> ## Documentation Index
> Fetch the complete documentation index at: https://neuraltrust-92b43583-develop.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Azure AI Search

> Connect Azure AI Search as a TrustTest knowledge base

`AzureKnowledgeBase` reads documents from an Azure AI Search index. Import it from the connector submodule — it is **not** re-exported from `trusttest.knowledge_base`.

```python theme={null}
from trusttest.knowledge_base.azure_search import AzureKnowledgeBase
```

The constructor takes `credentials` (`TokenCredential` or `AzureKeyCredential`), not `key=`.

## Installation

```bash theme={null}
uv add "trusttest[rag-azure]"
```

`trusttest[azure]` covers Azure identity / Azure OpenAI only. Use `rag-azure` for this connector.

## Constructor

| Parameter          | Type                                    | Description                                      |
| ------------------ | --------------------------------------- | ------------------------------------------------ |
| `credentials`      | `TokenCredential \| AzureKeyCredential` | Required. API key or Azure AD credential         |
| `service_endpoint` | `str`                                   | Or `AZURE_SEARCH_SERVICE_ENDPOINT`               |
| `index_name`       | `str`                                   | Or `AZURE_SEARCH_INDEX_NAME`                     |
| `fields_mapping`   | `dict[str, str]`                        | Defaults to `{"id": "id", "content": "content"}` |
| `language`         | `LanguageType`                          | Optional; otherwise detected from documents      |
| `embeddings_model` | `EmbeddingsModel`                       | From `set_config` if omitted                     |
| `llm_client`       | `LLMClient`                             | Topic summarizer                                 |
| `seed_topics`      | `list[str]`                             | Skip auto-clustering when set                    |
| `max_doc_count`    | `int`                                   | Cap on retrieved documents                       |

## Example

```python theme={null}
import os

from azure.core.credentials import AzureKeyCredential
from dotenv import load_dotenv

from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluators import AnswerRelevanceEvaluator
from trusttest.knowledge_base.azure_search import AzureKnowledgeBase
from trusttest.probes.rag import RAGProbe, BenignQuestion
from trusttest.targets.testing import DummyTarget

load_dotenv(override=True)

knowledge_base = AzureKnowledgeBase(
    credentials=AzureKeyCredential(os.getenv("AZURE_SEARCH_KEY")),
    service_endpoint=os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT"),
    index_name=os.getenv("AZURE_SEARCH_INDEX_NAME"),
    fields_mapping={"content": "chunk", "id": "chunk_id"},
    language="Spanish",
)

probe = RAGProbe(
    target=DummyTarget(),
    knowledge_base=knowledge_base,
    num_questions=2,
    question_types=[BenignQuestion.SIMPLE],
)

scenario = EvaluationScenario(
    name="RAG Functional",
    evaluator_suite=EvaluatorSuite(
        evaluators=[AnswerRelevanceEvaluator()],
        criteria="any_fail",
    ),
)

test_set = probe.get_test_set()
results = scenario.evaluate(test_set)
results.display_summary()
```