ai-agent-book/chapter9/gaia-experience/AWorld/examples/gaia/gaia_agent_runner.py

import json
import logging
import os
import re
import subprocess
import sys
import traceback
from typing import AsyncGenerator
import uuid
from aworld.cmd.utils.agent_ui_parser import (
    AWorldWebAgentUI,
    BaseToolResultParser,
    ToolCard,
    ToolResultParserFactory,
)
from aworld.config.conf import AgentConfig, TaskConfig
from aworld.agents.llm_agent import Agent
from aworld.core.task import Task
from aworld.output.artifact import ArtifactType
from aworld.output.workspace import WorkSpace
from aworld.runner import Runners
from aworld.output.ui.base import AworldUI
from aworld.output.base import Output, ToolResultOutput
from .utils import (
    add_file_path,
    load_dataset_meta_dict,
    question_scorer,
)
from .prompt import system_prompt

logger = logging.getLogger(__name__)


class GaiaSearchToolResultParser(BaseToolResultParser):
    async def parse(self, output: ToolResultOutput, workspace: WorkSpace):
        tool_card = ToolCard.from_tool_result(output)

        query = ""
        try:
            args = json.loads(tool_card.arguments)
            query = args.get("query")
            # aworld search server
            if not query:
                query = args.get("query_list")
        except Exception:
            pass

        result_items = []
        try:
            results = json.loads(tool_card.results)
            result_items = results.get("message", {}).get("results", [])
            # aworld search server return url, not link
            if result_items and isinstance(result_items, list):
                for item in result_items:
                    if not item.get("link", None) and item.get("url", None):
                        item["link"] = item.get("url")
        except Exception:
            pass

        if len(result_items) > 0:
            tool_card.results = ""

        tool_card.card_type = "tool_call_card_link_list"
        tool_card.card_data = {
            "title": "🔎 Gaia Search",
            "query": query,
            "search_items": result_items,
        }

        artifact_id = str(uuid.uuid4())
        await workspace.create_artifact(
            artifact_type=ArtifactType.WEB_PAGES,
            artifact_id=artifact_id,
            content=result_items,
            metadata={
                "query": query,
            },
        )
        tool_card.artifacts.append(
            {
                "artifact_type": ArtifactType.WEB_PAGES.value,
                "artifact_id": artifact_id,
            }
        )

        return f"""
\n\n**🔎 Gaia Search**\n\n
```tool_card
{json.dumps(tool_card.model_dump(), ensure_ascii=False, indent=2)}
```\n
"""


class CustomToolResultParserFactory(ToolResultParserFactory):
    def get_parser(self, tool_type: str, tool_name: str):
        if tool_name in ("search_server", "search"):
            return GaiaSearchToolResultParser()
        return super().get_parser(tool_type, tool_name)


# Module-level flag to ensure dependencies are installed only once per program run
_install_dependencies_flag = False


class GaiaAgentRunner:
    """
    Gaia Agent Runner
    """

    def _install_dependencies(self):
        try:
            current_dir = os.path.dirname(os.path.abspath(__file__))
            requirements_file = os.path.join(current_dir, "requirements.txt")

            if os.path.exists(requirements_file):
                logger.info(f"Installing dependencies from {requirements_file}")
                subprocess.check_call(
                    [
                        sys.executable,
                        "-m",
                        "pip",
                        "install",
                        "-U",
                        "-r",
                        requirements_file,
                    ]
                )
                subprocess.check_call(
                    [
                        sys.executable,
                        "-m",
                        "pip",
                        "install",
                        "--no-deps",
                        "-U",
                        "marker-pdf",
                        "anthropic==0.46.0",
                    ]
                )
                logger.info("Dependencies installed successfully")
            else:
                logger.warning(f"Requirements file not found at {requirements_file}")
        except Exception as e:
            logger.error(f"Failed to install dependencies: {e}")

    def __init__(
        self,
        llm_provider: str,
        llm_model_name: str,
        llm_base_url: str,
        llm_api_key: str,
        llm_temperature: float = 0.0,
        mcp_config: dict = None,
        session_id: str = None,
    ):
        global _install_dependencies_flag
        if not _install_dependencies_flag:
            self._install_dependencies()
            _install_dependencies_flag = True

        self.session_id = session_id or str(uuid.uuid4())
        self.agent_config = AgentConfig(
            llm_provider=llm_provider,
            llm_model_name=llm_model_name,
            llm_api_key=llm_api_key,
            llm_base_url=llm_base_url,
            llm_temperature=llm_temperature,
        )

        if mcp_config is None:
            mcp_path = os.path.join(
                os.path.dirname(os.path.abspath(__file__)), "mcp.json"
            )
            with open(mcp_path, "r") as f:
                mcp_config = json.load(f)
                logger.info(f"Gaia Agent Runner mcp_config: {mcp_config}")

        self.super_agent = Agent(
            conf=self.agent_config,
            name="gaia_super_agent",
            system_prompt=system_prompt,
            mcp_config=mcp_config,
            mcp_servers=(
                os.getenv("GAIA_MCP_SERVERS", "").split(",") if os.getenv("GAIA_MCP_SERVERS", "") else ""
                or mcp_config.get("mcpServers", {}).keys()
            ),
        )

        self.gaia_dataset_path = os.path.abspath(
            os.getenv(
                "GAIA_DATASET_PATH",
                os.path.join(
                    os.path.dirname(os.path.abspath(__file__)), "GAIA", "2023"
                ),
            )
        )
        self.full_dataset = load_dataset_meta_dict(self.gaia_dataset_path)
        logger.info(
            f"Gaia Agent Runner initialized: super_agent={self.super_agent}, agent_config={self.agent_config}, gaia_dataset_path={self.gaia_dataset_path}, full_dataset={len(self.full_dataset)}"
        )

    async def run(self, prompt: str):
        yield (f"\n### GAIA Agent Start!")

        mcp_servers = "\n- ✅ ".join(self.super_agent.mcp_servers)
        yield (f"\n```gaia_agent_status\n- ✅ {mcp_servers}\n```\n")

        question = None
        data_item = None
        task_id = None
        try:
            json_data = json.loads(prompt)
            task_id = json_data["task_id"]

            data_item = self.full_dataset[task_id]
            question = add_file_path(data_item, file_path=self.gaia_dataset_path)[
                "Question"
            ]
            yield (
                f"\n### Gaia Question\n```gaia_question\n{json.dumps(data_item, indent=2)}\n```\n"
            )
        except Exception as e:
            pass

        if not question:
            logger.warning(
                "Could not find GAIA question for prompt, chat using prompt directly!"
            )
            yield (f"\n{prompt}\n")
            question = prompt

        try:
            task = Task(
                id=task_id + "." + uuid.uuid1().hex if task_id else uuid.uuid1().hex,
                input=question,
                agent=self.super_agent,
                conf=TaskConfig(max_steps=20),
                session_id=self.session_id,
                endless_threshold=50,
            )

            last_output: Output = None
            rich_ui = AWorldWebAgentUI(
                session_id=self.session_id,
                workspace=WorkSpace.from_local_storages(workspace_id=self.session_id),
                tool_result_parser_factory=CustomToolResultParserFactory(),
            )
            async for output in Runners.streamed_run_task(task).stream_events():
                logger.info(f"Gaia Agent Ouput: {output}")
                res = await AworldUI.parse_output(output, rich_ui)
                for item in res if isinstance(res, list) else [res]:
                    if isinstance(item, AsyncGenerator):
                        async for sub_item in item:
                            yield sub_item
                            if sub_item and str(sub_item).strip():
                                last_output = sub_item
                    else:
                        yield item
                        if item and str(item).strip():
                            last_output = item

            logger.info(f"Gaia Agent Last Output: {last_output}")

            if data_item and last_output:
                final_response = self._judge_answer(data_item, last_output)
                yield final_response

        except Exception as e:
            logger.error(f"Error processing {prompt}, error: {traceback.format_exc()}")

    def _judge_answer(self, data_item: dict, result: Output):
        answer = result
        match = re.search(r"<answer>(.*?)</answer>", answer)
        if match:
            answer = match.group(1)
            logger.info(f"Agent answer: {answer}")
            logger.info(f"Correct answer: {data_item['Final answer']}")

            if question_scorer(answer, data_item["Final answer"]):
                logger.info(f"Question {data_item['task_id']} Correct!")
            else:
                logger.info(f"Question {data_item['task_id']} Incorrect!")

            # Create the new result record
            correct = question_scorer(answer, data_item["Final answer"])
            new_result = {
                "task_id": data_item["task_id"],
                "level": data_item["Level"],
                "question": data_item["Question"],
                "answer": data_item["Final answer"],
                "response": answer,
                "is_correct": correct,
            }
            return f"\n## Final Result: {'✅' if correct else '❌'}\n \n```gaia_result\n{json.dumps(new_result, indent=2)}\n```"
        else:
            new_result = answer
            return f"\n## Final Result:\n \n```gaia_result\n{json.dumps(new_result, indent=2)}\n```"


if __name__ == "__main__":
    import asyncio
    import argparse
    from datetime import datetime

    logger = logging.getLogger(__name__)

    output_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "output")
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)

    output_file = os.path.join(
        output_dir, f"output_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md"
    )

    async def main():
        parser = argparse.ArgumentParser()
        parser.add_argument("--prompt", type=str, default="")
        args = parser.parse_args()

        try:
            prompt = args.prompt

            llm_provider = os.getenv("LLM_PROVIDER", "openai")
            llm_model_name = os.getenv("LLM_MODEL_NAME")
            llm_api_key = os.getenv("LLM_API_KEY")
            llm_base_url = os.getenv("LLM_BASE_URL")
            llm_temperature = os.getenv("LLM_TEMPERATURE", 0.0)

            def send_output(output):
                with open(output_file, "a") as f:
                    f.write(f"{output}\n")

            async for i in GaiaAgentRunner(
                llm_provider=llm_provider,
                llm_model_name=llm_model_name,
                llm_base_url=llm_base_url,
                llm_api_key=llm_api_key,
                llm_temperature=llm_temperature,
            ).run(prompt):
                send_output(i)
        except Exception as e:
            logger.error(
                f"Error processing {args.prompt}, error: {traceback.format_exc()}"
            )

    asyncio.run(main())