test: replace ElasticsearchDS with InMemoryDS when it makes sense; support scale_score in InMemoryDS (#4283)

* replace elasticds with imds - first draft

* fix

* fix tests and implement scale_score in imds bm25

* add docstrings for scale_score

haystack/document_stores/memory.py

@@ -16,6 +16,7 @@ import torch
 from tqdm.auto import tqdm
 import rank_bm25
 import pandas as pd
+from scipy.special import expit
 
 from haystack.schema import Document, FilterType, Label
 from haystack.errors import DuplicateDocumentError, DocumentStoreError
@@ -953,7 +954,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         index: Optional[str] = None,
         headers: Optional[Dict[str, str]] = None,
         all_terms_must_match: bool = False,
-        scale_score: bool = False,
+        scale_score: bool = True,
     ) -> List[Document]:
         """
         Scan through documents in DocumentStore and return a small number documents
@@ -961,6 +962,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         :param query: The query.
         :param top_k: How many documents to return per query.
         :param index: The name of the index in the DocumentStore from which to retrieve documents.
+        :param scale_score: Whether to scale the similarity score to the unit interval (range of [0,1]).
         """
 
         if headers:
@@ -973,10 +975,6 @@ class InMemoryDocumentStore(KeywordDocumentStore):
             logger.warning(
                 "InMemoryDocumentStore does not support filters for BM25 retrieval. This parameter is ignored."
             )
-        if scale_score is True:
-            logger.warning(
-                "InMemoryDocumentStore does not support scale_score for BM25 retrieval. This parameter is ignored."
-            )
 
         index = index or self.index
         if index not in self.bm25:
@@ -989,6 +987,9 @@ class InMemoryDocumentStore(KeywordDocumentStore):
 
         tokenized_query = self.bm25_tokenization_regex(query.lower())
         docs_scores = self.bm25[index].get_scores(tokenized_query)
+        if scale_score is True:
+            # scaling probability from BM25
+            docs_scores = [float(expit(np.asarray(score / 8))) for score in docs_scores]
         top_docs_positions = np.argsort(docs_scores)[::-1][:top_k]
 
         textual_docs_list = [doc for doc in self.indexes[index].values() if doc.content_type in ["text", "table"]]
@@ -1009,7 +1010,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         index: Optional[str] = None,
         headers: Optional[Dict[str, str]] = None,
         all_terms_must_match: bool = False,
-        scale_score: bool = False,
+        scale_score: bool = True,
     ) -> List[List[Document]]:
         """
         Scan through documents in DocumentStore and return a small number documents
@@ -1018,6 +1019,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         :param query: The query.
         :param top_k: How many documents to return per query.
         :param index: The name of the index in the DocumentStore from which to retrieve documents.
+        :param scale_score: Whether to scale the similarity score to the unit interval (range of [0,1]).
         """
 
         if headers:
@@ -1030,10 +1032,6 @@ class InMemoryDocumentStore(KeywordDocumentStore):
             logger.warning(
                 "InMemoryDocumentStore does not support filters for BM25 retrieval. This parameter is ignored."
             )
-        if scale_score is True:
-            logger.warning(
-                "InMemoryDocumentStore does not support scale_score for BM25 retrieval. This parameter is ignored."
-            )
 
         index = index or self.index
         if index not in self.bm25:
@@ -1043,6 +1041,6 @@ class InMemoryDocumentStore(KeywordDocumentStore):
 
         result_documents = []
         for query in queries:
-            result_documents.append(self.query(query=query, top_k=top_k, index=index))
+            result_documents.append(self.query(query=query, top_k=top_k, index=index, scale_score=scale_score))
 
         return result_documents

test/conftest.py

@@ -863,6 +863,7 @@ def get_document_store(
             index=index,
             similarity=similarity,
             use_bm25=True,
+            bm25_parameters={"k1": 1.2, "b": 0.75},  # parameters similar to those of Elasticsearch
         )
 
     elif document_store_type == "elasticsearch":

test/nodes/test_extractor_translation.py

@@ -1,21 +1,17 @@
 import pytest
 
 from haystack.pipelines import TranslationWrapperPipeline, ExtractiveQAPipeline
-from haystack.nodes import DensePassageRetriever, EmbeddingRetriever
 from .test_summarizer import SPLIT_DOCS
 
 
 # Keeping few (retriever,document_store,reader) combination to reduce test time
 @pytest.mark.integration
-@pytest.mark.elasticsearch
 @pytest.mark.summarizer
 @pytest.mark.parametrize("retriever,document_store,reader", [("embedding", "memory", "farm")], indirect=True)
 def test_extractive_qa_pipeline_with_translator(
     document_store, retriever, reader, en_to_de_translator, de_to_en_translator
 ):
     document_store.write_documents(SPLIT_DOCS)
-
-    if isinstance(retriever, EmbeddingRetriever) or isinstance(retriever, DensePassageRetriever):
     document_store.update_embeddings(retriever=retriever)
 
     query = "Wo steht der Eiffelturm?"

test/pipelines/test_eval.py

@@ -161,6 +161,7 @@ def test_eval_reader(reader, document_store, use_confidence_scores):
         assert reader_eval_results["top_n_accuracy"] == 100.0
 
 
+# using ElasticsearchDocumentStore, since InMemoryDocumentStore doesn't return meaningful BM25 scores when there are very few documents
 @pytest.mark.elasticsearch
 @pytest.mark.parametrize("document_store", ["elasticsearch"], indirect=True)
 @pytest.mark.parametrize("open_domain", [True, False])

test/pipelines/test_pipeline_debug_and_validation.py

@@ -21,8 +21,7 @@ class MockRetriever(BaseMockRetriever):
             raise ValueError("TEST ERROR!")
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_node_names_validation(document_store_with_docs, tmp_path):
     pipeline = Pipeline()
     pipeline.add_node(
@@ -52,28 +51,27 @@ def test_node_names_validation(document_store_with_docs, tmp_path):
     assert "top_k" not in exception_raised
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_debug_attributes_global(document_store_with_docs, tmp_path):
-    es_retriever = BM25Retriever(document_store=document_store_with_docs)
+    bm25_retriever = BM25Retriever(document_store=document_store_with_docs)
     reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2", num_processes=0)
 
     pipeline = Pipeline()
-    pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
+    pipeline.add_node(component=bm25_retriever, name="BM25Retriever", inputs=["Query"])
-    pipeline.add_node(component=reader, name="Reader", inputs=["ESRetriever"])
+    pipeline.add_node(component=reader, name="Reader", inputs=["BM25Retriever"])
 
     prediction = pipeline.run(
-        query="Who lives in Berlin?", params={"ESRetriever": {"top_k": 10}, "Reader": {"top_k": 3}}, debug=True
+        query="Who lives in Berlin?", params={"BM25Retriever": {"top_k": 10}, "Reader": {"top_k": 3}}, debug=True
     )
     assert "_debug" in prediction.keys()
-    assert "ESRetriever" in prediction["_debug"].keys()
+    assert "BM25Retriever" in prediction["_debug"].keys()
     assert "Reader" in prediction["_debug"].keys()
-    assert "input" in prediction["_debug"]["ESRetriever"].keys()
+    assert "input" in prediction["_debug"]["BM25Retriever"].keys()
-    assert "output" in prediction["_debug"]["ESRetriever"].keys()
+    assert "output" in prediction["_debug"]["BM25Retriever"].keys()
     assert "input" in prediction["_debug"]["Reader"].keys()
     assert "output" in prediction["_debug"]["Reader"].keys()
-    assert prediction["_debug"]["ESRetriever"]["input"]
+    assert prediction["_debug"]["BM25Retriever"]["input"]
-    assert prediction["_debug"]["ESRetriever"]["output"]
+    assert prediction["_debug"]["BM25Retriever"]["output"]
     assert prediction["_debug"]["Reader"]["input"]
     assert prediction["_debug"]["Reader"]["output"]
 
@@ -81,57 +79,55 @@ def test_debug_attributes_global(document_store_with_docs, tmp_path):
     json.dumps(prediction, default=str)
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_debug_attributes_per_node(document_store_with_docs, tmp_path):
-    es_retriever = BM25Retriever(document_store=document_store_with_docs)
+    bm25_retriever = BM25Retriever(document_store=document_store_with_docs)
     reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2", num_processes=0)
 
     pipeline = Pipeline()
-    pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
+    pipeline.add_node(component=bm25_retriever, name="BM25Retriever", inputs=["Query"])
-    pipeline.add_node(component=reader, name="Reader", inputs=["ESRetriever"])
+    pipeline.add_node(component=reader, name="Reader", inputs=["BM25Retriever"])
 
     prediction = pipeline.run(
-        query="Who lives in Berlin?", params={"ESRetriever": {"top_k": 10, "debug": True}, "Reader": {"top_k": 3}}
+        query="Who lives in Berlin?", params={"BM25Retriever": {"top_k": 10, "debug": True}, "Reader": {"top_k": 3}}
     )
     assert "_debug" in prediction.keys()
-    assert "ESRetriever" in prediction["_debug"].keys()
+    assert "BM25Retriever" in prediction["_debug"].keys()
     assert "Reader" not in prediction["_debug"].keys()
-    assert "input" in prediction["_debug"]["ESRetriever"].keys()
+    assert "input" in prediction["_debug"]["BM25Retriever"].keys()
-    assert "output" in prediction["_debug"]["ESRetriever"].keys()
+    assert "output" in prediction["_debug"]["BM25Retriever"].keys()
-    assert prediction["_debug"]["ESRetriever"]["input"]
+    assert prediction["_debug"]["BM25Retriever"]["input"]
-    assert prediction["_debug"]["ESRetriever"]["output"]
+    assert prediction["_debug"]["BM25Retriever"]["output"]
 
     # Avoid circular reference: easiest way to detect those is to use json.dumps
     json.dumps(prediction, default=str)
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_debug_attributes_for_join_nodes(document_store_with_docs, tmp_path):
-    es_retriever_1 = BM25Retriever(document_store=document_store_with_docs)
+    bm25_retriever_1 = BM25Retriever(document_store=document_store_with_docs)
-    es_retriever_2 = BM25Retriever(document_store=document_store_with_docs)
+    bm25_retriever_2 = BM25Retriever(document_store=document_store_with_docs)
 
     pipeline = Pipeline()
-    pipeline.add_node(component=es_retriever_1, name="ESRetriever1", inputs=["Query"])
+    pipeline.add_node(component=bm25_retriever_1, name="BM25Retriever1", inputs=["Query"])
-    pipeline.add_node(component=es_retriever_2, name="ESRetriever2", inputs=["Query"])
+    pipeline.add_node(component=bm25_retriever_2, name="BM25Retriever2", inputs=["Query"])
-    pipeline.add_node(component=JoinDocuments(), name="JoinDocuments", inputs=["ESRetriever1", "ESRetriever2"])
+    pipeline.add_node(component=JoinDocuments(), name="JoinDocuments", inputs=["BM25Retriever1", "BM25Retriever2"])
 
     prediction = pipeline.run(query="Who lives in Berlin?", debug=True)
     assert "_debug" in prediction.keys()
-    assert "ESRetriever1" in prediction["_debug"].keys()
+    assert "BM25Retriever1" in prediction["_debug"].keys()
-    assert "ESRetriever2" in prediction["_debug"].keys()
+    assert "BM25Retriever2" in prediction["_debug"].keys()
     assert "JoinDocuments" in prediction["_debug"].keys()
-    assert "input" in prediction["_debug"]["ESRetriever1"].keys()
+    assert "input" in prediction["_debug"]["BM25Retriever1"].keys()
-    assert "output" in prediction["_debug"]["ESRetriever1"].keys()
+    assert "output" in prediction["_debug"]["BM25Retriever1"].keys()
-    assert "input" in prediction["_debug"]["ESRetriever2"].keys()
+    assert "input" in prediction["_debug"]["BM25Retriever2"].keys()
-    assert "output" in prediction["_debug"]["ESRetriever2"].keys()
+    assert "output" in prediction["_debug"]["BM25Retriever2"].keys()
     assert "input" in prediction["_debug"]["JoinDocuments"].keys()
     assert "output" in prediction["_debug"]["JoinDocuments"].keys()
-    assert prediction["_debug"]["ESRetriever1"]["input"]
+    assert prediction["_debug"]["BM25Retriever1"]["input"]
-    assert prediction["_debug"]["ESRetriever1"]["output"]
+    assert prediction["_debug"]["BM25Retriever1"]["output"]
-    assert prediction["_debug"]["ESRetriever2"]["input"]
+    assert prediction["_debug"]["BM25Retriever2"]["input"]
-    assert prediction["_debug"]["ESRetriever2"]["output"]
+    assert prediction["_debug"]["BM25Retriever2"]["output"]
     assert prediction["_debug"]["JoinDocuments"]["input"]
     assert prediction["_debug"]["JoinDocuments"]["output"]
 
@@ -139,37 +135,36 @@ def test_debug_attributes_for_join_nodes(document_store_with_docs, tmp_path):
     json.dumps(prediction, default=str)
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_with_docs", ["elasticsearch"], indirect=True)
 def test_global_debug_attributes_override_node_ones(document_store_with_docs, tmp_path):
-    es_retriever = BM25Retriever(document_store=document_store_with_docs)
+    bm25_retriever = BM25Retriever(document_store=document_store_with_docs)
     reader = FARMReader(model_name_or_path="deepset/minilm-uncased-squad2", num_processes=0)
 
     pipeline = Pipeline()
-    pipeline.add_node(component=es_retriever, name="ESRetriever", inputs=["Query"])
+    pipeline.add_node(component=bm25_retriever, name="BM25Retriever", inputs=["Query"])
-    pipeline.add_node(component=reader, name="Reader", inputs=["ESRetriever"])
+    pipeline.add_node(component=reader, name="Reader", inputs=["BM25Retriever"])
 
     prediction = pipeline.run(
         query="Who lives in Berlin?",
-        params={"ESRetriever": {"top_k": 10, "debug": True}, "Reader": {"top_k": 3, "debug": True}},
+        params={"BM25Retriever": {"top_k": 10, "debug": True}, "Reader": {"top_k": 3, "debug": True}},
         debug=False,
     )
     assert "_debug" not in prediction.keys()
 
     prediction = pipeline.run(
         query="Who lives in Berlin?",
-        params={"ESRetriever": {"top_k": 10, "debug": False}, "Reader": {"top_k": 3, "debug": False}},
+        params={"BM25Retriever": {"top_k": 10, "debug": False}, "Reader": {"top_k": 3, "debug": False}},
         debug=True,
     )
     assert "_debug" in prediction.keys()
-    assert "ESRetriever" in prediction["_debug"].keys()
+    assert "BM25Retriever" in prediction["_debug"].keys()
     assert "Reader" in prediction["_debug"].keys()
-    assert "input" in prediction["_debug"]["ESRetriever"].keys()
+    assert "input" in prediction["_debug"]["BM25Retriever"].keys()
-    assert "output" in prediction["_debug"]["ESRetriever"].keys()
+    assert "output" in prediction["_debug"]["BM25Retriever"].keys()
     assert "input" in prediction["_debug"]["Reader"].keys()
     assert "output" in prediction["_debug"]["Reader"].keys()
-    assert prediction["_debug"]["ESRetriever"]["input"]
+    assert prediction["_debug"]["BM25Retriever"]["input"]
-    assert prediction["_debug"]["ESRetriever"]["output"]
+    assert prediction["_debug"]["BM25Retriever"]["output"]
     assert prediction["_debug"]["Reader"]["input"]
     assert prediction["_debug"]["Reader"]["output"]
 

test/pipelines/test_standard_pipelines.py

@@ -293,10 +293,9 @@ def test_most_similar_documents_pipeline_with_filters_batch(retriever, document_
             assert document.meta["source"] in ["wiki3", "wiki4", "wiki5"]
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 def test_join_merge_no_weights(document_store_dot_product_with_docs):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -309,17 +308,16 @@ def test_join_merge_no_weights(document_store_dot_product_with_docs):
 
     join_node = JoinDocuments(join_mode="merge")
     p = Pipeline()
-    p.add_node(component=es, name="R1", inputs=["Query"])
+    p.add_node(component=bm25, 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"]) == 5
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 def test_join_merge_with_weights(document_store_dot_product_with_docs):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -332,18 +330,17 @@ def test_join_merge_with_weights(document_store_dot_product_with_docs):
 
     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=bm25, 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 math.isclose(results["documents"][0].score, 0.5481393431183286, rel_tol=0.0001)
+    assert math.isclose(results["documents"][0].score, 0.5336782589721345, rel_tol=0.0001)
     assert len(results["documents"]) == 2
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 def test_join_concatenate(document_store_dot_product_with_docs):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -356,17 +353,16 @@ def test_join_concatenate(document_store_dot_product_with_docs):
 
     join_node = JoinDocuments(join_mode="concatenate")
     p = Pipeline()
-    p.add_node(component=es, name="R1", inputs=["Query"])
+    p.add_node(component=bm25, 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"]) == 5
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 def test_join_concatenate_with_topk(document_store_dot_product_with_docs):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -379,7 +375,7 @@ def test_join_concatenate_with_topk(document_store_dot_product_with_docs):
 
     join_node = JoinDocuments(join_mode="concatenate")
     p = Pipeline()
-    p.add_node(component=es, name="R1", inputs=["Query"])
+    p.add_node(component=bm25, name="R1", inputs=["Query"])
     p.add_node(component=dpr, name="R2", inputs=["Query"])
     p.add_node(component=join_node, name="Join", inputs=["R1", "R2"])
     one_result = p.run(query=query, params={"Join": {"top_k_join": 1}})
@@ -388,11 +384,10 @@ def test_join_concatenate_with_topk(document_store_dot_product_with_docs):
     assert len(two_results["documents"]) == 2
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 @pytest.mark.parametrize("reader", ["farm"], indirect=True)
 def test_join_with_reader(document_store_dot_product_with_docs, reader):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -405,7 +400,7 @@ def test_join_with_reader(document_store_dot_product_with_docs, reader):
 
     join_node = JoinDocuments()
     p = Pipeline()
-    p.add_node(component=es, name="R1", inputs=["Query"])
+    p.add_node(component=bm25, 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"])
@@ -414,10 +409,9 @@ def test_join_with_reader(document_store_dot_product_with_docs, reader):
     assert results["answers"][0].answer == "Berlin" or results["answers"][1].answer == "Berlin"
 
 
-@pytest.mark.elasticsearch
+@pytest.mark.parametrize("document_store_dot_product_with_docs", ["memory"], indirect=True)
-@pytest.mark.parametrize("document_store_dot_product_with_docs", ["elasticsearch"], indirect=True)
 def test_join_with_rrf(document_store_dot_product_with_docs):
-    es = BM25Retriever(document_store=document_store_dot_product_with_docs)
+    bm25 = BM25Retriever(document_store=document_store_dot_product_with_docs)
     dpr = DensePassageRetriever(
         document_store=document_store_dot_product_with_docs,
         query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
@@ -430,7 +424,7 @@ def test_join_with_rrf(document_store_dot_product_with_docs):
 
     join_node = JoinDocuments(join_mode="reciprocal_rank_fusion")
     p = Pipeline()
-    p.add_node(component=es, name="R1", inputs=["Query"])
+    p.add_node(component=bm25, 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)
@@ -444,7 +438,9 @@ def test_join_with_rrf(document_store_dot_product_with_docs):
         0.031009615384615385,
     ]
 
-    assert all([doc.score == expected_scores[idx] for idx, doc in enumerate(results["documents"])])
+    assert all(
+        doc.score == pytest.approx(expected_scores[idx], abs=1e-3) for idx, doc in enumerate(results["documents"])
+    )
 
 
 def test_query_keyword_statement_classifier():