haystack/test/test_pipeline.py

from pathlib import Path

import pytest

from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
from haystack.pipeline import TranslationWrapperPipeline, JoinDocuments, ExtractiveQAPipeline, Pipeline, FAQPipeline, \
    DocumentSearchPipeline, RootNode
from haystack.retriever.dense import DensePassageRetriever
from haystack.retriever.sparse import ElasticsearchRetriever


@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
def test_load_and_save_yaml(document_store_with_docs, tmp_path):
    # test correct load of indexing pipeline from yaml
    pipeline = Pipeline.load_from_yaml(Path("samples/pipeline/test_pipeline.yaml"), pipeline_name="indexing_pipeline")
    pipeline.run(file_path=Path("samples/pdf/sample_pdf_1.pdf"), top_k_retriever=10, top_k_reader=3)

    # test correct load of query pipeline from yaml
    pipeline = Pipeline.load_from_yaml(Path("samples/pipeline/test_pipeline.yaml"), pipeline_name="query_pipeline")
    prediction = pipeline.run(query="Who made the PDF specification?", top_k_retriever=10, top_k_reader=3)
    assert prediction["query"] == "Who made the PDF specification?"
    assert prediction["answers"][0]["answer"] == "Adobe Systems"

    # test invalid pipeline name
    with pytest.raises(Exception):
        Pipeline.load_from_yaml(path=Path("samples/pipeline/test_pipeline.yaml"), pipeline_name="invalid")

    # test config export
    pipeline.save_to_yaml(tmp_path / "test.yaml")
    with open(tmp_path/"test.yaml", "r", encoding='utf-8') as stream:
        saved_yaml = stream.read()
    expected_yaml = '''
        components:
        - name: ESRetriever
          params:
            document_store: ElasticsearchDocumentStore
          type: ElasticsearchRetriever
        - name: ElasticsearchDocumentStore
          params:
            index: haystack_test_document
            label_index: haystack_test_label
          type: ElasticsearchDocumentStore
        - name: Reader
          params:
            model_name_or_path: deepset/roberta-base-squad2
            no_ans_boost: -10
          type: FARMReader
        pipelines:
        - name: query
          nodes:
          - inputs:
            - Query
            name: ESRetriever
          - inputs:
            - ESRetriever
            name: Reader
          type: Query
        version: '0.8'
    '''
    assert saved_yaml.replace(" ", "").replace("\n", "") == expected_yaml.replace(" ", "").replace("\n", "")


@pytest.mark.slow
@pytest.mark.elasticsearch
@pytest.mark.parametrize(
    "retriever_with_docs, document_store_with_docs", [("elasticsearch", "elasticsearch")], indirect=True
)
def test_graph_creation(reader, retriever_with_docs, document_store_with_docs):
    pipeline = Pipeline()
    pipeline.add_node(name="ES", component=retriever_with_docs, inputs=["Query"])

    with pytest.raises(AssertionError):
        pipeline.add_node(name="Reader", component=retriever_with_docs, inputs=["ES.output_2"])

    with pytest.raises(AssertionError):
        pipeline.add_node(name="Reader", component=retriever_with_docs, inputs=["ES.wrong_edge_label"])

    with pytest.raises(Exception):
        pipeline.add_node(name="Reader", component=retriever_with_docs, inputs=["InvalidNode"])

    with pytest.raises(Exception):
        pipeline = Pipeline()
        pipeline.add_node(name="ES", component=retriever_with_docs, inputs=["InvalidNode"])


@pytest.mark.slow
@pytest.mark.elasticsearch
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_answers(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    prediction = pipeline.run(query="Who lives in Berlin?", top_k_retriever=10, top_k_reader=3)
    assert prediction is not None
    assert prediction["query"] == "Who lives in Berlin?"
    assert prediction["answers"][0]["answer"] == "Carla"
    assert prediction["answers"][0]["probability"] <= 1
    assert prediction["answers"][0]["probability"] >= 0
    assert prediction["answers"][0]["meta"]["meta_field"] == "test1"
    assert prediction["answers"][0]["context"] == "My name is Carla and I live in Berlin"

    assert len(prediction["answers"]) == 3


@pytest.mark.elasticsearch
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_offsets(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    prediction = pipeline.run(query="Who lives in Berlin?", top_k_retriever=10, top_k_reader=5)

    assert prediction["answers"][0]["offset_start"] == 11
    assert prediction["answers"][0]["offset_end"] == 16
    start = prediction["answers"][0]["offset_start"]
    end = prediction["answers"][0]["offset_end"]
    assert prediction["answers"][0]["context"][start:end] == prediction["answers"][0]["answer"]


@pytest.mark.slow
@pytest.mark.elasticsearch
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_answers_single_result(reader, retriever_with_docs):
    pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    query = "testing finder"
    prediction = pipeline.run(query=query, top_k_retriever=1, top_k_reader=1)
    assert prediction is not None
    assert len(prediction["answers"]) == 1


@pytest.mark.elasticsearch
@pytest.mark.parametrize(
    "retriever,document_store",
    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("embedding", "elasticsearch")],
    indirect=True,
)
def test_faq_pipeline(retriever, document_store):
    documents = [
        {"text": "How to test module-1?", 'meta': {"source": "wiki1", "answer": "Using tests for module-1"}},
        {"text": "How to test module-2?", 'meta': {"source": "wiki2", "answer": "Using tests for module-2"}},
        {"text": "How to test module-3?", 'meta': {"source": "wiki3", "answer": "Using tests for module-3"}},
        {"text": "How to test module-4?", 'meta': {"source": "wiki4", "answer": "Using tests for module-4"}},
        {"text": "How to test module-5?", 'meta': {"source": "wiki5", "answer": "Using tests for module-5"}},
    ]

    document_store.write_documents(documents)
    document_store.update_embeddings(retriever)

    pipeline = FAQPipeline(retriever=retriever)

    output = pipeline.run(query="How to test this?", top_k_retriever=3)
    assert len(output["answers"]) == 3
    assert output["answers"][0]["query"].startswith("How to")
    assert output["answers"][0]["answer"].startswith("Using tests")

    if isinstance(document_store, ElasticsearchDocumentStore):
        output = pipeline.run(query="How to test this?", filters={"source": ["wiki2"]}, top_k_retriever=5)
        assert len(output["answers"]) == 1


@pytest.mark.elasticsearch
@pytest.mark.parametrize(
    "retriever,document_store",
    [("embedding", "memory"), ("embedding", "faiss"), ("embedding", "milvus"), ("embedding", "elasticsearch")],
    indirect=True,
)
def test_document_search_pipeline(retriever, document_store):
    documents = [
        {"text": "Sample text for document-1", 'meta': {"source": "wiki1"}},
        {"text": "Sample text for document-2", 'meta': {"source": "wiki2"}},
        {"text": "Sample text for document-3", 'meta': {"source": "wiki3"}},
        {"text": "Sample text for document-4", 'meta': {"source": "wiki4"}},
        {"text": "Sample text for document-5", 'meta': {"source": "wiki5"}},
    ]

    document_store.write_documents(documents)
    document_store.update_embeddings(retriever)

    pipeline = DocumentSearchPipeline(retriever=retriever)
    output = pipeline.run(query="How to test this?", top_k_retriever=4)
    assert len(output.get('documents', [])) == 4

    if isinstance(document_store, ElasticsearchDocumentStore):
        output = pipeline.run(query="How to test this?", filters={"source": ["wiki2"]}, top_k_retriever=5)
        assert len(output["documents"]) == 1


@pytest.mark.slow
@pytest.mark.elasticsearch
@pytest.mark.parametrize("retriever_with_docs", ["tfidf"], indirect=True)
def test_extractive_qa_answers_with_translator(reader, retriever_with_docs, en_to_de_translator, de_to_en_translator):
    base_pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever_with_docs)
    pipeline = TranslationWrapperPipeline(
        input_translator=de_to_en_translator,
        output_translator=en_to_de_translator,
        pipeline=base_pipeline
    )

    prediction = pipeline.run(query="Wer lebt in Berlin?", top_k_retriever=10, top_k_reader=3)
    assert prediction is not None
    assert prediction["query"] == "Wer lebt in Berlin?"
    assert "Carla" in prediction["answers"][0]["answer"]
    assert prediction["answers"][0]["probability"] <= 1
    assert prediction["answers"][0]["probability"] >= 0
    assert prediction["answers"][0]["meta"]["meta_field"] == "test1"
    assert prediction["answers"][0]["context"] == "My name is Carla and I live in Berlin"


@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
@pytest.mark.parametrize("reader", ["farm"], indirect=True)
def test_join_document_pipeline(document_store_with_docs, reader):
    es = ElasticsearchRetriever(document_store=document_store_with_docs)
    dpr = DensePassageRetriever(
        document_store=document_store_with_docs,
        query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
        passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base",
        use_gpu=False,
    )
    document_store_with_docs.update_embeddings(dpr)

    query = "Where does Carla lives?"

    # test merge without weights
    join_node = JoinDocuments(join_mode="merge")
    p = Pipeline()
    p.add_node(component=es, name="R1", inputs=["Query"])
    p.add_node(component=dpr, name="R2", inputs=["Query"])
    p.add_node(component=join_node, name="Join", inputs=["R1", "R2"])
    results = p.run(query=query)
    assert len(results["documents"]) == 3

    # test merge with weights
    join_node = JoinDocuments(join_mode="merge", weights=[1000, 1], top_k_join=2)
    p = Pipeline()
    p.add_node(component=es, name="R1", inputs=["Query"])
    p.add_node(component=dpr, name="R2", inputs=["Query"])
    p.add_node(component=join_node, name="Join", inputs=["R1", "R2"])
    results = p.run(query=query)
    assert results["documents"][0].score > 1000
    assert len(results["documents"]) == 2

    # test concatenate
    join_node = JoinDocuments(join_mode="concatenate")
    p = Pipeline()
    p.add_node(component=es, name="R1", inputs=["Query"])
    p.add_node(component=dpr, name="R2", inputs=["Query"])
    p.add_node(component=join_node, name="Join", inputs=["R1", "R2"])
    results = p.run(query=query)
    assert len(results["documents"]) == 3

    # test join_node with reader
    join_node = JoinDocuments()
    p = Pipeline()
    p.add_node(component=es, name="R1", inputs=["Query"])
    p.add_node(component=dpr, name="R2", inputs=["Query"])
    p.add_node(component=join_node, name="Join", inputs=["R1", "R2"])
    p.add_node(component=reader, name="Reader", inputs=["Join"])
    results = p.run(query=query)
    assert results["answers"][0]["answer"] == "Berlin"


def test_parallel_paths_in_pipeline_graph():
    class A(RootNode):
        def run(self, **kwargs):
            kwargs["output"] = "A"
            return kwargs, "output_1"

    class B(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "B"
            return kwargs, "output_1"

    class C(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "C"
            return kwargs, "output_1"

    class D(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "D"
            return kwargs, "output_1"

    class E(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "E"
            return kwargs, "output_1"

    class JoinNode(RootNode):
        def run(self, **kwargs):
            kwargs["output"] = kwargs["inputs"][0]["output"] + kwargs["inputs"][1]["output"]
            return kwargs, "output_1"

    pipeline = Pipeline()
    pipeline.add_node(name="A", component=A(), inputs=["Query"])
    pipeline.add_node(name="B", component=B(), inputs=["A"])
    pipeline.add_node(name="C", component=C(), inputs=["B"])
    pipeline.add_node(name="E", component=E(), inputs=["C"])
    pipeline.add_node(name="D", component=D(), inputs=["B"])
    pipeline.add_node(name="F", component=JoinNode(), inputs=["D", "E"])
    output = pipeline.run(query="test")
    assert output["output"] == "ABDABCE"

    pipeline = Pipeline()
    pipeline.add_node(name="A", component=A(), inputs=["Query"])
    pipeline.add_node(name="B", component=B(), inputs=["A"])
    pipeline.add_node(name="C", component=C(), inputs=["B"])
    pipeline.add_node(name="D", component=D(), inputs=["B"])
    pipeline.add_node(name="E", component=JoinNode(), inputs=["C", "D"])
    output = pipeline.run(query="test")
    assert output["output"] == "ABCABD"


def test_parallel_paths_in_pipeline_graph_with_branching():
    class AWithOutput1(RootNode):
        outgoing_edges = 2
        def run(self, **kwargs):
            kwargs["output"] = "A"
            return kwargs, "output_1"

    class AWithOutput2(RootNode):
        outgoing_edges = 2
        def run(self, **kwargs):
            kwargs["output"] = "A"
            return kwargs, "output_2"

    class AWithOutputAll(RootNode):
        outgoing_edges = 2
        def run(self, **kwargs):
            kwargs["output"] = "A"
            return kwargs, "output_all"

    class B(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "B"
            return kwargs, "output_1"

    class C(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "C"
            return kwargs, "output_1"

    class D(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "D"
            return kwargs, "output_1"

    class E(RootNode):
        def run(self, **kwargs):
            kwargs["output"] += "E"
            return kwargs, "output_1"

    class JoinNode(RootNode):
        def run(self, **kwargs):
            if kwargs.get("inputs"):
                kwargs["output"] = ""
                for input_dict in kwargs["inputs"]:
                    kwargs["output"] += (input_dict["output"])
            return kwargs, "output_1"

    pipeline = Pipeline()
    pipeline.add_node(name="A", component=AWithOutput1(), inputs=["Query"])
    pipeline.add_node(name="B", component=B(), inputs=["A.output_1"])
    pipeline.add_node(name="C", component=C(), inputs=["A.output_2"])
    pipeline.add_node(name="D", component=E(), inputs=["B"])
    pipeline.add_node(name="E", component=D(), inputs=["B"])
    pipeline.add_node(name="F", component=JoinNode(), inputs=["D", "E", "C"])
    output = pipeline.run(query="test")
    assert output["output"] == "ABEABD"

    pipeline = Pipeline()
    pipeline.add_node(name="A", component=AWithOutput2(), inputs=["Query"])
    pipeline.add_node(name="B", component=B(), inputs=["A.output_1"])
    pipeline.add_node(name="C", component=C(), inputs=["A.output_2"])
    pipeline.add_node(name="D", component=E(), inputs=["B"])
    pipeline.add_node(name="E", component=D(), inputs=["B"])
    pipeline.add_node(name="F", component=JoinNode(), inputs=["D", "E", "C"])
    output = pipeline.run(query="test")
    assert output["output"] == "AC"

    pipeline = Pipeline()
    pipeline.add_node(name="A", component=AWithOutputAll(), inputs=["Query"])
    pipeline.add_node(name="B", component=B(), inputs=["A.output_1"])
    pipeline.add_node(name="C", component=C(), inputs=["A.output_2"])
    pipeline.add_node(name="D", component=E(), inputs=["B"])
    pipeline.add_node(name="E", component=D(), inputs=["B"])
    pipeline.add_node(name="F", component=JoinNode(), inputs=["D", "E", "C"])
    output = pipeline.run(query="test")
    assert output["output"] == "ACABEABD"