testbed/deepset-ai__haystack/e2e/pipelines/test_dense_doc_search.py

# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0

import json

from haystack import Pipeline
from haystack.components.converters import PyPDFToDocument, TextFileToDocument
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
from haystack.components.joiners import DocumentJoiner
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.components.routers import FileTypeRouter
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore


def test_dense_doc_search_pipeline(tmp_path, samples_path):
    # Create the indexing pipeline
    indexing_pipeline = Pipeline()
    indexing_pipeline.add_component(
        instance=FileTypeRouter(mime_types=["text/plain", "application/pdf"]), name="file_type_router"
    )
    indexing_pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
    indexing_pipeline.add_component(instance=PyPDFToDocument(), name="pdf_file_converter")
    indexing_pipeline.add_component(instance=DocumentJoiner(), name="joiner")
    indexing_pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
    indexing_pipeline.add_component(
        instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30), name="splitter"
    )
    indexing_pipeline.add_component(
        instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
    )
    indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="writer")

    indexing_pipeline.connect("file_type_router.text/plain", "text_file_converter.sources")
    indexing_pipeline.connect("file_type_router.application/pdf", "pdf_file_converter.sources")
    indexing_pipeline.connect("text_file_converter.documents", "joiner.documents")
    indexing_pipeline.connect("pdf_file_converter.documents", "joiner.documents")
    indexing_pipeline.connect("joiner.documents", "cleaner.documents")
    indexing_pipeline.connect("cleaner.documents", "splitter.documents")
    indexing_pipeline.connect("splitter.documents", "embedder.documents")
    indexing_pipeline.connect("embedder.documents", "writer.documents")

    # Draw the indexing pipeline
    indexing_pipeline.draw(tmp_path / "test_dense_doc_search_indexing_pipeline.png")

    # Serialize the indexing pipeline to YAML.
    with open(tmp_path / "test_dense_doc_search_indexing_pipeline.yaml", "w") as f:
        indexing_pipeline.dump(f)

    # Load the indexing pipeline back
    with open(tmp_path / "test_dense_doc_search_indexing_pipeline.yaml", "r") as f:
        indexing_pipeline = Pipeline.load(f)

    indexing_result = indexing_pipeline.run({"file_type_router": {"sources": list(samples_path.iterdir())}})
    filled_document_store = indexing_pipeline.get_component("writer").document_store

    assert indexing_result["writer"]["documents_written"] == 2
    assert filled_document_store.count_documents() == 2

    # Create the querying pipeline
    query_pipeline = Pipeline()
    query_pipeline.add_component(
        instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
    )
    query_pipeline.add_component(
        instance=InMemoryEmbeddingRetriever(document_store=filled_document_store, top_k=20), name="embedding_retriever"
    )
    query_pipeline.connect("text_embedder", "embedding_retriever")

    querying_result = query_pipeline.run({"text_embedder": {"text": "Who lives in Rome?"}})
    assert querying_result["embedding_retriever"]["documents"][0].content == "My name is Giorgio and I live in Rome."

    # Draw the querying pipeline
    query_pipeline.draw(tmp_path / "test_dense_doc_search_query_pipeline.png")

    # Serialize the querying pipeline to JSON
    with open(tmp_path / "test_dense_doc_search_query_pipeline.json", "w") as f:
        print(json.dumps(query_pipeline.to_dict(), indent=4))
        json.dump(query_pipeline.to_dict(), f)

    # Load the querying pipeline back
    with open(tmp_path / "test_dense_doc_search_query_pipeline.json", "r") as f:
        query_pipeline = Pipeline.from_dict(json.load(f))