diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..13a04d8 --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +**/__pycache__ +storage diff --git a/app/agents/dummy/agent.py b/app/agents/dummy/agent.py deleted file mode 100644 index dde7fa3..0000000 --- a/app/agents/dummy/agent.py +++ /dev/null @@ -1,33 +0,0 @@ -from llama_agents import AgentService, SimpleMessageQueue -from llama_index.core.agent import FunctionCallingAgentWorker -from llama_index.core.tools import FunctionTool -from llama_index.core.settings import Settings -from app.utils import load_from_env - - -DEFAULT_DUMMY_AGENT_DESCRIPTION = "I'm a dummy agent which does nothing." - - -def dummy_function(): - """ - This function does nothing. - """ - return "" - - -def init_dummy_agent(message_queue: SimpleMessageQueue) -> AgentService: - agent = FunctionCallingAgentWorker( - tools=[FunctionTool.from_defaults(fn=dummy_function)], - llm=Settings.llm, - prefix_messages=[], - ).as_agent() - - return AgentService( - service_name="dummy_agent", - agent=agent, - message_queue=message_queue.client, - description=load_from_env("AGENT_DUMMY_DESCRIPTION", throw_error=False) - or DEFAULT_DUMMY_AGENT_DESCRIPTION, - host=load_from_env("AGENT_DUMMY_HOST", throw_error=False) or "127.0.0.1", - port=int(load_from_env("AGENT_DUMMY_PORT")), - ) diff --git a/app/agents/query_engine/agent.py b/app/agents/query_engine/agent.py deleted file mode 100644 index bee1f01..0000000 --- a/app/agents/query_engine/agent.py +++ /dev/null @@ -1,52 +0,0 @@ -import os -from llama_agents import AgentService, SimpleMessageQueue -from llama_index.core.agent import FunctionCallingAgentWorker -from llama_index.core.tools import QueryEngineTool, ToolMetadata -from llama_index.core.settings import Settings -from app.engine.index import get_index -from app.utils import load_from_env - - -DEFAULT_QUERY_ENGINE_AGENT_DESCRIPTION = ( - "Used to answer the questions using the provided context data." -) - - -def get_query_engine_tool() -> QueryEngineTool: - """ - Provide an agent worker that can be used to query the index. - """ - index = get_index() - if index is None: - raise ValueError("Index not found. Please create an index first.") - query_engine = index.as_query_engine(similarity_top_k=int(os.getenv("TOP_K", 3))) - return QueryEngineTool( - query_engine=query_engine, - metadata=ToolMetadata( - name="context_data", - description=""" - Provide the provided context information. - Use a detailed plain text question as input to the tool. - """, - ), - ) - - -def init_query_engine_agent( - message_queue: SimpleMessageQueue, -) -> AgentService: - """ - Initialize the agent service. - """ - agent = FunctionCallingAgentWorker( - tools=[get_query_engine_tool()], llm=Settings.llm, prefix_messages=[] - ).as_agent() - return AgentService( - service_name="context_query_agent", - agent=agent, - message_queue=message_queue.client, - description=load_from_env("AGENT_QUERY_ENGINE_DESCRIPTION", throw_error=False) - or DEFAULT_QUERY_ENGINE_AGENT_DESCRIPTION, - host=load_from_env("AGENT_QUERY_ENGINE_HOST", throw_error=False) or "127.0.0.1", - port=int(load_from_env("AGENT_QUERY_ENGINE_PORT")), - ) diff --git a/app/config.py b/app/config.py new file mode 100644 index 0000000..29fa8d9 --- /dev/null +++ b/app/config.py @@ -0,0 +1 @@ +DATA_DIR = "data" diff --git a/app/core/agent_call.py b/app/core/agent_call.py new file mode 100644 index 0000000..77fa9a5 --- /dev/null +++ b/app/core/agent_call.py @@ -0,0 +1,38 @@ +from typing import Any, List + +from llama_index.core.tools import FunctionTool +from llama_index.core.workflow import Workflow + +from app.core.prefix import PrintPrefix +from app.core.function_call import FunctionCallingAgent + + +def create_call_workflow_fn(agent: Workflow) -> FunctionTool: + def call_workflow_fn(input: str) -> str: + raise NotImplementedError + + async def acall_workflow_fn(input: str) -> str: + print(f"Calling agent {agent.name} with input: '{input}'") + with PrintPrefix(f"[{agent.name}]"): + ret = await agent.run(input=input) + print(f"Finished calling agent {agent.name}") + return ret["response"] + + return FunctionTool.from_defaults( + fn=call_workflow_fn, # not necessary with https://github.com/run-llama/llama_index/pull/15638/files + async_fn=acall_workflow_fn, + name=f"call_{agent.name}", + description=f"Use this tool to delegate a task to the agent {agent.name}", + ) + + +class AgentCallingAgent(FunctionCallingAgent): + def __init__( + self, + *args: Any, + agents: List[Workflow] | None = None, + **kwargs: Any, + ) -> None: + agents = agents or [] + tools = [create_call_workflow_fn(agent) for agent in agents] + super().__init__(*args, tools=tools, **kwargs) diff --git a/app/core/control_plane.py b/app/core/control_plane.py deleted file mode 100644 index 2ac3a1e..0000000 --- a/app/core/control_plane.py +++ /dev/null @@ -1,19 +0,0 @@ -from llama_index.llms.openai import OpenAI -from llama_agents import AgentOrchestrator, ControlPlaneServer -from app.core.message_queue import message_queue -from app.utils import load_from_env - - -control_plane_host = ( - load_from_env("CONTROL_PLANE_HOST", throw_error=False) or "127.0.0.1" -) -control_plane_port = load_from_env("CONTROL_PLANE_PORT", throw_error=False) or "8001" - - -# setup control plane -control_plane = ControlPlaneServer( - message_queue=message_queue, - orchestrator=AgentOrchestrator(llm=OpenAI()), - host=control_plane_host, - port=int(control_plane_port) if control_plane_port else None, -) diff --git a/app/core/function_call.py b/app/core/function_call.py new file mode 100644 index 0000000..05ce30a --- /dev/null +++ b/app/core/function_call.py @@ -0,0 +1,136 @@ +from typing import Any, List + +from llama_index.core.llms.function_calling import FunctionCallingLLM +from llama_index.core.memory import ChatMemoryBuffer +from llama_index.core.tools.types import BaseTool +from llama_index.core.workflow import Workflow, StartEvent, StopEvent, step + +from llama_index.core.llms import ChatMessage +from llama_index.core.tools import ToolSelection, ToolOutput +from llama_index.core.workflow import Event +from llama_index.core.settings import Settings + + +class InputEvent(Event): + input: list[ChatMessage] + + +class ToolCallEvent(Event): + tool_calls: list[ToolSelection] + + +class FunctionOutputEvent(Event): + output: ToolOutput + + +class FunctionCallingAgent(Workflow): + def __init__( + self, + *args: Any, + llm: FunctionCallingLLM | None = None, + tools: List[BaseTool] | None = None, + system_prompt: str | None = None, + verbose: bool = True, + timeout: float = 120.0, + name: str, + **kwargs: Any, + ) -> None: + super().__init__(*args, verbose=verbose, timeout=timeout, **kwargs) + self.tools = tools or [] + self.name = name + + if llm is None: + llm = Settings.llm + self.llm = llm + assert self.llm.metadata.is_function_calling_model + + self.system_prompt = system_prompt + + self.memory = ChatMemoryBuffer.from_defaults(llm=self.llm) + self.sources = [] + + @step() + async def prepare_chat_history(self, ev: StartEvent) -> InputEvent: + # clear sources + self.sources = [] + + # set system prompt + if self.system_prompt is not None: + system_msg = ChatMessage(role="system", content=self.system_prompt) + self.memory.put(system_msg) + + # get user input + user_input = ev.input + user_msg = ChatMessage(role="user", content=user_input) + self.memory.put(user_msg) + + # get chat history + chat_history = self.memory.get() + return InputEvent(input=chat_history) + + @step() + async def handle_llm_input(self, ev: InputEvent) -> ToolCallEvent | StopEvent: + chat_history = ev.input + + response = await self.llm.achat_with_tools( + self.tools, chat_history=chat_history + ) + self.memory.put(response.message) + + tool_calls = self.llm.get_tool_calls_from_response( + response, error_on_no_tool_call=False + ) + + if not tool_calls: + return StopEvent(result={"response": response, "sources": [*self.sources]}) + else: + return ToolCallEvent(tool_calls=tool_calls) + + @step() + async def handle_tool_calls(self, ev: ToolCallEvent) -> InputEvent: + tool_calls = ev.tool_calls + tools_by_name = {tool.metadata.get_name(): tool for tool in self.tools} + + tool_msgs = [] + + # call tools -- safely! + for tool_call in tool_calls: + tool = tools_by_name.get(tool_call.tool_name) + additional_kwargs = { + "tool_call_id": tool_call.tool_id, + "name": tool.metadata.get_name(), + } + if not tool: + tool_msgs.append( + ChatMessage( + role="tool", + content=f"Tool {tool_call.tool_name} does not exist", + additional_kwargs=additional_kwargs, + ) + ) + continue + + try: + tool_output = await tool.acall(**tool_call.tool_kwargs) + self.sources.append(tool_output) + tool_msgs.append( + ChatMessage( + role="tool", + content=tool_output.content, + additional_kwargs=additional_kwargs, + ) + ) + except Exception as e: + tool_msgs.append( + ChatMessage( + role="tool", + content=f"Encountered error in tool call: {e}", + additional_kwargs=additional_kwargs, + ) + ) + + for msg in tool_msgs: + self.memory.put(msg) + + chat_history = self.memory.get() + return InputEvent(input=chat_history) diff --git a/app/core/message_queue.py b/app/core/message_queue.py deleted file mode 100644 index cebca44..0000000 --- a/app/core/message_queue.py +++ /dev/null @@ -1,12 +0,0 @@ -from llama_agents import SimpleMessageQueue -from app.utils import load_from_env - -message_queue_host = ( - load_from_env("MESSAGE_QUEUE_HOST", throw_error=False) or "127.0.0.1" -) -message_queue_port = load_from_env("MESSAGE_QUEUE_PORT", throw_error=False) or "8000" - -message_queue = SimpleMessageQueue( - host=message_queue_host, - port=int(message_queue_port) if message_queue_port else None, -) diff --git a/app/core/prefix.py b/app/core/prefix.py new file mode 100644 index 0000000..5fdebee --- /dev/null +++ b/app/core/prefix.py @@ -0,0 +1,16 @@ +import builtins + + +class PrintPrefix: + def __init__(self, prefix): + self.prefix = prefix + self.original_print = builtins.print + + def __enter__(self): + builtins.print = self._print_with_prefix + + def __exit__(self, exc_type, exc_val, exc_tb): + builtins.print = self.original_print + + def _print_with_prefix(self, *args, **kwargs): + self.original_print(self.prefix, *args, **kwargs) diff --git a/app/core/task_result.py b/app/core/task_result.py deleted file mode 100644 index 9b0737e..0000000 --- a/app/core/task_result.py +++ /dev/null @@ -1,88 +0,0 @@ -import json -from logging import getLogger -from pathlib import Path -from fastapi import FastAPI -from typing import Dict, Optional -from llama_agents import CallableMessageConsumer, QueueMessage -from llama_agents.message_queues.base import BaseMessageQueue -from llama_agents.message_consumers.base import BaseMessageQueueConsumer -from llama_agents.message_consumers.remote import RemoteMessageConsumer -from app.utils import load_from_env -from app.core.message_queue import message_queue - - -logger = getLogger(__name__) - - -class TaskResultService: - def __init__( - self, - message_queue: BaseMessageQueue, - name: str = "human", - host: str = "127.0.0.1", - port: Optional[int] = 8002, - ) -> None: - self.name = name - self.host = host - self.port = port - - self._message_queue = message_queue - - # app - self._app = FastAPI() - self._app.add_api_route( - "/", self.home, methods=["GET"], tags=["Human Consumer"] - ) - self._app.add_api_route( - "/process_message", - self.process_message, - methods=["POST"], - tags=["Human Consumer"], - ) - - @property - def message_queue(self) -> BaseMessageQueue: - return self._message_queue - - def as_consumer(self, remote: bool = False) -> BaseMessageQueueConsumer: - if remote: - return RemoteMessageConsumer( - url=( - f"http://{self.host}:{self.port}/process_message" - if self.port - else f"http://{self.host}/process_message" - ), - message_type=self.name, - ) - - return CallableMessageConsumer( - message_type=self.name, - handler=self.process_message, - ) - - async def process_message(self, message: QueueMessage) -> None: - Path("task_results").mkdir(exist_ok=True) - with open("task_results/task_results.json", "+a") as f: - json.dump(message.model_dump(), f) - f.write("\n") - - async def home(self) -> Dict[str, str]: - return {"message": "hello, human."} - - async def register_to_message_queue(self) -> None: - """Register to the message queue.""" - await self.message_queue.register_consumer(self.as_consumer(remote=True)) - - -human_consumer_host = ( - load_from_env("HUMAN_CONSUMER_HOST", throw_error=False) or "127.0.0.1" -) -human_consumer_port = load_from_env("HUMAN_CONSUMER_PORT", throw_error=False) or "8002" - - -human_consumer_server = TaskResultService( - message_queue=message_queue, - host=human_consumer_host, - port=int(human_consumer_port) if human_consumer_port else None, - name="human", -) diff --git a/main.py b/main.py index 03dc98a..9c0dcbc 100644 --- a/main.py +++ b/main.py @@ -1,28 +1,36 @@ # flake8: noqa: E402 +import asyncio from dotenv import load_dotenv +from app.agents.researcher.agent import get_query_engine_tool +from app.core.agent_call import AgentCallingAgent +from app.core.function_call import FunctionCallingAgent from app.settings import init_settings + load_dotenv() init_settings() -from llama_agents import ServerLauncher -from app.core.message_queue import message_queue -from app.core.control_plane import control_plane -from app.core.task_result import human_consumer_server -from app.agents.query_engine.agent import init_query_engine_agent -from app.agents.dummy.agent import init_dummy_agent -agents = [ - init_query_engine_agent(message_queue), - init_dummy_agent(message_queue), -] +async def main(): + researcher = FunctionCallingAgent( + name="researcher", + tools=[get_query_engine_tool()], + system_prompt="You are a researcher agent. You are given a researching task. You must use your tools to complete the research.", + ) + reviewer = AgentCallingAgent( + name="reviewer", + system_prompt="You are an expert in reviewing blog posts. You are given a task to write a blog post. Before starting to write the post, consult the researcher agent to get the information you need. Don't make up any information yourself.", + ) + writer = AgentCallingAgent( + name="writer", + agents=[researcher, reviewer], + system_prompt="""You are an expert in writing blog posts. You are given a task to write a blog post. Before starting to write the post, consult the researcher agent to get the information you need. Don't make up any information yourself. + After creating a draft for the post, send it to the reviewer agent to receive some feedback and make sure to incorporate the feedback from the reviewer. + You can consult the reviewer and researcher multiple times. Only finish the task once the reviewer is satisfied.""", + ) + ret = await writer.run(input="Write a blog post about letter standards") + print(ret["response"]) -launcher = ServerLauncher( - agents, - control_plane, - message_queue, - additional_consumers=[human_consumer_server.as_consumer()], -) if __name__ == "__main__": - launcher.launch_servers() + asyncio.run(main()) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..ad97473 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,2264 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.0" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"}, + {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = 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