app.py

import streamlit as st
import requests
import json
import os
import pandas as pd
from sentence_transformers import CrossEncoder
import numpy as np


# Credentials ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

corpus_id = os.environ['VECTARA_CORPUS_ID']
customer_id = os.environ['VECTARA_CUSTOMER_ID']
api_key = os.environ['VECTARA_API_KEY']

"""
        "api_key": os.environ.get("VECTARA_API_KEY", ""),
        "customer_id": os.environ.get("VECTARA_CUSTOMER_ID", ""),
        "corpus_id": os.environ.get("VECTARA_CORPUS_ID", ""),      

"""

# Get Data +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


def get_post_headers() -> dict:
    """Returns headers that should be attached to each post request."""
    return {
        "x-api-key": api_key,
        "customer-id": customer_id,
        "Content-Type": "application/json",
    }

def query_vectara(query: str, filter_str="", lambda_val=0.0) -> str:
    corpus_key = {
        "customerId": customer_id,
        "corpusId": corpus_id,
        "lexicalInterpolationConfig": {"lambda": lambda_val},
    }
    if filter_str:
        corpus_key["metadataFilter"] = filter_str

    data = {
        "query": [
            {
                "query": query,
                "start": 0,
                "numResults": 10,
                "contextConfig": {
                    "sentencesBefore": 2,
                    "sentencesAfter": 2
                },
                "corpusKey": [corpus_key],
                "summary": [
                    {
                        "responseLang": "eng",
                        "maxSummarizedResults": 5,
                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
                    },
                ]                    
            }
        ]
    }

    response = requests.post(
        headers=get_post_headers(),
        url="https://api.vectara.io/v1/query",
        data=json.dumps(data),
        timeout=30,
    )

    if response.status_code != 200:
        st.error(f"Query failed (code {response.status_code}, reason {response.reason}, details {response.text})")
        return ""

    result = response.json()

    answer = result["responseSet"][0]["summary"][0]["text"]
    return re.sub(r'\[\d+(,\d+){0,5}\]', '', answer)

    

# Streamlit UI
st.title('Vectara Query Interface')

# User input for query
user_query = st.text_input("Enter your query:", "")

# Advanced options
st.sidebar.header("Advanced Options")
filter_str = st.sidebar.text_input("Filter String:", "")
lambda_val = st.sidebar.slider("Lambda Value:", min_value=0.0, max_value=1.0, value=0.0)

if st.button('Search'):
    if user_query:
        with st.spinner('Querying Vectara...'):
            output = query_vectara(user_query, filter_str, lambda_val)
            st.markdown("## Result")
            st.write(output)
    else:
        st.error("Please enter a query to search.")






# 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 = {

        "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.")