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* 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
100 lines
4.4 KiB
Python
100 lines
4.4 KiB
Python
from haystack import Document, Pipeline
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from haystack.components.embedders import SentenceTransformersTextEmbedder
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from haystack.components.rankers import TransformersSimilarityRanker
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from haystack.components.retrievers.in_memory import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
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from haystack.components.joiners.document_joiner import DocumentJoiner
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from haystack.document_stores.in_memory import InMemoryDocumentStore
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from haystack.evaluation.eval import eval
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def test_hybrid_doc_search_pipeline():
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# Create the pipeline
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document_store = InMemoryDocumentStore()
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hybrid_pipeline = Pipeline()
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hybrid_pipeline.add_component(instance=InMemoryBM25Retriever(document_store=document_store), name="bm25_retriever")
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hybrid_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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hybrid_pipeline.add_component(
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instance=InMemoryEmbeddingRetriever(document_store=document_store), name="embedding_retriever"
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)
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hybrid_pipeline.add_component(instance=DocumentJoiner(), name="joiner")
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hybrid_pipeline.add_component(instance=TransformersSimilarityRanker(top_k=2), name="ranker")
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hybrid_pipeline.connect("bm25_retriever", "joiner")
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hybrid_pipeline.connect("text_embedder", "embedding_retriever")
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hybrid_pipeline.connect("embedding_retriever", "joiner")
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hybrid_pipeline.connect("joiner", "ranker")
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# Populate the document store
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documents = [
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Document(content="My name is Jean and I live in Paris."),
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Document(content="My name is Mark and I live in Berlin."),
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Document(content="My name is Mario and I live in the capital of Italy."),
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Document(content="My name is Giorgio and I live in Rome."),
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]
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hybrid_pipeline.get_component("bm25_retriever").document_store.write_documents(documents)
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questions = ["Who lives in Paris?", "Who lives in Berlin?", "Who lives in Rome?"]
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inputs = [
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{"bm25_retriever": {"query": question}, "text_embedder": {"text": question}, "ranker": {"query": question}}
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for question in questions
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]
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expected_outputs = [
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{
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"ranker": {
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"documents": [
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Document(
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id="6c90b78ad94e4e634e2a067b5fe2d26d4ce95405ec222cbaefaeb09ab4dce81e",
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content="My name is Jean and I live in Paris.",
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score=2.2277960777282715,
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),
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Document(
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id="10a183e965c2e107e20507c717f16559c58a8ba4bc7c577ea8dc32a8d6ca7a20",
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content="My name is Mark and I live in Berlin.",
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score=-7.304897308349609,
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),
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]
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}
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},
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{
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"ranker": {
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"documents": [
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Document(
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id="10a183e965c2e107e20507c717f16559c58a8ba4bc7c577ea8dc32a8d6ca7a20",
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content="My name is Mark and I live in Berlin.",
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score=3.694173812866211,
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),
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Document(
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id="f7533b5c6c968680d0ef8e38f366d4e68b7ac0d7238f1b1b366d15cb9c33efd8",
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content="My name is Mario and I live in the capital of Italy.",
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score=-9.008655548095703,
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),
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]
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}
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},
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{
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"ranker": {
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"documents": [
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Document(
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id="fb0f1efe94b3c78aa1c4e5a17a5ef8270f70e89d36a3665c8362675e8a769a27",
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content="My name is Giorgio and I live in Rome.",
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score=3.487802028656006,
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),
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Document(
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id="f7533b5c6c968680d0ef8e38f366d4e68b7ac0d7238f1b1b366d15cb9c33efd8",
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content="My name is Mario and I live in the capital of Italy.",
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score=-2.873128890991211,
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),
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]
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}
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},
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]
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eval_result = eval(hybrid_pipeline, inputs=inputs, expected_outputs=expected_outputs)
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assert eval_result.inputs == inputs
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assert eval_result.expected_outputs == expected_outputs
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assert len(eval_result.outputs) == len(expected_outputs) == len(inputs)
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assert eval_result.runnable.to_dict() == hybrid_pipeline.to_dict()
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