Update app.py

app.py

@@ -10,6 +10,11 @@ model_name = "Dolphin-Phi"
 # Load the chosen LLM model
 llm = pipeline("text-generation", model=Dolphin-Phi)
 
+#Vectara config:
+# customer_id =
+# corpus_id = 
+# api_key = 
+
 # DSPy-based prompt generation
 from dspy.agents import Agent
 from dspy.utils import SentenceSplitter, SentimentAnalyzer, NamedEntityRecognizer
@@ -40,10 +45,10 @@ def dspy_generate_agent_prompts(prompt):
     for sentence in sentences:
         entities = ner.process(sentence)
         for entity in entities:
-            if entity.label_ in ["FOOD", "ORG", "LOCATION"]:  # Customize entity labels based on your needs
+            if entity.label_ in ["FOOD", "ORG", "LOCATION"]:  # Customize entity labels based on needs
                 extracted_entities.setdefault(entity.label_, []).append(entity.text)
 
-    # 4. Craft prompts for each agent
+    # 4. Craft prompts for each agent (incomplete)
     agent_prompts = []
 
     # **Sentiment Analyzer Prompt:**