"""This is an example of a terminal-based ChatGPT clone using an OpenAIAssistantAgent and event-based orchestration.""" import argparse import asyncio import logging import os import re from typing import List import aiofiles import openai from agnext.application import SingleThreadedAgentRuntime from agnext.chat.agents.oai_assistant import OpenAIAssistantAgent from agnext.chat.memory import BufferedChatMemory from agnext.chat.patterns.group_chat_manager import GroupChatManager from agnext.chat.types import PublishNow, TextMessage from agnext.components import TypeRoutedAgent, message_handler from agnext.core import AgentId, AgentRuntime, CancellationToken from openai import AsyncAssistantEventHandler from openai.types.beta.thread import ToolResources from openai.types.beta.threads import Message, Text, TextDelta from openai.types.beta.threads.runs import RunStep, RunStepDelta from typing_extensions import override sep = "-" * 50 class UserProxyAgent(TypeRoutedAgent): # type: ignore def __init__( # type: ignore self, client: openai.AsyncClient, # type: ignore assistant_id: str, thread_id: str, vector_store_id: str, ) -> None: # type: ignore super().__init__( description="A human user", ) # type: ignore self._client = client self._assistant_id = assistant_id self._thread_id = thread_id self._vector_store_id = vector_store_id @message_handler() # type: ignore async def on_text_message(self, message: TextMessage, cancellation_token: CancellationToken) -> None: # type: ignore # TODO: render image if message has image. # print(f"{message.source}: {message.content}") pass async def _get_user_input(self, prompt: str) -> str: loop = asyncio.get_event_loop() return await loop.run_in_executor(None, input, prompt) @message_handler() # type: ignore async def on_publish_now(self, message: PublishNow, cancellation_token: CancellationToken) -> None: # type: ignore while True: user_input = await self._get_user_input(f"\n{sep}\nYou: ") # Parse upload file command '[upload code_interpreter | file_search filename]'. match = re.search(r"\[upload\s+(code_interpreter|file_search)\s+(.+)\]", user_input) if match: # Purpose of the file. purpose = match.group(1) # Extract file path. file_path = match.group(2) if not os.path.exists(file_path): print(f"File not found: {file_path}") continue # Filename. file_name = os.path.basename(file_path) # Read file content. async with aiofiles.open(file_path, "rb") as f: file_content = await f.read() if purpose == "code_interpreter": # Upload file. file = await self._client.files.create(file=(file_name, file_content), purpose="assistants") # Get existing file ids from tool resources. thread = await self._client.beta.threads.retrieve(thread_id=self._thread_id) tool_resources: ToolResources = thread.tool_resources if thread.tool_resources else ToolResources() assert tool_resources.code_interpreter is not None if tool_resources.code_interpreter.file_ids: file_ids = tool_resources.code_interpreter.file_ids else: file_ids = [file.id] # Update thread with new file. await self._client.beta.threads.update( thread_id=self._thread_id, tool_resources={"code_interpreter": {"file_ids": file_ids}}, ) elif purpose == "file_search": # Upload file to vector store. file_batch = await self._client.beta.vector_stores.file_batches.upload_and_poll( vector_store_id=self._vector_store_id, files=[(file_name, file_content)], ) assert file_batch.status == "completed" print(f"Uploaded file: {file_name}") continue elif user_input.startswith("[upload"): print("Invalid upload command. Please use '[upload code_interpreter | file_search filename]'.") continue elif user_input.strip().lower() == "exit": # Exit handler. return else: # Publish user input and exit handler. await self.publish_message(TextMessage(content=user_input, source=self.metadata["name"])) return class EventHandler(AsyncAssistantEventHandler): @override async def on_text_delta(self, delta: TextDelta, snapshot: Text) -> None: print(delta.value, end="", flush=True) @override async def on_run_step_created(self, run_step: RunStep) -> None: details = run_step.step_details if details.type == "tool_calls": for tool in details.tool_calls: if tool.type == "code_interpreter": print("\nGenerating code to interpret:\n\n```python") @override async def on_run_step_done(self, run_step: RunStep) -> None: details = run_step.step_details if details.type == "tool_calls": for tool in details.tool_calls: if tool.type == "code_interpreter": print("\n```\nExecuting code...") @override async def on_run_step_delta(self, delta: RunStepDelta, snapshot: RunStep) -> None: details = delta.step_details if details is not None and details.type == "tool_calls": for tool in details.tool_calls or []: if tool.type == "code_interpreter" and tool.code_interpreter and tool.code_interpreter.input: print(tool.code_interpreter.input, end="", flush=True) @override async def on_message_created(self, message: Message) -> None: print(f"{sep}\nAssistant:\n") @override async def on_message_done(self, message: Message) -> None: # print a citation to the file searched if not message.content: return content = message.content[0] if not content.type == "text": return text_content = content.text annotations = text_content.annotations citations: List[str] = [] for index, annotation in enumerate(annotations): text_content.value = text_content.value.replace(annotation.text, f"[{index}]") if file_citation := getattr(annotation, "file_citation", None): client = openai.AsyncClient() cited_file = await client.files.retrieve(file_citation.file_id) citations.append(f"[{index}] {cited_file.filename}") if citations: print("\n".join(citations)) def assistant_chat(runtime: AgentRuntime) -> AgentId: oai_assistant = openai.beta.assistants.create( model="gpt-4-turbo", description="An AI assistant that helps with everyday tasks.", instructions="Help the user with their task.", tools=[{"type": "code_interpreter"}, {"type": "file_search"}], ) vector_store = openai.beta.vector_stores.create() thread = openai.beta.threads.create( tool_resources={"file_search": {"vector_store_ids": [vector_store.id]}}, ) assistant = runtime.register_and_get( "Assistant", lambda: OpenAIAssistantAgent( description="An AI assistant that helps with everyday tasks.", client=openai.AsyncClient(), assistant_id=oai_assistant.id, thread_id=thread.id, assistant_event_handler_factory=lambda: EventHandler(), ), ) user = runtime.register_and_get( "User", lambda: UserProxyAgent( client=openai.AsyncClient(), assistant_id=oai_assistant.id, thread_id=thread.id, vector_store_id=vector_store.id, ), ) # Create a group chat manager to facilitate a turn-based conversation. runtime.register( "GroupChatManager", lambda: GroupChatManager( description="A group chat manager.", runtime=runtime, memory=BufferedChatMemory(buffer_size=10), participants=[assistant, user], ), ) return user async def main() -> None: usage = """Chat with an AI assistant backed by OpenAI Assistant API. You can upload files to the assistant using the command: [upload code_interpreter | file_search filename] where 'code_interpreter' or 'file_search' is the purpose of the file and 'filename' is the path to the file. For example: [upload code_interpreter data.csv] This will upload data.csv to the assistant for use with the code interpreter tool. Type "exit" to exit the chat. """ runtime = SingleThreadedAgentRuntime() user = assistant_chat(runtime) print(usage) # Request the user to start the conversation. runtime.send_message(PublishNow(), user) while True: # TODO: have a way to exit the loop. await runtime.process_next() await asyncio.sleep(1) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Chat with an AI assistant.") parser.add_argument("--verbose", action="store_true", help="Enable verbose logging.") args = parser.parse_args() if args.verbose: logging.basicConfig(level=logging.WARNING) logging.getLogger("agnext").setLevel(logging.DEBUG) handler = logging.FileHandler("assistant.log") logging.getLogger("agnext").addHandler(handler) asyncio.run(main())