agent.py

import asyncio
import datetime
import os
import pprint
from llama_index.core.agent.workflow.workflow_events import AgentInput, AgentOutput, AgentStream, ToolCall, ToolCallResult
from llama_index.core.llms import ChatMessage
# from llama_index.llms.ollama import Ollama
from llama_index.core.agent.workflow import AgentWorkflow
from llama_index.core.tools import FunctionTool
from workflows.events import StopEvent, StartEvent, StepState, StepStateChanged
from llama_index.tools.arxiv import ArxivToolSpec
from llama_index.tools.wikipedia import WikipediaToolSpec
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.llms.nebius import NebiusLLM

import tools

"""
TODO: 4. Run HF Space and submit answers to the scoring server.
TODO: 5. Get a certificate for the course.
"""

class BasicAgent:
    def __init__(self, verbose: bool = False):
        self.verbose = verbose
        # llm = Ollama(
        #     model="gpt-oss:20b",
        #     request_timeout=120.0
        # )
        # llm = HuggingFaceInferenceAPI(
        #     model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
        # )
        llm = NebiusLLM(
            api_key=os.getenv("NEBIUS_API_KEY"),
            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
            api_base="https://api.tokenfactory.nebius.com/v1"
        )
        self.workflow = AgentWorkflow.from_tools_or_functions(
            [
                FunctionTool.from_defaults(tools.multiply), 
                FunctionTool.from_defaults(tools.add), 
                # FunctionTool.from_defaults(tools.transcribeAudio),
                # FunctionTool.from_defaults(tools.describeImage),
                FunctionTool.from_defaults(tools.directFetchTool),
                FunctionTool.from_defaults(tools.webSearchTool),
                *ArxivToolSpec().to_tool_list(),
                *WikipediaToolSpec().to_tool_list(),
                ],
            llm=llm,
            system_prompt=f"Today's date is {datetime.datetime.now().strftime('%Y-%m-%d')}." 
        )
    
    async def __call__(self, question: str) -> str:
        """Async call method that returns the final result without streaming"""
        print(f"Agent received question : {question}\n")
        
        if self.verbose:
            answer = await self.stream_answers(question)
            if answer is None:
                return ''
            return str(answer)
        
        answer = await self.workflow.run(user_msg=question)
        print(f"Agent returning answer: {answer}")
        if answer is None:
            return ''
        return str(answer)
    
    def call_sync(self, question: str) -> str:
        """Synchronous wrapper for __call__ method (for compatibility with sync code)"""
        return asyncio.run(self.__call__(question))
    
    async def stream_answers(self, question: str) -> None:
        
        handler = self.workflow.run(user_msg=question, max_iterations=10)
        async for event in handler.stream_events():
            if isinstance(event, AgentInput):
                # print(f"\nAgent input: {event.input}")
                pass
            elif isinstance(event, AgentOutput):
                # print(f"\nAgent output: {event}")
                pass
            elif isinstance(event, ToolCall):
                print(f"\n\tCalled tool: {event.tool_name} {event.tool_kwargs}")
            elif isinstance(event, ToolCallResult):
                print(f"\n\t{event.tool_name} {event.tool_kwargs} -> {str(event.tool_output)[200:]}")
            elif isinstance(event, StopEvent):
                return event.result
            elif isinstance(event, AgentStream):
                if event.delta:
                    print(event.delta, end="", flush=True)
                elif event.thinking_delta:
                    print(event.thinking_delta, end="", flush=True)
            else:
                pprint.pprint(f"Received event: {type(event)} {event}")


if __name__ == "__main__":
    async def main():
        from dotenv import load_dotenv
        load_dotenv()
        # agent = BasicAgent()
        
        llm = NebiusLLM(
            api_key=os.getenv("NEBIUS_API_KEY"),
            model="Qwen/Qwen3-235B-A22B-Instruct-2507",
            api_base="https://api.tokenfactory.nebius.com/v1"
        )
        # result = llm.complete("Hello!")
        result = llm.chat([ChatMessage("Who are you?")])
        print(result)
        
        # Example without streaming (using __call__)
        # print("\n=== Non-Streaming Example ===")
        # result = await agent("What is 5 + 7?")
        # print(f"Final result: {result}")
        # result = await agent.stream_answers("Who did the actor who played Ray in the Polish-language version of Everybody Loves Raymond play in Magda M.? Give only the first name.")
        # print(f"\nFinal result: {result}")
    
    asyncio.run(main())