fix: ignore dimension for custom mock embeddings

haystack/components/embedders/mock_document_embedder.py

@@ -81,13 +81,13 @@ def __init__(
         :param meta_fields_to_embed: List of metadata fields to embed along with the document text.
         :param embedding_separator: Separator used to concatenate the metadata fields to the document text.
         :param progress_bar: Accepted for interface compatibility with real Document Embedders and ignored.
-        :raises ValueError: If both `embedding` and `embedding_fn` are provided, if `dimension` is not positive, or
-            if `embedding` is an empty list.
+        :raises ValueError: If both `embedding` and `embedding_fn` are provided, if `embedding` is an empty list,
+            or if neither is provided and `dimension` is not positive.
         :raises TypeError: If `embedding` is not a sequence of numbers.
         """
         if embedding is not None and embedding_fn is not None:
             raise ValueError("Pass either 'embedding' or 'embedding_fn', not both.")
-        if dimension <= 0:
+        if embedding is None and embedding_fn is None and dimension <= 0:
             raise ValueError("'dimension' must be a positive integer.")
 
         self.embedding = _coerce_embedding(embedding, name="'embedding'") if embedding is not None else None

haystack/components/embedders/mock_text_embedder.py

@@ -70,13 +70,13 @@ def __init__(
         :param meta: Additional metadata merged into the output `meta`.
         :param prefix: A string to add at the beginning of the text before embedding.
         :param suffix: A string to add at the end of the text before embedding.
-        :raises ValueError: If both `embedding` and `embedding_fn` are provided, if `dimension` is not positive, or
-            if `embedding` is an empty list.
+        :raises ValueError: If both `embedding` and `embedding_fn` are provided, if `embedding` is an empty list,
+            or if neither is provided and `dimension` is not positive.
         :raises TypeError: If `embedding` is not a sequence of numbers.
         """
         if embedding is not None and embedding_fn is not None:
             raise ValueError("Pass either 'embedding' or 'embedding_fn', not both.")
-        if dimension <= 0:
+        if embedding is None and embedding_fn is None and dimension <= 0:
             raise ValueError("'dimension' must be a positive integer.")
 
         self.embedding = _coerce_embedding(embedding, name="'embedding'") if embedding is not None else None

releasenotes/notes/fix-mock-embedder-dimension-f58b01fc4f125811.yaml

@@ -0,0 +1,6 @@
+fixes:
+  - |
+    ``MockTextEmbedder`` and ``MockDocumentEmbedder`` now accept non-positive
+    ``dimension`` values when ``embedding`` or ``embedding_fn`` is provided,
+    matching the documented behavior. The default deterministic embedding mode
+    still requires a positive ``dimension``.

test/components/embedders/test_mock_document_embedder.py

@@ -18,6 +18,7 @@ class TestMockDocumentEmbedder:
         ("args", "kwargs", "match"),
         [
             (([0.1],), {"embedding_fn": _ones}, "either 'embedding' or 'embedding_fn'"),
+            ((), {"dimension": 0}, "must be a positive integer"),
             ((), {"dimension": -1}, "must be a positive integer"),
         ],
     )
@@ -38,12 +39,16 @@ def test_consistent_with_text_embedder(self):
         doc_embedding = MockDocumentEmbedder(dimension=8).run([Document(content="pizza")])["documents"][0].embedding
         assert text_embedding == doc_embedding
 
-    def test_fixed_embedding(self):
-        result = MockDocumentEmbedder([0.5, 0.5]).run([Document(content="a"), Document(content="b")])
+    @pytest.mark.parametrize("dimension", [768, 0, -1])
+    def test_fixed_embedding(self, dimension):
+        embedder = MockDocumentEmbedder([0.5, 0.5], dimension=dimension)
+        result = embedder.run([Document(content="a"), Document(content="b")])
         assert all(doc.embedding == [0.5, 0.5] for doc in result["documents"])
 
-    def test_embedding_fn(self):
-        result = MockDocumentEmbedder(embedding_fn=_ones).run([Document(content="a")])
+    @pytest.mark.parametrize("dimension", [768, 0, -1])
+    def test_embedding_fn(self, dimension):
+        embedder = MockDocumentEmbedder(embedding_fn=_ones, dimension=dimension)
+        result = embedder.run([Document(content="a")])
         assert result["documents"][0].embedding == [1.0, 1.0, 1.0]
 
     def test_meta_fields_to_embed_affect_embedding(self):

test/components/embedders/test_mock_text_embedder.py

@@ -27,6 +27,7 @@ class TestMockTextEmbedder:
         [
             (([0.1, 0.2],), {"embedding_fn": _ones}, ValueError, "either 'embedding' or 'embedding_fn'"),
             ((), {"dimension": 0}, ValueError, "must be a positive integer"),
+            ((), {"dimension": -1}, ValueError, "must be a positive integer"),
             (([],), {}, ValueError, "must not be empty"),
             ((["not", "numbers"],), {}, TypeError, "must be a sequence of numbers"),
         ],
@@ -51,13 +52,16 @@ def test_deterministic_distinguishes_texts(self):
             MockTextEmbedder(dimension=8).run("x")["embedding"] == MockTextEmbedder(dimension=8).run("x")["embedding"]
         )
 
-    def test_fixed_embedding(self):
-        embedder = MockTextEmbedder([0.1, 0.2, 0.3])
+    @pytest.mark.parametrize("dimension", [768, 0, -1])
+    def test_fixed_embedding(self, dimension):
+        embedder = MockTextEmbedder([0.1, 0.2, 0.3], dimension=dimension)
         assert embedder.run("anything")["embedding"] == [0.1, 0.2, 0.3]
         assert embedder.run("something else")["embedding"] == [0.1, 0.2, 0.3]
 
-    def test_embedding_fn(self):
-        assert MockTextEmbedder(embedding_fn=_ones).run("hello")["embedding"] == [1.0, 1.0, 1.0]
+    @pytest.mark.parametrize("dimension", [768, 0, -1])
+    def test_embedding_fn(self, dimension):
+        embedder = MockTextEmbedder(embedding_fn=_ones, dimension=dimension)
+        assert embedder.run("hello")["embedding"] == [1.0, 1.0, 1.0]
 
     def test_embedding_fn_invalid_return_raises(self):
         # embedding_fn deliberately returns a non-vector to exercise the runtime type check