""" Conversation thread management system for AgentPress. This module provides comprehensive conversation management, including: - Thread creation and persistence - Message handling with support for text and images - Tool registration and execution - LLM interaction with streaming support - Error handling and cleanup """ import json import uuid from typing import List, Dict, Any, Optional, Type, Union, AsyncGenerator, Tuple, Callable, Literal from services.llm import make_llm_api_call from agentpress.tool import Tool, ToolResult from agentpress.tool_registry import ToolRegistry from agentpress.response_processor import ( ResponseProcessor, ProcessorConfig ) from services.supabase import DBConnection from utils.logger import logger # Type alias for tool choice ToolChoice = Literal["auto", "required", "none"] class ThreadManager: """Manages conversation threads with LLM models and tool execution. Provides comprehensive conversation management, handling message threading, tool registration, and LLM interactions with support for both standard and XML-based tool execution patterns. """ def __init__(self): """Initialize ThreadManager.""" self.db = DBConnection() self.tool_registry = ToolRegistry() self.response_processor = ResponseProcessor( tool_registry=self.tool_registry, add_message_callback=self.add_message ) def add_tool(self, tool_class: Type[Tool], function_names: Optional[List[str]] = None, **kwargs): """Add a tool to the ThreadManager.""" self.tool_registry.register_tool(tool_class, function_names, **kwargs) async def add_message( self, thread_id: str, type: str, content: Union[Dict[str, Any], List[Any], str], is_llm_message: bool = False, metadata: Optional[Dict[str, Any]] = None ): """Add a message to the thread in the database. Args: thread_id: The ID of the thread to add the message to. type: The type of the message (e.g., 'text', 'image_url', 'tool_call', 'tool', 'user', 'assistant'). content: The content of the message. Can be a dictionary, list, or string. It will be stored as JSONB in the database. is_llm_message: Flag indicating if the message originated from the LLM. Defaults to False (user message). metadata: Optional dictionary for additional message metadata. Defaults to None, stored as an empty JSONB object if None. """ logger.debug(f"Adding message of type '{type}' to thread {thread_id}") client = await self.db.client # Prepare data for insertion data_to_insert = { 'thread_id': thread_id, 'type': type, 'content': json.dumps(content) if isinstance(content, (dict, list)) else content, 'is_llm_message': is_llm_message, 'metadata': json.dumps(metadata or {}), # Ensure metadata is always a JSON object } try: result = await client.table('messages').insert(data_to_insert).execute() logger.info(f"Successfully added message to thread {thread_id}") except Exception as e: logger.error(f"Failed to add message to thread {thread_id}: {str(e)}", exc_info=True) raise async def get_messages(self, thread_id: str) -> List[Dict[str, Any]]: """Get all messages for a thread. Args: thread_id: The ID of the thread to get messages for. Returns: List of message objects. """ logger.debug(f"Getting messages for thread {thread_id}") client = await self.db.client try: result = await client.rpc('get_llm_formatted_messages', {'p_thread_id': thread_id}).execute() # Parse the returned data which might be stringified JSON if not result.data: return [] # Return properly parsed JSON objects messages = [] for item in result.data: if isinstance(item, str): try: parsed_item = json.loads(item) messages.append(parsed_item) except json.JSONDecodeError: logger.error(f"Failed to parse message: {item}") else: messages.append(item) # Ensure tool_calls have properly formatted function arguments for message in messages: if message.get('tool_calls'): for tool_call in message['tool_calls']: if isinstance(tool_call, dict) and 'function' in tool_call: # Ensure function.arguments is a string if 'arguments' in tool_call['function'] and not isinstance(tool_call['function']['arguments'], str): # Log and fix the issue # logger.warning(f"Found non-string arguments in tool_call, converting to string") tool_call['function']['arguments'] = json.dumps(tool_call['function']['arguments']) return messages except Exception as e: logger.error(f"Failed to get messages for thread {thread_id}: {str(e)}", exc_info=True) return [] async def run_thread( self, thread_id: str, system_prompt: Dict[str, Any], stream: bool = True, temporary_message: Optional[Dict[str, Any]] = None, llm_model: str = "gpt-4o", llm_temperature: float = 0, llm_max_tokens: Optional[int] = None, processor_config: Optional[ProcessorConfig] = None, tool_choice: ToolChoice = "auto", native_max_auto_continues: int = 25, max_xml_tool_calls: int = 0, include_xml_examples: bool = False, ) -> Union[Dict[str, Any], AsyncGenerator]: """Run a conversation thread with LLM integration and tool execution. Args: thread_id: The ID of the thread to run system_prompt: System message to set the assistant's behavior stream: Use streaming API for the LLM response temporary_message: Optional temporary user message for this run only llm_model: The name of the LLM model to use llm_temperature: Temperature parameter for response randomness (0-1) llm_max_tokens: Maximum tokens in the LLM response processor_config: Configuration for the response processor tool_choice: Tool choice preference ("auto", "required", "none") native_max_auto_continues: Maximum number of automatic continuations when finish_reason="tool_calls" (0 disables auto-continue) max_xml_tool_calls: Maximum number of XML tool calls to allow (0 = no limit) include_xml_examples: Whether to include XML tool examples in the system prompt Returns: An async generator yielding response chunks or error dict """ logger.info(f"Starting thread execution for thread {thread_id}") logger.debug(f"Parameters: model={llm_model}, temperature={llm_temperature}, max_tokens={llm_max_tokens}") logger.debug(f"Auto-continue: max={native_max_auto_continues}, XML tool limit={max_xml_tool_calls}") # Control whether we need to auto-continue due to tool_calls finish reason auto_continue = True auto_continue_count = 0 # Define inner function to handle a single run async def _run_once(temp_msg=None): try: # Ensure processor_config is available in this scope nonlocal processor_config # Use a default config if none was provided if processor_config is None: processor_config = ProcessorConfig() # Apply max_xml_tool_calls if specified and not already set if max_xml_tool_calls > 0: processor_config.max_xml_tool_calls = max_xml_tool_calls # Add XML examples to system prompt if requested if include_xml_examples and processor_config.xml_tool_calling: xml_examples = self.tool_registry.get_xml_examples() if xml_examples: # logger.debug(f"Adding {len(xml_examples)} XML examples to system prompt") # Create or append to content if isinstance(system_prompt['content'], str): examples_content = """ In this environment you have access to a set of tools you can use to answer the user's question. The tools are specified in XML format. {{ FORMATTING INSTRUCTIONS }} String and scalar parameters should be specified as attributes, while content goes between tags. Note that spaces for string values are not stripped. The output is parsed with regular expressions. Here are the XML tools available with examples: """ for tag_name, example in xml_examples.items(): examples_content += f"<{tag_name}> Example: {example}\n" system_prompt['content'] += examples_content else: # If content is not a string (might be a list or dict), log a warning logger.warning("System prompt content is not a string, cannot add XML examples") # 1. Get messages from thread for LLM call messages = await self.get_messages(thread_id) # 2. Prepare messages for LLM call + add temporary message if it exists prepared_messages = [system_prompt] # Find the last user message index last_user_index = -1 for i, msg in enumerate(messages): if msg.get('role') == 'user': last_user_index = i # Insert temporary message before the last user message if it exists if temp_msg and last_user_index >= 0: prepared_messages.extend(messages[:last_user_index]) prepared_messages.append(temp_msg) prepared_messages.extend(messages[last_user_index:]) logger.debug("Added temporary message before the last user message") else: # If no user message or no temporary message, just add all messages prepared_messages.extend(messages) if temp_msg: prepared_messages.append(temp_msg) logger.debug("Added temporary message to the end of prepared messages") # 3. Create or use processor config - this is now redundant since we handle it above # but kept for consistency and clarity logger.debug(f"Processor config: XML={processor_config.xml_tool_calling}, Native={processor_config.native_tool_calling}, " f"Execute tools={processor_config.execute_tools}, Strategy={processor_config.tool_execution_strategy}, " f"XML limit={processor_config.max_xml_tool_calls}") # 4. Prepare tools for LLM call openapi_tool_schemas = None if processor_config.native_tool_calling: openapi_tool_schemas = self.tool_registry.get_openapi_schemas() logger.debug(f"Retrieved {len(openapi_tool_schemas) if openapi_tool_schemas else 0} OpenAPI tool schemas") # 5. Make LLM API call logger.info("Making LLM API call") try: llm_response = await make_llm_api_call( prepared_messages, llm_model, temperature=llm_temperature, max_tokens=llm_max_tokens, tools=openapi_tool_schemas, tool_choice=tool_choice if processor_config.native_tool_calling else None, stream=stream ) logger.debug("Successfully received raw LLM API response stream/object") except Exception as e: logger.error(f"Failed to make LLM API call: {str(e)}", exc_info=True) raise # 6. Process LLM response using the ResponseProcessor if stream: logger.info("Processing streaming response") response_generator = self.response_processor.process_streaming_response( llm_response=llm_response, thread_id=thread_id, config=processor_config ) return response_generator else: logger.info("Processing non-streaming response") try: response = await self.response_processor.process_non_streaming_response( llm_response=llm_response, thread_id=thread_id, config=processor_config ) return response except Exception as e: logger.error(f"Error in non-streaming response: {str(e)}", exc_info=True) raise except Exception as e: logger.error(f"Error in run_thread: {str(e)}", exc_info=True) return { "status": "error", "message": str(e) } # Define a wrapper generator that handles auto-continue logic async def auto_continue_wrapper(): nonlocal auto_continue, auto_continue_count while auto_continue and (native_max_auto_continues == 0 or auto_continue_count < native_max_auto_continues): # Reset auto_continue for this iteration auto_continue = False # Run the thread once response_gen = await _run_once(temporary_message if auto_continue_count == 0 else None) # Handle error responses if isinstance(response_gen, dict) and "status" in response_gen and response_gen["status"] == "error": yield response_gen return # Process each chunk async for chunk in response_gen: # Check if this is a finish reason chunk with tool_calls or xml_tool_limit_reached if chunk.get('type') == 'finish': if chunk.get('finish_reason') == 'tool_calls': # Only auto-continue if enabled (max > 0) if native_max_auto_continues > 0: logger.info(f"Detected finish_reason='tool_calls', auto-continuing ({auto_continue_count + 1}/{native_max_auto_continues})") auto_continue = True auto_continue_count += 1 # Don't yield the finish chunk to avoid confusing the client continue elif chunk.get('finish_reason') == 'xml_tool_limit_reached': # Don't auto-continue if XML tool limit was reached logger.info(f"Detected finish_reason='xml_tool_limit_reached', stopping auto-continue") auto_continue = False # Still yield the chunk to inform the client # Otherwise just yield the chunk normally yield chunk # If not auto-continuing, we're done if not auto_continue: break # If we've reached the max auto-continues, log a warning if auto_continue and auto_continue_count >= native_max_auto_continues: logger.warning(f"Reached maximum auto-continue limit ({native_max_auto_continues}), stopping.") yield { "type": "content", "content": f"\n[Agent reached maximum auto-continue limit of {native_max_auto_continues}]" } # If auto-continue is disabled (max=0), just run once if native_max_auto_continues == 0: logger.info("Auto-continue is disabled (native_max_auto_continues=0)") return await _run_once(temporary_message) # Otherwise return the auto-continue wrapper generator return auto_continue_wrapper()