Update app.py

app.py

@@ -1,272 +1,820 @@
-def main():
-    """Main function to run the RAG application"""
-    # Path for configuration file
-    CONFIG_FILE_PATH = "rag_config.json"
+import os
+import sys
+import logging
+from pathlib import Path
+import json
+from datetime import datetime
+from typing import List, Dict, Any, Optional, Tuple, Union
+import traceback
+
+# Configure detailed logging with file output
+LOG_DIR = "logs"
+os.makedirs(LOG_DIR, exist_ok=True)
+log_file = os.path.join(LOG_DIR, f"rag_system_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")
+
+# Set up root logger with both file and console handlers
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler(log_file),
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger("rag_system")
+logger.info(f"Starting RAG system. Log file: {log_file}")
+
+# Importing necessary libraries with error handling
+try:
+    import torch
+    import numpy as np
+    from sentence_transformers import SentenceTransformer
+    import chromadb
+    from chromadb.utils import embedding_functions
+    import gradio as gr
+    from openai import OpenAI
+    import google.generativeai as genai
+    logger.info("All required libraries successfully imported")
+except ImportError as e:
+    logger.critical(f"Failed to import required libraries: {e}")
+    print(f"ERROR: Missing required libraries. Please install with: pip install -r requirements.txt")
+    print(f"Specific error: {e}")
+    sys.exit(1)
+
+# Version info for tracking
+VERSION = "1.1.0"
+logger.info(f"RAG System Version: {VERSION}")
+
+# Custom CSS for better UI
+custom_css = """
+    .gradio-container {
+        max-width: 1200px;
+        margin: auto;
+    }
+    .gr-prose h1 {
+        font-size: 2.5rem;
+        margin-bottom: 1rem;
+        color: #1a5276;
+    }
+    .gr-prose h3 {
+        font-size: 1.25rem;
+        font-weight: 600;
+        margin-top: 1rem;
+        margin-bottom: 0.5rem;
+        color: #2874a6;
+    }
+    .container {
+        margin: 0 auto;
+        padding: 2rem;
+    }
+    .gr-box {
+        border-radius: 8px;
+        box-shadow: 0 1px 3px rgba(0,0,0,0.12), 0 1px 2px rgba(0,0,0,0.24);
+        padding: 1rem;
+        margin-bottom: 1rem;
+        background-color: #f9f9f9;
+    }
+    .footer {
+        text-align: center;
+        font-size: 0.8rem;
+        color: #666;
+        margin-top: 2rem;
+    }
+"""
+
+class Config:
+    """
+    Configuration for vector store and RAG system.
     
-    try:
-        # Try to load configuration from file, or use defaults
-        if os.path.exists(CONFIG_FILE_PATH):
-            config = Config.from_file(CONFIG_FILE_PATH)
-        else:
-            config = Config(
-                local_dir="./chroma_db",  # Store Chroma files in dedicated directory
-                collection_name="markdown_docs"
-            )
-            # Save default configuration
-            config.save_to_file(CONFIG_FILE_PATH)
-        
-        print(f"Starting Document Knowledge Assistant v{VERSION}")
-        print(f"Log file: {log_file}")
+    This class centralizes all configuration parameters for the application,
+    making it easier to modify settings and ensure consistency.
+    
+    Attributes:
+        local_dir (str): Directory for ChromaDB persistence
+        embedding_model (str): Name of the embedding model to use
+        collection_name (str): Name of the ChromaDB collection
+        default_top_k (int): Default number of results to return
+        openai_model (str): Default OpenAI model to use
+        gemini_model (str): Default Gemini model to use
+        temperature (float): Temperature setting for LLM generation
+        max_tokens (int): Maximum tokens for LLM response
+        system_name (str): Name of the system for UI
+        context_limit (int): Maximum characters to include in context
+    """
+    
+    def __init__(self, 
+                 local_dir: str = "./chroma_db",
+                 embedding_model: str = "all-MiniLM-L6-v2",
+                 collection_name: str = "markdown_docs",
+                 default_top_k: int = 8,  # Increased from 5 to 8 for more context
+                 openai_model: str = "gpt-4o-mini",
+                 gemini_model: str = "gemini-1.5-flash",
+                 temperature: float = 0.3,
+                 max_tokens: int = 2000,  # Increased from 1000 to 2000 for more comprehensive responses
+                 system_name: str = "Document Knowledge Assistant",
+                 context_limit: int = 16000):  # Increased context limit for more comprehensive context
+        self.local_dir = local_dir
+        self.embedding_model = embedding_model
+        self.collection_name = collection_name
+        self.default_top_k = default_top_k
+        self.openai_model = openai_model
+        self.gemini_model = gemini_model
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.system_name = system_name
+        self.context_limit = context_limit
         
-        # Initialize vector store manager with existing collection
-        vector_store = VectorStoreManager(config)
+        # Create local directory if it doesn't exist
+        os.makedirs(local_dir, exist_ok=True)
         
-        # Initialize RAG system without API keys initially
-        rag_system = RAGSystem(vector_store, config)
+        logger.info(f"Initialized configuration: {self.__dict__}")
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert configuration to dictionary for serialization"""
+        return self.__dict__
+    
+    @classmethod
+    def from_file(cls, config_path: str) -> 'Config':
+        """Load configuration from JSON file"""
+        try:
+            with open(config_path, 'r') as f:
+                config_dict = json.load(f)
+            logger.info(f"Loaded configuration from {config_path}")
+            return cls(**config_dict)
+        except Exception as e:
+            logger.error(f"Failed to load configuration from {config_path}: {e}")
+            logger.info("Using default configuration")
+            return cls()
+    
+    def save_to_file(self, config_path: str) -> bool:
+        """Save configuration to JSON file"""
+        try:
+            with open(config_path, 'w') as f:
+                json.dump(self.to_dict(), f, indent=2)
+            logger.info(f"Saved configuration to {config_path}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to save configuration to {config_path}: {e}")
+            return False
+
+class EmbeddingEngine:
+    """
+    Handle embeddings with a lightweight model.
+    
+    This class manages the embedding model used to convert text to vector 
+    representations for semantic search.
+    
+    Attributes:
+        model (SentenceTransformer): The loaded embedding model
+        model_name (str): Name of the successfully loaded model
+        vector_size (int): Dimension of the embedding vectors
+        device (str): Device used for inference ('cuda' or 'cpu')
+    """
+    
+    def __init__(self, model_name="all-MiniLM-L6-v2"):
+        """
+        Initialize the embedding engine with the specified model.
         
-        # Create the Gradio interface with custom CSS
-        with gr.Blocks(title="Document Knowledge Assistant", css=custom_css) as app:
-            gr.Markdown(f"# Document Knowledge Assistant v{VERSION}")
-            gr.Markdown("Ask questions about your documents and get comprehensive AI-powered answers")
+        Args:
+            model_name (str): Name of the embedding model to load
             
-            # Main layout
-            with gr.Row():
-                # Left column for asking questions
-                with gr.Column(scale=3):
-                    with gr.Box():
-                        gr.Markdown("### Ask Your Question")
-                        query_input = gr.Textbox(
-                            label="", 
-                            placeholder="What would you like to know about your documents?",
-                            lines=3
-                        )
-                        
-                        with gr.Row():
-                            query_button = gr.Button("Ask Question", variant="primary", scale=3)
-                            clear_button = gr.Button("Clear", variant="secondary", scale=1)
-                    
-                    with gr.Box():
-                        gr.Markdown("### Answer")
-                        response_output = gr.Markdown()
+        Raises:
+            SystemExit: If no embedding model could be loaded
+        """
+        # Use GPU if available
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        logger.info(f"Using device for embeddings: {self.device}")
+        
+        # Try multiple model options in order of preference
+        model_options = [
+            model_name,
+            "all-MiniLM-L6-v2",      # Good balance of speed and quality
+            "paraphrase-MiniLM-L3-v2", # Faster but less accurate
+            "all-mpnet-base-v2"      # Higher quality but larger model
+        ]
+        
+        self.model = None
+        
+        # Try each model in order until one works
+        for model_option in model_options:
+            try:
+                logger.info(f"Attempting to load embedding model: {model_option}")
+                self.model = SentenceTransformer(model_option)
                 
-                # Right column for settings
-                with gr.Column(scale=1):
-                    # API Keys and model selection
-                    with gr.Accordion("AI Model Settings", open=True):
-                        gr.Markdown("### AI Configuration")
-                        model_choice = gr.Radio(
-                            choices=["openai", "gemini"], 
-                            value="openai", 
-                            label="AI Provider",
-                            info=f"Select your preferred AI model"
-                        )
-                        
-                        api_key_input = gr.Textbox(
-                            label="API Key",
-                            placeholder="Enter your API key here...",
-                            type="password",
-                            info="Your key is not stored between sessions"
-                        )
-                        
-                        save_key_button = gr.Button("Save API Key", variant="primary")
-                        api_status = gr.Markdown("")
-                    
-                    # Advanced search controls
-                    with gr.Accordion("Advanced Settings", open=False):
-                        gr.Markdown("### Search & Response Settings")
-                        num_results = gr.Slider(
-                            minimum=3, 
-                            maximum=15, 
-                            value=config.default_top_k, 
-                            step=1, 
-                            label="Documents to search",
-                            info="Higher values provide more context"
-                        )
-                        
-                        temperature_slider = gr.Slider(
-                            minimum=0.0,
-                            maximum=1.0,
-                            value=config.temperature,
-                            step=0.05,
-                            label="Creativity",
-                            info="Lower = more factual, Higher = more creative"
-                        )
-                        
-                        max_tokens_slider = gr.Slider(
-                            minimum=500,
-                            maximum=4000,
-                            value=config.max_tokens,
-                            step=100,
-                            label="Response Length",
-                            info="Maximum words in response"
-                        )
-                    
-                    # Database stats - simplified
-                    with gr.Accordion("System Info", open=False):
-                        stats_display = gr.Markdown(get_db_stats(vector_store))
-                        
-                        gr.Markdown(f"""
-                        **System Details:**
-                        - Version: {VERSION}
-                        - Embedding: {vector_store.embedding_engine.model_name}
-                        - Device: {vector_store.embedding_engine.device}
-                        """)
-                        refresh_button = gr.Button("Refresh", variant="secondary", size="sm")
+                # Move model to device
+                self.model.to(self.device)
+                
+                logger.info(f"Successfully loaded embedding model: {model_option}")
+                self.model_name = model_option
+                self.vector_size = self.model.get_sentence_embedding_dimension()
+                logger.info(f"Embedding vector size: {self.vector_size}")
+                break
+                
+            except Exception as e:
+                logger.warning(f"Failed to load embedding model {model_option}: {str(e)}")
+        
+        if self.model is None:
+            error_msg = "Failed to load any embedding model. Please check your internet connection or install models locally."
+            logger.critical(error_msg)
+            raise SystemExit(error_msg)
+    
+    def embed(self, texts: List[str]) -> np.ndarray:
+        """
+        Generate embeddings for a list of texts.
+        
+        Args:
+            texts (List[str]): List of texts to embed
             
-            # Hidden element for search results (not visible to user)
-            with gr.Accordion("Debug Information", open=False, visible=False):
-                search_output = gr.Markdown()
+        Returns:
+            np.ndarray: Array of embeddings
             
-            # Query history at the bottom (optional section)
-            with gr.Accordion("Recent Questions", open=False):
-                history_list = gr.Dataframe(
-                    headers=["Time", "Question", "Model"],
-                    datatype=["str", "str", "str"],
-                    row_count=5,
-                    col_count=(3, "fixed"),
-                    interactive=False
-                )
-                
-            # Footer
-            gr.Markdown(
-                """<div class="footer">Document Knowledge Assistant helps you get insights from your documents using AI. 
-                Powered by Retrieval Augmented Generation.</div>"""
-            )
+        Raises:
+            ValueError: If the input is invalid
+            RuntimeError: If embedding fails
+        """
+        if not texts:
+            raise ValueError("Cannot embed empty list of texts")
+        
+        try:
+            embeddings = self.model.encode(texts, convert_to_numpy=True)
+            return embeddings
+        except Exception as e:
+            logger.error(f"Error generating embeddings: {e}")
+            raise RuntimeError(f"Failed to generate embeddings: {e}")
+
+class VectorStoreManager:
+    """
+    Manage Chroma vector store operations - upload, query, etc.
+    
+    This class provides an interface to the ChromaDB vector database,
+    handling document storage, retrieval, and management.
+    
+    Attributes:
+        config (Config): Configuration parameters
+        client (chromadb.PersistentClient): ChromaDB client
+        collection (chromadb.Collection): The active ChromaDB collection
+        embedding_engine (EmbeddingEngine): Engine for generating embeddings
+    """
+    
+    def __init__(self, config: Config):
+        """
+        Initialize the vector store manager.
+        
+        Args:
+            config (Config): Configuration parameters
             
-            # Query history storage
-            query_history = []
+        Raises:
+            SystemExit: If the vector store cannot be initialized
+        """
+        self.config = config
             
-            # Function to update API key based on selected model
-            def update_api_key(api_key, model):
-                if not api_key.strip():
-                    return "❌ API key cannot be empty"
-                    
-                if model == "openai":
-                    success = rag_system.setup_openai(api_key)
-                    model_name = f"OpenAI {config.openai_model}"
+        # Initialize Chroma client (local persistence)
+        logger.info(f"Initializing Chroma at {config.local_dir}")
+        try:
+            self.client = chromadb.PersistentClient(path=config.local_dir)
+            logger.info("ChromaDB client initialized successfully")
+        except Exception as e:
+            error_msg = f"Failed to initialize ChromaDB client: {e}"
+            logger.critical(error_msg)
+            raise SystemExit(error_msg)
+        
+        # Get or create collection
+        try:
+            # Initialize embedding model
+            logger.info("Loading embedding model...")
+            self.embedding_engine = EmbeddingEngine(config.embedding_model)
+            logger.info(f"Using embedding model: {self.embedding_engine.model_name}")
+            
+            # Create embedding function
+            sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
+                model_name=self.embedding_engine.model_name
+            )
+            
+            # Try to get existing collection or create a new one
+            try:
+                self.collection = self.client.get_collection(
+                    name=config.collection_name,
+                    embedding_function=sentence_transformer_ef
+                )
+                logger.info(f"Using existing collection: {config.collection_name}")
+            except Exception as e:
+                logger.warning(f"Error getting collection: {e}")
+                # Attempt to get a list of available collections
+                collections = self.client.list_collections()
+                if collections:
+                    logger.info(f"Available collections: {[c.name for c in collections]}")
+                    # Use the first available collection if any
+                    self.collection = self.client.get_collection(
+                        name=collections[0].name,
+                        embedding_function=sentence_transformer_ef
+                    )
+                    logger.info(f"Using collection: {collections[0].name}")
                 else:
-                    success = rag_system.setup_gemini(api_key)
-                    model_name = f"Google {config.gemini_model}"
+                    # Create new collection if none exist
+                    self.collection = self.client.create_collection(
+                        name=config.collection_name,
+                        embedding_function=sentence_transformer_ef,
+                        metadata={"hnsw:space": "cosine"}
+                    )
+                    logger.info(f"Created new collection: {config.collection_name}")
                 
-                if success:
-                    return f"✅ {model_name} connected successfully"
-                else:
-                    return f"❌ Connection failed. Please check your API key and try again."
+        except Exception as e:
+            error_msg = f"Error initializing Chroma collection: {e}"
+            logger.critical(error_msg)
+            raise SystemExit(error_msg)
+    
+    def query(self, query_text: str, n_results: int = 5) -> List[Dict]:
+        """
+        Query the vector store with a text query.
+        
+        Args:
+            query_text (str): The query text
+            n_results (int): Number of results to return
             
-            # Query function that returns both response and search results
-            def query_and_search(query, n_results, model, temperature, max_tokens):
-                # Update configuration with current UI values
-                config.temperature = float(temperature)
-                config.max_tokens = int(max_tokens)
-                
-                start_time = datetime.now()
-                
-                if not query.strip():
-                    return "Please enter a question to get an answer.", "", query_history[-5:] if query_history else []
+        Returns:
+            List[Dict]: List of results with document text, metadata, and similarity score
+        """
+        if not query_text.strip():
+            logger.warning("Empty query received")
+            return []
+        
+        try:
+            logger.info(f"Querying vector store with: '{query_text[:50]}...' (top {n_results})")
+            
+            # Query the collection
+            search_results = self.collection.query(
+                query_texts=[query_text],
+                n_results=n_results,
+                include=["documents", "metadatas", "distances"]
+            )
+            
+            # Format results
+            results = []
+            if search_results["documents"] and len(search_results["documents"][0]) > 0:
+                for i in range(len(search_results["documents"][0])):
+                    results.append({
+                        'document': search_results["documents"][0][i],
+                        'metadata': search_results["metadatas"][0][i] if search_results["metadatas"] else {},
+                        'score': 1.0 - search_results["distances"][0][i],  # Convert distance to similarity
+                        'distance': search_results["distances"][0][i]
+                    })
                 
+                logger.info(f"Found {len(results)} results for query")
+            else:
+                logger.info("No results found for query")
+            
+            return results
+        except Exception as e:
+            logger.error(f"Error querying collection: {e}")
+            logger.debug(traceback.format_exc())
+            return []
+
+    def add_document(self, 
+                    document: str, 
+                    doc_id: str, 
+                    metadata: Dict[str, Any]) -> bool:
+        """
+        Add a document to the vector store.
+        
+        Args:
+            document (str): The document text
+            doc_id (str): Unique identifier for the document
+            metadata (Dict[str, Any]): Metadata about the document
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            logger.info(f"Adding document '{doc_id}' to vector store")
+            
+            # Add the document to the collection
+            self.collection.add(
+                documents=[document],
+                ids=[doc_id],
+                metadatas=[metadata]
+            )
+            
+            logger.info(f"Successfully added document '{doc_id}'")
+            return True
+        except Exception as e:
+            logger.error(f"Error adding document to collection: {e}")
+            return False
+
+    def delete_document(self, doc_id: str) -> bool:
+        """
+        Delete a document from the vector store.
+        
+        Args:
+            doc_id (str): ID of the document to delete
+            
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        try:
+            logger.info(f"Deleting document '{doc_id}' from vector store")
+            self.collection.delete(ids=[doc_id])
+            logger.info(f"Successfully deleted document '{doc_id}'")
+            return True
+        except Exception as e:
+            logger.error(f"Error deleting document from collection: {e}")
+            return False
+
+    def get_statistics(self) -> Dict[str, Any]:
+        """
+        Get statistics about the vector store.
+        
+        Returns:
+            Dict[str, Any]: Statistics about the vector store
+        """
+        stats = {
+            'collection_name': self.config.collection_name,
+            'embedding_model': self.embedding_engine.model_name,
+            'embedding_dimensions': self.embedding_engine.vector_size,
+            'device': self.embedding_engine.device
+        }
+        
+        try:
+            # Get collection count
+            collection_count = self.collection.count()
+            stats['total_documents'] = collection_count
+            
+            # Get unique metadata values
+            if collection_count > 0:
                 try:
-                    # Verify that API keys are configured
-                    if (model == "openai" and rag_system.openai_client is None) or \
-                       (model == "gemini" and not rag_system.gemini_configured):
-                        return "Please configure your API key first. Enter your API key in the settings panel and click 'Save API Key'.", "", query_history[-5:] if query_history else []
-                    
-                    # Call the RAG system's query and generate function
-                    response, search_output_text = rag_system.query_and_generate(
-                        query=query, 
-                        n_results=int(n_results),
-                        model=model
-                    )
-                    
-                    # Add to history
-                    timestamp = datetime.now().strftime("%H:%M")
-                    query_history.append([timestamp, query, model])
-                    
-                    # Keep only the last 100 queries
-                    if len(query_history) > 100:
-                        query_history.pop(0)
-                    
-                    # Update the history display with the most recent entries (reverse chronological)
-                    recent_history = list(reversed(query_history[-5:])) if len(query_history) >= 5 else list(reversed(query_history))
-                    
-                    # Calculate elapsed time
-                    elapsed_time = (datetime.now() - start_time).total_seconds()
-                    
-                    # Add subtle timing information to the response
-                    response_with_timing = f"{response}\n\n<small>Answered in {elapsed_time:.1f}s</small>"
-                    
-                    return response_with_timing, search_output_text, recent_history
-                
+                    # Get a sample of document metadata
+                    sample_results = self.collection.get(limit=min(collection_count, 100))
+                    if sample_results and 'metadatas' in sample_results and sample_results['metadatas']:
+                        # Count unique files if filename exists in metadata
+                        filenames = set()
+                        for metadata in sample_results['metadatas']:
+                            if 'filename' in metadata:
+                                filenames.add(metadata['filename'])
+                        stats['unique_files'] = len(filenames)
                 except Exception as e:
-                    error_msg = f"Error processing query: {str(e)}"
-                    logger.error(error_msg)
-                    logger.error(traceback.format_exc())
-                    return "I encountered an error while processing your question. Please try again or check your API key settings.", "", query_history[-5:] if query_history else []
+                    logger.warning(f"Error getting metadata statistics: {e}")
             
-            # Function to clear the input and results
-            def clear_inputs():
-                return "", "", "", query_history[-5:] if query_history else []
+            logger.info(f"Vector store statistics: {stats}")
+        except Exception as e:
+            logger.error(f"Error getting statistics: {e}")
+            stats['error'] = str(e)
+        
+        return stats
+
+class RAGSystem:
+    """
+    Retrieval-Augmented Generation with multiple LLM providers.
+    
+    This class handles the RAG workflow: retrieval of relevant documents,
+    formatting context, and generating responses with different LLM providers.
+    
+    Attributes:
+        vector_store (VectorStoreManager): Manager for vector store operations
+        openai_client (Optional[OpenAI]): OpenAI client
+        gemini_configured (bool): Whether Gemini API is configured
+        config (Config): Configuration parameters
+    """
+    
+    def __init__(self, vector_store: VectorStoreManager, config: Config):
+        """
+        Initialize the RAG system.
+        
+        Args:
+            vector_store (VectorStoreManager): Vector store manager
+            config (Config): Configuration parameters
+        """
+        self.vector_store = vector_store
+        self.config = config
+        self.openai_client = None
+        self.gemini_configured = False
+        
+        logger.info("Initialized RAG system")
+    
+    def setup_openai(self, api_key: str) -> bool:
+        """
+        Set up OpenAI client with API key.
+        
+        Args:
+            api_key (str): OpenAI API key
             
-            # Set up events
-            save_key_button.click(
-                fn=update_api_key,
-                inputs=[api_key_input, model_choice],
-                outputs=api_status
-            )
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        if not api_key.strip():
+            logger.warning("Empty OpenAI API key provided")
+            return False
             
-            query_button.click(
-                fn=query_and_search,
-                inputs=[query_input, num_results, model_choice, temperature_slider, max_tokens_slider],
-                outputs=[response_output, search_output, history_list]
+        try:
+            logger.info("Setting up OpenAI client")
+            self.openai_client = OpenAI(api_key=api_key)
+            # Test the API key with a simple request
+            response = self.openai_client.chat.completions.create(
+                model=self.config.openai_model,
+                messages=[
+                    {"role": "system", "content": "You are a helpful assistant."},
+                    {"role": "user", "content": "Test connection"}
+                ],
+                max_tokens=10
             )
+            logger.info("OpenAI client configured successfully")
+            return True
+        except Exception as e:
+            logger.error(f"Error initializing OpenAI client: {e}")
+            self.openai_client = None
+            return False
+    
+    def setup_gemini(self, api_key: str) -> bool:
+        """
+        Set up Gemini with API key.
+        
+        Args:
+            api_key (str): Google AI API key
             
-            refresh_button.click(
-                fn=lambda: get_db_stats(vector_store),
-                inputs=None,
-                outputs=stats_display
-            )
+        Returns:
+            bool: True if successful, False otherwise
+        """
+        if not api_key.strip():
+            logger.warning("Empty Gemini API key provided")
+            return False
             
-            clear_button.click(
-                fn=clear_inputs,
-                inputs=None,
-                outputs=[query_input, response_output, search_output, history_list]
-            )
+        try:
+            logger.info("Setting up Gemini client")
+            genai.configure(api_key=api_key)
             
-            # Handle Enter key in query input
-            query_input.submit(
-                fn=query_and_search,
-                inputs=[query_input, num_results, model_choice, temperature_slider, max_tokens_slider],
-                outputs=[response_output, search_output, history_list]
+            # Test the API key with a simple request
+            model = genai.GenerativeModel(self.config.gemini_model)
+            response = model.generate_content("Test connection")
+            
+            self.gemini_configured = True
+            logger.info("Gemini client configured successfully")
+            return True
+        except Exception as e:
+            logger.error(f"Error configuring Gemini: {e}")
+            self.gemini_configured = False
+            return False
+    
+    def format_context(self, documents: List[Dict]) -> str:
+        """
+        Format retrieved documents into context for the LLM.
+        
+        Args:
+            documents (List[Dict]): List of retrieved documents
+            
+        Returns:
+            str: Formatted context for the LLM
+        """
+        if not documents:
+            logger.warning("No documents provided for context formatting")
+            return "No relevant documents found."
+        
+        logger.info(f"Formatting {len(documents)} documents for context")
+        context_parts = []
+        
+        for i, doc in enumerate(documents):
+            metadata = doc['metadata']
+            # Extract document metadata in a robust way
+            title = metadata.get('title', metadata.get('filename', 'Unknown document'))
+            
+            # Format header with just essential metadata for cleaner context
+            header = f"Document {i+1} - {title}"
+            
+            # For readability, limit length of context document (using config value)
+            doc_text = doc['document']
+            if len(doc_text) > (self.config.context_limit // len(documents)):  
+                # Divide context limit among the documents
+                max_length = self.config.context_limit // len(documents)
+                doc_text = doc_text[:max_length] + "... [Document truncated for brevity]"
+                
+            context_parts.append(f"{header}:\n{doc_text}\n")
+        
+        full_context = "\n".join(context_parts)
+        logger.info(f"Created context with {len(full_context)} characters")
+        
+        return full_context
+    
+    def generate_response_openai(self, query: str, context: str) -> str:
+        """
+        Generate a response using OpenAI model with context.
+        
+        Args:
+            query (str): User query
+            context (str): Formatted document context
+            
+        Returns:
+            str: Generated response
+        """
+        if not self.openai_client:
+            logger.warning("OpenAI API key not configured for response generation")
+            return "Please configure an OpenAI API key to use this feature. Enter your API key in the field and click 'Save API Key'."
+        
+        # Improved system prompt for better, more comprehensive responses
+        system_prompt = """
+        You are an exceptionally helpful, clear, and friendly AI research assistant. Your goal is to provide comprehensive, well-structured, and insightful answers based on the provided document context.
+
+        Guidelines for your response:
+        
+        1. USE ONLY the information contained in the provided context documents to form your answer. If the context doesn't contain enough information to provide a complete answer, acknowledge this limitation clearly.
+        
+        2. Always provide well-structured, detailed responses between 300-500 words that thoroughly address the user's question.
+        
+        3. Format your response with clear headings, bullet points, or numbered lists when appropriate to enhance readability.
+        
+        4. Cite your sources by referring to the document numbers (e.g., "According to Document 1...") to support your claims.
+        
+        5. Use a friendly, conversational, and supportive tone that makes complex information accessible.
+        
+        6. If different documents offer conflicting information, acknowledge these differences and present both perspectives without bias.
+        
+        7. When appropriate, organize information into logical categories or chronological order to improve clarity.
+        
+        8. Use examples from the documents to illustrate key points when available.
+        
+        9. Conclude with a brief summary of the main points if the answer is complex.
+        
+        10. Remember to stay focused on the user's specific question while providing sufficient context for complete understanding.
+        """
+        
+        try:
+            logger.info(f"Generating response with OpenAI ({self.config.openai_model})")
+            
+            start_time = datetime.now()
+            response = self.openai_client.chat.completions.create(
+                model=self.config.openai_model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": f"Context:\n{context}\n\nQuestion: {query}"}
+                ],
+                temperature=self.config.temperature,
+                max_tokens=self.config.max_tokens,
             )
             
-            # Auto-fill examples
-            examples = [
-                ["What are the main features of this application?"],
-                ["How does the retrieval augmented generation work?"],
-                ["Can you explain the embedding models used in this system?"],
-            ]
+            generation_time = (datetime.now() - start_time).total_seconds()
+            response_text = response.choices[0].message.content
             
-            gr.Examples(
-                examples=examples,
-                inputs=query_input,
-                outputs=[response_output, search_output, history_list],
-                fn=lambda q: query_and_search(q, num_results.value, model_choice.value, temperature_slider.value, max_tokens_slider.value),
-                cache_examples=False,
+            logger.info(f"Generated response with OpenAI in {generation_time:.2f} seconds")
+            return response_text
+        except Exception as e:
+            error_msg = f"Error generating response with OpenAI: {str(e)}"
+            logger.error(error_msg)
+            return f"I encountered an error while generating your response. Please try again or check your API key. Error details: {str(e)}"
+    
+    def generate_response_gemini(self, query: str, context: str) -> str:
+        """
+        Generate a response using Gemini with context.
+        
+        Args:
+            query (str): User query
+            context (str): Formatted document context
+            
+        Returns:
+            str: Generated response
+        """
+        if not self.gemini_configured:
+            logger.warning("Gemini API key not configured for response generation")
+            return "Please configure a Google AI API key to use this feature. Enter your API key in the field and click 'Save API Key'."
+        
+        # Improved Gemini prompt for more comprehensive and user-friendly responses
+        prompt = f"""
+        You are a knowledgeable and friendly research assistant who excels at providing clear, comprehensive, and well-structured responses. Your goal is to help users understand complex information from documents in an accessible way.
+
+        **Guidelines for Your Response:**
+        
+        - Create a detailed, well-organized response of approximately 300-500 words that thoroughly addresses the user's question.
+        - Use ONLY information from the provided context documents.
+        - Structure your answer with clear paragraphs, and use headings, bullet points, or numbered lists when appropriate.
+        - Maintain a friendly, conversational tone that makes information accessible and engaging.
+        - When citing information, reference specific documents by number (e.g., "As mentioned in Document 2...").
+        - If the context doesn't contain enough information for a complete answer, acknowledge this limitation while providing what you can from the available context.
+        - If documents contain conflicting information, present both perspectives fairly.
+        - Conclude with a brief summary if the topic is complex.
+        
+        **Context Documents:**
+        {context}
+        
+        **User's Question:** 
+        {query}
+
+        **Your Response:** 
+        """
+
+        try:
+            logger.info(f"Generating response with Gemini ({self.config.gemini_model})")
+            
+            start_time = datetime.now()
+            model = genai.GenerativeModel(self.config.gemini_model)
+            
+            generation_config = {
+                "temperature": self.config.temperature,
+                "max_output_tokens": self.config.max_tokens,
+                "top_p": 0.9,
+                "top_k": 40
+            }
+            
+            response = model.generate_content(
+                prompt,
+                generation_config=generation_config
             )
             
-        # Launch the interface with a nice theme
-        app.launch(
-            share=False,  # Set to True to create a public link
-            server_name="0.0.0.0",  # Listen on all interfaces
-            server_port=7860,  # Default Gradio port
-            debug=False,  # Set to True during development
-            auth=None,  # Add (username, password) tuple for basic auth
-            favicon_path="favicon.ico" if os.path.exists("favicon.ico") else None,
-            show_error=True
-        )
+            generation_time = (datetime.now() - start_time).total_seconds()
+            response_text = response.text
+            
+            logger.info(f"Generated response with Gemini in {generation_time:.2f} seconds")
+            return response_text
+        except Exception as e:
+            error_msg = f"Error generating response with Gemini: {str(e)}"
+            logger.error(error_msg)
+            return f"I encountered an error while generating your response. Please try again or check your API key. Error details: {str(e)}"
+    
+    def query_and_generate(self, 
+                          query: str, 
+                          n_results: int = 5, 
+                          model: str = "openai") -> Tuple[str, str]:
+        """
+        Retrieve relevant documents and generate a response using the specified model.
+        
+        Args:
+            query (str): User query
+            n_results (int): Number of documents to retrieve
+            model (str): Model provider to use ('openai' or 'gemini')
+            
+        Returns:
+            Tuple[str, str]: (Generated response, Search results)
+        """
+        if not query.strip():
+            logger.warning("Empty query received")
+            return "Please enter a question to get a response.", "No search performed."
+        
+        logger.info(f"Processing query: '{query[:50]}...' with {model} model")
+        
+        # Query vector store
+        documents = self.vector_store.query(query, n_results=n_results)
+        
+        # Format search results (for logs and hidden UI component)
+        # We'll format this in a way that's more useful for reference but not shown in UI
+        formatted_results = []
+        for i, res in enumerate(documents):
+            metadata = res['metadata']
+            title = metadata.get('title', metadata.get('filename', 'Unknown'))
+            score = res['score']
+            
+            # Only include a very brief preview for reference
+            preview = res['document'][:100] + '...' if len(res['document']) > 100 else res['document']
+            formatted_results.append(f"Document {i+1}: {title} (Relevance: {score:.2f})")
+        
+        search_output_text = "\n".join(formatted_results) if formatted_results else "No relevant documents found."
+        
+        if not documents:
+            logger.warning("No relevant documents found")
+            return "I couldn't find relevant information in the knowledge base to answer your question. Could you try rephrasing your question or ask about a different topic?", search_output_text
+        
+        # Format context
+        context = self.format_context(documents)
+        
+        # Generate response with the appropriate model
+        if model == "openai":
+            response = self.generate_response_openai(query, context)
+        elif model == "gemini":
+            response = self.generate_response_gemini(query, context)
+        else:
+            error_msg = f"Unknown model: {model}"
+            logger.error(error_msg)
+            return error_msg, search_output_text
+        
+        return response, search_output_text
+
+def get_db_stats(vector_store: VectorStoreManager) -> str:
+    """
+    Function to get vector store statistics.
+    
+    Args:
+        vector_store (VectorStoreManager): Vector store manager
+        
+    Returns:
+        str: Formatted statistics string
+    """
+    try:
+        stats = vector_store.get_statistics()
+        total_docs = stats.get('total_documents', 0)
+        
+        stats_text = f"Documents in knowledge base: {total_docs}"
+        return stats_text
     except Exception as e:
-        logger.critical(f"Error starting application: {e}")
-        print(f"Error starting application: {e}")
-        sys.exit(1)
+        logger.error(f"Error getting statistics: {e}")
+        return "Error getting database statistics"
+
+# Helper function for loading documents (can be expanded in future versions)
+def load_document(file_path: str, chunk_size: int = 2000, chunk_overlap: int = 200) -> bool:
+    """
+    Load a document into the vector store.
+    
+    Args:
+        file_path (str): Path to the document
+        chunk_size (int): Size of chunks to split the document into
+        chunk_overlap (int): Overlap between chunks
+        
+    Returns:
+        bool: True if successful, False otherwise
+    """
+    try:
 try:
         logger.info(f"Loading document: {file_path}")