Create session_history.py

utils/session_history.py

@@ -0,0 +1,250 @@
+import os
+import openai
+import logging
+import json
+import gradio as gr
+from typing import Optional
+
+class ResponseManager:
+"""
+This class initializes the OpenAI client and provides methods to create responses,
+maintain conversation history, and handle user queries.
+"""
+
+def __init__(self, 
+             vector_store_id: Optional[str] = None, 
+             api_key: Optional[str] = None, 
+             meta_prompt_file: Optional[str] = None,
+             model: str = "gpt-4o-mini",
+             temperature: float = 0,
+             max_output_tokens: int = 800,
+             max_num_results: int = 15):
+    """
+    Initialize the ResponseManager with optional parameters for configuration.
+    :param vector_store_id: The ID of the vector store to use for file search.
+    :param api_key: The OpenAI API key for authentication.
+    :param meta_prompt_file: Path to the meta prompt file (default: 'config/meta_prompt.txt').
+    :param model: The OpenAI model to use (default: 'gpt-4o-mini').
+    :param temperature: The temperature for response generation (default: 0).
+    :param max_output_tokens: The maximum number of output tokens (default: 800).
+    :param max_num_results: The maximum number of search results to return (default: 15).
+    """
+    # Load vector_store_id and api_key from environment variables if not provided
+    self.vector_store_id = vector_store_id or os.getenv('VECTOR_STORE_ID')
+    if not self.vector_store_id:
+        logging.error("VECTOR_STORE_ID is not provided or set in the environment.")
+        raise ValueError("VECTOR_STORE_ID is required.")
+
+    self.api_key = api_key or os.getenv('OPENAI_API_KEY')
+    if not self.api_key:
+        logging.error("OPENAI_API_KEY is not provided or set in the environment.")
+        raise ValueError("OPENAI_API_KEY is required.")
+
+    # Initialize other attributes
+    self.meta_prompt_file = meta_prompt_file or 'config/meta_prompt.txt'
+    self.previous_response_id = None
+
+    # Initialize the OpenAI client
+    self.client = openai.OpenAI(api_key=self.api_key)
+
+    # Load the meta prompt from the specified file
+    self.meta_prompt = self._load_meta_prompt(self.meta_prompt_file)
+
+    # Set default parameters for response generation
+    self.model = model
+    self.temperature = temperature
+    self.max_output_tokens = max_output_tokens
+    self.max_num_results = max_num_results
+
+def _load_meta_prompt(self, meta_prompt_file: str) -> str:
+    """
+    Load the meta prompt from the specified file.
+    :param meta_prompt_file: Path to the meta prompt file.
+    :return: The meta prompt as a string.
+    """
+    if not os.path.exists(meta_prompt_file):
+        logging.error(f"Meta prompt file '{meta_prompt_file}' not found.")
+        raise FileNotFoundError(f"Meta prompt file '{meta_prompt_file}' not found.")
+    with open(meta_prompt_file, 'r', encoding='utf-8') as file:
+        meta_prompt = file.read().strip()
+    logging.info(f"Meta prompt loaded successfully from '{meta_prompt_file}'.")
+    return meta_prompt
+
+def generate_response(self, query: str, history: list) -> tuple:
+    """
+    Generate a response to a user query using the OpenAI API.
+    This method interacts with the OpenAI API to create a response based on the user's query.
+    It supports optional parameters for model configuration and handles errors gracefully.
+    Args:
+        query (str): The user query to respond to.
+        history (list): The conversation history from the chatbot.
+    Returns:
+        tuple: (updated conversation list for display, updated conversation list for state)
+    """
+    # Prepare the input for the API call
+    input_data = [{"role": "developer", "content": self.meta_prompt}] if self.previous_response_id is None else []
+    input_data.append({"role": "user", "content": query})
+    
+    # Validate the query
+    if not query.strip():
+        logging.warning("Empty or invalid query received.")
+        warning_message = "Please enter a valid query."
+        input_data.append({"role": "assistant", "content": warning_message})
+        new_history = history + input_data
+        return new_history, new_history
+
+    try:
+        logging.info("Sending request to OpenAI API...")
+        response = self.client.responses.create(
+            model=self.model,
+            previous_response_id=self.previous_response_id,
+            input=input_data,
+            tools=[{
+                "type": "file_search",
+                "vector_store_ids": [self.vector_store_id],
+                "max_num_results": self.max_num_results
+            }],
+            truncation="auto",
+            temperature=self.temperature,
+            max_output_tokens=self.max_output_tokens
+        )
+        self.previous_response_id = response.id
+        logging.info("Response received successfully.")
+        input_data.append({"role": "assistant", "content": response.output_text})
+        new_history = history + input_data
+        return new_history, new_history
+    except Exception as e:
+        logging.error(f"An error occurred while generating a response: {e}")
+        error_message = "Sorry, I couldn't generate a response at this time. Please try again later."
+        input_data.append({"role": "assistant", "content": error_message})
+        new_history = history + input_data
+        return new_history, new_history
+ 
+class ChatbotInterface:
+def init(self,
+config_path: str = 'config/gradio_config.json',
+model: str = "gpt-4o-mini",
+temperature: float = 0,
+max_output_tokens: int = 800,
+max_num_results: int = 15,
+vector_store_id: Optional[str] = None,
+api_key: Optional[str] = None,
+meta_prompt_file: Optional[str] = None):
+"""
+Initialize the ChatbotInterface with configuration and custom parameters for ResponseManager.
+:param config_path: Path to the configuration JSON file.
+:param model: The OpenAI model to use (default: 'gpt-4o-mini').
+:param temperature: The temperature for response generation (default: 0).
+:param max_output_tokens: The maximum number of output tokens (default: 800).
+:param max_num_results: The maximum number of search results to return (default: 15).
+:param vector_store_id: The ID of the vector store to use for file search.
+:param api_key: The OpenAI API key for authentication.
+:param meta_prompt_file: Path to the meta prompt file.
+"""
+self.config = self.load_config(config_path)
+self.title = self.config["chatbot_title"]
+self.description = self.config["chatbot_description"]
+self.input_label = self.config["chatbot_input_label"]
+self.input_placeholder = self.config["chatbot_input_placeholder"]
+self.output_label = self.config["chatbot_output_label"]
+self.reset_button = self.config["chatbot_reset_button"]
+self.submit_button = self.config["chatbot_submit_button"]
+
+    # Initialize ResponseManager with custom parameters
+    try:
+        self.response_manager = ResponseManager(
+            model=model,
+            temperature=temperature,
+            max_output_tokens=max_output_tokens,
+            max_num_results=max_num_results,
+            vector_store_id=vector_store_id,
+            api_key=api_key,
+            meta_prompt_file=meta_prompt_file
+        )
+        self.generate_response = self.response_manager.generate_response  
+        logging.info(
+            "ChatbotInterface initialized with the following parameters:\n"
+            f"  - Model: {model}\n"
+            f"  - Temperature: {temperature}\n"
+            f"  - Max Output Tokens: {max_output_tokens}\n"
+            f"  - Max Number of Results: {max_num_results}\n"
+            f"  - Vector Store ID: {vector_store_id}\n"
+            f"  - API Key: {'Provided' if api_key else 'Not Provided'}\n"
+            f"  - Meta Prompt File: {meta_prompt_file or 'Default'}"
+        )
+    except Exception as e:
+        logging.error(f"Failed to initialize ResponseManager: {e}")
+        raise
+
+   
+@staticmethod
+def load_config(config_path: str) -> dict:
+    """
+    Load the configuration for Gradio GUI interface from the JSON file.
+    :param config_path: Path to the configuration JSON file.
+    :return: Configuration dictionary.
+    """
+    logging.info(f"Loading configuration from {config_path}...")
+    if not os.path.exists(config_path):
+        logging.error(f"Configuration file not found: {config_path}")
+        raise FileNotFoundError(f"Configuration file not found: {config_path}")
+
+    with open(config_path, 'r') as config_file:
+        config = json.load(config_file)
+
+    required_keys = [
+        "chatbot_title", "chatbot_description", "chatbot_input_label",
+        "chatbot_input_placeholder", "chatbot_output_label",
+        "chatbot_reset_button", "chatbot_submit_button"
+    ]
+    for key in required_keys:
+        if key not in config:
+            logging.error(f"Missing required configuration key: {key}")
+            raise ValueError(f"Missing required configuration key: {key}")
+
+    logging.info("Configuration loaded successfully.")
+    return config
+
+def reset_output(self) -> list:
+    """
+    Reset the chatbot output.
+    :return: An empty list to reset the output.
+    """
+    return []
+
+def create_interface(self) -> gr.Blocks:
+    """
+    Create the Gradio Blocks interface.
+    :return: A Gradio Blocks interface object.
+    """
+    logging.info("Creating Gradio interface...")
+
+    # Define the Gradio Blocks interface
+    with gr.Blocks() as demo:
+        gr.Markdown(f"## {self.title}\n{self.description}")
+
+        # Chatbot history component    
+        chatbot_output = gr.Chatbot(label=self.output_label, type="messages")
+        
+        # Adding a session-specific state to store conversation history.
+        conversation_state = gr.State([])
+
+        # User input
+        user_input = gr.Textbox(
+            lines=2,
+            label=self.input_label,
+            placeholder=self.input_placeholder
+        )
+
+        # Buttons
+        with gr.Row():
+            reset = gr.Button(self.reset_button, variant="secondary")
+            submit = gr.Button(self.submit_button, variant="primary")
+
+        submit.click(fn=self.generate_response, inputs=[user_input, conversation_state], outputs=[chatbot_output, conversation_state])
+        user_input.submit(fn=self.generate_response, inputs=[user_input, conversation_state], outputs=[chatbot_output, conversation_state])
+        reset.click(fn=self.reset_output, inputs=None, outputs=chatbot_output)
+
+
+    logging.info("Gradio interface created successfully.")
+    return demo