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72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
from dataclasses import dataclass
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from temporalio import activity
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from ollama import chat, ChatResponse
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import json
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from typing import Sequence
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from temporalio.common import RawValue
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@dataclass
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class ToolPromptInput:
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prompt: str
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context_instructions: str
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class ToolActivities:
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@activity.defn
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def prompt_llm(self, input: ToolPromptInput) -> dict:
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model_name = "qwen2.5:14b"
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messages = [
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{
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"role": "system",
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"content": input.context_instructions
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+ ". The current date is "
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+ get_current_date_human_readable(),
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},
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{
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"role": "user",
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"content": input.prompt,
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},
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]
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response: ChatResponse = chat(model=model_name, messages=messages)
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print(f"Chat response: {response.message.content}")
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try:
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data = json.loads(response.message.content)
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except json.JSONDecodeError as e:
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print(f"Invalid JSON: {e}")
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print(response.message.content)
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raise json.JSONDecodeError
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return data
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def get_current_date_human_readable():
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"""
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Returns the current date in a human-readable format.
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Example: Wednesday, January 1, 2025
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"""
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from datetime import datetime
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return datetime.now().strftime("%A, %B %d, %Y")
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@activity.defn(dynamic=True)
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def dynamic_tool_activity(args: Sequence[RawValue]) -> dict:
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from tools import get_handler
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tool_name = activity.info().activity_type # e.g. "FindEvents"
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tool_args = activity.payload_converter().from_payload(args[0].payload, dict)
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activity.logger.info(f"Running dynamic tool '{tool_name}' with args: {tool_args}")
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# Delegate to the relevant function
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handler = get_handler(tool_name)
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result = handler(tool_args)
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# Optionally log or augment the result
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activity.logger.info(f"Tool '{tool_name}' result: {result}")
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return result
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