Update app.py

app.py

@@ -6,64 +6,23 @@ from decouple import Config
 config = Config('.env')
 
 def query_vectara(question):
-    # Get the user's message from the chat history
     user_message = question
 
-    # Query Vectara API
-    customer_id = config('CUSTOMER_ID')  # Read from .env file
-    corpus_id = config('CORPUS_ID')  # Read from .env file
-    api_key = config('API_KEY')  # Read from .env file
+    # Read authentication parameters from the .env file
+    customer_id = config('CUSTOMER_ID')
+    corpus_id = config('CORPUS_ID')
+    api_key = config('API_KEY')
 
-    query_url = "https://api.vectara.io:443/v1/query"
+    # Define the query URL
+    query_url = f"https://api.vectara.io:443/v1/query?customer_id={customer_id}&corpus_id={corpus_id}"
 
     headers = {
-        "Content-Type": "application/json",
-        "authorization": f"Bearer {api_key}",  # Corrected authorization header
-        "customer-id": customer_id,
+        "x-api-key": api_key,  # Use the x-api-key header for authentication
     }
 
     query_body = {
-        "query": [
-            {
-                "query": user_message,
-                "queryContext": "",
-                "start": 0,
-                "numResults": 10,
-                "contextConfig": {
-                    "charsBefore": 0,
-                    "charsAfter": 0,
-                    "sentencesBefore": 2,
-                    "sentencesAfter": 2,
-                    "startTag": "%START_SNIPPET%",
-                    "endTag": "%END_SNIPPET%",
-                },
-                "rerankingConfig": {
-                    "rerankerId": 272725718,
-                    "mmrConfig": {
-                        "diversityBias": 0.3
-                    }
-                },
-                "corpusKey": [
-                    {
-                        "customerId": customer_id,
-                        "corpusId": corpus_id,
-                        "semantics": 0,
-                        "metadataFilter": "",
-                        "lexicalInterpolationConfig": {
-                            "lambda": 0
-                        },
-                        "dim": []
-                    }
-                ],
-                "summary": [
-                    {
-                        "maxSummarizedResults": 5,
-                        "responseLang": "eng",
-                        "summarizerPromptName": "vectara-summary-ext-v1.2.0"
-                    }
-                ]
-            }
-        ]
+        "query": user_message,
+        "num_results": 10
     }
 
     query_response = requests.post(query_url, json=query_body, headers=headers)
@@ -76,7 +35,6 @@ def query_vectara(question):
 
     return response_message
 
-# Create a Gradio ChatInterface with only a text input
 iface = gr.Interface(
     fn=query_vectara,
     inputs=[gr.Textbox(label="Input Text")],