mirror of
https://github.com/msoedov/agentic_security.git
synced 2026-06-24 14:19:55 +02:00
Alexander Myasoedov
124 lines
3.7 KiB
Markdown
124 lines
3.7 KiB
Markdown
# Operator Module
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The `operator.py` module provides tools for managing and operating on datasets using an agent-based approach. It is designed to facilitate the execution of operations on datasets through a structured and validated process.
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## Classes
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### AgentSpecification
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Defines the specification for an LLM/agent:
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- `name`: Name of the LLM/agent
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- `version`: Version of the LLM/agent
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- `description`: Description of the LLM/agent
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- `capabilities`: List of capabilities
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- `configuration`: Configuration settings
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### OperatorToolBox
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Main class for dataset operations:
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- `__init__(spec: AgentSpecification, datasets: list[dict[str, Any]])`: Initialize with agent spec and datasets. This sets up the toolbox with the necessary specifications and datasets for operation.
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- `get_spec()`: Get the agent specification. Returns the `AgentSpecification` object associated with the toolbox.
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- `get_datasets()`: Get the datasets. Returns a list of datasets that the toolbox operates on.
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- `validate()`: Validate the toolbox. Checks if the toolbox is correctly set up with valid specifications and datasets.
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- `stop()`: Stop the toolbox. Halts any ongoing operations within the toolbox.
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- `run()`: Run the toolbox. Initiates the execution of operations as defined in the toolbox.
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- `get_results()`: Get operation results. Retrieves the results of operations performed by the toolbox.
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- `get_failures()`: Get failures. Provides a list of any failures encountered during operations.
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- `run_operation(operation: str)`: Run a specific operation. Executes a given operation on the datasets, returning the result or failure message.
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## Agent Tools
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The `dataset_manager_agent` provides these tools:
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### validate_toolbox
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Validates the OperatorToolBox:
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```python
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@dataset_manager_agent.tool
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async def validate_toolbox(ctx: RunContext[OperatorToolBox]) -> str
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```
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### execute_operation
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Executes an operation on a dataset:
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```python
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@dataset_manager_agent.tool
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async def execute_operation(ctx: RunContext[OperatorToolBox], operation: str) -> str
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```
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### retrieve_results
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Retrieves operation results:
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```python
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@dataset_manager_agent.tool
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async def retrieve_results(ctx: RunContext[OperatorToolBox]) -> str
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```
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### retrieve_failures
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Retrieves failures:
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```python
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@dataset_manager_agent.tool
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async def retrieve_failures(ctx: RunContext[OperatorToolBox]) -> str
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```
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## Usage Examples
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### Initializing the OperatorToolBox
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To initialize the `OperatorToolBox`, you need to provide an `AgentSpecification` and a list of datasets:
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```python
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spec = AgentSpecification(
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name="GPT-4",
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version="4.0",
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description="A powerful language model",
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capabilities=["text-generation", "question-answering"],
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configuration={"max_tokens": 100},
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)
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datasets = [{"name": "dataset1"}, {"name": "dataset2"}]
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toolbox = OperatorToolBox(spec=spec, datasets=datasets)
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```
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### Synchronous Usage
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```python
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def run_dataset_manager_agent_sync():
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prompts = [
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"Validate the toolbox.",
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"Execute operation on 'dataset2'.",
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"Retrieve the results.",
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"Retrieve any failures."
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]
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for prompt in prompts:
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result = dataset_manager_agent.run_sync(prompt, deps=toolbox)
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print(f"Response: {result.data}")
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```
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### Asynchronous Usage
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```python
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async def run_dataset_manager_agent_async():
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prompts = [
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"Validate the toolbox.",
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"Execute operation on 'dataset2'.",
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"Retrieve the results.",
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"Retrieve any failures."
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]
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for prompt in prompts:
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result = await dataset_manager_agent.run(prompt, deps=toolbox)
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print(f"Response: {result.data}")
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```
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These updates provide a more detailed and comprehensive understanding of the `operator.py` module, its classes, and its usage.
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