chore: import upstream snapshot with attribution

test/components/evaluators/test_context_relevance_evaluator.py

@@ -0,0 +1,346 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+import os
+
+import pytest
+
+from haystack import Pipeline
+from haystack.components.evaluators import ContextRelevanceEvaluator
+from haystack.components.generators.chat.openai import OpenAIChatGenerator
+from haystack.dataclasses.chat_message import ChatMessage
+from haystack.utils.auth import Secret
+
+
+class TestContextRelevanceEvaluator:
+    def test_init_default(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator()
+
+        assert component.instructions == (
+            "Please extract only sentences from the provided context which are absolutely relevant and "
+            "required to answer the following question. If no relevant sentences are found, or if you "
+            "believe the question cannot be answered from the given context, return an empty list, example: []"
+        )
+        assert component.inputs == [("questions", list[str]), ("contexts", list[list[str]])]
+        assert component.outputs == ["relevant_statements"]
+        assert component.examples == [
+            {
+                "inputs": {
+                    "questions": "What is the capital of Germany?",
+                    "contexts": ["Berlin is the capital of Germany. Berlin and was founded in 1244."],
+                },
+                "outputs": {"relevant_statements": ["Berlin is the capital of Germany."]},
+            },
+            {
+                "inputs": {
+                    "questions": "What is the capital of France?",
+                    "contexts": [
+                        "Berlin is the capital of Germany and was founded in 1244.",
+                        "Europe is a continent with 44 countries.",
+                        "Madrid is the capital of Spain.",
+                    ],
+                },
+                "outputs": {"relevant_statements": []},
+            },
+            {
+                "inputs": {"questions": "What is the capital of Italy?", "contexts": ["Rome is the capital of Italy."]},
+                "outputs": {"relevant_statements": ["Rome is the capital of Italy."]},
+            },
+        ]
+
+        assert isinstance(component._chat_generator, OpenAIChatGenerator)
+        assert component._chat_generator.api_key.resolve_value() == "test-api-key"
+        assert component._chat_generator.generation_kwargs == {"response_format": {"type": "json_object"}, "seed": 42}
+
+    def test_key_resolved_at_warm_up_not_init(self, monkeypatch):
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        component = ContextRelevanceEvaluator()
+        with pytest.raises(ValueError, match="None of the .* environment variables are set"):
+            component.warm_up()
+
+    def test_init_with_parameters(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator(
+            examples=[
+                {"inputs": {"questions": "Damn, this is straight outta hell!!!"}, "outputs": {"custom_score": 1}},
+                {"inputs": {"questions": "Football is the most popular sport."}, "outputs": {"custom_score": 0}},
+            ]
+        )
+
+        assert component.examples == [
+            {"inputs": {"questions": "Damn, this is straight outta hell!!!"}, "outputs": {"custom_score": 1}},
+            {"inputs": {"questions": "Football is the most popular sport."}, "outputs": {"custom_score": 0}},
+        ]
+
+        assert isinstance(component._chat_generator, OpenAIChatGenerator)
+        assert component._chat_generator.api_key.resolve_value() == "test-api-key"
+        assert component._chat_generator.generation_kwargs == {"response_format": {"type": "json_object"}, "seed": 42}
+
+    def test_init_with_chat_generator(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        chat_generator = OpenAIChatGenerator(generation_kwargs={"response_format": {"type": "json_object"}, "seed": 42})
+        component = ContextRelevanceEvaluator(chat_generator=chat_generator)
+
+        assert component._chat_generator is chat_generator
+
+    def test_to_dict_with_parameters(self, monkeypatch):
+        monkeypatch.setenv("ENV_VAR", "test-api-key")
+        chat_generator = OpenAIChatGenerator(
+            generation_kwargs={"response_format": {"type": "json_object"}, "seed": 42},
+            api_key=Secret.from_env_var("ENV_VAR"),
+        )
+
+        component = ContextRelevanceEvaluator(
+            chat_generator=chat_generator,
+            examples=[{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}],
+            raise_on_failure=False,
+            progress_bar=False,
+        )
+        data = component.to_dict()
+        assert data == {
+            "type": "haystack.components.evaluators.context_relevance.ContextRelevanceEvaluator",
+            "init_parameters": {
+                "chat_generator": chat_generator.to_dict(),
+                "examples": [{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}],
+                "progress_bar": False,
+                "raise_on_failure": False,
+            },
+        }
+
+    def test_from_dict(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        chat_generator = OpenAIChatGenerator(generation_kwargs={"response_format": {"type": "json_object"}, "seed": 42})
+
+        data = {
+            "type": "haystack.components.evaluators.context_relevance.ContextRelevanceEvaluator",
+            "init_parameters": {
+                "chat_generator": chat_generator.to_dict(),
+                "examples": [{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}],
+            },
+        }
+
+        component = ContextRelevanceEvaluator.from_dict(data)
+        assert isinstance(component._chat_generator, OpenAIChatGenerator)
+        assert component._chat_generator.api_key.resolve_value() == "test-api-key"
+        assert component._chat_generator.generation_kwargs == {"response_format": {"type": "json_object"}, "seed": 42}
+        assert component.examples == [{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}]
+
+    def test_pipeline_serde(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+
+        component = ContextRelevanceEvaluator()
+        pipeline = Pipeline()
+        pipeline.add_component("evaluator", component)
+
+        serialized_pipeline = pipeline.dumps()
+        deserialized_pipeline = Pipeline.loads(serialized_pipeline)
+        assert deserialized_pipeline == pipeline
+
+    def test_run_calculates_mean_score(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator()
+
+        def chat_generator_run(self, *args, **kwargs):
+            if "Football" in kwargs["messages"][0].text:
+                return {"replies": [ChatMessage.from_assistant('{"relevant_statements": ["a", "b"], "score": 1}')]}
+            return {"replies": [ChatMessage.from_assistant('{"relevant_statements": [], "score": 0}')]}
+
+        monkeypatch.setattr("haystack.components.evaluators.llm_evaluator.OpenAIChatGenerator.run", chat_generator_run)
+
+        questions = ["Which is the most popular global sport?", "Who created the Python language?"]
+        contexts = [
+            [
+                "The popularity of sports can be measured in various ways, including TV viewership, social media "
+                "presence, number of participants, and economic impact. Football is undoubtedly the world's most "
+                "popular sport with major events like the FIFA World Cup and sports personalities like Ronaldo and "
+                "Messi, drawing a followership of more than 4 billion people."
+            ],
+            [
+                "Python is design philosophy emphasizes code readability, and its language constructs aim to help "
+                "programmers write clear, logical code for both small and large-scale software projects."
+            ],
+        ]
+        results = component.run(questions=questions, contexts=contexts)
+
+        assert results == {
+            "results": [{"score": 1, "relevant_statements": ["a", "b"]}, {"score": 0, "relevant_statements": []}],
+            "score": 0.5,
+            "meta": None,
+            "individual_scores": [1, 0],
+        }
+
+    def test_run_no_statements_extracted(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator()
+
+        def chat_generator_run(self, *args, **kwargs):
+            if "Football" in kwargs["messages"][0].text:
+                return {"replies": [ChatMessage.from_assistant('{"relevant_statements": ["a", "b"], "score": 1}')]}
+            return {"replies": [ChatMessage.from_assistant('{"relevant_statements": [], "score": 0}')]}
+
+        monkeypatch.setattr("haystack.components.evaluators.llm_evaluator.OpenAIChatGenerator.run", chat_generator_run)
+
+        questions = ["Which is the most popular global sport?", "Who created the Python language?"]
+        contexts = [
+            [
+                "The popularity of sports can be measured in various ways, including TV viewership, social media "
+                "presence, number of participants, and economic impact. Football is undoubtedly the world's most "
+                "popular sport with major events like the FIFA World Cup and sports personalities like Ronaldo and "
+                "Messi, drawing a followership of more than 4 billion people."
+            ],
+            [],
+        ]
+        results = component.run(questions=questions, contexts=contexts)
+        assert results == {
+            "results": [{"score": 1, "relevant_statements": ["a", "b"]}, {"score": 0, "relevant_statements": []}],
+            "score": 0.5,
+            "meta": None,
+            "individual_scores": [1, 0],
+        }
+
+    def test_run_missing_parameters(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator()
+        with pytest.raises(ValueError, match="LLM evaluator expected input parameter"):
+            component.run()
+
+    def test_run_returns_nan_raise_on_failure_false(self, monkeypatch, caplog):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator(raise_on_failure=False)
+
+        def chat_generator_run(self, *args, **kwargs):
+            if "Python" in kwargs["messages"][0].text:
+                raise Exception("OpenAI API request failed.")
+            return {"replies": [ChatMessage.from_assistant('{"relevant_statements": ["c", "d"], "score": 1}')]}
+
+        monkeypatch.setattr("haystack.components.evaluators.llm_evaluator.OpenAIChatGenerator.run", chat_generator_run)
+
+        questions = ["Which is the most popular global sport?", "Who created the Python language?"]
+        contexts = [
+            [
+                "The popularity of sports can be measured in various ways, including TV viewership, social media "
+                "presence, number of participants, and economic impact. Football is undoubtedly the world's most "
+                "popular sport with major events like the FIFA World Cup and sports personalities like Ronaldo and "
+                "Messi, drawing a followership of more than 4 billion people."
+            ],
+            [
+                "Python, created by Guido van Rossum in the late 1980s, is a high-level general-purpose programming "
+                "language. Its design philosophy emphasizes code readability, and its language constructs aim to help "
+                "programmers write clear, logical code for both small and large-scale software projects."
+            ],
+        ]
+        with caplog.at_level("WARNING", logger="haystack.components.evaluators.context_relevance"):
+            results = component.run(questions=questions, contexts=contexts)
+
+        assert results["score"] == 1
+        assert results["results"][0] == {"relevant_statements": ["c", "d"], "score": 1}
+        assert results["results"][1]["relevant_statements"] == []
+        assert math.isnan(results["results"][1]["score"])
+
+        assert "1 query(s) failed and were excluded from the score." in caplog.text
+
+    @pytest.mark.skipif(
+        not os.environ.get("OPENAI_API_KEY", None),
+        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+    )
+    @pytest.mark.integration
+    def test_live_run(self):
+        questions = ["Who created the Python language?"]
+        contexts = [["Python, created by Guido van Rossum, is a high-level general-purpose programming language."]]
+
+        evaluator = ContextRelevanceEvaluator(chat_generator=OpenAIChatGenerator(model="gpt-4.1-nano"))
+        result = evaluator.run(questions=questions, contexts=contexts)
+
+        required_fields = {"results"}
+        assert all(field in result for field in required_fields)
+        nested_required_fields = {"score", "relevant_statements"}
+        assert all(field in result["results"][0] for field in nested_required_fields)
+
+        assert "meta" in result
+        assert "prompt_tokens" in result["meta"][0]["usage"]
+        assert "completion_tokens" in result["meta"][0]["usage"]
+        assert "total_tokens" in result["meta"][0]["usage"]
+
+
+class TestContextRelevanceEvaluatorAsync:
+    @pytest.mark.asyncio
+    async def test_run_async_calculates_mean_score(self, monkeypatch):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator()
+
+        async def chat_generator_run_async(self, *args, **kwargs):
+            if "Football" in kwargs["messages"][0].text:
+                return {"replies": [ChatMessage.from_assistant('{"relevant_statements": ["a", "b"], "score": 1}')]}
+            return {"replies": [ChatMessage.from_assistant('{"relevant_statements": [], "score": 0}')]}
+
+        monkeypatch.setattr(
+            "haystack.components.evaluators.llm_evaluator.OpenAIChatGenerator.run_async", chat_generator_run_async
+        )
+
+        questions = ["Which is the most popular global sport?", "Who created the Python language?"]
+        contexts = [
+            ["Football is the world's most popular sport."],
+            ["Python is a cross-platform programming language."],
+        ]
+
+        results = await component.run_async(questions=questions, contexts=contexts)
+
+        assert results == {
+            "results": [{"score": 1, "relevant_statements": ["a", "b"]}, {"score": 0, "relevant_statements": []}],
+            "score": 0.5,
+            "meta": None,
+            "individual_scores": [1, 0],
+        }
+
+    @pytest.mark.asyncio
+    async def test_run_async_returns_nan_raise_on_failure_false(self, monkeypatch, caplog):
+        monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
+        component = ContextRelevanceEvaluator(raise_on_failure=False)
+
+        async def chat_generator_run_async(self, *args, **kwargs):
+            if "Python" in kwargs["messages"][0].text:
+                raise Exception("OpenAI API request failed.")
+            return {"replies": [ChatMessage.from_assistant('{"relevant_statements": ["c", "d"], "score": 1}')]}
+
+        monkeypatch.setattr(
+            "haystack.components.evaluators.llm_evaluator.OpenAIChatGenerator.run_async", chat_generator_run_async
+        )
+
+        questions = ["Which is the most popular global sport?", "Who created the Python language?"]
+        contexts = [["Football is popular."], ["Python was created by Guido van Rossum."]]
+
+        with caplog.at_level("WARNING", logger="haystack.components.evaluators.context_relevance"):
+            results = await component.run_async(questions=questions, contexts=contexts)
+
+        assert results["score"] == 1
+        assert results["results"][0] == {"relevant_statements": ["c", "d"], "score": 1}
+        assert results["results"][1]["relevant_statements"] == []
+        assert math.isnan(results["results"][1]["score"])
+
+        assert "1 query(s) failed and were excluded from the score." in caplog.text
+
+    @pytest.mark.asyncio
+    @pytest.mark.skipif(
+        not os.environ.get("OPENAI_API_KEY", None),
+        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
+    )
+    @pytest.mark.integration
+    async def test_live_run_async(self):
+        questions = ["Who created the Python language?"]
+        contexts = [["Python, created by Guido van Rossum, is a high-level general-purpose programming language."]]
+
+        evaluator = ContextRelevanceEvaluator(chat_generator=OpenAIChatGenerator(model="gpt-4.1-nano"))
+        result = await evaluator.run_async(questions=questions, contexts=contexts)
+
+        required_fields = {"results"}
+        assert all(field in result for field in required_fields)
+        nested_required_fields = {"score", "relevant_statements"}
+        assert all(field in result["results"][0] for field in nested_required_fields)
+
+        assert "meta" in result
+        assert "prompt_tokens" in result["meta"][0]["usage"]
+        assert "completion_tokens" in result["meta"][0]["usage"]
+        assert "total_tokens" in result["meta"][0]["usage"]