meta']['name']
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relevance = round(answers[i]['probability']*100,2)
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result.append({'context':context,'answer':answer,'source':meta_name,'relevance':relevance})
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return result, response_raw
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