Update evaluate.py

giskard/rag/evaluate.py

@@ -50,14 +50,30 @@ def evaluate(
     RAGReport
         The report of the evaluation.
     """
-    if testset is None and knowledge_base is None:
-        raise ValueError("At least one of testset or knowledge base must be provided to the evaluate function.")
 
-    if testset is None and not isinstance(answer_fn, Sequence):
-        raise ValueError(
+    validate_inputs(answer_fn, knowledge_base, testset)
+    testset = testset or generate_testset(knowledge_base)
+    answers = retrieve_answers(answer_fn, testset)
+    llm_client = llm_client or get_default_client()
+    metrics = get_metrics(metrics, llm_client, agent_description)
+    metrics_results = compute_metrics(metrics, testset, answers)
+    report = get_report(testset, answers, metrics_results, knowledge_base)
+    add_recommendation(report, llm_client, metrics)
+    track_analytics(report, testset, knowledge_base, agent_description, metrics)
+
+    return report
+
+def validate_inputs(answer_fn, knowledge_base, testset):
+    if testset is None:
+        if knowledge_base is None:
+            raise ValueError("At least one of testset or knowledge base must be provided to the evaluate function.")
+        if not isinstance(answer_fn, Sequence):
+            raise ValueError(
             "If the testset is not provided, the answer_fn must be a list of answers to ensure the matching between questions and answers."
         )
 
+        testset = generate_testset(knowledge_base)
+
     # Check basic types, in case the user passed the params in the wrong order
     if knowledge_base is not None and not isinstance(knowledge_base, KnowledgeBase):
         raise ValueError(
@@ -69,21 +85,19 @@ def evaluate(
             f"testset must be a QATestset object (got {type(testset)} instead). Are you sure you passed the parameters in the right order?"
         )
 
-    if testset is None:
-        testset = generate_testset(knowledge_base)
-
-    answers = answer_fn if isinstance(answer_fn, Sequence) else _compute_answers(answer_fn, testset)
-
-    llm_client = llm_client or get_default_client()
+def retrieve_answers(answer_fn, testset):
+    return answer_fn if isinstance(answer_fn, Sequence) else _compute_answers(answer_fn, testset)
 
-    # @TODO: improve this
+def get_metrics(metrics, llm_client, agent_description):
     metrics = list(metrics) if metrics is not None else []
     if not any(isinstance(metric, CorrectnessMetric) for metric in metrics):
         # By default only correctness is computed as it is required to build the report
         metrics.insert(
             0, CorrectnessMetric(name="correctness", llm_client=llm_client, agent_description=agent_description)
         )
+    return metrics
 
+def compute_metrics(metrics, testset, answers):
     metrics_results = defaultdict(dict)
 
     for metric in metrics:
@@ -97,8 +111,12 @@ def evaluate(
             total=len(answers),
         ):
             metrics_results[sample["id"]].update(metric(sample, answer))
+    return metrics_results
 
-    report = RAGReport(testset, answers, metrics_results, knowledge_base)
+def get_report(testset, answers, metrics_results, knowledge_base):
+    return RAGReport(testset, answers, metrics_results, knowledge_base)
+
+def add_recommendation(report, llm_client, metrics):
     recommendation = get_rag_recommendation(
         report.topics,
         report.correctness_by_question_type().to_dict()[metrics[0].name],
@@ -107,6 +125,7 @@ def evaluate(
     )
     report._recommendation = recommendation
 
+def track_analytics(report, testset, knowledge_base, agent_description, metrics):
     analytics.track(
         "raget:evaluation",
         {
@@ -117,8 +136,6 @@ def evaluate(
             "correctness": report.correctness,
         },
     )
-    return report
-
 
 def _compute_answers(answer_fn, testset):
     answers = []