from __future__ import annotations from pydantic import BaseModel, PrivateAttr, Field # type: ignore from typing import ( Literal, get_args, List, AsyncGenerator, TYPE_CHECKING, Optional, ClassVar, Any, TypeAlias, Dict, Tuple ) from abc import ABC from .. setup_logging import setup_logging from .. import defines from abc import ABC import logging from .. message import Message from .. import tools as Tools import json import time import inspect logger = setup_logging() # Only import Context for type checking if TYPE_CHECKING: from .. context import Context from .types import registry from .. conversation import Conversation from .. message import Message class Agent(BaseModel, ABC): """ Base class for all agent types. This class defines the common attributes and methods for all agent types. """ # Agent management with pydantic agent_type: Literal["base"] = "base" _agent_type: ClassVar[str] = agent_type # Add this for registration # context_size is shared across all subclasses _context_size: ClassVar[int] = int(defines.max_context * 0.5) @property def context_size(self) -> int: return Agent._context_size @context_size.setter def context_size(self, value: int): Agent._context_size = value # Agent properties system_prompt: str # Mandatory conversation: Conversation = Conversation() context_tokens: int = 0 context: Optional[Context] = Field(default=None, exclude=True) # Avoid circular reference, require as param, and prevent serialization _content_seed: str = PrivateAttr(default="") def set_optimal_context_size(self, llm: Any, model: str, prompt: str, ctx_buffer=2048) -> int: # # Get more accurate token count estimate using tiktoken or similar # response = llm.generate( # model=model, # prompt=prompt, # options={ # "num_ctx": self.context_size, # "num_predict": 0, # } # Don't generate any tokens, just tokenize # ) # # The prompt_eval_count gives you the token count of your input # tokens = response.get("prompt_eval_count", 0) # Most models average 1.3-1.5 tokens per word word_count = len(prompt.split()) tokens = int(word_count * 1.4) # Add buffer for safety total_ctx = tokens + ctx_buffer if total_ctx > self.context_size: logger.info(f"Increasing context size from {self.context_size} to {total_ctx}") # Grow the context size if necessary self.context_size = max(self.context_size, total_ctx) # Use actual model maximum context size return self.context_size # Class and pydantic model management def __init_subclass__(cls, **kwargs) -> None: """Auto-register subclasses""" super().__init_subclass__(**kwargs) # Register this class if it has an agent_type if hasattr(cls, 'agent_type') and cls.agent_type != Agent._agent_type: registry.register(cls.agent_type, cls) def model_dump(self, *args, **kwargs) -> Any: # Ensure context is always excluded, even with exclude_unset=True kwargs.setdefault("exclude", set()) if isinstance(kwargs["exclude"], set): kwargs["exclude"].add("context") elif isinstance(kwargs["exclude"], dict): kwargs["exclude"]["context"] = True return super().model_dump(*args, **kwargs) @classmethod def valid_agent_types(cls) -> set[str]: """Return the set of valid agent_type values.""" return set(get_args(cls.__annotations__["agent_type"])) def set_context(self, context): object.__setattr__(self, "context", context) # Agent methods def get_agent_type(self): return self._agent_type async def prepare_message(self, message:Message) -> AsyncGenerator[Message, None]: """ Prepare message with context information in message.preamble """ logging.info(f"{self.agent_type} - {inspect.stack()[1].function}") if not self.context: raise ValueError("Context is not set for this agent.") # Generate RAG content if enabled, based on the content rag_context = "" if message.enable_rag: # Gather RAG results, yielding each result # as it becomes available for message in self.context.generate_rag_results(message): logging.info(f"RAG: {message.status} - {message.response}") if message.status == "error": yield message return if message.status != "done": yield message if "rag" in message.metadata and message.metadata["rag"]: for rag in message.metadata["rag"]: for doc in rag["documents"]: rag_context += f"{doc}\n" message.preamble = {} if rag_context: message.preamble["context"] = rag_context if self.context.user_resume: message.preamble["resume"] = self.context.user_resume if message.preamble: preamble_types = [f"<|{p}|>" for p in message.preamble.keys()] preamble_types_AND = " and ".join(preamble_types) preamble_types_OR = " or ".join(preamble_types) message.preamble["rules"] = f"""\ - Answer the question based on the information provided in the {preamble_types_AND} sections by incorporate it seamlessly and refer to it using natural language instead of mentioning {preamble_types_OR} or quoting it directly. - If there is no information in these sections, answer based on your knowledge, or use any available tools. - Avoid phrases like 'According to the {preamble_types[0]}' or similar references to the {preamble_types_OR}. """ message.preamble["question"] = "Respond to:" message.system_prompt = self.system_prompt message.status = "done" yield message return async def process_tool_calls(self, llm: Any, model: str, message: Message, tool_message: Any, messages: List[Any]) -> AsyncGenerator[Message, None]: logging.info(f"{self.agent_type} - {inspect.stack()[1].function}") if not self.context: raise ValueError("Context is not set for this agent.") if not message.metadata["tools"]: raise ValueError("tools field not initialized") tool_metadata = message.metadata["tools"] tool_metadata["messages"] = messages tool_metadata["tool_calls"] = [] message.status = "tooling" for i, tool_call in enumerate(tool_message.tool_calls): arguments = tool_call.function.arguments tool = tool_call.function.name # Yield status update before processing each tool message.response = f"Processing tool {i+1}/{len(tool_message.tool_calls)}: {tool}..." yield message logging.info(f"LLM - {message.response}") # Process the tool based on its type match tool: case "TickerValue": ticker = arguments.get("ticker") if not ticker: ret = None else: ret = Tools.TickerValue(ticker) case "AnalyzeSite": url = arguments.get("url") question = arguments.get("question", "what is the summary of this content?") # Additional status update for long-running operations message.response = f"Retrieving and summarizing content from {url}..." yield message ret = await Tools.AnalyzeSite(llm=llm, model=model, url=url, question=question) case "DateTime": tz = arguments.get("timezone") ret = Tools.DateTime(tz) case "WeatherForecast": city = arguments.get("city") state = arguments.get("state") message.response = f"Fetching weather data for {city}, {state}..." yield message ret = Tools.WeatherForecast(city, state) case _: ret = None # Build response for this tool tool_response = { "role": "tool", "content": json.dumps(ret), "name": tool_call.function.name } tool_metadata["tool_calls"].append(tool_response) if len(tool_metadata["tool_calls"]) == 0: message.status = "done" yield message return message_dict = { "role": tool_message.get("role", "assistant"), "content": tool_message.get("content", ""), "tool_calls": [ { "function": { "name": tc["function"]["name"], "arguments": tc["function"]["arguments"] } } for tc in tool_message.tool_calls ] } messages.append(message_dict) messages.extend(tool_metadata["tool_calls"]) message.status = "thinking" # Decrease creativity when processing tool call requests message.response = "" start_time = time.perf_counter() for response in llm.chat( model=model, messages=messages, stream=True, options={ **message.metadata["options"], # "temperature": 0.5, } ): # logging.info(f"LLM::Tools: {'done' if response.done else 'processing'} - {response.message}") message.status = "streaming" message.response += response.message.content if not response.done: yield message if response.done: message.metadata["eval_count"] += response.eval_count message.metadata["eval_duration"] += response.eval_duration message.metadata["prompt_eval_count"] += response.prompt_eval_count message.metadata["prompt_eval_duration"] += response.prompt_eval_duration self.context_tokens = response.prompt_eval_count + response.eval_count message.status = "done" yield message end_time = time.perf_counter() message.metadata["timers"]["llm_with_tools"] = f"{(end_time - start_time):.4f}" return async def generate_llm_response(self, llm: Any, model: str, message: Message) -> AsyncGenerator[Message, None]: logging.info(f"{self.agent_type} - {inspect.stack()[1].function}") if not self.context: raise ValueError("Context is not set for this agent.") messages = [ { "role": "system", "content": message.system_prompt } ] messages.extend([ item for m in self.conversation.messages for item in [ {"role": "user", "content": m.prompt.strip()}, {"role": "assistant", "content": m.response.strip()} ] ]) messages.append({ "role": "user", "content": message.context_prompt.strip(), }) message.metadata["messages"] = messages message.metadata["options"]={ "seed": 8911, "num_ctx": self.context_size, #"temperature": 0.9, # Higher temperature to encourage tool usage } message.metadata["timers"] = {} use_tools = message.enable_tools and len(self.context.tools) > 0 message.metadata["tools"] = { "available": Tools.llm_tools(self.context.tools), "used": False } tool_metadata = message.metadata["tools"] if use_tools: message.status = "thinking" message.response = f"Performing tool analysis step 1/2..." yield message logging.info("Checking for LLM tool usage") start_time = time.perf_counter() # Tools are enabled and available, so query the LLM with a short token target to see if it will # use the tools tool_metadata["messages"] = [{ "role": "system", "content": self.system_prompt}, {"role": "user", "content": message.prompt}] response = llm.chat( model=model, messages=tool_metadata["messages"], tools=tool_metadata["available"], options={ **message.metadata["options"], #"num_predict": 1024, # "Low" token limit to cut off after tool call }, stream=False # No need to stream the probe ) end_time = time.perf_counter() message.metadata["timers"]["tool_check"] = f"{(end_time - start_time):.4f}" if not response.message.tool_calls: logging.info("LLM indicates tools will not be used") # The LLM will not use tools, so disable use_tools so we can stream the full response use_tools = False if use_tools: logging.info("LLM indicates tools will be used") # Tools are enabled and available and the LLM indicated it will use them tool_metadata["attempted"] = response.message.tool_calls message.response = f"Performing tool analysis step 2/2 (tool use suspected)..." yield message logging.info(f"Performing LLM call with tools") start_time = time.perf_counter() response = llm.chat( model=model, messages=tool_metadata["messages"], # messages, tools=tool_metadata["available"], options={ **message.metadata["options"], }, stream=False ) end_time = time.perf_counter() message.metadata["timers"]["non_streaming"] = f"{(end_time - start_time):.4f}" if not response: message.status = "error" message.response = "No response from LLM." yield message return if response.message.tool_calls: tool_metadata["used"] = response.message.tool_calls # Process all yielded items from the handler start_time = time.perf_counter() async for message in self.process_tool_calls(llm=llm, model=model, message=message, tool_message=response.message, messages=messages): if message.status == "error": yield message return yield message end_time = time.perf_counter() message.metadata["timers"]["process_tool_calls"] = f"{(end_time - start_time):.4f}" message.status = "done" return logging.info("LLM indicated tools will be used, and then they weren't") message.response = response.message.content message.status = "done" yield message return # not use_tools yield message # Reset the response for streaming message.response = "" start_time = time.perf_counter() for response in llm.chat( model=model, messages=messages, options={ **message.metadata["options"], }, stream=True, ): if not response: message.status = "error" message.response = "No response from LLM." yield message return message.status = "streaming" message.response += response.message.content if not response.done: yield message if response.done: message.metadata["eval_count"] += response.eval_count message.metadata["eval_duration"] += response.eval_duration message.metadata["prompt_eval_count"] += response.prompt_eval_count message.metadata["prompt_eval_duration"] += response.prompt_eval_duration self.context_tokens = response.prompt_eval_count + response.eval_count message.status = "done" yield message end_time = time.perf_counter() message.metadata["timers"]["streamed"] = f"{(end_time - start_time):.4f}" return async def process_message(self, llm: Any, model: str, message:Message) -> AsyncGenerator[Message, None]: logging.info(f"{self.agent_type} - {inspect.stack()[1].function}") if not self.context: raise ValueError("Context is not set for this agent.") if self.context.processing: logging.info("TODO: Implement delay queing; busy for same agent, otherwise return queue size and estimated wait time") message.status = "error" message.response = "Busy processing another request." yield message return self.context.processing = True message.metadata["system_prompt"] = f"<|system|>\n{self.system_prompt.strip()}\n" message.context_prompt = "" for p in message.preamble.keys(): message.context_prompt += f"\n<|{p}|>\n{message.preamble[p].strip()}\n" message.context_prompt += f"{message.prompt}" # Estimate token length of new messages message.response = f"Optimizing context..." message.status = "thinking" yield message message.metadata["context_size"] = self.set_optimal_context_size(llm, model, prompt=message.context_prompt) message.response = f"Processing {'RAG augmented ' if message.metadata['rag'] else ''}query..." message.status = "thinking" yield message async for message in self.generate_llm_response(llm, model, message): # logging.info(f"LLM: {message.status} - {f'...{message.response[-20:]}' if len(message.response) > 20 else message.response}") if message.status == "error": yield message self.context.processing = False return yield message # Done processing, add message to conversation message.status = "done" self.conversation.add_message(message) self.context.processing = False return # Register the base agent registry.register(Agent._agent_type, Agent)