feat: initial commit of agent task executor framework
- Add core task execution framework - Add LLM integration with DeepSeek - Add text analysis task implementation - Add configuration management - Add tests and documentation
This commit is contained in:
zhukang
parent
8c39084a31
commit
dd797ab5e4
12
.env.example
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12
.env.example
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# LLM Configuration
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# You can set these environment variables to override the configuration in llm_config.yaml
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# API Key (will override secure storage)
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LLM_API_KEY=your-api-key-here
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# Base URL (will override provider default)
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LLM_API_BASE=https://api.deepseek.com
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# Provider settings
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LLM_PROVIDER=deepseek
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LLM_MODEL=deepseek-chat
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26
.gitignore
vendored
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26
.gitignore
vendored
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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.pytest_cache/
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# Virtual Environment
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.venv/
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venv/
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ENV/
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# Environment Variables
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.env
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# IDE
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.vscode/
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.idea/
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# Secure Configuration
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config/secure.json
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config/secure.key
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# Distribution
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dist/
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build/
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*.egg-info/
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1
config/__init__.py
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1
config/__init__.py
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"""Configuration management package."""
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config/config_loader.py
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config/config_loader.py
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import os
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from typing import Dict, Any
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import yaml
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from pathlib import Path
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from dotenv import load_dotenv
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from .secure_config import SecureConfig
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def get_api_settings(config: Dict[str, Any], secure_config: Dict[str, Any]) -> Dict[str, str]:
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"""Get API settings from config and environment variables."""
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llm_config = config["llm"]
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provider = llm_config["provider"]
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# Get API key with priority:
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# 1. Environment variables
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# 2. Secure config
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# 3. Regular config
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api_key = (
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os.getenv("LLM_API_KEY") or
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os.getenv("OPENAI_API_KEY") or
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secure_config.get("api_key") or
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llm_config["api"].get("api_key")
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)
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# Get API base URL with fallback chain:
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# 1. Environment variable
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# 2. Config api.api_base
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# 3. Provider-specific default
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api_base = (
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os.getenv("LLM_API_BASE") or
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llm_config["api"].get("api_base") or
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llm_config.get(provider, {}).get("api_base")
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)
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return {
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"api_key": api_key,
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"api_base": api_base
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}
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def save_api_key(api_key: str):
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"""Securely save API key."""
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secure = SecureConfig()
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secure_path = Path(os.getcwd()) / "config" / "secure.json"
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secure.save_secure_config({"api_key": api_key}, secure_path)
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def load_config() -> Dict[str, Any]:
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"""Load configuration from YAML file and secure storage."""
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load_dotenv()
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config_dir = Path(os.getcwd()) / "config"
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# Load main config
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with open(config_dir / "llm_config.yaml", "r") as f:
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config = yaml.safe_load(f)
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# Load secure config
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secure = SecureConfig()
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secure_config = secure.load_secure_config(config_dir / "secure.json")
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# Get API settings
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api_settings = get_api_settings(config, secure_config)
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# Update config with API settings
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llm_config = config["llm"]
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llm_config["api_key"] = api_settings["api_key"]
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llm_config["api_base"] = api_settings["api_base"]
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return config
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22
config/llm_config.yaml
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config/llm_config.yaml
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llm:
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provider: deepseek # can be: openai, azure, anthropic, deepseek, custom
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model: deepseek-chat
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max_tokens: 2000
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temperature: 0.7
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timeout: 30
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# API configuration
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# These values can be overridden by environment variables
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# or secure storage
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api:
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api_base: null # will use provider default if not set
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# Provider-specific settings
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openai:
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api_base: https://api.openai.com/v1
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azure:
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api_base: null # Set your Azure endpoint here
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anthropic:
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api_base: https://api.anthropic.com
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deepseek:
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api_base: https://api.deepseek.com
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config/secure_config.py
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config/secure_config.py
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import os
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import json
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from pathlib import Path
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from base64 import b64encode, b64decode
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from cryptography.fernet import Fernet
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from cryptography.hazmat.primitives import hashes
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from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
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from typing import Dict, Any, Optional
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class SecureConfig:
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def __init__(self, config_path: str = None):
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self.config_path = Path(config_path or Path(__file__).parent / "secure.key")
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self._ensure_key_exists()
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self.fernet = self._get_fernet()
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def _ensure_key_exists(self):
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"""Ensure encryption key exists, generate if not."""
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self.config_path.parent.mkdir(parents=True, exist_ok=True)
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if not self.config_path.exists():
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# Generate a new encryption key
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key = Fernet.generate_key()
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with open(self.config_path, "wb") as f:
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f.write(key)
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# Set restrictive permissions on Windows or Unix
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if os.name == 'nt': # Windows
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import win32security
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import win32api
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import ntsecuritycon as con
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# Get current user's SID
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username = win32api.GetUserName()
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sid = win32security.LookupAccountName(None, username)[0]
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# Create a new DACL with full access for the current user
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dacl = win32security.ACL()
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dacl.AddAccessAllowedAce(
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win32security.ACL_REVISION,
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con.FILE_ALL_ACCESS,
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sid
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)
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# Create and set the security descriptor
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security_desc = win32security.SECURITY_DESCRIPTOR()
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security_desc.SetSecurityDescriptorDacl(1, dacl, 0)
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win32security.SetFileSecurity(
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str(self.config_path),
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win32security.DACL_SECURITY_INFORMATION,
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security_desc
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)
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else: # Unix
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os.chmod(self.config_path, 0o600)
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def _get_fernet(self) -> Fernet:
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"""Get Fernet instance for encryption/decryption."""
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with open(self.config_path, "rb") as f:
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key = f.read()
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return Fernet(key)
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def encrypt_value(self, value: str) -> str:
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"""Encrypt a string value."""
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if not value:
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return None
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return self.fernet.encrypt(value.encode()).decode()
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def decrypt_value(self, encrypted_value: str) -> Optional[str]:
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"""Decrypt an encrypted string value."""
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if not encrypted_value:
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return None
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try:
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return self.fernet.decrypt(encrypted_value.encode()).decode()
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except Exception:
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return None
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def save_secure_config(self, config: Dict[str, Any], file_path: str):
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"""Save configuration with encrypted sensitive values."""
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file_path = Path(file_path)
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file_path.parent.mkdir(parents=True, exist_ok=True)
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secure_config = {}
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# Encrypt sensitive values
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if "api_key" in config:
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secure_config["api_key"] = self.encrypt_value(config["api_key"])
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# Save to file
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with open(file_path, "w") as f:
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json.dump(secure_config, f, indent=2)
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# Set restrictive permissions
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if os.name == 'nt': # Windows
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import win32security
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import win32api
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import ntsecuritycon as con
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# Get current user's SID
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username = win32api.GetUserName()
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sid = win32security.LookupAccountName(None, username)[0]
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# Create a new DACL with full access for the current user
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dacl = win32security.ACL()
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dacl.AddAccessAllowedAce(
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win32security.ACL_REVISION,
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con.FILE_ALL_ACCESS,
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sid
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)
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# Create and set the security descriptor
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security_desc = win32security.SECURITY_DESCRIPTOR()
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security_desc.SetSecurityDescriptorDacl(1, dacl, 0)
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win32security.SetFileSecurity(
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str(file_path),
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win32security.DACL_SECURITY_INFORMATION,
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security_desc
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)
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else: # Unix
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os.chmod(file_path, 0o600)
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def load_secure_config(self, file_path: str) -> Dict[str, Any]:
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"""Load and decrypt configuration."""
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if not Path(file_path).exists():
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return {}
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with open(file_path, "r") as f:
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secure_config = json.load(f)
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# Decrypt sensitive values
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config = {}
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if "api_key" in secure_config:
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config["api_key"] = self.decrypt_value(secure_config["api_key"])
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return config
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110
llm_client.py
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110
llm_client.py
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from abc import ABC, abstractmethod
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from typing import Dict, Any, List, Optional
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import os
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import json
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import httpx
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from dataclasses import dataclass
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from enum import Enum
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class LLMProvider(Enum):
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OPENAI = "openai"
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AZURE = "azure"
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ANTHROPIC = "anthropic"
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DEEPSEEK = "deepseek"
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CUSTOM = "custom"
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@dataclass
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class LLMConfig:
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provider: LLMProvider
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api_key: str
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api_base: Optional[str] = None
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model: str = "gpt-4"
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max_tokens: int = 2000
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temperature: float = 0.7
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timeout: int = 30
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class BaseLLMClient(ABC):
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@abstractmethod
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async def generate(self,
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messages: List[Dict[str, str]],
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**kwargs) -> Dict[str, Any]:
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pass
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class OpenAICompatibleClient(BaseLLMClient):
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def __init__(self, config: LLMConfig):
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self.config = config
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async def generate(self,
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messages: List[Dict[str, str]],
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**kwargs) -> Dict[str, Any]:
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"""
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Generate a response using OpenAI-compatible API.
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Args:
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messages: List of message dictionaries with 'role' and 'content'
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**kwargs: Additional parameters to pass to the API
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Returns:
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Dict containing the API response
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"""
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try:
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# Prepare request data
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request_data = {
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"model": self.config.model,
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"messages": messages,
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"max_tokens": self.config.max_tokens,
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"temperature": self.config.temperature,
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**kwargs
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}
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# Make API request
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async with httpx.AsyncClient(
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base_url=self.config.api_base or "https://api.openai.com/v1",
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timeout=self.config.timeout,
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headers={
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"Authorization": f"Bearer {self.config.api_key}",
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"Content-Type": "application/json"
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}
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) as client:
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response = await client.post(
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"/v1/chat/completions",
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json=request_data
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)
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# Check for HTTP errors
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response.raise_for_status()
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# Parse response
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response_data = response.json()
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# Add usage information if available
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if "usage" in response_data:
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response_data["usage"] = {
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"prompt_tokens": response_data["usage"].get("prompt_tokens", 0),
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"completion_tokens": response_data["usage"].get("completion_tokens", 0),
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"total_tokens": response_data["usage"].get("total_tokens", 0)
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}
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return response_data
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except httpx.HTTPError as http_err:
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# Handle HTTP errors
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error_msg = f"HTTP error occurred: {str(http_err)}"
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try:
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error_data = http_err.response.json()
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if "error" in error_data:
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error_msg = f"API error: {error_data['error'].get('message', str(error_data['error']))}"
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except:
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pass
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return {"error": error_msg}
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except Exception as e:
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# Handle other errors
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return {"error": f"Error generating response: {str(e)}"}
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def create_llm_client(config: LLMConfig) -> BaseLLMClient:
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"""Factory function to create appropriate LLM client."""
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if config.provider in [LLMProvider.OPENAI, LLMProvider.AZURE, LLMProvider.DEEPSEEK, LLMProvider.CUSTOM]:
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return OpenAICompatibleClient(config)
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else:
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raise ValueError(f"Unsupported LLM provider: {config.provider}")
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199
llm_executor.py
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199
llm_executor.py
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import os
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from typing import Dict, Any, List, Optional
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from tenacity import retry, stop_after_attempt, wait_exponential
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from dotenv import load_dotenv
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import json
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from llm_client import LLMConfig, LLMProvider, create_llm_client
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from config.config_loader import load_config
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load_dotenv()
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class LLMExecutor:
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def __init__(self, model: str = None):
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# Load configuration
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config_dict = load_config()
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llm_config = config_dict["llm"]
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# Get provider-specific settings
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provider = LLMProvider(llm_config["provider"])
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api_base = (
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llm_config["api"].get("api_base") or
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llm_config.get(provider.value, {}).get("api_base")
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)
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# Initialize LLM client with configuration
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config = LLMConfig(
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provider=provider,
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api_key=llm_config["api_key"],
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api_base=api_base,
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model=model or llm_config["model"],
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max_tokens=llm_config.get("max_tokens", 2000),
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temperature=llm_config.get("temperature", 0.7),
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timeout=llm_config.get("timeout", 30),
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)
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self.llm_client = create_llm_client(config)
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self.conversation_history = []
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@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
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async def execute_step(self,
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step_instruction: str,
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step_input: Dict[str, Any],
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context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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"""
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Execute a single step using the LLM.
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Args:
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step_instruction: The instruction for the current step
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step_input: Input data for the step
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context: Optional context information
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Returns:
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Dict containing the LLM's response and any generated artifacts
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"""
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try:
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# Format the prompt
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messages = [
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{"role": "system", "content": """You are an AI assistant helping with task execution.
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Please process the input according to the instruction and return a JSON response.
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The input text may contain Chinese characters. Please handle them correctly.
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For example, if asked to validate text, return: {"valid": true/false, "reason": "explanation"}
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For text preprocessing, return: {"preprocessed_text": "cleaned text"}
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For summary generation, return: {"summary": "generated summary"}
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For keyword extraction, return: {"keywords": ["keyword1", "keyword2", ...]}
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For final analysis, return: {"analysis": "final analysis text"}
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Please ensure all Chinese characters are preserved and handled correctly."""},
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{"role": "user", "content": f"""Instruction: {step_instruction}
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Input: {json.dumps(step_input, indent=2, ensure_ascii=False)}"""}
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]
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# Call LLM
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try:
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response = await self.llm_client.generate(messages)
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if "error" in response:
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raise Exception(f"LLM API error: {response['error']}")
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if "choices" not in response or not response["choices"]:
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raise Exception("No response from LLM API")
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# Extract and parse response
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content = response["choices"][0]["message"]["content"]
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try:
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# Try to extract JSON from markdown code block if present
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if "```json" in content:
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json_str = content.split("```json")[1].split("```")[0].strip()
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output = json.loads(json_str)
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else:
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output = json.loads(content)
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except json.JSONDecodeError:
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# If response is not JSON, wrap it in a result field
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output = {"result": content}
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return {
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"success": True,
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"output": output
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}
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except Exception as api_error:
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raise Exception(f"LLM API call failed: {str(api_error)}")
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except Exception as e:
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return {
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"success": False,
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"error": str(e)
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}
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def _build_messages(self,
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instruction: str,
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input_data: Dict[str, Any],
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context: Optional[Dict[str, Any]] = None) -> List[Dict[str, str]]:
|
||||
"""Build the message list for the LLM conversation."""
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": """You are an AI agent tasked with executing specific steps in a larger task.
|
||||
Follow these guidelines:
|
||||
1. Focus only on the current step's instruction
|
||||
2. Use the provided input data and context
|
||||
3. Return results in a clear, structured format
|
||||
4. If you need additional information, specify it in your response
|
||||
5. If you encounter errors, provide detailed error messages"""
|
||||
}
|
||||
]
|
||||
|
||||
# Add conversation history
|
||||
messages.extend(self.conversation_history)
|
||||
|
||||
# Add context if provided
|
||||
if context:
|
||||
messages.append({
|
||||
"role": "system",
|
||||
"content": f"Context for current step: {context}"
|
||||
})
|
||||
|
||||
# Add current instruction and input
|
||||
messages.append({
|
||||
"role": "user",
|
||||
"content": f"""
|
||||
Instruction: {instruction}
|
||||
Input Data: {input_data}
|
||||
|
||||
Please execute this step and provide your response in the following format:
|
||||
- Result: (main output)
|
||||
- Additional Info: (any additional information or explanations)
|
||||
- Required Next: (any information required for next steps)
|
||||
"""
|
||||
})
|
||||
|
||||
return messages
|
||||
|
||||
def _parse_response(self, response: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Parse the LLM response into a structured format."""
|
||||
content = response["content"]
|
||||
|
||||
# Parse the response into sections
|
||||
sections = {
|
||||
"result": "",
|
||||
"additional_info": "",
|
||||
"required_next": ""
|
||||
}
|
||||
|
||||
current_section = None
|
||||
for line in content.split('\n'):
|
||||
line = line.strip()
|
||||
if line.startswith('- Result:'):
|
||||
current_section = "result"
|
||||
sections[current_section] = line.replace('- Result:', '').strip()
|
||||
elif line.startswith('- Additional Info:'):
|
||||
current_section = "additional_info"
|
||||
sections[current_section] = line.replace('- Additional Info:', '').strip()
|
||||
elif line.startswith('- Required Next:'):
|
||||
current_section = "required_next"
|
||||
sections[current_section] = line.replace('- Required Next:', '').strip()
|
||||
elif current_section and line:
|
||||
sections[current_section] += f"\n{line}"
|
||||
|
||||
return sections
|
||||
|
||||
def _update_conversation_history(self, user_message: Dict[str, str],
|
||||
assistant_response: Dict[str, Any]):
|
||||
"""Update the conversation history with the latest interaction."""
|
||||
self.conversation_history.append(user_message)
|
||||
self.conversation_history.append({
|
||||
"role": "assistant",
|
||||
"content": f"""
|
||||
Result: {assistant_response['result']}
|
||||
Additional Info: {assistant_response['additional_info']}
|
||||
Required Next: {assistant_response['required_next']}
|
||||
"""
|
||||
})
|
||||
|
||||
# Keep only the last 10 messages to prevent context window from growing too large
|
||||
if len(self.conversation_history) > 10:
|
||||
self.conversation_history = self.conversation_history[-10:]
|
||||
|
||||
def clear_history(self):
|
||||
"""Clear the conversation history."""
|
||||
self.conversation_history = []
|
||||
|
||||
async def close(self):
|
||||
"""Clean up resources."""
|
||||
await self.llm_client.close()
|
||||
27
pyproject.toml
Normal file
27
pyproject.toml
Normal file
@ -0,0 +1,27 @@
|
||||
[project]
|
||||
name = "agent-task-executor"
|
||||
version = "0.1.0"
|
||||
description = "An LLM-based agent task execution framework"
|
||||
authors = [
|
||||
{name = "Your Name", email = "your.email@example.com"},
|
||||
]
|
||||
dependencies = [
|
||||
"python-dateutil>=2.8.2",
|
||||
"typing-extensions>=4.4.0",
|
||||
"openai>=1.0.0",
|
||||
"python-dotenv>=1.0.0",
|
||||
"tenacity>=8.2.0",
|
||||
"httpx>=0.24.0",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[tool.rye]
|
||||
managed = true
|
||||
dev-dependencies = [
|
||||
"pytest>=7.3.1",
|
||||
"black>=23.7.0",
|
||||
"isort>=5.12.0",
|
||||
]
|
||||
12
requirements.txt
Normal file
12
requirements.txt
Normal file
@ -0,0 +1,12 @@
|
||||
python-dateutil>=2.8.2
|
||||
typing-extensions>=4.4.0
|
||||
openai>=1.0.0
|
||||
python-dotenv>=1.0.0
|
||||
tenacity>=8.2.0
|
||||
PyYAML>=6.0.2
|
||||
cryptography>=44.0.0
|
||||
pywin32>=308; platform_system == "Windows"
|
||||
|
||||
# Test dependencies
|
||||
pytest>=8.3.4
|
||||
pytest-asyncio>=0.25.2
|
||||
110
sample_task.py
Normal file
110
sample_task.py
Normal file
@ -0,0 +1,110 @@
|
||||
from task_executor import TaskExecutor, TaskStatus
|
||||
from typing import Dict, Any
|
||||
import time
|
||||
|
||||
class SampleTaskExecutor(TaskExecutor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.task_steps = [
|
||||
{
|
||||
"id": "input_validation",
|
||||
"name": "Validate Input",
|
||||
"required_info": ["input_data"]
|
||||
},
|
||||
{
|
||||
"id": "resource_check",
|
||||
"name": "Check Resources",
|
||||
"required_info": ["system_resources"]
|
||||
},
|
||||
{
|
||||
"id": "task_processing",
|
||||
"name": "Process Task",
|
||||
"required_info": ["processed_data"]
|
||||
},
|
||||
{
|
||||
"id": "result_validation",
|
||||
"name": "Validate Results",
|
||||
"required_info": ["validation_criteria"]
|
||||
}
|
||||
]
|
||||
|
||||
def validate_input(self, input_data: Dict[str, Any]) -> bool:
|
||||
"""Validate specific input requirements for the sample task."""
|
||||
required_fields = ["input_data", "validation_criteria"]
|
||||
return all(field in input_data for field in required_fields)
|
||||
|
||||
def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
|
||||
"""Execute a specific step of the sample task."""
|
||||
try:
|
||||
# Simulate step execution
|
||||
time.sleep(1) # Simulate work being done
|
||||
|
||||
step_state = {
|
||||
"step_id": step_id,
|
||||
"status": TaskStatus.IN_PROGRESS,
|
||||
"required_info": step_data.get("required_info", []),
|
||||
"available_info": [],
|
||||
"missing_info": [],
|
||||
"resources_used": {
|
||||
"time": time.time() - self.start_time,
|
||||
"memory": "100MB" # Simulated memory usage
|
||||
}
|
||||
}
|
||||
|
||||
# Update execution state
|
||||
self.current_step = {
|
||||
"id": step_id,
|
||||
"name": step_data["name"],
|
||||
"status": TaskStatus.COMPLETED.value,
|
||||
"progress": 100
|
||||
}
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Step {step_id} failed: {str(e)}")
|
||||
return False
|
||||
|
||||
def execute(self, task_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Execute the sample task with all its steps."""
|
||||
try:
|
||||
if not self.validate_input(task_input):
|
||||
raise ValueError("Invalid task input")
|
||||
|
||||
self.logger.info(f"Starting sample task execution: {self.task_id}")
|
||||
|
||||
# Execute each step in sequence
|
||||
for step in self.task_steps:
|
||||
if not self.execute_step(step["id"], step):
|
||||
raise Exception(f"Step {step['id']} failed")
|
||||
|
||||
if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
|
||||
self.create_checkpoint()
|
||||
|
||||
return self.get_status_update()
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Sample task failed: {str(e)}")
|
||||
return self.get_status_update()
|
||||
|
||||
def main():
|
||||
# Example usage
|
||||
executor = SampleTaskExecutor()
|
||||
|
||||
# Sample task input
|
||||
task_input = {
|
||||
"input_data": "sample data",
|
||||
"validation_criteria": ["criteria1", "criteria2"],
|
||||
"system_resources": {
|
||||
"memory": "1GB",
|
||||
"cpu": "2 cores"
|
||||
}
|
||||
}
|
||||
|
||||
# Execute the task
|
||||
result = executor.execute(task_input)
|
||||
print(f"Task execution completed with status: {result['current_step']['status']}")
|
||||
print(f"Execution path: {result['execution_path']}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
scripts/__init__.py
Normal file
1
scripts/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
"""Scripts package for utility scripts."""
|
||||
10
scripts/setup_config.py
Normal file
10
scripts/setup_config.py
Normal file
@ -0,0 +1,10 @@
|
||||
from config.config_loader import save_api_key
|
||||
|
||||
def main():
|
||||
"""Set up the configuration with API key."""
|
||||
api_key = "sk-652f44592f6d4d19bad104c54f5fbf4a"
|
||||
save_api_key(api_key)
|
||||
print("API key has been securely saved.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
190
task_executor.py
Normal file
190
task_executor.py
Normal file
@ -0,0 +1,190 @@
|
||||
import time
|
||||
import uuid
|
||||
from typing import List, Dict, Any
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
import json
|
||||
import logging
|
||||
import asyncio
|
||||
from llm_executor import LLMExecutor
|
||||
|
||||
class TaskStatus(Enum):
|
||||
PENDING = "pending"
|
||||
IN_PROGRESS = "in_progress"
|
||||
COMPLETED = "completed"
|
||||
FAILED = "failed"
|
||||
|
||||
@dataclass
|
||||
class StepState:
|
||||
step_id: str
|
||||
status: TaskStatus
|
||||
required_info: List[str]
|
||||
available_info: List[str]
|
||||
missing_info: List[str]
|
||||
resources_used: Dict[str, Any]
|
||||
|
||||
class TaskExecutor:
|
||||
MAX_RETRIES = 3
|
||||
TIMEOUT = 300 # seconds
|
||||
CHECKPOINT_INTERVAL = 5 # steps
|
||||
|
||||
def __init__(self, llm_model: str = None):
|
||||
"""Initialize TaskExecutor."""
|
||||
self.task_id = str(uuid.uuid4())
|
||||
self.start_time = time.time()
|
||||
self.checkpoints = []
|
||||
self.execution_path = []
|
||||
self.current_step = None
|
||||
self.retry_count = 0
|
||||
self.llm_executor = LLMExecutor(model=llm_model)
|
||||
self.task_input = None
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
self.logger = logging.getLogger(f"TaskExecutor-{self.task_id}")
|
||||
|
||||
def get_status_update(self) -> dict:
|
||||
"""Generate a status update for the current execution state."""
|
||||
return {
|
||||
"task_id": self.task_id,
|
||||
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
|
||||
"current_step": self.current_step,
|
||||
"checkpoints": self.checkpoints,
|
||||
"resources": {
|
||||
"used": {
|
||||
"time": time.time() - self.start_time,
|
||||
"memory": "N/A" # To be implemented
|
||||
},
|
||||
"available": {
|
||||
"time": self.TIMEOUT - (time.time() - self.start_time),
|
||||
"memory": "N/A" # To be implemented
|
||||
}
|
||||
},
|
||||
"execution_path": self.execution_path,
|
||||
"status": TaskStatus.COMPLETED.value if self.current_step and self.current_step.get("status") == TaskStatus.COMPLETED.value else TaskStatus.IN_PROGRESS.value
|
||||
}
|
||||
|
||||
async def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
|
||||
"""Execute a single step of the task using LLM."""
|
||||
try:
|
||||
self.current_step = {
|
||||
"id": step_id,
|
||||
"name": step_data.get("name", "Unknown"),
|
||||
"status": TaskStatus.IN_PROGRESS.value,
|
||||
"progress": 0
|
||||
}
|
||||
|
||||
# Check if execution should continue
|
||||
if time.time() - self.start_time > self.TIMEOUT:
|
||||
raise TimeoutError("Task execution timeout")
|
||||
|
||||
# Execute step using LLM
|
||||
step_result = await self.llm_executor.execute_step(
|
||||
step_instruction=step_data.get("instruction", ""),
|
||||
step_input=step_data.get("input", {}),
|
||||
context=step_data.get("context", {})
|
||||
)
|
||||
|
||||
if not step_result["success"]:
|
||||
raise Exception(f"Step failed: {step_result['error']}")
|
||||
|
||||
# Update step status
|
||||
self.execution_path.append({
|
||||
"step_id": step_id,
|
||||
"result": step_result["output"]
|
||||
})
|
||||
|
||||
self.current_step["status"] = TaskStatus.COMPLETED.value
|
||||
self.current_step["progress"] = 100
|
||||
|
||||
# Create checkpoint if needed
|
||||
if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
|
||||
self.create_checkpoint()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.current_step["status"] = TaskStatus.FAILED.value
|
||||
return self.handle_error(e)
|
||||
|
||||
def validate_input(self, input_data: Dict[str, Any]) -> bool:
|
||||
"""Validate the input data for the task."""
|
||||
return True
|
||||
|
||||
def create_checkpoint(self):
|
||||
"""Create a checkpoint of the current execution state."""
|
||||
checkpoint = {
|
||||
"timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
|
||||
"task_id": self.task_id,
|
||||
"current_step": self.current_step,
|
||||
"execution_path": self.execution_path.copy(),
|
||||
"resources": {
|
||||
"used": {
|
||||
"time": time.time() - self.start_time,
|
||||
"memory": "N/A" # To be implemented
|
||||
}
|
||||
}
|
||||
}
|
||||
self.checkpoints.append(checkpoint)
|
||||
self.logger.info(f"Created checkpoint: {json.dumps(checkpoint, indent=2)}")
|
||||
|
||||
def rollback_to_checkpoint(self, checkpoint_index: int):
|
||||
"""Rollback the execution to a specific checkpoint."""
|
||||
if 0 <= checkpoint_index < len(self.checkpoints):
|
||||
checkpoint = self.checkpoints[checkpoint_index]
|
||||
# Implement state restoration logic
|
||||
self.logger.info(f"Rolling back to checkpoint: {checkpoint['timestamp']}")
|
||||
return True
|
||||
return False
|
||||
|
||||
def get_next_actions(self) -> List[str]:
|
||||
"""Determine the next possible actions based on current state."""
|
||||
actions = []
|
||||
if self.current_step and self.current_step["status"] == TaskStatus.FAILED.value:
|
||||
actions.extend(["retry", "rollback", "abort"])
|
||||
elif self.current_step and self.current_step["status"] == TaskStatus.COMPLETED.value:
|
||||
actions.extend(["continue", "checkpoint"])
|
||||
return actions
|
||||
|
||||
def handle_error(self, error: Exception):
|
||||
"""Handle execution errors."""
|
||||
self.logger.error(f"Error occurred: {str(error)}")
|
||||
self.retry_count += 1
|
||||
|
||||
if self.retry_count >= self.MAX_RETRIES:
|
||||
self.logger.error("Max retries reached. Terminating execution.")
|
||||
return False
|
||||
|
||||
# Implement error recovery logic
|
||||
return True
|
||||
|
||||
async def execute(self, task_input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Execute the complete task."""
|
||||
try:
|
||||
# Store task input
|
||||
self.task_input = task_input
|
||||
|
||||
if not self.validate_input(task_input):
|
||||
raise ValueError("Invalid task input")
|
||||
|
||||
self.logger.info(f"Starting task execution: {self.task_id}")
|
||||
|
||||
# Execute each step in sequence
|
||||
if hasattr(self, 'task_steps'):
|
||||
for step in self.task_steps:
|
||||
if not await self.execute_step(step["id"], step):
|
||||
raise Exception(f"Step {step['id']} failed")
|
||||
|
||||
if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
|
||||
self.create_checkpoint()
|
||||
else:
|
||||
raise ValueError("No task steps defined")
|
||||
|
||||
return self.get_status_update()
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Task failed: {str(e)}")
|
||||
return self.get_status_update()
|
||||
1
tasksamples/__init__.py
Normal file
1
tasksamples/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
|
||||
200
tasksamples/text_analysis_task.py
Normal file
200
tasksamples/text_analysis_task.py
Normal file
@ -0,0 +1,200 @@
|
||||
from task_executor import TaskExecutor, TaskStatus
|
||||
from typing import Dict, Any
|
||||
import time
|
||||
import json
|
||||
import re
|
||||
|
||||
class TextAnalysisExecutor(TaskExecutor):
|
||||
def __init__(self):
|
||||
super().__init__(llm_model="deepseek-chat")
|
||||
self.task_steps = [
|
||||
{
|
||||
"id": "input_validation",
|
||||
"name": "Validate Input",
|
||||
"required_info": ["text"],
|
||||
"instruction": "Validate if the input text is not empty and contains valid Chinese characters. Check for proper UTF-8 encoding."
|
||||
},
|
||||
{
|
||||
"id": "text_preprocessing",
|
||||
"name": "Preprocess Text",
|
||||
"required_info": ["text"],
|
||||
"instruction": "Clean and preprocess the text by: 1) Normalizing Chinese punctuation, 2) Removing unnecessary whitespace while preserving sentence structure, 3) Standardizing traditional/simplified characters if needed."
|
||||
},
|
||||
{
|
||||
"id": "generate_summary",
|
||||
"name": "Generate Summary",
|
||||
"required_info": ["preprocessed_text"],
|
||||
"instruction": "Generate a concise summary in Chinese that captures the main points. Maintain the original language style and terminology."
|
||||
},
|
||||
{
|
||||
"id": "extract_keywords",
|
||||
"name": "Extract Keywords",
|
||||
"required_info": ["preprocessed_text"],
|
||||
"instruction": "Extract the most important Chinese keywords and key phrases from the text. Include both technical terms and contextual phrases."
|
||||
},
|
||||
{
|
||||
"id": "final_analysis",
|
||||
"name": "Final Analysis",
|
||||
"required_info": ["summary", "keywords"],
|
||||
"instruction": "Combine the summary and keywords into a comprehensive analysis report in Chinese. Structure the report with clear sections for summary, key points, and insights."
|
||||
}
|
||||
]
|
||||
|
||||
def validate_input(self, input_data: Dict[str, Any]) -> bool:
|
||||
"""Validate specific input requirements for the text analysis task."""
|
||||
if "text" not in input_data:
|
||||
return False
|
||||
text = input_data.get("text", "")
|
||||
return isinstance(text, str) and len(text.strip()) > 0
|
||||
|
||||
async def execute_step(self, step_id: str, step_data: Dict[str, Any]) -> bool:
|
||||
"""Execute a specific step of the text analysis task."""
|
||||
try:
|
||||
self.current_step = {
|
||||
"id": step_id,
|
||||
"name": step_data["name"],
|
||||
"status": TaskStatus.IN_PROGRESS.value,
|
||||
"progress": 0
|
||||
}
|
||||
|
||||
# Get step instruction and input
|
||||
instruction = step_data.get("instruction", "")
|
||||
step_input = {}
|
||||
|
||||
# Prepare step-specific input
|
||||
if step_id == "input_validation":
|
||||
text = self.task_input.get("text", "")
|
||||
# Validate Chinese text encoding
|
||||
try:
|
||||
text.encode('utf-8').decode('utf-8')
|
||||
except UnicodeError:
|
||||
return {"error": "Invalid text encoding. Please ensure the text is properly encoded in UTF-8."}
|
||||
step_input = {"text": text}
|
||||
|
||||
elif step_id == "text_preprocessing":
|
||||
text = self.task_input.get("text", "")
|
||||
# Basic Chinese text preprocessing
|
||||
# Normalize whitespace while preserving Chinese text structure
|
||||
text = re.sub(r'\s+', ' ', text).strip()
|
||||
# Normalize Chinese punctuation (simple example)
|
||||
text = text.replace('，', ',').replace('。', '.').replace('：', ':')
|
||||
step_input = {"text": text}
|
||||
elif step_id == "generate_summary":
|
||||
# Get the preprocessed text from previous step
|
||||
prev_result = next(
|
||||
(step["result"] for step in self.execution_path if step["step_id"] == "text_preprocessing"),
|
||||
{}
|
||||
)
|
||||
step_input = {"text": prev_result.get("preprocessed_text", self.task_input.get("text", ""))}
|
||||
elif step_id == "extract_keywords":
|
||||
# Get the preprocessed text from previous step
|
||||
prev_result = next(
|
||||
(step["result"] for step in self.execution_path if step["step_id"] == "text_preprocessing"),
|
||||
{}
|
||||
)
|
||||
step_input = {"text": prev_result.get("preprocessed_text", self.task_input.get("text", ""))}
|
||||
elif step_id == "final_analysis":
|
||||
# Get summary and keywords from previous steps
|
||||
summary_result = next(
|
||||
(step["result"] for step in self.execution_path if step["step_id"] == "generate_summary"),
|
||||
{}
|
||||
)
|
||||
keywords_result = next(
|
||||
(step["result"] for step in self.execution_path if step["step_id"] == "extract_keywords"),
|
||||
{}
|
||||
)
|
||||
step_input = {
|
||||
"summary": summary_result.get("summary", ""),
|
||||
"keywords": keywords_result.get("keywords", [])
|
||||
}
|
||||
|
||||
# Execute step using LLM
|
||||
step_result = await self.llm_executor.execute_step(
|
||||
step_instruction=instruction,
|
||||
step_input=step_input
|
||||
)
|
||||
|
||||
if not step_result.get("success", False):
|
||||
raise Exception(f"Step failed: {step_result.get('error', 'Unknown error')}")
|
||||
|
||||
# Update execution path
|
||||
self.execution_path.append({
|
||||
"step_id": step_id,
|
||||
"result": step_result.get("output", {})
|
||||
})
|
||||
|
||||
# Update step status
|
||||
self.current_step["status"] = TaskStatus.COMPLETED.value
|
||||
self.current_step["progress"] = 100
|
||||
|
||||
# Create checkpoint if needed
|
||||
if len(self.execution_path) % self.CHECKPOINT_INTERVAL == 0:
|
||||
self.create_checkpoint()
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Step {step_id} failed: {str(e)}")
|
||||
self.current_step["status"] = TaskStatus.FAILED.value
|
||||
return False
|
||||
|
||||
def main():
|
||||
# Set console encoding to UTF-8
|
||||
import sys, io
|
||||
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
|
||||
|
||||
# Create the executor
|
||||
executor = TextAnalysisExecutor()
|
||||
|
||||
# Sample Chinese text for analysis
|
||||
sample_text = """
|
||||
从 ChatGPT 到 Devin：AI 编程的四个发展阶段与范式转变 Koji：我们再聊一聊 AI 编程。编程领域今年取得了非常令人兴奋的进展。雨森一直有很强的框架归纳和总结能力。前不久你跟我分享过你提炼出来的 AI 编程发展四段论，要不要在播客里和大家分享一下？ 雨森：这其实是和很多朋友一起探讨得出的结果，是大家智慧的结晶。AI 编程从 ChatGPT 出现到现在也就两年出头的时间，但已经经历了四个阶段。 第一个阶段是让 AI 直接写代码，典型代表是早期的 ChatGPT、Claude。我们给它一个需求，比如「帮我写个贪吃蛇」，它就给出一段代码。在这个过程中，它既不知道我为什么要写贪吃蛇，也不知道代码运行情况如何。可能要我去本地编译运行后发现报错，再把错误告诉它，它才能给出调试后的结果。这时的 AI 完全就像一个只能通过邮件交流的笔友，是简单的问答模式。 第二阶段是以 GitHub Copilot 为代表，AI 开始拥有上下文，它可以把整个组织的代码库作为 context。这样 AI 就获得了大量新的背景信息。但这时用户还是需要手动把代码贴到 IDE 里面进行调试。我觉得这是 2.0 阶段，就是我们让 AI 拥有了 codebase 作为上下文。 2024 年一个非常大的进步是以 Cursor 为代表的编程 Copilot 的出现。它的核心理念是预测用户未来要写什么代码。根据你的代码库以及刚才写的代码，它预测你接下来要写什么代码、创建什么文件、做什么操作。这里面对于生成代码的质量和数量，以及文件的创建和修改都有很大提升。后来 Windsurf 还加入了对命令行操作的自动化，这样 AI 就能很好地使用我的电脑。原来的 AI 是在一张纸上写代码，我把代码抄走运行；现在 AI 可以在我的电脑上创建文件、执行命令行操作，进入到「我为你写」的阶段。 当我们觉得这已经很令人兴奋时，Devin 的出现带来了几个重要突破：首先，它可以异步工作。Cursor、Windsurf 这些工具虽然一步操作做的事情比较多，但仍然需要持续的注意力，即「我说一步它做一步」。而 Devin 可以持续工作，把用户的注意力释放出来。这是因为它多了一个 Planner，可以规划任务。 其次，它可以通过虚拟机执行更多操作，做更多调试工作。比如你写个网站，它可以自己用虚拟机去访问这个网站，检查前端后端的业务逻辑是否正确，并且可以随时打断和调整。大家用 Cursor 或者 ChatGPT 都知道，你无法在它输出的中间做调整，必须等它输出完后才能修改。但 Devin 就像真人一样，你可以在它完成任务时给出新指令，它会把这个结合到已有的 Planner 里调整计划。这就从「为你写」进化到了「为你做」。 总结一下这四个阶段：第一阶段是让 AI 写代码，代表是 ChatGPT；第二阶段是 AI 开放代码库，代表是 GitHub Copilot；第三阶段是 AI 可以自动写代码并执行，代表是 Cursor 和 Windsurf；第四阶段是 AI 虚拟员工，Devin 开创了一个很好的先例。
|
||||
"""
|
||||
|
||||
# Prepare input
|
||||
task_input = {
|
||||
"text": sample_text
|
||||
}
|
||||
|
||||
# Execute the task
|
||||
import asyncio
|
||||
result = asyncio.run(executor.execute(task_input))
|
||||
|
||||
# Print results with proper formatting
|
||||
print("\n 文本分析结果:")
|
||||
print("=" * 50)
|
||||
|
||||
# Print each step's result with proper formatting
|
||||
for step in result.get("execution_path", []):
|
||||
step_id = step["step_id"]
|
||||
result_data = step["result"]
|
||||
|
||||
print(f"\n {step_id.upper()}:")
|
||||
print("-" * 30)
|
||||
|
||||
if step_id == "input_validation":
|
||||
print(" 输入验证完成")
|
||||
elif step_id == "text_preprocessing":
|
||||
print(" 文本预处理完成")
|
||||
if "preprocessed_text" in result_data:
|
||||
print("\n处理后的文本:")
|
||||
print(result_data["preprocessed_text"])
|
||||
elif step_id == "generate_summary":
|
||||
print("\n 文本摘要:")
|
||||
if "summary" in result_data:
|
||||
print(result_data["summary"])
|
||||
elif step_id == "extract_keywords":
|
||||
print("\n 关键词:")
|
||||
if "keywords" in result_data:
|
||||
keywords = result_data["keywords"]
|
||||
if isinstance(keywords, list):
|
||||
print("、".join(keywords))
|
||||
else:
|
||||
print(keywords)
|
||||
elif step_id == "final_analysis":
|
||||
print("\n 最终分析:")
|
||||
if "analysis" in result_data:
|
||||
print(result_data["analysis"])
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
1
tests/__init__.py
Normal file
1
tests/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
|
||||
136
tests/test_config.py
Normal file
136
tests/test_config.py
Normal file
@ -0,0 +1,136 @@
|
||||
import os
|
||||
import json
|
||||
import pytest
|
||||
from pathlib import Path
|
||||
from config.secure_config import SecureConfig
|
||||
from config.config_loader import load_config, save_api_key
|
||||
|
||||
@pytest.fixture
|
||||
def temp_dir(tmp_path):
|
||||
"""Create a temporary directory for test files."""
|
||||
return tmp_path
|
||||
|
||||
@pytest.fixture
|
||||
def secure_config(temp_dir):
|
||||
"""Create a SecureConfig instance with a temporary key file."""
|
||||
return SecureConfig(str(temp_dir / "test_secure.key"))
|
||||
|
||||
@pytest.fixture
|
||||
def test_config_dir(temp_dir, monkeypatch):
|
||||
"""Set up a test configuration directory."""
|
||||
# Create config directory
|
||||
config_dir = temp_dir / "config"
|
||||
config_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Create test YAML config
|
||||
yaml_content = """
|
||||
llm:
|
||||
provider: openai
|
||||
model: gpt-4
|
||||
max_tokens: 2000
|
||||
temperature: 0.7
|
||||
timeout: 30
|
||||
api:
|
||||
api_base: null
|
||||
openai:
|
||||
api_base: https://api.openai.com/v1
|
||||
"""
|
||||
config_path = config_dir / "llm_config.yaml"
|
||||
config_path.write_text(yaml_content)
|
||||
|
||||
# Set up environment for config loader
|
||||
monkeypatch.setenv("PYTHONPATH", str(temp_dir))
|
||||
monkeypatch.chdir(temp_dir)
|
||||
|
||||
return temp_dir
|
||||
|
||||
class TestSecureConfig:
|
||||
def test_key_generation(self, secure_config, temp_dir):
|
||||
"""Test that encryption key is generated correctly."""
|
||||
key_path = temp_dir / "test_secure.key"
|
||||
assert key_path.exists()
|
||||
if os.name == 'nt': # Windows
|
||||
import win32security
|
||||
security = win32security.GetFileSecurity(
|
||||
str(key_path),
|
||||
win32security.DACL_SECURITY_INFORMATION
|
||||
)
|
||||
dacl = security.GetSecurityDescriptorDacl()
|
||||
assert dacl is not None
|
||||
# Verify only one ACE exists
|
||||
assert dacl.GetAceCount() == 1
|
||||
else: # Unix
|
||||
assert key_path.stat().st_mode & 0o777 == 0o600 # Check permissions
|
||||
|
||||
def test_encryption_decryption(self, secure_config):
|
||||
"""Test encryption and decryption of values."""
|
||||
test_value = "test-api-key-123"
|
||||
encrypted = secure_config.encrypt_value(test_value)
|
||||
decrypted = secure_config.decrypt_value(encrypted)
|
||||
assert decrypted == test_value
|
||||
|
||||
def test_null_handling(self, secure_config):
|
||||
"""Test handling of null/None values."""
|
||||
assert secure_config.encrypt_value(None) is None
|
||||
assert secure_config.decrypt_value(None) is None
|
||||
|
||||
def test_invalid_decrypt(self, secure_config):
|
||||
"""Test decryption of invalid data."""
|
||||
assert secure_config.decrypt_value("invalid-data") is None
|
||||
|
||||
class TestConfigLoader:
|
||||
def test_save_load_api_key(self, test_config_dir, monkeypatch):
|
||||
"""Test saving and loading API key."""
|
||||
test_api_key = "test-api-key-456"
|
||||
save_api_key(test_api_key)
|
||||
|
||||
# Verify secure.json was created with proper permissions
|
||||
secure_json = test_config_dir / "config" / "secure.json"
|
||||
assert secure_json.exists()
|
||||
|
||||
if os.name == 'nt': # Windows
|
||||
import win32security
|
||||
security = win32security.GetFileSecurity(
|
||||
str(secure_json),
|
||||
win32security.DACL_SECURITY_INFORMATION
|
||||
)
|
||||
dacl = security.GetSecurityDescriptorDacl()
|
||||
assert dacl is not None
|
||||
# Verify only one ACE exists
|
||||
assert dacl.GetAceCount() == 1
|
||||
else: # Unix
|
||||
assert secure_json.stat().st_mode & 0o777 == 0o600
|
||||
|
||||
# Load config and verify API key
|
||||
config = load_config()
|
||||
assert config["llm"]["api_key"] == test_api_key
|
||||
|
||||
def test_config_priority(self, test_config_dir, monkeypatch):
|
||||
"""Test configuration priority (env vars > secure storage > yaml)."""
|
||||
# Set up different values for each level
|
||||
env_api_key = "env-api-key"
|
||||
secure_api_key = "secure-api-key"
|
||||
|
||||
# Save to secure storage
|
||||
save_api_key(secure_api_key)
|
||||
|
||||
# Test without env var
|
||||
config = load_config()
|
||||
assert config["llm"]["api_key"] == secure_api_key
|
||||
|
||||
# Test with env var
|
||||
monkeypatch.setenv("LLM_API_KEY", env_api_key)
|
||||
config = load_config()
|
||||
assert config["llm"]["api_key"] == env_api_key
|
||||
|
||||
def test_api_base_config(self, test_config_dir, monkeypatch):
|
||||
"""Test API base URL configuration."""
|
||||
# Test default OpenAI base URL
|
||||
config = load_config()
|
||||
assert config["llm"]["api_base"] == "https://api.openai.com/v1"
|
||||
|
||||
# Test override with env var
|
||||
custom_base = "https://custom-api.example.com"
|
||||
monkeypatch.setenv("LLM_API_BASE", custom_base)
|
||||
config = load_config()
|
||||
assert config["llm"]["api_base"] == custom_base
|
||||
49
tests/test_llm.py
Normal file
49
tests/test_llm.py
Normal file
@ -0,0 +1,49 @@
|
||||
"""Test LLM API call."""
|
||||
import asyncio
|
||||
from config.config_loader import load_config
|
||||
import httpx
|
||||
|
||||
async def test_llm_call():
|
||||
"""Test LLM API call with a simple prompt."""
|
||||
config = load_config()
|
||||
llm_config = config["llm"]
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {llm_config['api_key']}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
# Deepseek chat completion payload
|
||||
payload = {
|
||||
"model": llm_config["model"],
|
||||
"messages": [
|
||||
{"role": "user", "content": "Say hello and introduce yourself in one sentence."}
|
||||
],
|
||||
"temperature": llm_config["temperature"],
|
||||
"max_tokens": llm_config["max_tokens"]
|
||||
}
|
||||
|
||||
print("Making API call to Deepseek...")
|
||||
print(f"Base URL: {llm_config['api_base']}")
|
||||
print(f"Model: {llm_config['model']}")
|
||||
|
||||
async with httpx.AsyncClient() as client:
|
||||
try:
|
||||
response = await client.post(
|
||||
f"{llm_config['api_base']}/v1/chat/completions",
|
||||
headers=headers,
|
||||
json=payload,
|
||||
timeout=llm_config["timeout"]
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
print("\nResponse:")
|
||||
print(result["choices"][0]["message"]["content"])
|
||||
|
||||
except httpx.HTTPStatusError as e:
|
||||
print(f"HTTP error occurred: {e.response.text}")
|
||||
except Exception as e:
|
||||
print(f"An error occurred: {str(e)}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_llm_call())
|
||||
323
uv.lock
generated
Normal file
323
uv.lock
generated
Normal file
@ -0,0 +1,323 @@
|
||||
version = 1
|
||||
requires-python = ">=3.11"
|
||||
|
||||
[[package]]
|
||||
name = "agent-task-executor"
|
||||
version = "0.1.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "httpx" },
|
||||
{ name = "openai" },
|
||||
{ name = "python-dateutil" },
|
||||
{ name = "python-dotenv" },
|
||||
{ name = "tenacity" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "httpx", specifier = ">=0.24.0" },
|
||||
{ name = "openai", specifier = ">=1.0.0" },
|
||||
{ name = "python-dateutil", specifier = ">=2.8.2" },
|
||||
{ name = "python-dotenv", specifier = ">=1.0.0" },
|
||||
{ name = "tenacity", specifier = ">=8.2.0" },
|
||||
{ name = "typing-extensions", specifier = ">=4.4.0" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.7.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
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Loading…
Reference in New Issue
Block a user