feat: add EmbeddingModelComponent for generating embeddings (#7204)

* feat: add EmbeddingModelComponent for generating embeddings

- Introduced a new EmbeddingModelComponent to generate embeddings using specified providers, starting with OpenAI.
- Updated the __init__.py file to include the new component in the exports.
- The component includes various input fields for configuration, such as provider selection, model name, API key, and additional parameters.

* update the tests.

---------

Co-authored-by: Edwin Jose <edwin.jose@datastax.com>

src/backend/base/langflow/components/embeddings/__init__.py

@@ -3,6 +3,7 @@ from .astra_vectorize import AstraVectorizeComponent
 from .azure_openai import AzureOpenAIEmbeddingsComponent
 from .cloudflare import CloudflareWorkersAIEmbeddingsComponent
 from .cohere import CohereEmbeddingsComponent
+from .embedding_model import EmbeddingModelComponent
 from .google_generative_ai import GoogleGenerativeAIEmbeddingsComponent
 from .huggingface_inference_api import HuggingFaceInferenceAPIEmbeddingsComponent
 from .lmstudioembeddings import LMStudioEmbeddingsComponent
@@ -21,6 +22,7 @@ __all__ = [
     "AzureOpenAIEmbeddingsComponent",
     "CloudflareWorkersAIEmbeddingsComponent",
     "CohereEmbeddingsComponent",
+    "EmbeddingModelComponent",
     "EmbeddingSimilarityComponent",
     "GoogleGenerativeAIEmbeddingsComponent",
     "HuggingFaceInferenceAPIEmbeddingsComponent",

src/backend/base/langflow/components/embeddings/embedding_model.py

@@ -0,0 +1,112 @@
+from typing import Any
+
+from langchain_openai import OpenAIEmbeddings
+
+from langflow.base.embeddings.model import LCEmbeddingsModel
+from langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES
+from langflow.field_typing import Embeddings
+from langflow.io import (
+    BoolInput,
+    DictInput,
+    DropdownInput,
+    FloatInput,
+    IntInput,
+    MessageTextInput,
+    SecretStrInput,
+)
+from langflow.schema.dotdict import dotdict
+
+
+class EmbeddingModelComponent(LCEmbeddingsModel):
+    display_name = "Embedding Model"
+    description = "Generate embeddings using a specified provider."
+    icon = "binary"
+    name = "EmbeddingModel"
+    category = "embeddings"
+
+    inputs = [
+        DropdownInput(
+            name="provider",
+            display_name="Model Provider",
+            options=["OpenAI"],
+            value="OpenAI",
+            info="Select the embedding model provider",
+            real_time_refresh=True,
+            options_metadata=[{"icon": "OpenAI"}],
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model Name",
+            options=OPENAI_EMBEDDING_MODEL_NAMES,
+            value=OPENAI_EMBEDDING_MODEL_NAMES[0],
+            info="Select the embedding model to use",
+        ),
+        SecretStrInput(
+            name="api_key",
+            display_name="OpenAI API Key",
+            info="Model Provider API key",
+            required=True,
+            show=True,
+            real_time_refresh=True,
+        ),
+        MessageTextInput(
+            name="api_base",
+            display_name="API Base URL",
+            info="Base URL for the API. Leave empty for default.",
+            advanced=True,
+        ),
+        IntInput(
+            name="dimensions",
+            display_name="Dimensions",
+            info="The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
+            advanced=True,
+        ),
+        IntInput(name="chunk_size", display_name="Chunk Size", advanced=True, value=1000),
+        FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True),
+        IntInput(name="max_retries", display_name="Max Retries", advanced=True, value=3),
+        BoolInput(name="show_progress_bar", display_name="Show Progress Bar", advanced=True),
+        DictInput(
+            name="model_kwargs",
+            display_name="Model Kwargs",
+            advanced=True,
+            info="Additional keyword arguments to pass to the model.",
+        ),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
+        provider = self.provider
+        model = self.model
+        api_key = self.api_key
+        api_base = self.api_base
+        dimensions = self.dimensions
+        chunk_size = self.chunk_size
+        request_timeout = self.request_timeout
+        max_retries = self.max_retries
+        show_progress_bar = self.show_progress_bar
+        model_kwargs = self.model_kwargs or {}
+
+        if provider == "OpenAI":
+            if not api_key:
+                msg = "OpenAI API key is required when using OpenAI provider"
+                raise ValueError(msg)
+            return OpenAIEmbeddings(
+                model=model,
+                dimensions=dimensions or None,
+                base_url=api_base or None,
+                api_key=api_key,
+                chunk_size=chunk_size,
+                max_retries=max_retries,
+                timeout=request_timeout or None,
+                show_progress_bar=show_progress_bar,
+                model_kwargs=model_kwargs,
+            )
+        msg = f"Unknown provider: {provider}"
+        raise ValueError(msg)
+
+    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:
+        if field_name == "provider" and field_value == "OpenAI":
+            build_config["model"]["options"] = OPENAI_EMBEDDING_MODEL_NAMES
+            build_config["model"]["value"] = OPENAI_EMBEDDING_MODEL_NAMES[0]
+            build_config["api_key"]["display_name"] = "OpenAI API Key"
+            build_config["api_base"]["display_name"] = "OpenAI API Base URL"
+        return build_config

src/backend/tests/unit/components/embeddings/test_embedding_model_component.py

@@ -0,0 +1,89 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+from langflow.base.models.openai_constants import OPENAI_EMBEDDING_MODEL_NAMES
+from langflow.components.embeddings.embedding_model import EmbeddingModelComponent
+
+from tests.base import ComponentTestBaseWithClient
+
+
+@pytest.mark.usefixtures("client")
+class TestEmbeddingModelComponent(ComponentTestBaseWithClient):
+    @pytest.fixture
+    def component_class(self):
+        return EmbeddingModelComponent
+
+    @pytest.fixture
+    def default_kwargs(self):
+        return {
+            "provider": "OpenAI",
+            "model": "text-embedding-3-small",
+            "api_key": "test-api-key",
+            "chunk_size": 1000,
+            "max_retries": 3,
+            "show_progress_bar": False,
+        }
+
+    @pytest.fixture
+    def file_names_mapping(self):
+        """Return the file names mapping for version-specific files."""
+
+    async def test_update_build_config_openai(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        build_config = {
+            "model": {"options": [], "value": ""},
+            "api_key": {"display_name": "API Key"},
+            "api_base": {"display_name": "API Base URL"},
+        }
+        updated_config = component.update_build_config(build_config, "OpenAI", "provider")
+        assert updated_config["model"]["options"] == OPENAI_EMBEDDING_MODEL_NAMES
+        assert updated_config["model"]["value"] == OPENAI_EMBEDDING_MODEL_NAMES[0]
+        assert updated_config["api_key"]["display_name"] == "OpenAI API Key"
+        assert updated_config["api_base"]["display_name"] == "OpenAI API Base URL"
+
+    @patch("langflow.components.embeddings.embedding_model.OpenAIEmbeddings")
+    async def test_build_embeddings_openai(self, mock_openai_embeddings, component_class, default_kwargs):
+        # Setup mock
+        mock_instance = MagicMock()
+        mock_openai_embeddings.return_value = mock_instance
+
+        # Create and configure the component
+        component = component_class(**default_kwargs)
+        component.provider = "OpenAI"
+        component.model = "text-embedding-3-small"
+        component.api_key = "test-key"
+        component.chunk_size = 1000
+        component.max_retries = 3
+        component.show_progress_bar = False
+
+        # Build the embeddings
+        embeddings = component.build_embeddings()
+
+        # Verify the OpenAIEmbeddings was called with the correct parameters
+        mock_openai_embeddings.assert_called_once_with(
+            model="text-embedding-3-small",
+            dimensions=None,
+            base_url=None,
+            api_key="test-key",
+            chunk_size=1000,
+            max_retries=3,
+            timeout=None,
+            show_progress_bar=False,
+            model_kwargs={},
+        )
+        assert embeddings == mock_instance
+
+    async def test_build_embeddings_openai_missing_api_key(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        component.provider = "OpenAI"
+        component.api_key = None
+
+        with pytest.raises(ValueError, match="OpenAI API key is required when using OpenAI provider"):
+            component.build_embeddings()
+
+    async def test_build_embeddings_unknown_provider(self, component_class, default_kwargs):
+        component = component_class(**default_kwargs)
+        component.provider = "Unknown"
+
+        with pytest.raises(ValueError, match="Unknown provider: Unknown"):
+            component.build_embeddings()