Upload app.py

app.py

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+
+import os
+os.environ['VECTARA_API_KEY'] = 'zqt_UXrBcnI2UXINZkrv4g1tQPhzj02vfdtqYJIDiA'
+os.environ['VECTARA_CORPUS_ID'] = '1'
+os.environ['VECTARA_CUSTOMER_ID']='1366999410'
+
+import os
+import json
+import requests
+import streamlit as st
+
+def vectara_query(query: str, config: dict) -> None:
+
+
+    corpus_key = [
+        {
+            "customerId": config["customer_id"],
+            "corpusId": config["corpus_id"],
+            "lexicalInterpolationConfig": {"lambda": config["lambda_val"]},
+        }
+    ]
+    data = {
+        "query": [
+            {
+                "query": query,
+                "start": 0,
+                "numResults": config["top_k"],
+                "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:
+        print(
+            "Query failed %s",
+            f"(code {response.status_code}, reason {response.reason}, details "
+            f"{response.text})",
+        )
+        return []
+
+    result = response.json()
+    responses = result["responseSet"][0]["response"]
+    documents = result["responseSet"][0]["document"]
+    summary = result["responseSet"][0]["summary"][0]["text"]
+
+    res = [[r['text'], r['score']] for r in responses]
+    return res, summary 
+
+
+
+
+# Set the environment variables
+os.environ['VECTARA_API_KEY'] = 'zqt_UXrBcnI2UXINZkrv4g1tQPhzj02vfdtqYJIDiA'
+os.environ['VECTARA_CORPUS_ID'] = '1'
+os.environ['VECTARA_CUSTOMER_ID'] = '1366999410'
+
+# Load config from environment variables
+api_key = os.environ.get("VECTARA_API_KEY", "")
+customer_id = os.environ.get("VECTARA_CUSTOMER_ID", "")
+corpus_id = os.environ.get("VECTARA_CORPUS_ID", "")
+
+config = {
+    "api_key": str(api_key),
+    "customer_id": str(customer_id),
+    "corpus_id": str(corpus_id),
+    "lambda_val": 0.025,
+    "top_k": 10,
+}
+
+# Streamlit app
+st.title("KitchenCreators App")
+
+# Input for the query
+query = st.text_input("Enter your query:", "What does Kitchen Creators do?")
+
+# Button to trigger the query
+if st.button("Run Query"):
+    results, summary = vectara_query(query, config)
+
+    # Display results
+    st.header("Results")
+    st.write(results)
+
+    # Display summary
+    st.header("Summary")
+    st.write(summary)