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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) | |