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fix(minor docs update):

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This commit is contained in:
Alexander Myasoedov
2025-03-09 21:23:13 +02:00
parent 56984c7078
commit c2671fd6f1
2 changed files with 21 additions and 35 deletions
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docs/probe_data.md
+8 -18
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@@ -54,20 +54,15 @@ The `probe_data` module is a core component of the Agentic Security project, res
- **Classes:**
- `PromptSelectionInterface`: Abstract base class for prompt selection strategies.
- Methods:
- `select_next_prompt(current_prompt: str, passed_guard: bool) -> str`: Selects next prompt
- `select_next_prompts(current_prompt: str, passed_guard: bool) -> list[str]`: Selects multiple prompts
- `update_rewards(previous_prompt: str, current_prompt: str, reward: float, passed_guard: bool) -> None`: Updates rewards
- `update_rewards(previous_prompt: str, current_prompt: str, reward: float, passed_guard: bool) -> null`: Updates rewards
- `RandomPromptSelector`: Basic random selection with history tracking.
- Parameters:
- `prompts: list[str]`: List of available prompts
- `history_size: int = 3`: Size of history to prevent cycles
- `CloudRLPromptSelector`: Cloud-based RL implementation with fallback.
- Parameters:
- `prompts: list[str]`: List of available prompts
- `api_url: str`: URL of RL service
@@ -75,9 +70,7 @@ The `probe_data` module is a core component of the Agentic Security project, res
- `history_size: int = 300`: Size of history
- `timeout: int = 5`: Request timeout
- `run_id: str = ""`: Unique run identifier
- `QLearningPromptSelector`: Local Q-learning implementation.
- Parameters:
- `prompts: list[str]`: List of available prompts
- `learning_rate: float = 0.1`: Learning rate
@@ -86,13 +79,11 @@ The `probe_data` module is a core component of the Agentic Security project, res
- `exploration_decay: float = 0.995`: Exploration decay rate
- `min_exploration: float = 0.01`: Minimum exploration rate
- `history_size: int = 300`: Size of history
- `Module`: Main class that uses CloudRLPromptSelector.
- Parameters:
- `prompt_groups: list[str]`: Groups of prompts
- `tools_inbox: asyncio.Queue`: Queue for tool communication
- `opts: dict = {}`: Configuration options
- **Module**: Main class that uses CloudRLPromptSelector.
- Parameters:
- `prompt_groups: list[str]`: Groups of prompts
- `tools_inbox: asyncio.Queue`: Queue for tool communication
- `opts: dict = {}`: Configuration options
## Usage Examples
@@ -119,10 +110,9 @@ from agentic_security.probe_data.modules.rl_model import QLearningPromptSelector
prompts = ["What is AI?", "Explain machine learning"]
selector = QLearningPromptSelector(prompts)
current_prompt = "What is AI?"
next_prompt = selector.select_next_prompt(current_prompt, passed_guard=True)
selector.update_rewards(current_prompt, next_prompt, reward=1.0, passed_guard=True)
next_prompt = selector.select_next_prompt(current_prompt, passed_guard=true)
selector.update_rewards(current_prompt, next_prompt, reward=1.0, passed_guard=true)
```
## Conclusion