Update app.py

app.py

@@ -4,7 +4,7 @@ import logging, os, sys, threading
 
 from datasets import load_dataset
 from dotenv import load_dotenv, find_dotenv
-from utils import process_records, connect_to_database, setup_vector_search_index
+from custom_utils import process_records, connect_to_database, setup_vector_search_index
 
 from pydantic import BaseModel
 from typing import Optional
@@ -23,108 +23,6 @@ RAG_ADVANCED = "Advanced RAG"
 
 logging.basicConfig(stream = sys.stdout, level = logging.INFO)
 logging.getLogger().addHandler(logging.StreamHandler(stream = sys.stdout))
-
-def vector_search(user_query, db, collection, additional_stages=[], vector_index="vector_index_text"):
-    """
-    Perform a vector search in the MongoDB collection based on the user query.
-
-    Args:
-    user_query (str): The user's query string.
-    db (MongoClient.database): The database object.
-    collection (MongoCollection): The MongoDB collection to search.
-    additional_stages (list): Additional aggregation stages to include in the pipeline.
-
-    Returns:
-    list: A list of matching documents.
-    """
-
-    # Generate embedding for the user query
-    query_embedding = custom_utils.get_embedding(user_query)
-
-    if query_embedding is None:
-        return "Invalid query or embedding generation failed."
-
-    # Define the vector search stage
-    vector_search_stage = {
-        "$vectorSearch": {
-            "index": vector_index, # specifies the index to use for the search
-            "queryVector": query_embedding, # the vector representing the query
-            "path": "text_embeddings", # field in the documents containing the vectors to search against
-            "numCandidates": 150, # number of candidate matches to consider
-            "limit": 20, # return top 20 matches
-        }
-    }
-
-    # Define the aggregate pipeline with the vector search stage and additional stages
-    pipeline = [vector_search_stage] + additional_stages
-
-    # Execute the search
-    results = collection.aggregate(pipeline)
-
-    explain_query_execution = db.command( # sends a database command directly to the MongoDB server
-        'explain', { # return information about how MongoDB executes a query or command without actually running it
-            'aggregate': collection.name, # specifies the name of the collection on which the aggregation is performed
-            'pipeline': pipeline, # the aggregation pipeline to analyze
-            'cursor': {} # indicates that default cursor behavior should be used
-        }, 
-        verbosity='executionStats') # detailed statistics about the execution of each stage of the aggregation pipeline
-
-    vector_search_explain = explain_query_execution['stages'][0]['$vectorSearch']
-    millis_elapsed = vector_search_explain['explain']['collectStats']['millisElapsed']
-
-    print(f"Total time for the execution to complete on the database server: {millis_elapsed} milliseconds")
-
-    return list(results)
-
-class SearchResultItem(BaseModel):
-    name: str
-    accommodates: Optional[int] = None
-    bedrooms: Optional[int] = None
-    address: custom_utils.Address
-    space: str = None
-
-def handle_user_query(query, db, collection, stages=[], vector_index="vector_index_text"):
-    # Assuming vector_search returns a list of dictionaries with keys 'title' and 'plot'
-    get_knowledge = vector_search(query, db, collection, stages, vector_index)
-
-    # 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 = custom_utils.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
     
 def invoke(openai_api_key, prompt, rag_option):
     if not openai_api_key: