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https://github.com/deepset-ai/haystack.git
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288ed150c9
* rename model parameter in the openai doc embedder * fix tests for openai doc embedder * rename model parameter in the openai text embedder * fix tests for openai text embedder * rename model parameter in the st doc embedder * fix tests for st doc embedder * rename model parameter in the st backend * fix tests for st backend * rename model parameter in the st text embedder * fix tests for st text embedder * fix docstring * fix pipeline utils * fix e2e * reno * fix the indexing pipeline _create_embedder function * fix e2e eval rag pipeline * pytest
83 lines
4.3 KiB
Python
83 lines
4.3 KiB
Python
import json
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from haystack import Pipeline
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from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
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from haystack.components.converters import PyPDFToDocument, TextFileToDocument
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from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
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from haystack.components.routers import FileTypeRouter
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from haystack.components.joiners import DocumentJoiner
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from haystack.components.writers import DocumentWriter
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
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def test_dense_doc_search_pipeline(tmp_path, samples_path):
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# Create the indexing pipeline
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indexing_pipeline = Pipeline()
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indexing_pipeline.add_component(
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instance=FileTypeRouter(mime_types=["text/plain", "application/pdf"]), name="file_type_router"
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)
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indexing_pipeline.add_component(instance=TextFileToDocument(), name="text_file_converter")
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indexing_pipeline.add_component(instance=PyPDFToDocument(), name="pdf_file_converter")
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indexing_pipeline.add_component(instance=DocumentJoiner(), name="joiner")
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indexing_pipeline.add_component(instance=DocumentCleaner(), name="cleaner")
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indexing_pipeline.add_component(
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instance=DocumentSplitter(split_by="sentence", split_length=250, split_overlap=30), name="splitter"
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)
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indexing_pipeline.add_component(
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instance=SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="embedder"
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)
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indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="writer")
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indexing_pipeline.connect("file_type_router.text/plain", "text_file_converter.sources")
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indexing_pipeline.connect("file_type_router.application/pdf", "pdf_file_converter.sources")
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indexing_pipeline.connect("text_file_converter.documents", "joiner.documents")
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indexing_pipeline.connect("pdf_file_converter.documents", "joiner.documents")
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indexing_pipeline.connect("joiner.documents", "cleaner.documents")
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indexing_pipeline.connect("cleaner.documents", "splitter.documents")
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indexing_pipeline.connect("splitter.documents", "embedder.documents")
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indexing_pipeline.connect("embedder.documents", "writer.documents")
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# Draw the indexing pipeline
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indexing_pipeline.draw(tmp_path / "test_dense_doc_search_indexing_pipeline.png")
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# Serialize the indexing pipeline to JSON
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with open(tmp_path / "test_dense_doc_search_indexing_pipeline.json", "w") as f:
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print(json.dumps(indexing_pipeline.to_dict(), indent=4))
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json.dump(indexing_pipeline.to_dict(), f)
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# Load the indexing pipeline back
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with open(tmp_path / "test_dense_doc_search_indexing_pipeline.json", "r") as f:
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indexing_pipeline = Pipeline.from_dict(json.load(f))
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indexing_result = indexing_pipeline.run({"file_type_router": {"sources": list(samples_path.iterdir())}})
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filled_document_store = indexing_pipeline.get_component("writer").document_store
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assert indexing_result["writer"]["documents_written"] == 2
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assert filled_document_store.count_documents() == 2
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# Create the querying pipeline
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query_pipeline = Pipeline()
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query_pipeline.add_component(
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instance=SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2"), name="text_embedder"
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)
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query_pipeline.add_component(
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instance=InMemoryEmbeddingRetriever(document_store=filled_document_store, top_k=20), name="embedding_retriever"
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)
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query_pipeline.connect("text_embedder", "embedding_retriever")
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querying_result = query_pipeline.run({"text_embedder": {"text": "Who lives in Rome?"}})
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assert querying_result["embedding_retriever"]["documents"][0].content == "My name is Giorgio and I live in Rome."
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# Draw the querying pipeline
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query_pipeline.draw(tmp_path / "test_dense_doc_search_query_pipeline.png")
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# Serialize the querying pipeline to JSON
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with open(tmp_path / "test_dense_doc_search_query_pipeline.json", "w") as f:
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print(json.dumps(query_pipeline.to_dict(), indent=4))
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json.dump(query_pipeline.to_dict(), f)
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# Load the querying pipeline back
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with open(tmp_path / "test_dense_doc_search_query_pipeline.json", "r") as f:
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query_pipeline = Pipeline.from_dict(json.load(f))
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