mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-17 14:00:52 +00:00
82 lines
4.2 KiB
Python
82 lines
4.2 KiB
Python
import json
|
|
|
|
from haystack import Pipeline
|
|
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
|
|
from haystack.components.converters import PyPDFToDocument, TextFileToDocument
|
|
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
|
|
from haystack.components.routers import FileTypeRouter
|
|
from haystack.components.joiners import DocumentJoiner
|
|
from haystack.components.writers import DocumentWriter
|
|
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
|
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
|
|
|
|
|
|
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))
|