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| import streamlit as st | |
| import requests | |
| import json | |
| import os | |
| import pandas as pd | |
| from sentence_transformers import CrossEncoder | |
| import numpy as np | |
| # Initialize the HHEM model | |
| model = CrossEncoder('vectara/hallucination_evaluation_model') | |
| # Function to compute HHEM scores | |
| def compute_hhem_scores(texts, summary): | |
| pairs = [[text, summary] for text in texts] | |
| scores = model.predict(pairs) | |
| return scores | |
| # Define the Vectara query function | |
| def vectara_query(query: str, config: dict): | |
| corpus_key = [{ | |
| "customerId": config["customer_id"], | |
| "corpusId": config["corpus_id"], | |
| "lexicalInterpolationConfig": {"lambda": config.get("lambda_val", 0.5)}, | |
| }] | |
| data = { | |
| "query": [{ | |
| "query": query, | |
| "start": 0, | |
| "numResults": config.get("top_k", 10), | |
| "contextConfig": { | |
| "sentencesBefore": 2, | |
| "sentencesAfter": 2, | |
| }, | |
| "corpusKey": corpus_key, | |
| "summary": [{ | |
| "responseLang": "eng", | |
| "maxSummarizedResults": 5, | |
| }] | |
| }] | |
| } | |
| headers = { | |
| "x-api-key": config["api_key"], | |
| "customer-id": config["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(f"Query failed (code {response.status_code}, reason {response.reason}, details {response.text})") | |
| return [], "" | |
| result = response.json() | |
| responses = result["responseSet"][0]["response"] | |
| summary = result["responseSet"][0]["summary"][0]["text"] | |
| res = [[r['text'], r['score']] for r in responses] | |
| return res, summary | |
| # Streamlit UI setup | |
| st.title("Vectara Content Query Interface") | |
| # User inputs | |
| query = st.text_input("Enter your query here", "") | |
| lambda_val = st.slider("Lambda Value", min_value=0.0, max_value=1.0, value=0.5) | |
| top_k = st.number_input("Top K Results", min_value=1, max_value=50, value=10) | |
| if st.button("Query Vectara"): | |
| config = { | |
| "api_key": os.environ.get("VECTARA_API_KEY", ""), | |
| "customer_id": os.environ.get("VECTARA_CUSTOMER_ID", ""), | |
| "corpus_id": os.environ.get("VECTARA_CORPUS_ID", ""), | |
| "lambda_val": lambda_val, | |
| "top_k": top_k, | |
| } | |
| results, summary = vectara_query(query, config) | |
| if results: | |
| st.subheader("Summary") | |
| st.write(summary) | |
| st.subheader("Top Results") | |
| # Extract texts from results | |
| texts = [r[0] for r in results[:5]] | |
| # Compute HHEM scores | |
| scores = compute_hhem_scores(texts, summary) | |
| # Prepare and display the dataframe | |
| df = pd.DataFrame({'Fact': texts, 'HHEM Score': scores}) | |
| st.dataframe(df) | |
| else: | |
| st.write("No results found.") | |