feat(langchain): Add support to google_genai provider in init_embeddings (#34388)

libs/langchain/langchain_classic/embeddings/base.py

@@ -9,6 +9,7 @@ _SUPPORTED_PROVIDERS = {
     "azure_openai": "langchain_openai",
     "bedrock": "langchain_aws",
     "cohere": "langchain_cohere",
+    "google_genai": "langchain_google_genai",
     "google_vertexai": "langchain_google_vertexai",
     "huggingface": "langchain_huggingface",
     "mistralai": "langchain_mistralai",
@@ -155,6 +156,7 @@ def init_embeddings(
             - `azure_openai`            -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
             - `bedrock`                 -> [`langchain-aws`](https://docs.langchain.com/oss/python/integrations/providers/aws)
             - `cohere`                  -> [`langchain-cohere`](https://docs.langchain.com/oss/python/integrations/providers/cohere)
+            - `google_genai`            -> [`langchain-google-genai`](https://docs.langchain.com/oss/python/integrations/providers/google)
             - `google_vertexai`         -> [`langchain-google-vertexai`](https://docs.langchain.com/oss/python/integrations/providers/google)
             - `huggingface`             -> [`langchain-huggingface`](https://docs.langchain.com/oss/python/integrations/providers/huggingface)
             - `mistraiai`               -> [`langchain-mistralai`](https://docs.langchain.com/oss/python/integrations/providers/mistralai)
@@ -207,6 +209,10 @@ def init_embeddings(
         from langchain_openai import AzureOpenAIEmbeddings
 
         return AzureOpenAIEmbeddings(model=model_name, **kwargs)
+    if provider == "google_genai":
+        from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+        return GoogleGenerativeAIEmbeddings(model=model_name, **kwargs)
     if provider == "google_vertexai":
         from langchain_google_vertexai import VertexAIEmbeddings
 

libs/langchain/tests/unit_tests/embeddings/test_base.py

@@ -9,19 +9,22 @@ from langchain_classic.embeddings.base import (
 )
 
 
-def test_parse_model_string() -> None:
+@pytest.mark.parametrize(
+    ("model_string", "expected_provider", "expected_model"),
+    [
+        ("openai:text-embedding-3-small", "openai", "text-embedding-3-small"),
+        ("bedrock:amazon.titan-embed-text-v1", "bedrock", "amazon.titan-embed-text-v1"),
+        ("huggingface:BAAI/bge-base-en:v1.5", "huggingface", "BAAI/bge-base-en:v1.5"),
+        ("google_genai:gemini-embedding-001", "google_genai", "gemini-embedding-001"),
+    ],
+)
+def test_parse_model_string(
+    model_string: str, expected_provider: str, expected_model: str
+) -> None:
     """Test parsing model strings into provider and model components."""
-    assert _parse_model_string("openai:text-embedding-3-small") == (
+    assert _parse_model_string(model_string) == (
-        "openai",
+        expected_provider,
-        "text-embedding-3-small",
+        expected_model,
-    )
-    assert _parse_model_string("bedrock:amazon.titan-embed-text-v1") == (
-        "bedrock",
-        "amazon.titan-embed-text-v1",
-    )
-    assert _parse_model_string("huggingface:BAAI/bge-base-en:v1.5") == (
-        "huggingface",
-        "BAAI/bge-base-en:v1.5",
     )
 
 

libs/langchain_v1/langchain/embeddings/base.py

@@ -10,6 +10,7 @@ _SUPPORTED_PROVIDERS = {
     "azure_openai": "langchain_openai",
     "bedrock": "langchain_aws",
     "cohere": "langchain_cohere",
+    "google_genai": "langchain_google_genai",
     "google_vertexai": "langchain_google_vertexai",
     "huggingface": "langchain_huggingface",
     "mistralai": "langchain_mistralai",
@@ -207,6 +208,10 @@ def init_embeddings(
         from langchain_openai import AzureOpenAIEmbeddings
 
         return AzureOpenAIEmbeddings(model=model_name, **kwargs)
+    if provider == "google_genai":
+        from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+        return GoogleGenerativeAIEmbeddings(model=model_name, **kwargs)
     if provider == "google_vertexai":
         from langchain_google_vertexai import VertexAIEmbeddings
 

libs/langchain_v1/tests/unit_tests/embeddings/test_base.py

@@ -9,19 +9,20 @@ from langchain.embeddings.base import (
 )
 
 
-def test_parse_model_string() -> None:
+@pytest.mark.parametrize(
+    ("model_string", "expected_provider", "expected_model"),
+    [
+        ("openai:text-embedding-3-small", "openai", "text-embedding-3-small"),
+        ("bedrock:amazon.titan-embed-text-v1", "bedrock", "amazon.titan-embed-text-v1"),
+        ("huggingface:BAAI/bge-base-en:v1.5", "huggingface", "BAAI/bge-base-en:v1.5"),
+        ("google_genai:gemini-embedding-001", "google_genai", "gemini-embedding-001"),
+    ],
+)
+def test_parse_model_string(model_string: str, expected_provider: str, expected_model: str) -> None:
     """Test parsing model strings into provider and model components."""
-    assert _parse_model_string("openai:text-embedding-3-small") == (
+    assert _parse_model_string(model_string) == (
-        "openai",
+        expected_provider,
-        "text-embedding-3-small",
+        expected_model,
-    )
-    assert _parse_model_string("bedrock:amazon.titan-embed-text-v1") == (
-        "bedrock",
-        "amazon.titan-embed-text-v1",
-    )
-    assert _parse_model_string("huggingface:BAAI/bge-base-en:v1.5") == (
-        "huggingface",
-        "BAAI/bge-base-en:v1.5",
     )