在本教程中，我们将使用 ClientAI 和 Ollama 构建一个人工智能驱动的任务规划器。我们的计划员会将目标分解为可操作的任务，创建现实的时间表并管理资源 - 所有这些都在您自己的机器上运行。
我们的任务规划器将能够：
有关 ClientAI 的文档，请参阅此处；有关 Github Repo，请参阅此处。
首先，为您的项目创建一个新目录：
mkdir local_task_planner cd local_task_planner
在 Ollama 支持下安装 ClientAI：
pip install clientai[ollama]
确保您的系统上安装了 Ollama。您可以从 Ollama 的网站获取。
创建我们的主要 Python 文件：
touch task_planner.py
让我们从核心导入开始：
from datetime import datetime, timedelta from typing import Dict, List import logging from clientai import ClientAI from clientai.agent import create_agent, tool from clientai.ollama import OllamaManager logger = logging.getLogger(__name__)
每个组件都起着至关重要的作用：
首先，让我们创建用于管理 AI 交互的 TaskPlanner 类：
class TaskPlanner:
"""A local task planning system using Ollama."""
def __init__(self):
"""Initialize the task planner with Ollama."""
self.manager = OllamaManager()
self.client = None
self.planner = None
def start(self):
"""Start the Ollama server and initialize the client."""
self.manager.start()
self.client = ClientAI("ollama", host="http://localhost:11434")
self.planner = create_agent(
client=self.client,
role="task planner",
system_prompt="""You are a practical task planner. Break down goals into
specific, actionable tasks with realistic time estimates and resource needs.
Use the tools provided to validate timelines and format plans properly.""",
model="llama3",
step="think",
tools=[validate_timeline, format_plan],
tool_confidence=0.8,
stream=True,
)
这个课程是我们的基础。它管理 Ollama 服务器生命周期，创建和配置我们的 AI 客户端，并设置具有特定功能的规划代理。
现在让我们构建人工智能将使用的工具。首先，时间线验证器：
@tool(name="validate_timeline")
def validate_timeline(tasks: Dict[str, int]) -> Dict[str, dict]:
"""
Validate time estimates and create a realistic timeline.
Args:
tasks: Dictionary of task names and estimated hours
Returns:
Dictionary with start dates and deadlines
"""
try:
current_date = datetime.now()
timeline = {}
accumulated_hours = 0
for task, hours in tasks.items():
try:
hours_int = int(float(str(hours)))
if hours_int <= 0:
logger.warning(f"Skipping task {task}: Invalid hours value {hours}")
continue
days_needed = hours_int / 6
start_date = current_date + timedelta(hours=accumulated_hours)
end_date = start_date + timedelta(days=days_needed)
timeline[task] = {
"start": start_date.strftime("%Y-%m-%d"),
"end": end_date.strftime("%Y-%m-%d"),
"hours": hours_int,
}
accumulated_hours += hours_int
except (ValueError, TypeError) as e:
logger.warning(f"Skipping task {task}: Invalid hours value {hours} - {e}")
continue
return timeline
except Exception as e:
logger.error(f"Error validating timeline: {str(e)}")
return {}
此验证器将时间估计转换为工作日，优雅地处理无效输入，创建现实的顺序调度并提供详细的调试日志记录。
接下来，让我们创建计划格式化程序：
@tool(name="format_plan")
def format_plan(
tasks: List[str],
timeline: Dict[str, dict],
resources: List[str]
) -> str:
"""
Format the plan in a clear, structured way.
Args:
tasks: List of tasks
timeline: Timeline from validate_timeline
resources: List of required resources
Returns:
Formatted plan as a string
"""
try:
plan = "== Project Plan ==\n\n"
plan += "Tasks and Timeline:\n"
for i, task in enumerate(tasks, 1):
if task in timeline:
t = timeline[task]
plan += f"\n{i}. {task}\n"
plan += f" Start: {t['start']}\n"
plan += f" End: {t['end']}\n"
plan += f" Estimated Hours: {t['hours']}\n"
plan += "\nRequired Resources:\n"
for resource in resources:
plan += f"- {resource}\n"
return plan
except Exception as e:
logger.error(f"Error formatting plan: {str(e)}")
return "Error: Unable to format plan"
在这里，我们希望通过正确的任务编号和有组织的时间线创建一致、可读的输出。
让我们为我们的计划者创建一个用户友好的界面：
def get_plan(self, goal: str) -> str:
"""
Generate a plan for the given goal.
Args:
goal: The goal to plan for
Returns:
A formatted plan string
"""
if not self.planner:
raise RuntimeError("Planner not initialized. Call start() first.")
return self.planner.run(goal)
def main():
planner = TaskPlanner()
try:
print("Task Planner (Local AI)")
print("Enter your goal, and I'll create a practical, timeline-based plan.")
print("Type 'quit' to exit.")
planner.start()
while True:
print("\n" + "=" * 50 + "\n")
goal = input("Enter your goal: ")
if goal.lower() == "quit":
break
try:
plan = planner.get_plan(goal)
print("\nYour Plan:\n")
for chunk in plan:
print(chunk, end="", flush=True)
except Exception as e:
print(f"Error: {str(e)}")
finally:
planner.stop()
if __name__ == "__main__":
main()
我们的界面提供：
以下是运行计划程序时您将看到的内容：
Task Planner (Local AI) Enter your goal, and I'll create a practical, timeline-based plan. Type 'quit' to exit. ================================================== Enter your goal: Create a personal portfolio website Your Plan: == Project Plan == Tasks and Timeline: 1. Requirements Analysis and Planning Start: 2024-12-08 End: 2024-12-09 Estimated Hours: 6 2. Design and Wireframing Start: 2024-12-09 End: 2024-12-11 Estimated Hours: 12 3. Content Creation Start: 2024-12-11 End: 2024-12-12 Estimated Hours: 8 4. Development Start: 2024-12-12 End: 2024-12-15 Estimated Hours: 20 Required Resources: - Design software (e.g., Figma) - Text editor or IDE - Web hosting service - Version control system
为您自己的任务规划器考虑这些增强功能：
要了解有关 ClientAI 的更多信息，请访问文档。
如果您对本教程有任何疑问或想分享您对任务计划程序的改进，请随时联系：
以上是使用 ClientAI 和 Ollama 构建本地 AI 任务规划器的详细内容。更多信息请关注PHP中文网其他相关文章！
