Refactor: Generalize OpenAICompatibleInferenceBot initialization

openai_compatible_inference_bot.py

@@ -3,67 +3,150 @@ import os
 import logging
 from abc import abstractmethod
 from base_telegram_inference_bot import BaseTelegramInferenceBot
-from openai import OpenAI
+from openai import OpenAI, AzureOpenAI # Import both
 
 class OpenAICompatibleInferenceBot(BaseTelegramInferenceBot):
-    def __init__(self):
+    DEFAULT_MAX_HISTORY_LENGTH = 20
-        super().__init__()
+    DEFAULT_MAX_TOKENS = 1000 # Default for _configure_model_and_tokens
-        # Client and model configuration will be handled by subclasses
-        self.client = None
-        self.model = None
-        self.max_tokens = None
 
-    def _configure_model_and_tokens(self, model_name, max_tokens_str, default_max_tokens=1000):
+    def __init__(
-        self.model = model_name if model_name else "default-model" 
+        self,
+        client: OpenAI | AzureOpenAI | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        api_version: str | None = None, # For Azure
+        azure_deployment: str | None = None, # Model for Azure, distinct from general model_name if needed
+        model_name: str | None = None, # General model name for the API call
+        max_tokens_str: str | None = None,
+        system_prompt_content: str | None = None,
+        system_prompt_path: str | None = None,
+        is_gemini: bool = False, # Hint for specific API key if others are not set
+        max_history_length: int | None = None
+    ):
+        super().__init__(system_prompt_content=system_prompt_content, system_prompt_path=system_prompt_path)
+
+        self.max_history_length = max_history_length if max_history_length is not None else self.DEFAULT_MAX_HISTORY_LENGTH
+        self.client = client
+
+        if not self.client:
+            _api_key = api_key
+            _base_url = base_url
+            _api_version = api_version
+            _azure_deployment_name = azure_deployment # This will be used as the model for Azure
+
+            # Determine if configuring for Azure OpenAI
+            is_azure = False
+            if _azure_deployment_name or (_base_url and "azure.com" in _base_url) or os.environ.get("AZURE_OPENAI_ENDPOINT"):
+                is_azure = True
+            
+            if is_azure:
+                _base_url = _base_url or os.environ.get("AZURE_OPENAI_ENDPOINT")
+                _api_key = _api_key or os.environ.get("AZURE_OPENAI_KEY")
+                _api_version = _api_version or os.environ.get("AZURE_OPENAI_API_VERSION")
+                # For Azure, the model parameter in API calls is the deployment name
+                _effective_model_name = _azure_deployment_name or model_name # Use deployment if available, else model_name
+                if not _base_url or not _api_key or not _api_version or not _effective_model_name:
+                    raise ValueError("For Azure OpenAI, endpoint, API key, API version, and deployment/model name must be configured.")
+                self.client = AzureOpenAI(
+                    api_key=_api_key,
+                    azure_endpoint=_base_url,
+                    api_version=_api_version
+                )
+                # The model to be used in API calls for Azure is the deployment name.
+                # _configure_model_and_tokens will set self.model to this.
+                model_name_for_config = _effective_model_name 
+                logging.info(f"Initialized AzureOpenAI client for deployment: {model_name_for_config} at {_base_url}")
+            else:
+                # Standard OpenAI or other OpenAI-compatible (like Gemini via base_url)
+                _base_url = _base_url or os.environ.get("OPENAI_API_BASE_URL") # For other compatible APIs
+                if not _api_key: # Try different ENV sources for API key
+                    if is_gemini and os.environ.get("GEMINI_API_KEY"):
+                        _api_key = os.environ.get("GEMINI_API_KEY")
+                    else:
+                        _api_key = os.environ.get("OPENAI_API_KEY")
+                
+                if not _api_key and not _base_url : # For completely local models with no key needed via base_url
+                     pass # Allow client to be created with no API key if base_url is set and points to local model
+                elif not _api_key:
+                     raise ValueError("API key must be provided for OpenAI compatible client if not Azure or local anonymous.")
+
+                self.client = OpenAI(api_key=_api_key, base_url=_base_url)
+                model_name_for_config = model_name # Use the general model_name for non-Azure
+                log_msg = f"Initialized OpenAI compatible client. Target URL: {_base_url if _base_url else 'OpenAI default'}."
+                logging.info(log_msg)
+        else:
+            # Client was provided directly
+            model_name_for_config = model_name # Use provided model_name
+            logging.info(f"Using provided client: {type(self.client)}")
+
+        # Configure the actual model name and max_tokens for API calls
+        self._configure_model_and_tokens(
+            model_name_for_config, 
+            max_tokens_str, 
+            default_max_tokens=self.DEFAULT_MAX_TOKENS
+        )
+
+    def _configure_model_and_tokens(self, model_name: str | None, max_tokens_str: str | None, default_max_tokens: int = 1000):
+        self.model = model_name if model_name else "default-model" # Fallback model name
         try:
-            self.max_tokens = int(max_tokens_str) if max_tokens_str is not None else default_max_tokens
+            # If max_tokens_str is explicitly "None" or empty, treat as None for API default
+            if max_tokens_str and max_tokens_str.lower() not in ["none", "", "null"]:
+                self.max_tokens = int(max_tokens_str)
+            else:
+                self.max_tokens = None # Use API default by not sending the parameter or sending null
         except ValueError:
-            logging.error(f"Invalid value for max_tokens: {max_tokens_str}. Using default {default_max_tokens}.")
+            logging.warning(f"Invalid value for max_tokens: {max_tokens_str}. Using API default (None). stalwart default was {default_max_tokens}")
-            self.max_tokens = default_max_tokens
+            self.max_tokens = None # Use API default
-        logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens}")
+        
+        logging.info(f"Configured to use model: {self.model} with max_tokens: {self.max_tokens if self.max_tokens is not None else 'API default'}")
 
     def get_llm_description(self) -> str:
-        return f"LLM: {self.model}, Max Tokens: {self.max_tokens}"
+        client_type = type(self.client).__name__
+        return f"Client: {client_type}, LLM: {self.model}, Max Tokens: {self.max_tokens if self.max_tokens is not None else 'API default'}"
 
     def get_chat_response(self, messages):
         if not self.client:
-            raise ValueError("OpenAI client not initialized. Subclasses must initialize it.")
+            # This should ideally not be hit if __init__ is successful
+            logging.error("OpenAI client not initialized before get_chat_response.")
+            raise ValueError("OpenAI client not initialized.")
         try:
+            # Pass self.max_tokens directly. If None, OpenAI library omits it or handles it.
             response = self.client.chat.completions.create(
-                model=self.model,
+                model=self.model, 
                 messages=messages,
                 tools=self.functions if hasattr(self, 'functions') and self.functions else None,
                 tool_choice="auto" if hasattr(self, 'functions') and self.functions else None,
-                max_tokens=self.max_tokens
+                max_tokens=self.max_tokens 
             )
             return response
         except Exception as e:
-            logging.error(f"API call failed: {e}")
+            logging.error(f"API call to model {self.model} failed: {e}")
             raise
 
     async def handle_message(self, user_id, user_message):
-        if user_id not in self.conversation_history:
+        if user_id not in self.conversation_history or not self.conversation_history[user_id]:
             self.conversation_history[user_id] = []
-            if hasattr(self, 'system_prompt') and self.system_prompt:
+            if self.system_prompt: # Use the loaded system_prompt
                  self.conversation_history[user_id].append({"role": "system", "content": self.system_prompt})
 
         self.conversation_history[user_id].append({"role": "user", "content": user_message})
-        messages = self.conversation_history[user_id]
+        messages = list(self.conversation_history[user_id]) # Work with a copy for this turn
 
         response = self.get_chat_response(messages)
         
         if not (response.choices and response.choices[0].message):
             logging.error("No valid response choice message from LLM.")
+            # Persist the user message in history even if LLM fails this turn
+            self.conversation_history[user_id] = messages 
             return "Error: Could not get a valid response from the LLM."
         
-        messages.append(response.choices[0].message) # Append the assistant's response message
+        assistant_message = response.choices[0].message
+        messages.append(assistant_message) 
 
-        tool_calls_from_response = []
+        tool_calls_from_response = list(assistant_message.tool_calls) if assistant_message.tool_calls else []
-        if response.choices[0].message.tool_calls:
-            tool_calls_from_response.extend(response.choices[0].message.tool_calls)
         
         tool_use_count = 0
-        MAX_TOOL_ITERATIONS = 200 
+        MAX_TOOL_ITERATIONS = 5 # OpenAI compatible typically uses fewer iterations than Anthropic
 
         while tool_calls_from_response and tool_use_count < MAX_TOOL_ITERATIONS:
             tool_results_for_model = [] 
@@ -71,20 +154,24 @@ class OpenAICompatibleInferenceBot(BaseTelegramInferenceBot):
             for tool_call in tool_calls_from_response:
                 tool_call_id = tool_call.id
                 function_to_call = tool_call.function
+                function_name = function_to_call.name
+                function_args_str = function_to_call.arguments
                 
-                logging.info(f"Attempting to call tool: {function_to_call.name} with args: {function_to_call.arguments}")
+                logging.info(f"Attempting to call tool: {function_name} with args: {function_args_str}")
                 try:
-                    tool_response_content = self.call_tool(function_to_call.name, function_to_call.arguments)
+                    # Arguments are already a string from the API, self.call_tool expects dict or string
+                    tool_response_content = self.call_tool(function_name, function_args_str)
+                    # Ensure content is string for OpenAI tool role
                     if not isinstance(tool_response_content, str):
                         tool_response_content = json.dumps(tool_response_content) 
                 except Exception as e:
-                    logging.error(f"Error calling tool {function_to_call.name}: {e}")
+                    logging.error(f"Error calling tool {function_name}: {e}")
-                    tool_response_content = f"Error executing tool {function_to_call.name}: {str(e)}"
+                    tool_response_content = f"Error executing tool {function_name}: {str(e)}"
                 
                 tool_results_for_model.append({
                     "role": "tool",
                     "tool_call_id": tool_call_id,
-                    "name": function_to_call.name,
+                    "name": function_name,
                     "content": tool_response_content
                 })
 
@@ -93,41 +180,51 @@ class OpenAICompatibleInferenceBot(BaseTelegramInferenceBot):
             response = self.get_chat_response(messages)
             if not (response.choices and response.choices[0].message):
                 logging.error("No valid response choice message from LLM after tool call.")
+                self.conversation_history[user_id] = messages # Persist state before error
                 return "Error: Could not get a valid response from the LLM after tool call."
 
-            messages.append(response.choices[0].message)
+            assistant_message = response.choices[0].message
+            messages.append(assistant_message)
 
-            tool_calls_from_response = [] 
+            tool_calls_from_response = list(assistant_message.tool_calls) if assistant_message.tool_calls else []
-            if response.choices[0].message.tool_calls:
-                tool_calls_from_response.extend(response.choices[0].message.tool_calls)
             
             tool_use_count += 1
             if tool_use_count >= MAX_TOOL_ITERATIONS and tool_calls_from_response:
                 logging.warning(f"Max tool iterations ({MAX_TOOL_ITERATIONS}) reached. Returning last assistant message.")
+                # Ensure final content is returned even if max iterations hit with pending tool calls
+                break 
 
-        # Conversation history management
+        self.conversation_history[user_id] = messages # Persist the full exchange for this turn
-        # This limit should be reviewed and potentially made configurable
+        # Apply history length limit
-        if len(self.conversation_history[user_id]) > 20: # Example limit, adjust as needed
+        if len(self.conversation_history[user_id]) > self.max_history_length:
-            self.conversation_history[user_id] = self.conversation_history[user_id][-20:]
+            # Keep system prompt if present as the first message, then trim the rest
+            if self.conversation_history[user_id][0]["role"] == "system":
+                system_msg = [self.conversation_history[user_id][0]]
+                trimmed_history = self.conversation_history[user_id][-(self.max_history_length-1):]
+                self.conversation_history[user_id] = system_msg + trimmed_history
+            else:
+                self.conversation_history[user_id] = self.conversation_history[user_id][-self.max_history_length:]
 
         final_assistant_message = messages[-1]
-        return final_assistant_message.content if final_assistant_message.role == "assistant" and final_assistant_message.content else "No content in final message."
+        return final_assistant_message.content if final_assistant_message.role == "assistant" and final_assistant_message.content is not None else "Assistant did not provide a textual response."
 
     async def start(self):
-        logging.info(f"{self.__class__.__name__} started.")
+        logging.info(f"{self.__class__.__name__} (Model: {self.model}) started.")
 
-    def clear(self, user_id):
+    # clear_conversation_history is inherited from BaseTelegramInferenceBot
-        super().clear_conversation_history(user_id)
 
     async def abort_processing(self, user_id):
+        # This is a soft abort for OpenAI compatible bots as API calls are synchronous within handle_message
         if user_id in self.processing_status:
-            self.processing_status[user_id]["processing"] = False
+            self.clear_processing_status(user_id) # Use base class method
-            self.clear(user_id)
+            logging.info(f"Processing aborted for user {user_id}.")
-            return "Processing aborted and conversation cleared."
+            # Optionally clear conversation history or let user do it explicitly
+            # super().clear_conversation_history(user_id) 
+            return "Processing aborted. You can send a new message or /clear the conversation."
         else:
-            self.clear(user_id)
+            # super().clear_conversation_history(user_id)
-            return "No active processing found to abort. Conversation cleared."
+            return "No active processing found to abort. If you wish, /clear the conversation history."
 
     @abstractmethod
     async def switch_model(self):
-        pass
+        pass