Updating doc strings for evaluate API (#3283)

# Description

Please add an informative description that covers that changes made by
the pull request and link all relevant issues.

# All Promptflow Contribution checklist:
- [ ] **The pull request does not introduce [breaking changes].**
- [ ] **CHANGELOG is updated for new features, bug fixes or other
significant changes.**
- [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).**
- [ ] **Create an issue and link to the pull request to get dedicated
review from promptflow team. Learn more: [suggested
workflow](../CONTRIBUTING.md#suggested-workflow).**

## General Guidelines and Best Practices
- [ ] Title of the pull request is clear and informative.
- [ ] There are a small number of commits, each of which have an
informative message. This means that previously merged commits do not
appear in the history of the PR. For more information on cleaning up the
commits in your PR, [see this
page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md).

### Testing Guidelines
- [ ] Pull request includes test coverage for the included changes.

src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py

@@ -305,12 +305,13 @@ def evaluate(
     target: Optional[Callable] = None,
     data: Optional[str] = None,
     evaluators: Optional[Dict[str, Callable]] = None,
-    evaluator_config: Optional[Dict[str, Dict[str, str]]] = {},
+    evaluator_config: Optional[Dict[str, Dict[str, str]]] = None,
     azure_ai_project: Optional[Dict] = None,
     output_path: Optional[str] = None,
     **kwargs,
 ):
-    """Evaluates target or data with built-in evaluation metrics
+    """Evaluates target or data with built-in or custom evaluators. If both target and data are provided,
+        data will be run through target function and then results will be evaluated.
 
     :keyword evaluation_name: Display name of the evaluation.
     :paramtype evaluation_name: Optional[str]
@@ -319,21 +320,71 @@ def evaluate(
     :keyword data: Path to the data to be evaluated or passed to target if target is set.
         Only .jsonl format files are supported.  `target` and `data` both cannot be None
     :paramtype data: Optional[str]
-    :keyword evaluator_config: Configuration for evaluators.
+    :keyword evaluators: Evaluators to be used for evaluation. It should be a dictionary with key as alias for evaluator
+        and value as the evaluator function.
+    :paramtype evaluators: Optional[Dict[str, Callable]
+    :keyword evaluator_config: Configuration for evaluators. The configuration should be a dictionary with evaluator
+        names as keys and a dictionary of column mappings as values. The column mappings should be a dictionary with
+        keys as the column names in the evaluator input and values as the column names in the input data or data
+        generated by target.
     :paramtype evaluator_config: Optional[Dict[str, Dict[str, str]]
-    :keyword output_path: The local folder path to save evaluation artifacts to if set
+    :keyword output_path: The local folder or file path to save evaluation results to if set. If folder path is provided
+          the results will be saved to a file named `evaluation_results.json` in the folder.
     :paramtype output_path: Optional[str]
-    :keyword azure_ai_project: Logs evaluation results to AI Studio
+    :keyword azure_ai_project: Logs evaluation results to AI Studio if set.
     :paramtype azure_ai_project: Optional[Dict]
-    :return: A EvaluationResult object.
-    :rtype: ~azure.ai.generative.evaluate.EvaluationResult
+    :return: Evaluation results.
+    :rtype: dict
+
+    :Example:
+
+    Evaluate API can be used as follows:
+
+    .. code-block:: python
+
+            from promptflow.core import AzureOpenAIModelConfiguration
+            from promptflow.evals.evaluate import evaluate
+            from promptflow.evals.evaluators import RelevanceEvaluator, CoherenceEvaluator
+
+
+            model_config = AzureOpenAIModelConfiguration(
+                azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
+                api_key=os.environ.get("AZURE_OPENAI_KEY"),
+                azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT")
+            )
+
+            coherence_eval = CoherenceEvaluator(model_config=model_config)
+            relevance_eval = RelevanceEvaluator(model_config=model_config)
+
+            path = "evaluate_test_data.jsonl"
+            result = evaluate(
+                data=path,
+                evaluators={
+                    "coherence": coherence_eval,
+                    "relevance": relevance_eval,
+                },
+                evaluator_config={
+                    "coherence": {
+                        "answer": "${data.answer}",
+                        "question": "${data.question}"
+                    },
+                    "relevance": {
+                        "answer": "${data.answer}",
+                        "context": "${data.context}",
+                        "question": "${data.question}"
+                    }
+                }
+            )
+
     """
 
     trace_destination = _trace_destination_from_project_scope(azure_ai_project) if azure_ai_project else None
 
     input_data_df = _validate_and_load_data(target, data, evaluators, output_path, azure_ai_project, evaluation_name)
 
     # Process evaluator config to replace ${target.} with ${data.}
+    if evaluator_config is None:
+        evaluator_config = {}
     evaluator_config = _process_evaluator_config(evaluator_config)
     _validate_columns(input_data_df, evaluators, target, evaluator_config)