2024-04-22 14:10:00 +02:00
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import os
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from typing import List
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import pytest
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from haystack.components.evaluators import ContextRelevanceEvaluator
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from haystack.utils.auth import Secret
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class TestContextRelevanceEvaluator:
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def test_init_default(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = ContextRelevanceEvaluator()
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assert component.api == "openai"
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assert component.generator.client.api_key == "test-api-key"
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assert component.instructions == (
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"Your task is to judge how relevant the provided context is for answering a question. "
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"First, please extract statements from the provided context. "
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"Second, calculate a relevance score for each statement in the context. "
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"The score is 1 if the statement is relevant to answer the question or 0 if it is not relevant."
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)
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assert component.inputs == [("questions", List[str]), ("contexts", List[List[str]])]
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assert component.outputs == ["statements", "statement_scores"]
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assert component.examples == [
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{
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"inputs": {
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"questions": "What is the capital of Germany?",
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"contexts": ["Berlin is the capital of Germany and was founded in 1244."],
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},
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"outputs": {
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"statements": ["Berlin is the capital of Germany.", "Berlin was founded in 1244."],
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"statement_scores": [1, 0],
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},
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},
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{
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"inputs": {
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"questions": "What is the capital of France?",
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"contexts": ["Berlin is the capital of Germany."],
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},
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"outputs": {"statements": ["Berlin is the capital of Germany."], "statement_scores": [0]},
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},
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{
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"inputs": {"questions": "What is the capital of Italy?", "contexts": ["Rome is the capital of Italy."]},
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"outputs": {"statements": ["Rome is the capital of Italy."], "statement_scores": [1]},
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},
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]
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def test_init_fail_wo_openai_api_key(self, monkeypatch):
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monkeypatch.delenv("OPENAI_API_KEY", raising=False)
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with pytest.raises(ValueError, match="None of the .* environment variables are set"):
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ContextRelevanceEvaluator()
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def test_init_with_parameters(self):
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component = ContextRelevanceEvaluator(
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api_key=Secret.from_token("test-api-key"),
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api="openai",
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examples=[
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{"inputs": {"questions": "Damn, this is straight outta hell!!!"}, "outputs": {"custom_score": 1}},
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{"inputs": {"questions": "Football is the most popular sport."}, "outputs": {"custom_score": 0}},
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],
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)
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assert component.generator.client.api_key == "test-api-key"
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assert component.api == "openai"
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assert component.examples == [
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{"inputs": {"questions": "Damn, this is straight outta hell!!!"}, "outputs": {"custom_score": 1}},
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{"inputs": {"questions": "Football is the most popular sport."}, "outputs": {"custom_score": 0}},
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]
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def test_from_dict(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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data = {
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"type": "haystack.components.evaluators.context_relevance.ContextRelevanceEvaluator",
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"init_parameters": {
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"api_key": {"env_vars": ["OPENAI_API_KEY"], "strict": True, "type": "env_var"},
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"api": "openai",
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"examples": [{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}],
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},
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}
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component = ContextRelevanceEvaluator.from_dict(data)
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assert component.api == "openai"
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assert component.generator.client.api_key == "test-api-key"
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assert component.examples == [{"inputs": {"questions": "What is football?"}, "outputs": {"score": 0}}]
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def test_run_calculates_mean_score(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = ContextRelevanceEvaluator()
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def generator_run(self, *args, **kwargs):
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if "Football" in kwargs["prompt"]:
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return {"replies": ['{"statements": ["a", "b"], "statement_scores": [1, 0]}']}
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else:
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return {"replies": ['{"statements": ["c", "d"], "statement_scores": [1, 1]}']}
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monkeypatch.setattr("haystack.components.generators.openai.OpenAIGenerator.run", generator_run)
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questions = ["Which is the most popular global sport?", "Who created the Python language?"]
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contexts = [
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[
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"The popularity of sports can be measured in various ways, including TV viewership, social media "
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"presence, number of participants, and economic impact. Football is undoubtedly the world's most "
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"popular sport with major events like the FIFA World Cup and sports personalities like Ronaldo and "
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"Messi, drawing a followership of more than 4 billion people."
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],
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[
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"Python, created by Guido van Rossum in the late 1980s, is a high-level general-purpose programming "
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"language. Its design philosophy emphasizes code readability, and its language constructs aim to help "
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"programmers write clear, logical code for both small and large-scale software projects."
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],
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]
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results = component.run(questions=questions, contexts=contexts)
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assert results == {
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"individual_scores": [0.5, 1],
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"results": [
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{"score": 0.5, "statement_scores": [1, 0], "statements": ["a", "b"]},
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{"score": 1, "statement_scores": [1, 1], "statements": ["c", "d"]},
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],
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"score": 0.75,
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}
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def test_run_missing_parameters(self, monkeypatch):
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monkeypatch.setenv("OPENAI_API_KEY", "test-api-key")
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component = ContextRelevanceEvaluator()
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with pytest.raises(TypeError, match="missing 2 required positional arguments"):
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component.run()
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@pytest.mark.skipif(
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not os.environ.get("OPENAI_API_KEY", None),
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reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
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)
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@pytest.mark.integration
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def test_live_run(self):
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questions = ["Who created the Python language?"]
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contexts = [["Python, created by Guido van Rossum, is a high-level general-purpose programming language."]]
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evaluator = ContextRelevanceEvaluator()
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result = evaluator.run(questions=questions, contexts=contexts)
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2024-04-23 18:01:25 +02:00
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required_fields = {"individual_scores", "results", "score"}
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assert all(field in result for field in required_fields)
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nested_required_fields = {"score", "statement_scores", "statements"}
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assert all(field in result["results"][0] for field in nested_required_fields)
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