Metadata-Version: 2.1
Name: azure-ai-evaluation
Version: 1.0.0b6
Summary: Microsoft Azure Evaluation Library for Python
Home-page: https://github.com/Azure/azure-sdk-for-python
Author: Microsoft Corporation
Author-email: azuresdkengsysadmins@microsoft.com
License: MIT License
Project-URL: Bug Reports, https://github.com/Azure/azure-sdk-for-python/issues
Project-URL: Source, https://github.com/Azure/azure-sdk-for-python
Keywords: azure,azure sdk
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: NOTICE.txt
Requires-Dist: promptflow-devkit>=1.15.0
Requires-Dist: promptflow-core>=1.15.0
Requires-Dist: pyjwt>=2.8.0
Requires-Dist: azure-identity>=1.16.0
Requires-Dist: azure-core>=1.30.2
Requires-Dist: nltk>=3.9.1
Provides-Extra: remote
Requires-Dist: azure-ai-inference>=1.0.0b4; extra == "remote"
Requires-Dist: promptflow-azure<2.0.0,>=1.15.0; extra == "remote"

# Azure AI Evaluation client library for Python

Use Azure AI Evaluation SDK to assess the performance of your generative AI applications. Generative AI application generations are quantitatively measured with mathematical based metrics, AI-assisted quality and safety metrics. Metrics are defined as `evaluators`. Built-in or custom evaluators can provide comprehensive insights into the application's capabilities and limitations.

Use Azure AI Evaluation SDK to:
- Evaluate existing data from generative AI applications
- Evaluate generative AI applications
- Evaluate by generating mathematical, AI-assisted quality and safety metrics

Azure AI SDK provides following to evaluate Generative AI Applications:
- [Evaluators][evaluators] - Generate scores individually or when used together with `evaluate` API.
- [Evaluate API][evaluate_api] - Python API to evaluate dataset or application using built-in or custom evaluators.

[Source code][source_code]
| [Package (PyPI)][evaluation_pypi]
| [API reference documentation][evaluation_ref_docs]
| [Product documentation][product_documentation]
| [Samples][evaluation_samples]


## Getting started

### Prerequisites

- Python 3.8 or later is required to use this package.
- [Optional] You must have [Azure AI Project][ai_project] or [Azure Open AI][azure_openai] to use AI-assisted evaluators

### Install the package

Install the Azure AI Evaluation SDK for Python with [pip][pip_link]:

```bash
pip install azure-ai-evaluation
```
If you want to track results in [AI Studio][ai_studio], install `remote` extra:
```python
pip install azure-ai-evaluation[remote]
```

## Key concepts

### Evaluators

Evaluators are custom or prebuilt classes or functions that are designed to measure the quality of the outputs from language models or generative AI applications.

#### Built-in evaluators

Built-in evaluators are out of box evaluators provided by Microsoft:
| Category  | Evaluator class                                                                                                                    |
|-----------|------------------------------------------------------------------------------------------------------------------------------------|
| [Performance and quality][performance_and_quality_evaluators] (AI-assisted)  | `GroundednessEvaluator`, `RelevanceEvaluator`, `CoherenceEvaluator`, `FluencyEvaluator`, `SimilarityEvaluator`, `RetrievalEvaluator` |
| [Performance and quality][performance_and_quality_evaluators] (NLP)  | `F1ScoreEvaluator`, `RougeScoreEvaluator`, `GleuScoreEvaluator`, `BleuScoreEvaluator`, `MeteorScoreEvaluator`|
| [Risk and safety][risk_and_safety_evaluators] (AI-assisted)    | `ViolenceEvaluator`, `SexualEvaluator`, `SelfHarmEvaluator`, `HateUnfairnessEvaluator`, `IndirectAttackEvaluator`, `ProtectedMaterialEvaluator`                                             |
| [Composite][composite_evaluators] | `QAEvaluator`, `ContentSafetyEvaluator`                                             |

For more in-depth information on each evaluator definition and how it's calculated, see [Evaluation and monitoring metrics for generative AI][evaluation_metrics].

```python
import os

from azure.ai.evaluation import evaluate, RelevanceEvaluator, ViolenceEvaluator, BleuScoreEvaluator

# NLP bleu score evaluator
bleu_score_evaluator = BleuScoreEvaluator()
result = bleu_score(
    response="Tokyo is the capital of Japan.",
    ground_truth="The capital of Japan is Tokyo."
)

# AI assisted quality evaluator
model_config = {
    "azure_endpoint": os.environ.get("AZURE_OPENAI_ENDPOINT"),
    "api_key": os.environ.get("AZURE_OPENAI_API_KEY"),
    "azure_deployment": os.environ.get("AZURE_OPENAI_DEPLOYMENT"),
}

relevance_evaluator = RelevanceEvaluator(model_config)
result = relevance_evaluator(
    query="What is the capital of Japan?",
    response="The capital of Japan is Tokyo."
)

# AI assisted safety evaluator
azure_ai_project = {
    "subscription_id": "<subscription_id>",
    "resource_group_name": "<resource_group_name>",
    "project_name": "<project_name>",
}

violence_evaluator = ViolenceEvaluator(azure_ai_project)
result = violence_evaluator(
    query="What is the capital of France?",
    response="Paris."
)
```

#### Custom evaluators

Built-in evaluators are great out of the box to start evaluating your application's generations. However you can build your own code-based or prompt-based evaluator to cater to your specific evaluation needs.

```python

# Custom evaluator as a function to calculate response length
def response_length(response, **kwargs):
    return len(response)

# Custom class based evaluator to check for blocked words
class BlocklistEvaluator:
    def __init__(self, blocklist):
        self._blocklist = blocklist

    def __call__(self, *, response: str, **kwargs):
        score = any([word in answer for word in self._blocklist])
        return {"score": score}

blocklist_evaluator = BlocklistEvaluator(blocklist=["bad, worst, terrible"])

result = response_length("The capital of Japan is Tokyo.")
result = blocklist_evaluator(answer="The capital of Japan is Tokyo.")

```

### Evaluate API
The package provides an `evaluate` API which can be used to run multiple evaluators together to evaluate generative AI application response.

#### Evaluate existing dataset

```python
from azure.ai.evaluation import evaluate

result = evaluate(
    data="data.jsonl", # provide your data here
    evaluators={
        "blocklist": blocklist_evaluator,
        "relevance": relevance_evaluator
    },
    # column mapping
    evaluator_config={
        "relevance": {
            "column_mapping": {
                "query": "${data.queries}"
                "ground_truth": "${data.ground_truth}"
                "response": "${outputs.response}"
            } 
        }
    }
    # Optionally provide your AI Studio project information to track your evaluation results in your Azure AI Studio project
    azure_ai_project = azure_ai_project,
    # Optionally provide an output path to dump a json of metric summary, row level data and metric and studio URL
    output_path="./evaluation_results.json"
)
```
For more details refer to [Evaluate on test dataset using evaluate()][evaluate_dataset]

#### Evaluate generative AI application
```python
from askwiki import askwiki

result = evaluate(
    data="data.jsonl",
    target=askwiki,
    evaluators={
        "relevance": relevance_eval
    },
    evaluator_config={
        "default": {
            "column_mapping": {
                "query": "${data.queries}"
                "context": "${outputs.context}"
                "response": "${outputs.response}"
            } 
        }
    }
)
```
Above code snippet refers to askwiki application in this [sample][evaluate_app].

For more details refer to [Evaluate on a target][evaluate_target]

### Simulator


Simulators allow users to generate synthentic data using their application. Simulator expects the user to have a callback method that invokes
their AI application.

#### Simulating with a Prompty

```yaml
---
name: ApplicationPrompty
description: Simulates an application
model:
  api: chat
  parameters:
    temperature: 0.0
    top_p: 1.0
    presence_penalty: 0
    frequency_penalty: 0
    response_format:
      type: text

inputs:
  conversation_history:
    type: dict

---
system:
You are a helpful assistant and you're helping with the user's query. Keep the conversation engaging and interesting.

Output with a string that continues the conversation, responding to the latest message from the user, given the conversation history:
{{ conversation_history }}

```

Query Response generaing prompty for gpt-4o with `json_schema` support
Use this file as an override.
```yaml
---
name: TaskSimulatorQueryResponseGPT4o
description: Gets queries and responses from a blob of text
model:
  api: chat
  parameters:
    temperature: 0.0
    top_p: 1.0
    presence_penalty: 0
    frequency_penalty: 0
    response_format:
      type: json_schema
      json_schema:
        name: QRJsonSchema
        schema: 
          type: object
          properties:
            items:
              type: array
              items:
                type: object
                properties:
                  q:
                    type: string
                  r:
                    type: string
                required:
                  - q
                  - r

inputs:
  text:
    type: string
  num_queries:
    type: integer


---
system:
You're an AI that helps in preparing a Question/Answer quiz from Text for "Who wants to be a millionaire" tv show
Both Questions and Answers MUST BE extracted from given Text
Frame Question in a way so that Answer is RELEVANT SHORT BITE-SIZED info from Text
RELEVANT info could be: NUMBER, DATE, STATISTIC, MONEY, NAME
A sentence should contribute multiple QnAs if it has more info in it
Answer must not be more than 5 words
Answer must be picked from Text as is
Question should be as descriptive as possible and must include as much context as possible from Text
Output must always have the provided number of QnAs
Output must be in JSON format.
Output must have {{num_queries}} objects in the format specified below. Any other count is unacceptable.
Text:
<|text_start|>
On January 24, 1984, former Apple CEO Steve Jobs introduced the first Macintosh. In late 2003, Apple had 2.06 percent of the desktop share in the United States.
Some years later, research firms IDC and Gartner reported that Apple's market share in the U.S. had increased to about 6%.
<|text_end|>
Output with 5 QnAs:
{
    "qna": [{
        "q": "When did the former Apple CEO Steve Jobs introduced the first Macintosh?",
        "r": "January 24, 1984"
    },
    {
        "q": "Who was the former Apple CEO that introduced the first Macintosh on January 24, 1984?",
        "r": "Steve Jobs"
    },
    {
        "q": "What percent of the desktop share did Apple have in the United States in late 2003?",
        "r": "2.06 percent"
    },
    {
        "q": "What were the research firms that reported on Apple's market share in the U.S.?",
        "r": "IDC and Gartner"
    },
    {
        "q": "What was the percentage increase of Apple's market share in the U.S., as reported by research firms IDC and Gartner?",
        "r": "6%"
    }]
}
Text:
<|text_start|>
{{ text }}
<|text_end|>
Output with {{ num_queries }} QnAs:
```

Application code:

```python
import json
import asyncio
from typing import Any, Dict, List, Optional
from azure.ai.evaluation.simulator import Simulator
from promptflow.client import load_flow
import os
import wikipedia

# Set up the model configuration without api_key, using DefaultAzureCredential
model_config = {
    "azure_endpoint": os.environ.get("AZURE_OPENAI_ENDPOINT"),
    "azure_deployment": os.environ.get("AZURE_DEPLOYMENT"),
    # not providing key would make the SDK pick up `DefaultAzureCredential`
    # use "api_key": "<your API key>"
    "api_version": "2024-08-01-preview" # keep this for gpt-4o
}

# Use Wikipedia to get some text for the simulation
wiki_search_term = "Leonardo da Vinci"
wiki_title = wikipedia.search(wiki_search_term)[0]
wiki_page = wikipedia.page(wiki_title)
text = wiki_page.summary[:1000]

def method_to_invoke_application_prompty(query: str, messages_list: List[Dict], context: Optional[Dict]):
    try:
        current_dir = os.path.dirname(__file__)
        prompty_path = os.path.join(current_dir, "application.prompty")
        _flow = load_flow(
            source=prompty_path,
            model=model_config,
            credential=DefaultAzureCredential()
        )
        response = _flow(
            query=query,
            context=context,
            conversation_history=messages_list
        )
        return response
    except Exception as e:
        print(f"Something went wrong invoking the prompty: {e}")
        return "something went wrong"

async def callback(
    messages: Dict[str, List[Dict]],
    stream: bool = False,
    session_state: Any = None,  # noqa: ANN401
    context: Optional[Dict[str, Any]] = None,
) -> dict:
    messages_list = messages["messages"]
    # Get the last message from the user
    latest_message = messages_list[-1]
    query = latest_message["content"]
    # Call your endpoint or AI application here
    response = method_to_invoke_application_prompty(query, messages_list, context)
    # Format the response to follow the OpenAI chat protocol format
    formatted_response = {
        "content": response,
        "role": "assistant",
        "context": "",
    }
    messages["messages"].append(formatted_response)
    return {"messages": messages["messages"], "stream": stream, "session_state": session_state, "context": context}

async def main():
    simulator = Simulator(model_config=model_config)
    current_dir = os.path.dirname(__file__)
    query_response_override_for_latest_gpt_4o = os.path.join(current_dir, "TaskSimulatorQueryResponseGPT4o.prompty")
    outputs = await simulator(
        target=callback,
        text=text,
        query_response_generating_prompty=query_response_override_for_latest_gpt_4o, # use this only with latest gpt-4o
        num_queries=2,
        max_conversation_turns=1,
        user_persona=[
            f"I am a student and I want to learn more about {wiki_search_term}",
            f"I am a teacher and I want to teach my students about {wiki_search_term}"
        ],
    )
    print(json.dumps(outputs, indent=2))

if __name__ == "__main__":
    # Ensure that the following environment variables are set in your environment:
    # AZURE_OPENAI_ENDPOINT and AZURE_DEPLOYMENT
    # Example:
    # os.environ["AZURE_OPENAI_ENDPOINT"] = "https://your-endpoint.openai.azure.com/"
    # os.environ["AZURE_DEPLOYMENT"] = "your-deployment-name"
    asyncio.run(main())
    print("done!")

```

#### Adversarial Simulator

```python
from azure.ai.evaluation.simulator import AdversarialSimulator, AdversarialScenario
from azure.identity import DefaultAzureCredential
from typing import Any, Dict, List, Optional
import asyncio


azure_ai_project = {
    "subscription_id": <subscription_id>,
    "resource_group_name": <resource_group_name>,
    "project_name": <project_name>
}

async def callback(
    messages: List[Dict],
    stream: bool = False,
    session_state: Any = None,
    context: Dict[str, Any] = None
) -> dict:
    messages_list = messages["messages"]
    # get last message
    latest_message = messages_list[-1]
    query = latest_message["content"]
    context = None
    if 'file_content' in messages["template_parameters"]:
        query += messages["template_parameters"]['file_content']
    # the next few lines explains how to use the AsyncAzureOpenAI's chat.completions
    # to respond to the simulator. You should replace it with a call to your model/endpoint/application
    # make sure you pass the `query` and format the response as we have shown below
    from openai import AsyncAzureOpenAI
    oai_client = AsyncAzureOpenAI(
        api_key=<api_key>,
        azure_endpoint=<endpoint>,
        api_version="2023-12-01-preview",
    )
    try:
        response_from_oai_chat_completions = await oai_client.chat.completions.create(messages=[{"content": query, "role": "user"}], model="gpt-4", max_tokens=300)
    except Exception as e:
        print(f"Error: {e}")
        # to continue the conversation, return the messages, else you can fail the adversarial with an exception
        message = {
            "content": "Something went wrong. Check the exception e for more details.",
            "role": "assistant",
            "context": None,
        }
        messages["messages"].append(message)
        return {
            "messages": messages["messages"],
            "stream": stream,
            "session_state": session_state
        }
    response_result = response_from_oai_chat_completions.choices[0].message.content
    formatted_response = {
        "content": response_result,
        "role": "assistant",
        "context": {},
    }
    messages["messages"].append(formatted_response)
    return {
        "messages": messages["messages"],
        "stream": stream,
        "session_state": session_state,
        "context": context
    }

```

#### Adversarial QA

```python
scenario = AdversarialScenario.ADVERSARIAL_QA
simulator = AdversarialSimulator(azure_ai_project=azure_ai_project, credential=DefaultAzureCredential())

outputs = asyncio.run(
    simulator(
        scenario=scenario,
        max_conversation_turns=1,
        max_simulation_results=3,
        target=callback
    )
)

print(outputs.to_eval_qr_json_lines())
```
#### Direct Attack Simulator

```python
scenario = AdversarialScenario.ADVERSARIAL_QA
simulator = DirectAttackSimulator(azure_ai_project=azure_ai_project, credential=DefaultAzureCredential())

outputs = asyncio.run(
    simulator(
        scenario=scenario,
        max_conversation_turns=1,
        max_simulation_results=2,
        target=callback
    )
)

print(outputs)
```

## Examples

In following section you will find examples of:
- [Evaluate an application][evaluate_app]
- [Evaluate different models][evaluate_models]
- [Custom Evaluators][custom_evaluators]

More examples can be found [here][evaluate_samples].

## Troubleshooting

### General

Please refer to [troubleshooting][evaluation_tsg] for common issues.

### Logging

This library uses the standard
[logging][python_logging] library for logging.
Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO
level.

Detailed DEBUG level logging, including request/response bodies and unredacted
headers, can be enabled on a client with the `logging_enable` argument.

See full SDK logging documentation with examples [here][sdk_logging_docs].

## Next steps

- View our [samples][evaluation_samples].
- View our [documentation][product_documentation]

## Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit [cla.microsoft.com][cla].

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct][code_of_conduct]. For more information see the [Code of Conduct FAQ][coc_faq] or contact [opencode@microsoft.com][coc_contact] with any additional questions or comments.

<!-- LINKS -->

[source_code]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/evaluation/azure-ai-evaluation
[evaluation_pypi]: https://pypi.org/project/azure-ai-evaluation/
[evaluation_ref_docs]: https://learn.microsoft.com/python/api/azure-ai-evaluation/azure.ai.evaluation?view=azure-python-preview
[evaluation_samples]: https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios
[product_documentation]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk
[python_logging]: https://docs.python.org/3/library/logging.html
[sdk_logging_docs]: https://docs.microsoft.com/azure/developer/python/azure-sdk-logging
[azure_core_readme]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md
[pip_link]: https://pypi.org/project/pip/
[azure_core_ref_docs]: https://aka.ms/azsdk-python-core-policies
[azure_core]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/core/azure-core/README.md
[azure_identity]: https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/identity/azure-identity
[cla]: https://cla.microsoft.com
[code_of_conduct]: https://opensource.microsoft.com/codeofconduct/
[coc_faq]: https://opensource.microsoft.com/codeofconduct/faq/
[coc_contact]: mailto:opencode@microsoft.com
[evaluate_target]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#evaluate-on-a-target
[evaluate_dataset]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#evaluate-on-test-dataset-using-evaluate
[evaluators]: https://learn.microsoft.com/python/api/azure-ai-evaluation/azure.ai.evaluation?view=azure-python-preview
[evaluate_api]: https://learn.microsoft.com/python/api/azure-ai-evaluation/azure.ai.evaluation?view=azure-python-preview#azure-ai-evaluation-evaluate
[evaluate_app]: https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/evaluate/evaluate_app
[evaluation_tsg]: https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/evaluation/azure-ai-evaluation/TROUBLESHOOTING.md
[ai_studio]: https://learn.microsoft.com/azure/ai-studio/what-is-ai-studio
[ai_project]: https://learn.microsoft.com/azure/ai-studio/how-to/create-projects?tabs=ai-studio
[azure_openai]: https://learn.microsoft.com/azure/ai-services/openai/
[evaluate_models]: https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/evaluate/evaluate_endpoints
[custom_evaluators]: https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/evaluate/evaluate_custom
[evaluate_samples]: https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/evaluate
[evaluation_metrics]: https://learn.microsoft.com/azure/ai-studio/concepts/evaluation-metrics-built-in
[performance_and_quality_evaluators]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#performance-and-quality-evaluators
[risk_and_safety_evaluators]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#risk-and-safety-evaluators
[composite_evaluators]: https://learn.microsoft.com/azure/ai-studio/how-to/develop/evaluate-sdk#composite-evaluators

# Release History

## 1.0.0b6 (Unreleased)

### Features Added

### Breaking Changes
- The `parallel` parameter has been removed from composite evaluators: `QAEvaluator`, `ContentSafetyChatEvaluator`, and `ContentSafetyMultimodalEvaluator`. To control evaluator parallelism, you can now use the `_parallel` keyword argument, though please note that this private parameter may change in the future.
- Parameters `query_response_generating_prompty_kwargs` and `user_simulator_prompty_kwargs` have been renamed to `query_response_generating_prompty_options` and `user_simulator_prompty_options` in the Simulator's __call__ method.

### Bugs Fixed
- Fixed an issue where the `output_path` parameter in the `evaluate` API did not support relative path.
- Output of adversarial simulators are of type `JsonLineList` and the helper function `to_eval_qr_json_lines` now outputs context from both user and assistant turns along with `category` if it exists in the conversation
- Fixed an issue where during long-running simulations, API token expires causing "Forbidden" error. Instead, users can now set an environment variable `AZURE_TOKEN_REFRESH_INTERVAL` to refresh the token more frequently to prevent expiration and ensure continuous operation of the simulation.
- Fixed an issue with the `ContentSafetyEvaluator` that caused parallel execution of sub-evaluators to fail. Parallel execution is now enabled by default again, but can still be disabled via the '_parallel' boolean keyword argument during class initialization.
- Fix `evaluate` function not producing aggregated metrics if ANY values to be aggregated were None, NaN, or
otherwise difficult to process. Such values are ignored fully, so the aggregated metric of `[1, 2, 3, NaN]`
would be 2, not 1.5.

### Other Changes
- Refined error messages for serviced-based evaluators and simulators.
- Tracing has been disabled due to Cosmos DB initialization issue.
- Introduced environment variable `AI_EVALS_DISABLE_EXPERIMENTAL_WARNING` to disable the warning message for experimental features.
- Changed the randomization pattern for `AdversarialSimulator` such that there is an almost equal number of Adversarial harm categories (e.g. Hate + Unfairness, Self-Harm, Violence, Sex) represented in the  `AdversarialSimulator` outputs. Previously, for 200 `max_simulation_results` a user might see 140 results belonging to the 'Hate + Unfairness' category and 40 results belonging to the 'Self-Harm' category. Now, user will see 50 results for each of Hate + Unfairness, Self-Harm, Violence, and Sex.
- For the `DirectAttackSimulator`, the prompt templates used to generate simulated outputs for each Adversarial harm category will no longer be in a randomized order by default. To override this behavior, pass `randomize_order=True` when you call the `DirectAttackSimulator`, for example:
```python
adversarial_simulator = DirectAttackSimulator(azure_ai_project=azure_ai_project, credential=DefaultAzureCredential())
outputs = asyncio.run(
    adversarial_simulator(
        scenario=scenario,
        target=callback,
        randomize_order=True
    )
)
```

## 1.0.0b5 (2024-10-28)

### Features Added
- Added `GroundednessProEvaluator`, which is a service-based evaluator for determining response groundedness.
- Groundedness detection in Non Adversarial Simulator via query/context pairs
```python
import importlib.resources as pkg_resources
package = "azure.ai.evaluation.simulator._data_sources"
resource_name = "grounding.json"
custom_simulator = Simulator(model_config=model_config)
conversation_turns = []
with pkg_resources.path(package, resource_name) as grounding_file:
    with open(grounding_file, "r") as file:
        data = json.load(file)
for item in data:
    conversation_turns.append([item])
outputs = asyncio.run(custom_simulator(
    target=callback,
    conversation_turns=conversation_turns,
    max_conversation_turns=1,
))
```
- Adding evaluator for multimodal use cases

### Breaking Changes
- Renamed environment variable `PF_EVALS_BATCH_USE_ASYNC` to `AI_EVALS_BATCH_USE_ASYNC`.
- `RetrievalEvaluator` now requires a `context` input in addition to `query` in single-turn evaluation.
- `RelevanceEvaluator` no longer takes `context` as an input. It now only takes `query` and `response` in single-turn evaluation.
- `FluencyEvaluator` no longer takes `query` as an input. It now only takes `response` in single-turn evaluation.
- AdversarialScenario enum does not include `ADVERSARIAL_INDIRECT_JAILBREAK`, invoking IndirectJailbreak or XPIA should be done with `IndirectAttackSimulator`
- Outputs of `Simulator` and `AdversarialSimulator` previously had `to_eval_qa_json_lines` and now has `to_eval_qr_json_lines`. Where `to_eval_qa_json_lines` had:
```json
{"question": <user_message>, "answer": <assistant_message>}
```
`to_eval_qr_json_lines` now has:
```json
{"query": <user_message>, "response": assistant_message}
```

### Bugs Fixed
- Non adversarial simulator works with `gpt-4o` models using the `json_schema` response format
- Fixed an issue where the `evaluate` API would fail with "[WinError 32] The process cannot access the file because it is being used by another process" when venv folder and target function file are in the same directory.
- Fix evaluate API failure when `trace.destination` is set to `none`
- Non adversarial simulator now accepts context from the callback

### Other Changes
- Improved error messages for the `evaluate` API by enhancing the validation of input parameters. This update provides more detailed and actionable error descriptions.
- `GroundednessEvaluator` now supports `query` as an optional input in single-turn evaluation. If `query` is provided, a different prompt template will be used for the evaluation.
- To align with our support of a diverse set of models, the following evaluators will now have a new key in their result output without the `gpt_` prefix. To maintain backwards compatibility, the old key with the `gpt_` prefix will still be present in the output; however, it is recommended to use the new key moving forward as the old key will be deprecated in the future.
  - `CoherenceEvaluator`
  - `RelevanceEvaluator`
  - `FluencyEvaluator`
  - `GroundednessEvaluator`
  - `SimilarityEvaluator`
  - `RetrievalEvaluator`
- The following evaluators will now have a new key in their result output including LLM reasoning behind the score. The new key will follow the pattern "<metric_name>_reason". The reasoning is the result of a more detailed prompt template being used to generate the LLM response. Note that this requires the maximum number of tokens used to run these evaluators to be increased.

    | Evaluator | New `max_token` for Generation |
    | --- | --- |
    | `CoherenceEvaluator` | 800 |
    | `RelevanceEvaluator` | 800 |
    | `FluencyEvaluator` | 800 |
    | `GroundednessEvaluator` | 800 |
    | `RetrievalEvaluator` | 1600 |
- Improved the error message for storage access permission issues to provide clearer guidance for users.

## 1.0.0b4 (2024-10-16)

### Breaking Changes

- Removed `numpy` dependency. All NaN values returned by the SDK have been changed to from `numpy.nan` to `math.nan`.
- `credential` is now required to be passed in for all content safety evaluators and `ProtectedMaterialsEvaluator`. `DefaultAzureCredential` will no longer be chosen if a credential is not passed.
- Changed package extra name from "pf-azure" to "remote".

### Bugs Fixed
- Adversarial Conversation simulations would fail with `Forbidden`. Added logic to re-fetch token in the exponential retry logic to retrive RAI Service response.
- Fixed an issue where the Evaluate API did not fail due to missing inputs when the target did not return columns required by the evaluators.

### Other Changes
- Enhance the error message to provide clearer instruction when required packages for the remote tracking feature are missing.
- Print the per-evaluator run summary at the end of the Evaluate API call to make troubleshooting row-level failures easier.

## 1.0.0b3 (2024-10-01)

### Features Added

- Added `type` field to `AzureOpenAIModelConfiguration` and `OpenAIModelConfiguration`
- The following evaluators now support `conversation` as an alternative input to their usual single-turn inputs:
  - `ViolenceEvaluator`
  - `SexualEvaluator`
  - `SelfHarmEvaluator`
  - `HateUnfairnessEvaluator`
  - `ProtectedMaterialEvaluator`
  - `IndirectAttackEvaluator`
  - `CoherenceEvaluator`
  - `RelevanceEvaluator`
  - `FluencyEvaluator`
  - `GroundednessEvaluator`
- Surfaced `RetrievalScoreEvaluator`, formally an internal part of `ChatEvaluator` as a standalone conversation-only evaluator.

### Breaking Changes

- Removed `ContentSafetyChatEvaluator` and `ChatEvaluator`
- The `evaluator_config` parameter of `evaluate` now maps in evaluator name to a dictionary `EvaluatorConfig`, which is a `TypedDict`. The
`column_mapping` between `data` or `target` and evaluator field names should now be specified inside this new dictionary:

Before:
```python
evaluate(
    ...,
    evaluator_config={
        "hate_unfairness": {
            "query": "${data.question}",
            "response": "${data.answer}",
        }
    },
    ...
)
```

After
```python
evaluate(
    ...,
    evaluator_config={
        "hate_unfairness": {
            "column_mapping": {
                "query": "${data.question}",
                "response": "${data.answer}",
             }
        }
    },
    ...
)
```

- Simulator now requires a model configuration to call the prompty instead of an Azure AI project scope. This enables the usage of simulator with Entra ID based auth.
Before:
```python
azure_ai_project = {
    "subscription_id": os.environ.get("AZURE_SUBSCRIPTION_ID"),
    "resource_group_name": os.environ.get("RESOURCE_GROUP"),
    "project_name": os.environ.get("PROJECT_NAME"),
}
sim = Simulator(azure_ai_project=azure_ai_project, credentails=DefaultAzureCredentials())
```
After:
```python
model_config = {
    "azure_endpoint": os.environ.get("AZURE_OPENAI_ENDPOINT"),
    "azure_deployment": os.environ.get("AZURE_DEPLOYMENT"),
}
sim = Simulator(model_config=model_config)
```
If `api_key` is not included in the `model_config`, the prompty runtime in `promptflow-core` will pick up `DefaultAzureCredential`.

### Bugs Fixed

- Fixed issue where Entra ID authentication was not working with `AzureOpenAIModelConfiguration`

## 1.0.0b2 (2024-09-24)

### Breaking Changes

- `data` and `evaluators` are now required keywords in `evaluate`.

## 1.0.0b1 (2024-09-20)

### Breaking Changes

- The `synthetic` namespace has been renamed to `simulator`, and sub-namespaces under this module have been removed
- The `evaluate` and `evaluators` namespaces have been removed, and everything previously exposed in those modules has been added to the root namespace `azure.ai.evaluation`
- The parameter name `project_scope` in content safety evaluators have been renamed to `azure_ai_project` for consistency with evaluate API and simulators.
- Model configurations classes are now of type `TypedDict` and are exposed in the `azure.ai.evaluation` module instead of coming from `promptflow.core`.
- Updated the parameter names for `question` and `answer` in built-in evaluators to more generic terms: `query` and `response`.

### Features Added

- First preview
- This package is port of `promptflow-evals`. New features will be added only to this package moving forward.
- Added a `TypedDict` for `AzureAIProject` that allows for better intellisense and type checking when passing in project information
