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a59bca3661
* Testing black on ui/ * Applying black on docstores * Add latest docstring and tutorial changes * Create a single GH action for Black and docs to reduce commit noise to the minimum, slightly refactor the OpenAPI action too * Remove comments * Relax constraints on pydoc-markdown * Split temporary black from the docs. Pydoc-markdown was obsolete and needs a separate PR to upgrade * Fix a couple of bugs * Add a type: ignore that was missing somehow * Give path to black * Apply Black * Apply Black * Relocate a couple of type: ignore * Update documentation * Make Linux CI run after applying Black * Triggering Black * Apply Black * Remove dependency, does not work well * Remove manually double trailing commas * Update documentation Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
132 lines
5.4 KiB
Python
132 lines
5.4 KiB
Python
import sys
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from typing import List
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import numpy as np
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import pytest
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from haystack.schema import Document
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from haystack.nodes.answer_generator import Seq2SeqGenerator
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from haystack.pipelines import TranslationWrapperPipeline, GenerativeQAPipeline
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from conftest import DOCS_WITH_EMBEDDINGS
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# Keeping few (retriever,document_store) combination to reduce test time
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@pytest.mark.slow
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@pytest.mark.generator
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@pytest.mark.parametrize(
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"retriever,document_store",
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[("embedding", "memory")],
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indirect=True,
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)
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def test_generator_pipeline_with_translator(
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document_store, retriever, rag_generator, en_to_de_translator, de_to_en_translator
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):
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document_store.write_documents(DOCS_WITH_EMBEDDINGS)
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query = "Was ist die Hauptstadt der Bundesrepublik Deutschland?"
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base_pipeline = GenerativeQAPipeline(retriever=retriever, generator=rag_generator)
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pipeline = TranslationWrapperPipeline(
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input_translator=de_to_en_translator, output_translator=en_to_de_translator, pipeline=base_pipeline
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)
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output = pipeline.run(query=query, params={"Generator": {"top_k": 2}, "Retriever": {"top_k": 1}})
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answers = output["answers"]
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assert len(answers) == 2
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assert "berlin" in answers[0].answer
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@pytest.mark.slow
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@pytest.mark.generator
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def test_rag_token_generator(rag_generator):
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query = "What is capital of the Germany?"
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generated_docs = rag_generator.predict(query=query, documents=DOCS_WITH_EMBEDDINGS, top_k=1)
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answers = generated_docs["answers"]
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assert len(answers) == 1
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assert "berlin" in answers[0].answer
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@pytest.mark.slow
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@pytest.mark.generator
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@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
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@pytest.mark.parametrize("retriever", ["embedding"], indirect=True)
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def test_generator_pipeline(document_store, retriever, rag_generator):
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document_store.write_documents(DOCS_WITH_EMBEDDINGS)
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query = "What is capital of the Germany?"
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pipeline = GenerativeQAPipeline(retriever=retriever, generator=rag_generator)
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output = pipeline.run(query=query, params={"Generator": {"top_k": 2}, "Retriever": {"top_k": 1}})
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answers = output["answers"]
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assert len(answers) == 2
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assert "berlin" in answers[0].answer
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@pytest.mark.skipif(sys.platform in ["win32", "cygwin"], reason="Gives memory allocation error on windows runner")
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@pytest.mark.slow
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@pytest.mark.generator
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@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
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@pytest.mark.parametrize("retriever", ["retribert"], indirect=True)
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@pytest.mark.embedding_dim(128)
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def test_lfqa_pipeline(document_store, retriever, eli5_generator):
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# reuse existing DOCS but regenerate embeddings with retribert
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docs: List[Document] = []
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for idx, d in enumerate(DOCS_WITH_EMBEDDINGS):
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docs.append(Document(d.content, str(idx)))
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document_store.write_documents(docs)
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document_store.update_embeddings(retriever)
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query = "Tell me about Berlin?"
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pipeline = GenerativeQAPipeline(retriever=retriever, generator=eli5_generator)
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output = pipeline.run(query=query, params={"top_k": 1})
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answers = output["answers"]
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assert len(answers) == 1
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assert "Germany" in answers[0].answer
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@pytest.mark.slow
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@pytest.mark.generator
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@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
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@pytest.mark.parametrize("retriever", ["retribert"], indirect=True)
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@pytest.mark.embedding_dim(128)
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def test_lfqa_pipeline_unknown_converter(document_store, retriever):
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# reuse existing DOCS but regenerate embeddings with retribert
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docs: List[Document] = []
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for idx, d in enumerate(DOCS_WITH_EMBEDDINGS):
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docs.append(Document(d.content, str(idx)))
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document_store.write_documents(docs)
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document_store.update_embeddings(retriever)
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seq2seq = Seq2SeqGenerator(model_name_or_path="patrickvonplaten/t5-tiny-random")
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query = "Tell me about Berlin?"
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pipeline = GenerativeQAPipeline(retriever=retriever, generator=seq2seq)
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# raises exception as we don't have converter for "patrickvonplaten/t5-tiny-random" in Seq2SeqGenerator
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with pytest.raises(Exception) as exception_info:
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output = pipeline.run(query=query, params={"top_k": 1})
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assert "doesn't have input converter registered for patrickvonplaten/t5-tiny-random" in str(exception_info.value)
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@pytest.mark.slow
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@pytest.mark.generator
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@pytest.mark.parametrize("document_store", ["memory"], indirect=True)
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@pytest.mark.parametrize("retriever", ["retribert"], indirect=True)
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@pytest.mark.embedding_dim(128)
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def test_lfqa_pipeline_invalid_converter(document_store, retriever):
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# reuse existing DOCS but regenerate embeddings with retribert
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docs: List[Document] = []
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for idx, d in enumerate(DOCS_WITH_EMBEDDINGS):
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docs.append(Document(d.content, str(idx)))
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document_store.write_documents(docs)
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document_store.update_embeddings(retriever)
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class _InvalidConverter:
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def __call__(self, some_invalid_para: str, another_invalid_param: str) -> None:
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pass
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seq2seq = Seq2SeqGenerator(
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model_name_or_path="patrickvonplaten/t5-tiny-random", input_converter=_InvalidConverter()
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)
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query = "This query will fail due to InvalidConverter used"
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pipeline = GenerativeQAPipeline(retriever=retriever, generator=seq2seq)
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# raises exception as we are using invalid method signature in _InvalidConverter
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with pytest.raises(Exception) as exception_info:
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output = pipeline.run(query=query, params={"top_k": 1})
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assert "does not have a valid __call__ method signature" in str(exception_info.value)
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