zhukang/agent-task-executor
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

docs: update project description and environment management in README

Browse Source
This commit is contained in:
zhukang 2025-01-14 20:52:19 +08:00
commit 8c39084a31
1 changed files with 237 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

237
README.md Normal file
View File

@ -0,0 +1,237 @@
# Agent Task Executor
A flexible task execution framework that uses LLMs (Large Language Models).
## Features
- Unique task identification
- State tracking and monitoring
- Checkpoint creation and rollback
- Resource usage monitoring
- Error handling and retry mechanism
- Execution path tracking
- Detailed status reporting
## Project Structure
```
agent_task_executor/
├── config/ # Configuration files and utilities
│ ├── config_loader.py # Configuration loading utilities
│ ├── llm_config.yaml # LLM configuration settings
│ ├── secure_config.py # Secure configuration handling
│ └── secure.json # Secure configuration data (encrypted)
│
├── tasksamples/ # Sample task implementations
│ ├── __init__.py
│ └── text_analysis_task.py # Text analysis task implementation
│
├── tests/ # Test suite
│ ├── test_config.py # Configuration tests
│ └── test_llm.py # LLM functionality tests
│
├── scripts/ # Utility scripts
│
├── llm_client.py # LLM API client implementation
├── llm_executor.py # Core LLM execution logic
├── task_executor.py # Task execution framework
├── sample_task.py # Sample task demonstration
│
├── requirements.txt # Python dependencies
├── pyproject.toml # Project metadata and build configuration
└── .env.example # Example environment variables
```
## Core Components
1. **Task Execution Framework**
- `task_executor.py`: Core framework for task execution
- `llm_executor.py`: LLM-specific execution logic
- `llm_client.py`: Client for interacting with LLM APIs
2. **Configuration Management**
- `config/`: Contains all configuration-related files
- Secure configuration handling with encryption support
- LLM-specific configurations
3. **Task Implementations**
- `tasksamples/`: Directory for task implementations
- Text analysis task with Chinese text support
- Extensible for additional task types
4. **Testing**
- Comprehensive test suite for configuration and LLM functionality
- Unit tests for core components
## Setup and Dependencies
The project uses Python with the following key dependencies:
- httpx: For making HTTP requests
- tenacity: For retry logic
- pyyaml: For configuration handling
- cryptography: For secure configuration
## Environment Management
### Package Management with UV
The project uses [uv](https://github.com/astral-sh/uv) for fast Python package management:
```bash
# Install uv (if not already installed)
pip install uv
# Create virtual environment and install dependencies
uv venv
uv pip install -r requirements.txt
# Activate virtual environment
# On Windows:
.venv\Scripts\activate
# On Unix or MacOS:
source .venv/bin/activate
```
### Lock File
The project uses `uv.lock` to ensure dependency consistency:
- `uv.lock`: Contains exact versions of all dependencies
- To update dependencies: `uv pip compile requirements.txt -o uv.lock`
- To sync with lock file: `uv pip sync uv.lock`
### Environment Variables
Required environment variables:
- `DEEPSEEK_API_KEY`: API key for DeepSeek LLM service
- `DEEPSEEK_API_BASE`: Base URL for DeepSeek API
- `CONFIG_PATH`: Path to configuration directory (default: ./config)
Create a `.env` file in the project root:
```bash
DEEPSEEK_API_KEY=your_api_key_here
DEEPSEEK_API_BASE=https://api.deepseek.com/v1
CONFIG_PATH=./config
```
### Configuration Files
1. `llm_config.yaml`: LLM service configuration
```yaml
llm:
provider: deepseek
model: deepseek-chat
temperature: 0.7
max_tokens: 2000
```
2. `secure.json`: Encrypted configuration for sensitive data
- Generated and managed by `secure_config.py`
- Uses encryption for storing API keys and secrets
## Usage
1. Install dependencies:
```bash
uv pip install -r requirements.txt
```
2. Run the sample task:
```bash
python sample_task.py
```
3. Create your own task executor:
```python
from task_executor import TaskExecutor
class CustomTaskExecutor(TaskExecutor):
def __init__(self):
super().__init__()
# Define your task steps
def validate_input(self, input_data):
# Implement input validation
pass
def execute_step(self, step_id, step_data):
# Implement step execution
pass
```
## Task Execution Framework
### Core Components
1. Task Status Tracking
- Unique task ID
- Step-by-step progress monitoring
- Resource usage tracking
- Execution path recording
2. Checkpointing System
- Automatic checkpoint creation every 5 steps
- Rollback capability
- State preservation
3. Error Handling
- Maximum 3 retry attempts
- Timeout monitoring (300 seconds)
- Detailed error logging
4. Status Reporting
- Real-time status updates
- Resource usage statistics
- Execution path history
- Error and warning logs
### Status Output Format
```json
{
"task_id": "unique_id",
"timestamp": "ISO-8601 timestamp",
"current_step": {
"id": "step_id",
"name": "step_name",
"status": "status",
"progress": "percentage"
},
"checkpoints": ["checkpoint_list"],
"resources": {
"used": "used_resources",
"available": "available_resources"
},
"execution_path": ["executed_steps"],
"next_actions": ["possible_actions"]
}
```
### 密钥管理
1. 创建了安全的配置管理系统：
SecureConfig 类用于加密存储敏感信息
使用 Fernet 对称加密
适当的文件权限管理（Windows 和 Unix）
2. 实现了配置加载器：
从 YAML 文件加载基本配置
从加密存储加载敏感信息
支持环境变量覆盖
3. 建立了清晰的配置优先级：
API 密钥：环境变量 > 安全存储 > YAML 配置
API 基础 URL：环境变量 > API 配置 > 提供商默认值
4. 完成了全面的测试覆盖：
安全配置的加密/解密
文件权限测试
配置优先级测试
API 设置测试
5. 添加了必要的依赖：
cryptography 用于加密
pywin32 用于 Windows 安全性
PyYAML 用于配置文件
6. 这个实现提供了：
更好的安全性：敏感信息使用加密存储
灵活性：多种配置方式和清晰的优先级
跨平台支持：同时支持 Windows 和 Unix
良好的测试覆盖
### 测试
pytest tests/test_config.py -v
## License
MIT License