# Python SDK

**1. Environment Setup**

```python
os.environ["RAGAAI_CATALYST_BASE_URL"] = "https://catalyst.raga.ai/api""
os.environ["OPENAI_API_KEY"] = "Your LLM API key"
```

* Configures the environment.
  * `RAGAAI_CATALYST_BASE_URL`: The base URL of the RagaAI Catalyst API for interaction.
  * `OPENAI_API_KEY`: Key to authenticate OpenAI-based LLM interactions.

***

**2. Initializing the RagaAICatalyst Client**

```python
catalyst = RagaAICatalyst(
    access_key="Generate access key form settings",
    secret_key="Generate access key form settings",
)
```

* Creates an authenticated client to interact with RagaAI Catalyst.
  * `access_key` and `secret_key`: Credentials to access RagaAI Catalyst.

***

**3. Initializing the Guardrails Manager**

```python
gdm = GuardrailsManager(project_name="Project Name")
```

* **Purpose**: Sets up a guardrails manager for managing guardrail-related configurations.
  * `project_name`: Links the guardrails to a specific project .

***

**4. Listing Available Guardrails**

```python
guardrails_list = gdm.list_guardrails()
print('guardrails_list:', guardrails_list)
```

* Retrieves a list of all guardrails configured in the project.
* **Output**: A list of guardrails available for use.

***

**5. Listing Fail Conditions**

```python
fail_conditions = gdm.list_fail_condition()
print('fail_conditions;', fail_conditions)
```

* Retrieves the conditions under which guardrails will flag a failure.
* **Output**: A list of fail conditions (e.g., `ALL_FAIL`, `SOME_FAIL`, etc.).

***

**6. Retrieving Deployment IDs**

```python
pdeployment_list = gdm.list_deployment_ids()
print('deployment_list:', deployment_list)
```

* Lists all deployment IDs associated with guardrails.
* **Output**: A list of deployment IDs, each representing a configuration of guardrails.

***

**7. Fetching Deployment Details**

```python
deployment_id_detail = gdm.get_deployment(17)
print('deployment_id_detail:', deployment_id_detail)
```

* Retrieves details of a specific deployment by its ID (`17` in this example).
* **Output**: Details of the deployment, including associated guardrails and configurations.

***

**8. Adding Guardrails to a Deployment**

```python
guardrails_config = {"guardrailFailConditions": ["FAIL"],
                     "deploymentFailCondition": "ALL_FAIL",
                     "alternateResponse": "Your alternate response"}
```

* Configures guardrail behavior when conditions fail.
  * `guardrailFailConditions`: Triggers guardrails when specific conditions are met.
  * `deploymentFailCondition`: Aggregates multiple failures (`ALL_FAIL` requires all guardrails to fail).
  * `alternateResponse`: Fallback response in case of guardrail-triggered failures.

```python
guardrails = [
    {
      "displayName": "Response_Evaluator",
      "name": "Response Evaluator",
      "config": {
          "mappings": [{"schemaName": "Text", "variableName": "Response"}],
          "params": {
              "isActive": {"value": False},
              "isHighRisk": {"value": True},
              "threshold": {"eq": 0},
              "competitors": {"value": ["Google", "Amazon"]}
          }
      }
    },
    {
      "displayName": "Regex_Check",
      "name": "Regex Check",
      "config": {
          "mappings": [{"schemaName": "Text", "variableName": "Response"}],
          "params": {
              "isActive": {"value": False},
              "isHighRisk": {"value": True},
              "threshold": {"lt1": 1}
          }
      }
    }
]
```

* **Purpose**: Defines guardrail configurations.
  * `displayName`: A user-friendly name for the guardrail.
  * `name`: The internal name of the guardrail.
  * `config`: Contains mappings, parameters, and settings for the guardrail's logic.

***

**9. Initializing the GuardExecutor**

```python
executor = GuardExecutor(17, gdm, field_map={'context': 'document'})
```

* **Purpose**: Initializes the executor to run evaluations using the deployment id.
  * 17: Deployment ID of the guardrails to apply.
  * `field_map`: Maps input fields (e.g., `context`) to expected variables (`document`).

***

**10. Preparing Input for Evaluation**

```python
message = {'role': 'user', 'content': 'What is the capital of France?'}
prompt_params = {'document': 'Paris is not the capital of france'}
model_params = {'temperature': .7, 'model': 'gpt-4o-mini'}
llm_caller = 'litellm'
```

* Supplies input for evaluation.
  * `message`: Represents the user query.
  * `prompt_params`: Contextual data provided to the model.
  * `model_params`: Configuration for the LLM response generation.
  * `llm_caller`: Specifies the API or library to call the LLM.

***

**11. Executing the Guardrails**

```python
executor([message], prompt_params, model_params, llm_caller)
```

* Runs the guardrails evaluation with the given inputs.