Add example for multi turn end to end rag

examples/evaluate_multi_turn_rag_end_to_end_dataset_with_given_predictions copy.py

@@ -0,0 +1,161 @@
+from unitxt import get_logger
+from unitxt.api import create_dataset, evaluate
+from unitxt.task import Task
+from unitxt.templates import JsonOutputTemplate
+
+logger = get_logger()
+
+#
+contexts = [
+    "Austin is the capital of Texas.",
+    "Houston is in Texas",
+    "Houston is the the largest city in the state but not the capital of it.",
+]
+
+# Set up question answer pairs in a dictionary
+dataset = [
+    {
+        "question": "What is the capital of Texas?",
+        "conversation_id": 0,
+        "turn_id": 0,
+        "reference_answers": ["Austin"],
+        "reference_contexts": [contexts[0]],
+        "reference_context_ids": [0],
+        "is_answerable_label": True,
+    },
+    {
+        "question": "Which is the the largest city in the state?",
+        "conversation_id": 0,
+        "turn_id": 1,
+        "reference_answers": ["Houston"],
+        "reference_contexts": [contexts[1], contexts[2]],
+        "reference_context_ids": [1, 2],
+        "is_answerable_label": True,
+    },
+    {
+        "question": "How much is 2+2?",
+        "conversation_id": 1,
+        "turn_id": 0,
+        "reference_answers": ["4"],
+        "reference_contexts": [""],
+        "reference_context_ids": [],
+        "is_answerable_label": True,
+    },
+    {
+        "question": "Multiply the answer by 5",
+        "conversation_id": 1,
+        "turn_id": 1,
+        "reference_answers": ["20"],
+        "reference_contexts": [""],
+        "reference_context_ids": [],
+        "is_answerable_label": True,
+    },
+]
+
+predictions = [
+    {
+        "answer": "Houston",
+        "contexts": [contexts[2]],
+        "context_ids": [2],
+        "is_answerable": True,
+    },
+    {
+        "answer": "Houston",
+        "contexts": [contexts[2]],
+        "context_ids": [2],
+        "is_answerable": True,
+    },
+    {
+        "answer": "4",
+        "contexts": [""],
+        "context_ids": [],
+        "is_answerable": True,
+    },
+    {
+        "answer": "25",
+        "contexts": [""],
+        "context_ids": [],
+        "is_answerable": True,
+    },
+]
+
+# select recommended metrics according to your available resources.
+metrics = [
+    "metrics.rag.end_to_end.recommended.cpu_only.all",
+    # "metrics.rag.end_to_end.recommended.small_llm.all",
+    # "metrics.rag.end_to_end.recommended.llmaj_watsonx.all",
+    # "metrics.rag.end_to_end.recommended.llmaj_rits.all"
+    # "metrics.rag.end_to_end.recommended.llmaj_azure.all"
+]
+
+multi_turn_rag_task = Task(
+    input_fields={
+        "question": "Union[str, Dialog]",
+        "conversation_id": "Any",
+        "turn_id": "Any",
+        "metadata_tags": "Dict[str, str]",
+    },
+    reference_fields={
+        "reference_answers": "List[str]",
+        "reference_contexts": "List[str]",
+        "reference_context_ids": "Union[List[int], List[str]]",
+        "is_answerable_label": "bool",
+    },
+    metrics=[
+        "metrics.rag.end_to_end.answer_correctness",
+        "metrics.rag.end_to_end.answer_faithfulness",
+        "metrics.rag.end_to_end.answer_reward",
+        "metrics.rag.end_to_end.context_correctness",
+        "metrics.rag.end_to_end.context_relevance",
+    ],
+    prediction_type="RagResponse",
+    augmentable_inputs=[
+        "question",
+    ],
+    defaults={
+        "metadata_tags": {},
+        "reference_answers": [],
+        "reference_contexts": [],
+        "reference_context_ids": [],
+        "is_answerable_label": True,
+    },
+)
+
+template = JsonOutputTemplate(
+    input_format="Conversation: {conversation_id} Turn: {turn_id} Question: {question}",
+    output_fields={
+        "reference_answers": "answer",
+        "reference_contexts": "contexts",
+        "reference_context_ids": "context_ids",
+    },
+    wrap_with_list_fields=[
+        "reference_contexts",
+        "reference_context_ids",
+    ],
+    postprocessors=[
+        "processors.load_json_predictions",
+    ],
+)
+
+dataset = create_dataset(
+    task=multi_turn_rag_task,
+    test_set=dataset,
+    split="test",
+    postprocessors=[],
+    metrics=metrics,
+    template=template,
+    group_by=["conversation_id"],
+)
+
+results = evaluate(predictions, dataset)
+
+# Print Results:
+
+print("Global Results:")
+print(results.global_scores.summary)
+
+print("Instance Results:")
+print(results.instance_scores.summary)
+
+print("Group results:")
+print(results.groups_scores.summary)