refactor: Google GenAI embedders - adaptations for Gemini Embedding 2 general availability

integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/document_embedder.py

@@ -102,7 +102,9 @@ def __init__(
             The name of the model to use for calculating embeddings.
             The default model is `gemini-embedding-001`.
         :param prefix:
-            A string to add at the beginning of each text.
+            A string to add at the beginning of each text. It can be used to specify a task type for
+            `gemini-embedding-2`. For available task types, see
+            [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
         :param suffix:
             A string to add at the end of each text.
         :param batch_size:
@@ -114,9 +116,12 @@ def __init__(
         :param embedding_separator:
             Separator used to concatenate the metadata fields to the document text.
         :param config:
-            A dictionary of keyword arguments to configure embedding content configuration `types.EmbedContentConfig`.
-            If not specified, it defaults to `{"task_type": "SEMANTIC_SIMILARITY"}`.
-            For more information, see the [Google AI Task types](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
+            A dictionary of keyword arguments to configure embedding content configuration.
+            See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
+            for the available options.
+            Specifying task types in `config` does not take effect for `gemini-embedding-2`.
+            See [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types) for more
+            information.
         """
         self._api_key = api_key
         self._api = api
@@ -129,7 +134,7 @@ def __init__(
         self._progress_bar = progress_bar
         self._meta_fields_to_embed = meta_fields_to_embed or []
         self._embedding_separator = embedding_separator
-        self._config = config if config is not None else {"task_type": "SEMANTIC_SIMILARITY"}
+        self._config = config
 
         self._client = _get_client(
             api_key=api_key,
@@ -207,7 +212,8 @@ def _embed_batch(
             range(0, len(texts_to_embed), batch_size), disable=not self._progress_bar, desc="Calculating embeddings"
         ):
             batch = texts_to_embed[i : i + batch_size]
-            args: dict[str, Any] = {"model": self._model, "contents": batch}
+            contents = [types.Content(parts=[types.Part.from_text(text=t)]) for t in batch]
+            args: dict[str, Any] = {"model": self._model, "contents": contents}
             if resolved_config:
                 args["config"] = resolved_config
 
@@ -239,7 +245,8 @@ async def _embed_batch_async(
             range(0, len(texts_to_embed), batch_size), disable=not self._progress_bar, desc="Calculating embeddings"
         ):
             batch = texts_to_embed[i : i + batch_size]
-            args: dict[str, Any] = {"model": self._model, "contents": batch}
+            contents = [types.Content(parts=[types.Part.from_text(text=t)]) for t in batch]
+            args: dict[str, Any] = {"model": self._model, "contents": contents}
             if self._config:
                 args["config"] = types.EmbedContentConfig(**self._config) if self._config else None
 

integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/multimodal_document_embedder.py

@@ -177,7 +177,7 @@ def __init__(
         file_path_meta_field: str = "file_path",
         root_path: str | None = None,
         image_size: tuple[int, int] | None = None,
-        model: str = "gemini-embedding-2-preview",
+        model: str = "gemini-embedding-2",
         batch_size: int = 6,
         progress_bar: bool = True,
         config: dict[str, Any] | None = None,
@@ -213,11 +213,10 @@ def __init__(
         :param progress_bar:
             If `True`, shows a progress bar when running.
         :param config:
-            A dictionary of keyword arguments to configure embedding content configuration `types.EmbedContentConfig`.
+            A dictionary of keyword arguments to configure embedding content configuration.
             You can for example set the output dimensionality of the embedding: `{"output_dimensionality": 768}`.
-            It also allows customizing the task type. If the task type is not specified, it defaults to
-            `{"task_type": "RETRIEVAL_DOCUMENT"}`.
-            For more information, see the [Google AI documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
+            See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
+            for the available options.
         """
         self._api_key = api_key
         self._api = api
@@ -229,9 +228,6 @@ def __init__(
         self._image_size = image_size
         self._batch_size = batch_size
         self._progress_bar = progress_bar
-
-        config = config or {}
-        config.setdefault("task_type", "RETRIEVAL_DOCUMENT")
         self._config = config
 
         self._client = _get_client(

integrations/google_genai/src/haystack_integrations/components/embedders/google_genai/text_embedder.py

@@ -99,13 +99,18 @@ def __init__(
             The name of the model to use for calculating embeddings.
             The default model is `gemini-embedding-001`.
         :param prefix:
-            A string to add at the beginning of each text to embed.
+            A string to add at the beginning of each text. It can be used to specify a task type for
+            `gemini-embedding-2`. For available task types, see
+            [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
         :param suffix:
             A string to add at the end of each text to embed.
         :param config:
-            A dictionary of keyword arguments to configure embedding content configuration `types.EmbedContentConfig`.
-            If not specified, it defaults to `{"task_type": "SEMANTIC_SIMILARITY"}`.
-            For more information, see the [Google AI Task types](https://ai.google.dev/gemini-api/docs/embeddings#task-types).
+            A dictionary of keyword arguments to configure embedding content configuration.
+            See [Google API documentation](https://googleapis.github.io/python-genai/genai.html#genai.types.EmbedContentConfig)
+            for the available options.
+            Specifying task types in `config` does not take effect for `gemini-embedding-2`.
+            See [Gemini documentation](https://ai.google.dev/gemini-api/docs/embeddings#task-types) for more
+            information.
         """
 
         self._api_key = api_key
@@ -115,7 +120,7 @@ def __init__(
         self._model_name = model
         self._prefix = prefix
         self._suffix = suffix
-        self._config = config if config is not None else {"task_type": "SEMANTIC_SIMILARITY"}
+        self._config = config
         self._client = _get_client(
             api_key=api_key,
             api=api,

integrations/google_genai/tests/test_document_embedder.py

@@ -39,7 +39,7 @@ def test_init_default(self, monkeypatch):
         assert embedder._progress_bar is True
         assert embedder._meta_fields_to_embed == []
         assert embedder._embedding_separator == "\n"
-        assert embedder._config == {"task_type": "SEMANTIC_SIMILARITY"}
+        assert embedder._config is None
 
     def test_init_with_parameters(self, monkeypatch):
         embedder = GoogleGenAIDocumentEmbedder(
@@ -86,7 +86,7 @@ def test_to_dict(self, monkeypatch):
                 "meta_fields_to_embed": [],
                 "embedding_separator": "\n",
                 "api_key": {"type": "env_var", "env_vars": ["GOOGLE_API_KEY", "GEMINI_API_KEY"], "strict": False},
-                "config": {"task_type": "SEMANTIC_SIMILARITY"},
+                "config": None,
                 "api": "gemini",
                 "vertex_ai_project": None,
                 "vertex_ai_location": None,
@@ -277,8 +277,11 @@ def test_embed_batch_passes_full_texts(self, monkeypatch):
 
         calls = embedder._client.models.embed_content.call_args_list
         assert len(calls) == 2
-        assert calls[0].kwargs["contents"] == ["first document text", "second document text"]
-        assert calls[1].kwargs["contents"] == ["third document text"]
+        assert [c.parts[0].text for c in calls[0].kwargs["contents"]] == [
+            "first document text",
+            "second document text",
+        ]
+        assert [c.parts[0].text for c in calls[1].kwargs["contents"]] == ["third document text"]
 
     @pytest.mark.asyncio
     async def test_embed_batch_async_passes_full_texts(self, monkeypatch):
@@ -300,24 +303,32 @@ async def test_embed_batch_async_passes_full_texts(self, monkeypatch):
 
         calls = embedder._client.aio.models.embed_content.call_args_list
         assert len(calls) == 2
-        assert calls[0].kwargs["contents"] == ["first document text", "second document text"]
-        assert calls[1].kwargs["contents"] == ["third document text"]
+        assert [c.parts[0].text for c in calls[0].kwargs["contents"]] == [
+            "first document text",
+            "second document text",
+        ]
+        assert [c.parts[0].text for c in calls[1].kwargs["contents"]] == ["third document text"]
 
     @pytest.mark.skipif(
         not os.environ.get("GOOGLE_API_KEY", None),
         reason="Export an env var called GOOGLE_API_KEY containing the Google API key to run this test.",
     )
     @pytest.mark.integration
-    def test_run(self):
-        model = "gemini-embedding-001"
-
+    @pytest.mark.parametrize(
+        "model,doc_config,query_config",
+        [
+            ("gemini-embedding-001", {"task_type": "RETRIEVAL_DOCUMENT"}, {"task_type": "RETRIEVAL_QUERY"}),
+            ("gemini-embedding-2", None, None),
+        ],
+    )
+    def test_run(self, model, doc_config, query_config):
         docs = [
             Document(content="The capybara is the largest rodent in the world and lives near rivers in South America."),
             Document(content="Dogs are domesticated mammals known for their loyalty and bond with humans."),
             Document(content="The tiger is the largest big cat, recognized by its orange coat with black stripes."),
         ]
 
-        embedder = GoogleGenAIDocumentEmbedder(model=model, config={"task_type": "RETRIEVAL_DOCUMENT"})
+        embedder = GoogleGenAIDocumentEmbedder(model=model, config=doc_config)
 
         result = embedder.run(documents=docs)
         documents_with_embeddings = result["documents"]
@@ -331,7 +342,7 @@ def test_run(self):
 
         assert result["meta"]["model"] == model
 
-        text_embedder = GoogleGenAITextEmbedder(model=model, config={"task_type": "RETRIEVAL_QUERY"})
+        text_embedder = GoogleGenAITextEmbedder(model=model, config=query_config)
         query_embedding = text_embedder.run("capybara")["embedding"]
         query_vec = np.array(query_embedding)
 
@@ -349,14 +360,13 @@ def test_run(self):
         reason="Export an env var called GOOGLE_API_KEY containing the Google API key to run this test.",
     )
     @pytest.mark.integration
-    async def test_run_async(self):
+    @pytest.mark.parametrize("model", ["gemini-embedding-001", "gemini-embedding-2"])
+    async def test_run_async(self, model):
         docs = [
             Document(content="I love cheese", meta={"topic": "Cuisine"}),
             Document(content="A transformer is a deep learning architecture", meta={"topic": "ML"}),
         ]
 
-        model = "gemini-embedding-001"
-
         embedder = GoogleGenAIDocumentEmbedder(model=model, meta_fields_to_embed=["topic"], embedding_separator=" | ")
 
         result = await embedder.run_async(documents=docs)

integrations/google_genai/tests/test_multimodal_document_embedder.py

@@ -103,7 +103,7 @@ def test_to_dict(self, monkeypatch):
                 "haystack_integrations.components.embedders.google_genai.multimodal_document_embedder.GoogleGenAIMultimodalDocumentEmbedder"
             ),
             "init_parameters": {
-                "model": "gemini-embedding-2-preview",
+                "model": "gemini-embedding-2",
                 "file_path_meta_field": "file_path",
                 "root_path": None,
                 "image_size": None,
@@ -123,7 +123,7 @@ def test_from_dict(self, monkeypatch):
                 "haystack_integrations.components.embedders.google_genai.multimodal_document_embedder.GoogleGenAIMultimodalDocumentEmbedder"
             ),
             "init_parameters": {
-                "model": "gemini-embedding-2-preview",
+                "model": "gemini-embedding-2",
                 "file_path_meta_field": "file_path",
                 "root_path": "some_root_path",
                 "image_size": (1024, 1024),
@@ -140,7 +140,7 @@ def test_from_dict(self, monkeypatch):
 
         embedder = component_from_dict(GoogleGenAIMultimodalDocumentEmbedder, data, "embedder")
         assert embedder._api_key.resolve_value() == "fake-api-key"
-        assert embedder._model == "gemini-embedding-2-preview"
+        assert embedder._model == "gemini-embedding-2"
         assert embedder._file_path_meta_field == "file_path"
         assert embedder._root_path == "some_root_path"
         assert embedder._image_size == (1024, 1024)
@@ -235,7 +235,7 @@ def test_run_with_mocked_client(self, test_files_path):
         assert len(result["documents"]) == 2
         for doc in result["documents"]:
             assert doc.embedding == [0.1, 0.2, 0.3]
-        assert result["meta"]["model"] == "gemini-embedding-2-preview"
+        assert result["meta"]["model"] == "gemini-embedding-2"
 
     @pytest.mark.asyncio
     async def test_run_async_with_mocked_client(self, test_files_path):
@@ -253,7 +253,7 @@ async def test_run_async_with_mocked_client(self, test_files_path):
         result = await embedder.run_async(documents=docs)
         assert len(result["documents"]) == 1
         assert result["documents"][0].embedding == [0.4, 0.5, 0.6]
-        assert result["meta"]["model"] == "gemini-embedding-2-preview"
+        assert result["meta"]["model"] == "gemini-embedding-2"
 
     @pytest.mark.integration
     @pytest.mark.skipif(
@@ -271,7 +271,7 @@ def test_live_run(self, test_files_path):
         assert len(result["documents"]) == 2
         for doc in result["documents"]:
             assert len(doc.embedding) == 3072
-        assert result["meta"]["model"] == "gemini-embedding-2-preview"
+        assert result["meta"]["model"] == "gemini-embedding-2"
 
     @pytest.mark.integration
     @pytest.mark.asyncio
@@ -290,4 +290,4 @@ async def test_live_run_async(self, test_files_path):
         assert len(result["documents"]) == 2
         for doc in result["documents"]:
             assert len(doc.embedding) == 3072
-        assert result["meta"]["model"] == "gemini-embedding-2-preview"
+        assert result["meta"]["model"] == "gemini-embedding-2"

integrations/google_genai/tests/test_text_embedder.py

@@ -5,7 +5,7 @@
 import os
 
 import pytest
-from google.genai.types import ContentEmbedding, EmbedContentConfig, EmbedContentResponse
+from google.genai.types import ContentEmbedding, EmbedContentResponse
 from haystack.utils.auth import Secret
 
 from haystack_integrations.components.embedders.google_genai import GoogleGenAITextEmbedder
@@ -20,7 +20,7 @@ def test_init_default(self, monkeypatch):
         assert embedder._model_name == "gemini-embedding-001"
         assert embedder._prefix == ""
         assert embedder._suffix == ""
-        assert embedder._config == {"task_type": "SEMANTIC_SIMILARITY"}
+        assert embedder._config is None
         assert embedder._api == "gemini"
         assert embedder._vertex_ai_project is None
         assert embedder._vertex_ai_location is None
@@ -50,7 +50,7 @@ def test_to_dict(self, monkeypatch):
                 "model": "gemini-embedding-001",
                 "prefix": "",
                 "suffix": "",
-                "config": {"task_type": "SEMANTIC_SIMILARITY"},
+                "config": None,
                 "api": "gemini",
                 "vertex_ai_project": None,
                 "vertex_ai_location": None,
@@ -112,14 +112,6 @@ def test_prepare_input(self, monkeypatch):
         assert prepared_input == {
             "model": "gemini-embedding-001",
             "contents": "The food was delicious",
-            "config": EmbedContentConfig(
-                http_options=None,
-                task_type="SEMANTIC_SIMILARITY",
-                title=None,
-                output_dimensionality=None,
-                mime_type=None,
-                auto_truncate=None,
-            ),
         }
 
     def test_prepare_output(self, monkeypatch):
@@ -149,9 +141,8 @@ def test_run_wrong_input_format(self):
         reason="Export an env var called GOOGLE_API_KEY containing the Google API key to run this test.",
     )
     @pytest.mark.integration
-    def test_run(self):
-        model = "gemini-embedding-001"
-
+    @pytest.mark.parametrize("model", ["gemini-embedding-001", "gemini-embedding-2"])
+    def test_run(self, model):
         embedder = GoogleGenAITextEmbedder(model=model)
         result = embedder.run(text="The food was delicious")
 
@@ -166,9 +157,8 @@ def test_run(self):
         reason="Export an env var called GOOGLE_API_KEY containing the Google API key to run this test.",
     )
     @pytest.mark.integration
-    async def test_run_async(self):
-        model = "gemini-embedding-001"
-
+    @pytest.mark.parametrize("model", ["gemini-embedding-001", "gemini-embedding-2"])
+    async def test_run_async(self, model):
         embedder = GoogleGenAITextEmbedder(model=model)
         result = await embedder.run_async(text="The food was delicious")