feat: Update FastembedRanker to auto call warm_up on first run

integrations/fastembed/examples/ranker_example.py

@@ -11,7 +11,6 @@
 ]
 
 ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-6-v2")
-ranker.warm_up()
 reranked_documents = ranker.run(query=query, documents=documents)["documents"]
 
 

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_document_embedder.py

@@ -29,8 +29,6 @@ class FastembedDocumentEmbedder:
         batch_size=256,
     )
 
-    doc_embedder.warm_up()
-
     # Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
     document_list = [
         Document(

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_document_embedder.py

@@ -28,8 +28,6 @@ class FastembedSparseDocumentEmbedder:
         batch_size=32,
     )
 
-    sparse_doc_embedder.warm_up()
-
     # Text taken from PubMed QA Dataset (https://huggingface.co/datasets/pubmed_qa)
     document_list = [
         Document(

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_sparse_text_embedder.py

@@ -28,7 +28,6 @@ class FastembedSparseTextEmbedder:
     sparse_text_embedder = FastembedSparseTextEmbedder(
         model="prithivida/Splade_PP_en_v1"
     )
-    sparse_text_embedder.warm_up()
 
     sparse_embedding = sparse_text_embedder.run(text)["sparse_embedding"]
     ```

integrations/fastembed/src/haystack_integrations/components/embedders/fastembed/fastembed_text_embedder.py

@@ -24,7 +24,6 @@ class FastembedTextEmbedder:
     text_embedder = FastembedTextEmbedder(
         model="BAAI/bge-small-en-v1.5"
     )
-    text_embedder.warm_up()
 
     embedding = text_embedder.run(text)["embedding"]
     ```

integrations/fastembed/src/haystack_integrations/components/rankers/fastembed/ranker.py

@@ -177,13 +177,12 @@ def run(self, query: str, documents: list[Document], top_k: int | None = None) -
             raise ValueError(msg)
 
         if self._model is None:
-            msg = "The ranker model has not been loaded. Please call warm_up() before running."
-            raise RuntimeError(msg)
+            self.warm_up()
 
         fastembed_input_docs = self._prepare_fastembed_input_docs(documents)
 
         scores = list(
-            self._model.rerank(
+            self._model.rerank(  # type: ignore[union-attr]
                 query=query,
                 documents=fastembed_input_docs,
                 batch_size=self.batch_size,

integrations/fastembed/tests/test_fastembed_document_embedder.py

@@ -281,12 +281,25 @@ def test_embed_metadata(self):
             parallel=None,
         )
 
+    def test_run_calls_warm_up(self):
+        embedder = FastembedDocumentEmbedder()
+        assert embedder.embedding_backend is None
+
+        mock_backend = MagicMock()
+        mock_backend.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        with patch.object(
+            embedder, "warm_up", side_effect=lambda: setattr(embedder, "embedding_backend", mock_backend)
+        ) as mock_warm_up:
+            embedder.run(documents=[Document(content="test document")])
+
+        mock_warm_up.assert_called_once()
+
     @pytest.mark.integration
     def test_run(self):
         embedder = FastembedDocumentEmbedder(
             model="BAAI/bge-small-en-v1.5",
         )
-        embedder.warm_up()
 
         doc = Document(content="Parton energy loss in QCD matter")
 

integrations/fastembed/tests/test_fastembed_ranker.py

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 
-from unittest.mock import MagicMock
+from unittest.mock import MagicMock, patch
 
 import numpy as np
 import pytest
@@ -257,10 +257,23 @@ def test_embed_metadata(self):
             parallel=None,
         )
 
+    def test_run_calls_warm_up(self):
+        """
+        Unit test to check that warm_up is called when run is called for the first time.
+        """
+        ranker = FastembedRanker()
+
+        mock_model = MagicMock()
+        mock_model.rerank.return_value = [0.5]
+
+        with patch.object(ranker, "warm_up", side_effect=lambda: setattr(ranker, "_model", mock_model)) as mock_warm_up:
+            ranker.run(query="test query", documents=[Document(content="test document")])
+
+        mock_warm_up.assert_called_once()
+
     @pytest.mark.integration
     def test_run(self):
         ranker = FastembedRanker(model_name="Xenova/ms-marco-MiniLM-L-6-v2", top_k=2)
-        ranker.warm_up()
 
         query = "Who is maintaining Qdrant?"
         documents = [

integrations/fastembed/tests/test_fastembed_sparse_document_embedder.py

@@ -304,6 +304,19 @@ def test_init_with_model_kwargs_parameters(self):
 
         assert embedder.model_kwargs == bm25_config
 
+    def test_run_calls_warm_up(self):
+        embedder = FastembedSparseDocumentEmbedder()
+
+        mock_backend = MagicMock()
+        mock_backend.embed.return_value = [{"indices": [0], "values": [0.5]}]
+
+        with patch.object(
+            embedder, "warm_up", side_effect=lambda: setattr(embedder, "embedding_backend", mock_backend)
+        ) as mock_warm_up:
+            embedder.run(documents=[Document(content="test document")])
+
+        mock_warm_up.assert_called_once()
+
     @pytest.mark.integration
     def test_run_with_model_kwargs(self):
         """
@@ -317,7 +330,6 @@ def test_run_with_model_kwargs(self):
             model="Qdrant/bm42-all-minilm-l6-v2-attentions",
             model_kwargs=bm42_config,
         )
-        embedder.warm_up()
 
         doc = Document(content="Example content using BM42")
 
@@ -336,7 +348,6 @@ def test_run(self):
         embedder = FastembedSparseDocumentEmbedder(
             model="prithivida/Splade_PP_en_v1",
         )
-        embedder.warm_up()
 
         doc = Document(content="Parton energy loss in QCD matter")
 

integrations/fastembed/tests/test_fastembed_sparse_text_embedder.py

@@ -224,6 +224,20 @@ def test_init_with_model_kwargs_parameters(self):
 
         assert embedder.model_kwargs == bm25_config
 
+    def test_run_calls_warm_up(self):
+        embedder = FastembedSparseTextEmbedder()
+        assert embedder.embedding_backend is None
+
+        mock_backend = MagicMock()
+        mock_backend.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        with patch.object(
+            embedder, "warm_up", side_effect=lambda: setattr(embedder, "embedding_backend", mock_backend)
+        ) as mock_warm_up:
+            embedder.run(text="test text")
+
+        mock_warm_up.assert_called_once()
+
     @pytest.mark.integration
     def test_run_with_model_kwargs(self):
         """
@@ -258,7 +272,6 @@ def test_run(self):
         embedder = FastembedSparseTextEmbedder(
             model="prithivida/Splade_PP_en_v1",
         )
-        embedder.warm_up()
 
         text = "Parton energy loss in QCD matter"
 

integrations/fastembed/tests/test_fastembed_text_embedder.py

@@ -202,12 +202,25 @@ def test_run_wrong_incorrect_format(self):
         with pytest.raises(TypeError, match="FastembedTextEmbedder expects a string as input"):
             embedder.run(text=list_integers_input)
 
+    def test_run_calls_warm_up(self):
+        embedder = FastembedTextEmbedder()
+        assert embedder.embedding_backend is None
+
+        mock_backend = MagicMock()
+        mock_backend.embed.return_value = [[0.1, 0.2, 0.3]]
+
+        with patch.object(
+            embedder, "warm_up", side_effect=lambda: setattr(embedder, "embedding_backend", mock_backend)
+        ) as mock_warm_up:
+            embedder.run(text="test text")
+
+        mock_warm_up.assert_called_once()
+
     @pytest.mark.integration
     def test_run(self):
         embedder = FastembedTextEmbedder(
             model="BAAI/bge-small-en-v1.5",
         )
-        embedder.warm_up()
 
         text = "Parton energy loss in QCD matter"