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import streamlit as st
import requests
import json
from langchain.vectorstores import Vectara
from sentence_transformers import CrossEncoder
# Input your API keys
vectara_customer_id = "1668875253"
vectara_corpus_id = 2
vectara_api_key = 'zqt_Y3kD9bueJq3QO5t_FISVQLmgTWMDhzgMgK9Isw'
# Initialize Vectara
vectara_instance = Vectara(
vectara_customer_id='1668875253',
vectara_corpus_id=2,
vectara_api_key='zqt_Y3kD9bueJq3QO5t_FISVQLmgTWMDhzgMgK9Isw',
)
# Model initialization
model = CrossEncoder('vectara/hallucination_evaluation_model')
# Streamlit app
st.title('Fact-Checked Finance RAG-Based App Using Vectara HHEM')
# Input message from the user
message = st.text_input('Enter your message')
# Button to trigger the processing
if st.button('Process'):
# Processing logic
corpus_key = [
{
"customerId": vectara_customer_id,
"corpusId": vectara_corpus_id,
"lexicalInterpolationConfig": {"lambda": 0.025},
}
]
data = {
"query": [
{
"query": message,
"start": 0,
"numResults": 10,
"contextConfig": {
"sentencesBefore": 2,
"sentencesAfter": 2,
},
"corpusKey": corpus_key,
"summary": [
{
"responseLang": "eng",
"maxSummarizedResults": 5,
}
]
}
]
}
headers = {
"x-api-key": vectara_api_key,
"customer-id": vectara_customer_id,
"Content-Type": "application/json",
}
response = requests.post(
headers=headers,
url="https://api.vectara.io/v1/query",
data=json.dumps(data),
)
if response.status_code != 200:
st.error("Query failed")
else:
result = response.json()
responses = result["responseSet"][0]["response"]
summary = result["responseSet"][0]["summary"][0]["text"]
res = [[r['text'], r['score']] for r in responses]
texts = [r[0] for r in res[:5]]
scores = [model.predict([text, summary]) for text in texts]
# text_elements = []
# docs = vectara_instance.similarity_search(message)
# for source_idx, source_doc in enumerate(docs[:5]):
# text_elements.append(source_doc.page_content)
ans = f"{summary}\n HHEM Scores: {scores}"
st.text(ans)
# st.text("Sources:")
# for text in text_elements:
# st.text(text)
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