Added ModelGarden Basic

Author: Niraj Nepal

py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/__init__.py

@@ -0,0 +1,8 @@
+from .model_garden import OpenAIFormatModelVersion, vertexai_name
+from .modelgarden_plugin import VertexAIModelGarden
+
+__all__ = [
+    OpenAIFormatModelVersion.__name__,
+    vertexai_name,
+    VertexAIModelGarden.__name__
+]

py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/model_garden.py

@@ -0,0 +1,95 @@
+# Copyright 2025 Google LLC
+# SPDX-License-Identifier: Apache-2.0
+import os
+
+from enum import StrEnum
+
+
+from genkit.plugins.vertex_ai import constants as const
+from genkit.core.typing import (
+    ModelInfo,
+    Supports,
+)
+from genkit.veneer.registry import GenkitRegistry
+from .openai_compatiblility import OpenAICompatibleModel, OpenAIConfig
+
+
+def vertexai_name(name: str) -> str:
+    """Create a Vertex AI action name.
+
+    Args:
+        name: Base name for the action.
+
+    Returns:
+        The fully qualified Vertex AI action name.
+    """
+    return f'vertexai/{name}'
+
+class OpenAIFormatModelVersion(StrEnum):
+    """Available versions of the llama model.
+
+    This enum defines the available versions of the llama model that
+    can be used through Vertex AI.
+    """
+    LLAMA_3_1 = 'llama-3.1'
+    LLAMA_3_2 = 'llama-3.2'
+
+
+SUPPORTED_OPENAI_FORMAT_MODELS: dict[str, ModelInfo] = {
+    OpenAIFormatModelVersion.LLAMA_3_1: ModelInfo(
+        versions=['meta/llama3-405b-instruct-maas'],
+        label='llama-3.1',
+        supports=Supports(
+            multiturn=True,
+            media=False,
+            tools=True,
+            systemRole=True,
+            output=['text', 'json']
+        )
+    ),
+    OpenAIFormatModelVersion.LLAMA_3_2: ModelInfo(
+        versions=['meta/llama-3.2-90b-vision-instruct-maas'],
+        label='llama-3.2',
+        supports=Supports(
+            multiturn=True,
+            media=True,
+            tools=True,
+            systemRole=True,
+            output=['text', 'json']
+        )
+    )
+}
+
+
+class ModelGarden:
+    @classmethod
+    def to_openai_compatible_model(
+        cls,
+        ai: GenkitRegistry,
+        model: str,
+        location: str,
+        project_id: str
+    ):
+        if model not in SUPPORTED_OPENAI_FORMAT_MODELS:
+            raise ValueError(f"Model '{model}' is not supported.")
+        model_version = SUPPORTED_OPENAI_FORMAT_MODELS[model].versions[0]
+        open_ai_compat = OpenAICompatibleModel(
+            model_version,
+            project_id,
+            location
+        )
+        supports = SUPPORTED_OPENAI_FORMAT_MODELS[model].supports.model_dump()
+
+        ai.define_model(
+            name=f'vertexai/{model}',
+            fn=open_ai_compat.generate,
+            config_schema=OpenAIConfig,
+            metadata={
+                'model': {
+                    'supports': supports
+                }
+            }
+
+        )
+
+

py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/modelgarden_plugin.py

@@ -0,0 +1,75 @@
+# Copyright 2025 Google LLC
+# SPDX-License-Identifier: Apache-2.0
+
+
+"""ModelGarden API Compatible Plugin for Genkit."""
+
+from pydantic import BaseModel, ConfigDict
+from .model_garden import (
+    SUPPORTED_OPENAI_FORMAT_MODELS,
+    ModelGarden
+)
+from genkit.veneer.plugin import Plugin
+from genkit.veneer.registry import GenkitRegistry
+from genkit.plugins.vertex_ai import constants as const
+import pdb
+import os
+from pprint import pprint
+
+
+class CommonPluginOptions(BaseModel):
+    model_config = ConfigDict(extra='forbid', populate_by_name=True)
+
+    project_id: str | None = None
+    location: str | None = None
+    models: list[str] | None = None
+
+
+def vertexai_name(name: str) -> str:
+    """Create a Vertex AI action name.
+
+    Args:
+        name: Base name for the action.
+
+    Returns:
+        The fully qualified Vertex AI action name.
+    """
+    return f'vertexai/{name}'
+
+class VertexAIModelGarden(Plugin):
+    """Model Garden plugin for Genkit.
+
+    This plugin provides integration with Google Cloud's Vertex AI platform,
+    enabling the use of Vertex AI models and services within the Genkit
+    framework. It handles initialization of the Model Garden client and
+    registration of model actions.
+    """
+
+    name = "modelgarden"
+
+    def __init__(self, **kwargs):
+        """Initialize the plugin by registering actions with the registry."""
+        self.plugin_options = CommonPluginOptions(
+            project_id=kwargs.get('project_id', os.getenv(const.GCLOUD_PROJECT)),
+            location=kwargs.get('location', const.DEFAULT_REGION),
+            models=kwargs.get('models')
+        )
+
+    def initialize(self, ai: GenkitRegistry) -> None:
+        """Handles actions for various openaicompatible models."""
+        for model in self.plugin_options.models:
+            openai_model = next(
+                (
+                    key
+                    for key, _ in SUPPORTED_OPENAI_FORMAT_MODELS.items()
+                    if key == model
+                ),
+                None
+            )
+            if openai_model:
+                ModelGarden.to_openai_compatible_model(
+                    ai,
+                    model=openai_model,
+                    location=self.plugin_options.location,
+                    project_id=self.plugin_options.project_id
+                )

py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/openai_compatiblility/__init__.py

@@ -0,0 +1,3 @@
+from .openai_compatibility import OpenAICompatibleModel
+
+__all__ = [OpenAICompatibleModel.__name__]

py/plugins/vertex-ai/src/genkit/plugins/vertex_ai/model_garden/openai_compatiblility/openai_compatibility.py

@@ -0,0 +1,169 @@
+# Copyright 2025 Google LLC
+# SPDX-License-Identifier: Apache-2.0
+
+import pdb
+from enum import StrEnum
+from genkit.core.typing import (
+    ModelInfo,
+    Supports,
+    GenerationCommonConfig,
+    GenerateRequest,
+    GenerateResponse,
+    Message,
+    Role,
+    TextPart,
+    GenerateResponseChunk
+)
+from genkit.core.action import ActionRunContext
+from openai import OpenAI as OpenAIClient
+from google.auth import default, transport
+from typing import Annotated
+
+from pydantic import BaseModel, ConfigDict
+
+class ChatMessage(BaseModel):
+    model_config = ConfigDict(extra='forbid', populate_by_name=True)
+
+    role: str
+    content: str
+
+
+class OpenAIConfig(GenerationCommonConfig):
+    """Config for OpenAI model."""
+    frequency_penalty: Annotated[float, range(-2, 2)] | None = None
+    logit_bias: dict[str, Annotated[float, range(-100, 100)]] | None = None
+    logprobs: bool | None = None
+    presence_penalty: Annotated[float, range(-2, 2)] | None = None
+    seed: int | None = None
+    top_logprobs: Annotated[int, range(0, 20)] | None = None
+    user: str | None = None
+
+
+class ChatCompletionRole(StrEnum):
+    """Available roles supported by openai-compatible models."""
+    USER = 'user'
+    ASSISTANT = 'assistant'
+    SYSTEM = 'system'
+    TOOL = 'tool'
+
+
+class OpenAICompatibleModel:
+    "Handles openai compatible model support in model_garden"""
+
+    def __init__(self, model: str, project_id: str, location: str):
+        self._model = model
+        self._client = self.client_factory(location, project_id)
+
+    def client_factory(self, location: str, project_id: str) -> OpenAIClient:
+        """Initiates an openai compatible client object and return it."""
+        if project_id:
+            credentials, _ = default()
+        else:
+            credentials, project_id = default()
+
+        credentials.refresh(transport.requests.Request())
+        base_url = f'https://{location}-aiplatform.googleapis.com/v1beta1/projects/{project_id}/locations/{location}/endpoints/openapi'
+        return OpenAIClient(api_key=credentials.token, base_url=base_url)
+
+
+    def to_openai_messages(self, messages: list[Message]) -> list[ChatMessage]:
+        if not messages:
+            raise ValueError('No messages provided in the request.')
+        return [
+            ChatMessage(
+                role=OpenAICompatibleModel.to_openai_role(m.role.value),
+                content=''.join(
+                    part.root.text
+                    for part in m.content
+                    if part.root.text is not None
+                ),
+            )
+            for m in messages
+        ]
+    def generate(
+        self, request: GenerateRequest, ctx: ActionRunContext
+    ) -> GenerateResponse:
+        openai_config: dict = {
+            'messages': self.to_openai_messages(request.messages),
+            'model': self._model
+        }
+        if ctx.is_streaming:
+            openai_config['stream'] = True
+            stream = self._client.chat.completions.create(**openai_config)
+            for chunk in stream:
+                choice = chunk.choices[0]
+                if not choice.delta.content:
+                    continue
+
+                response_chunk = GenerateResponseChunk(
+                    role=Role.MODEL,
+                    index=choice.index,
+                    content=[TextPart(text=choice.delta.content)],
+                )
+
+                ctx.send_chunk(response_chunk)
+
+        else:
+            response = self._client.chat.completions.create(**openai_config)
+            return GenerateResponse(
+                request=request,
+                message=Message(
+                role=Role.MODEL,
+                content=[TextPart(text=response.choices[0].message.content)],
+            ),
+        )
+
+    @staticmethod
+    def to_openai_role(role: Role) -> ChatCompletionRole:
+        """Converts Role enum to corrosponding OpenAI Compatible role."""
+        match role:
+            case Role.USER:
+                return ChatCompletionRole.USER
+            case Role.MODEL:
+                return ChatCompletionRole.ASSISTANT  # "model" maps to "assistant"
+            case Role.SYSTEM:
+                return ChatCompletionRole.SYSTEM
+            case Role.TOOL:
+                return ChatCompletionRole.TOOL
+            case _:
+                raise ValueError(f"Role '{role}' doesn't map to an OpenAI role.")
+
+
+
+class OllamaVersion(StrEnum):
+    """Available versions of the llama model.
+
+    This enum defines the available versions of the llama model that
+    can be used through Vertex AI.
+    """
+    LLAMA_3_1 = 'llama-3.1'
+    LLAMA_3_2 = 'llama-3.2'
+    LLAMA3_405_B = 'llama3-405b'
+
+
+SUPPORTED_MODELS = {
+    OllamaVersion.LLAMA_3_1: ModelInfo(
+        versions=['meta/llama3-405b-instruct-maas'],
+        label='Llama 3.1',
+        supports=Supports(
+            multiturn=True, media=False, tools=True,
+            systemRole=True, output=['text', 'json']
+        )
+    ),
+    OllamaVersion.LLAMA_3_2: ModelInfo(
+        versions=['meta/llama-3.2-90b-vision-instruct-maas'],
+        label='Llama 3.2',
+        supports=Supports(
+            multiturn=True, media=True, tools=True,
+            systemRole=True, output=['text', 'json']
+        )
+    ),
+    OllamaVersion.LLAMA3_405_B: ModelInfo(
+        versions=[],
+        label='Llama 3.1 405b',
+        supports=Supports(
+            multiturn=True, media=False, tools=True,
+            systemRole=True, output=['text']
+        )
+    )
+}