Create app.py

app.py

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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 = "3939498282"
+vectara_corpus_id = 2
+vectara_api_key = 'zqt_6s_5KqwCCxK5tosYGbpSie8n2-hO7LdlxBWUBA'
+
+# Initialize Vectara
+vectara_instance = Vectara(
+    vectara_customer_id='3939498282',
+    vectara_corpus_id=2,
+    vectara_api_key='zqt_Y3kD9bueJq3QO5t_FISVQLmgTWMDhzgMgK9Isw',
+)
+
+# Model initialization
+model = CrossEncoder('vectara/hallucination_evaluation_model')
+
+# Streamlit app
+st.title('RAG-Based App')
+
+# 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]):
+            source_name = f"Source {source_idx + 1}"
+            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)