mirror of
https://github.com/temporal-community/temporal-ai-agent.git
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5d55a9fe80
* initial mcp * food ordering with mcp * prompt eng * splitting out goals and updating docs * a diff so I can get tests from codex * a diff so I can get tests from codex * oops, missing files * tests, file formatting * readme and setup updates * setup.md link fixes * readme change * readme change * readme change * stripe food setup script * single agent mode default * prompt engineering for better multi agent performance * performance should be greatly improved * Update goals/finance.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update activities/tool_activities.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * co-pilot PR suggested this change, and now fixed it * stronger wording around json format response * formatting * moved docs to dir * moved image assets under docs * cleanup env example, stripe guidance * cleanup --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
import asyncio
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import concurrent.futures
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import logging
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import os
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from dotenv import load_dotenv
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from temporalio.worker import Worker
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from activities.tool_activities import (
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ToolActivities,
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dynamic_tool_activity,
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mcp_list_tools,
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)
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from shared.config import TEMPORAL_TASK_QUEUE, get_temporal_client
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from workflows.agent_goal_workflow import AgentGoalWorkflow
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async def main():
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# Load environment variables
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load_dotenv(override=True)
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# Print LLM configuration info
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llm_model = os.environ.get("LLM_MODEL", "openai/gpt-4")
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print(f"Worker will use LLM model: {llm_model}")
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# Create the client
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client = await get_temporal_client()
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# Initialize the activities class
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activities = ToolActivities()
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print(f"ToolActivities initialized with LLM model: {llm_model}")
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# If using Ollama, pre-load the model to avoid cold start latency
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if llm_model.startswith("ollama"):
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print("\n======== OLLAMA MODEL INITIALIZATION ========")
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print("Ollama models need to be loaded into memory on first use.")
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print("This may take 30+ seconds depending on your hardware and model size.")
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print("Please wait while the model is being loaded...")
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# This call will load the model and measure initialization time
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success = activities.warm_up_ollama()
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if success:
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print("===========================================================")
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print("✅ Ollama model successfully pre-loaded and ready for requests!")
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print("===========================================================\n")
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else:
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print("===========================================================")
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print("⚠️ Ollama model pre-loading failed. The worker will continue,")
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print("but the first actual request may experience a delay while")
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print("the model is loaded on-demand.")
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print("===========================================================\n")
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print("Worker ready to process tasks!")
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logging.basicConfig(level=logging.INFO)
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# Run the worker
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with concurrent.futures.ThreadPoolExecutor(max_workers=100) as activity_executor:
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worker = Worker(
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client,
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task_queue=TEMPORAL_TASK_QUEUE,
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workflows=[AgentGoalWorkflow],
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activities=[
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activities.agent_validatePrompt,
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activities.agent_toolPlanner,
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activities.get_wf_env_vars,
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activities.mcp_tool_activity,
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dynamic_tool_activity,
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mcp_list_tools,
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],
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activity_executor=activity_executor,
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)
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print(f"Starting worker, connecting to task queue: {TEMPORAL_TASK_QUEUE}")
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await worker.run()
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if __name__ == "__main__":
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asyncio.run(main())
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