Update openai_embedding.py

openai_embedding.py

@@ -1,5 +1,7 @@
 import openai
 
+from IPython.display import display, HTML
+
 openai.api_key = OPENAI_API_KEY
 
 def get_embedding(text):
@@ -17,4 +19,47 @@ def get_embedding(text):
         return embedding
     except Exception as e:
         print(f"Error in get_embedding: {e}")
-        return None
+        return None
+
+def handle_user_query(query, db, collection):
+    # Assuming vector_search returns a list of dictionaries with keys 'title' and 'plot'
+    get_knowledge = vector_search(query, db, collection)
+
+    # Check if there are any results
+    if not get_knowledge:
+        return "No results found.", "No source information available."
+        
+     # Convert search results into a list of SearchResultItem models
+    search_results_models = [
+        SearchResultItem(**result)
+        for result in get_knowledge
+    ]
+
+    # Convert search results into a DataFrame for better rendering in Jupyter
+    search_results_df = pd.DataFrame([item.dict() for item in search_results_models])
+
+    # Generate system response using OpenAI's completion
+    completion = openai.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system", 
+                "content": "You are a airbnb listing recommendation system."},
+            {
+                "role": "user", 
+                "content": f"Answer this user query: {query} with the following context:\n{search_results_df}"
+            }
+        ]
+    )
+
+    system_response = completion.choices[0].message.content
+
+    # Print User Question, System Response, and Source Information
+    print(f"- User Question:\n{query}\n")
+    print(f"- System Response:\n{system_response}\n")
+
+    # Display the DataFrame as an HTML table
+    display(HTML(search_results_df.to_html()))
+
+    # Return structured response and source info as a string
+    return system_response