add WatsonX inference adapter

llama_stack/providers/remote/inference/watsonx/__init__.py

@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.inference import Inference
+
+from .config import WatsonXConfig
+
+
+async def get_adapter_impl(config: WatsonXConfig, _deps) -> Inference:
+    # import dynamically so `llama stack build` does not fail due to missing dependencies
+    from .watsonx import WatsonXInferenceAdapter
+
+    if not isinstance(config, WatsonXConfig):
+        raise RuntimeError(f"Unexpected config type: {type(config)}")
+    adapter = WatsonXInferenceAdapter(config)
+    return adapter
+
+
+__all__ = ["get_adapter_impl", "WatsonXConfig"]

llama_stack/providers/remote/inference/watsonx/config.py

@@ -0,0 +1,46 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import os
+from typing import Optional, Dict, Any
+
+from llama_stack.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+class WatsonXProviderDataValidator(BaseModel):
+    url: str
+    api_key: str
+    project_id: str
+
+
+@json_schema_type
+class WatsonXConfig(BaseModel):
+
+    url: str = Field(
+        default_factory=lambda: os.getenv("WATSONX_BASE_URL", "https://us-south.ml.cloud.ibm.com"),
+        description="A base url for accessing the Watsonx.ai",
+    )
+    api_key: Optional[str] = Field(
+        default_factory=lambda: os.getenv("WATSONX_API_KEY"),
+        description="The Watsonx API key, only needed of using the hosted service",
+    )
+    project_id: Optional[str] = Field(
+        default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"),
+        description="The Project ID key, only needed of using the hosted service",
+    )
+    timeout: int = Field(
+        default=60,
+        description="Timeout for the HTTP requests",
+    )
+
+    @classmethod
+    def sample_run_config(cls, **kwargs) -> Dict[str, Any]:
+        return {
+            "url": "${env.WATSONX_BASE_URL:https://us-south.ml.cloud.ibm.com}",
+            "api_key": "${env.WATSONX_API_KEY:}",
+            "project_id": "${env.WATSONX_PROJECT_ID:}"
+        }

llama_stack/providers/remote/inference/watsonx/models.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.models.llama.datatypes import CoreModelId
+from llama_stack.providers.utils.inference.model_registry import build_hf_repo_model_entry
+
+MODEL_ENTRIES = [
+    build_hf_repo_model_entry(
+        "meta-llama/llama-3-3-70b-instruct",
+        CoreModelId.llama3_3_70b_instruct.value,
+    )
+]
+

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -0,0 +1,87 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Optional, Union, AsyncIterator
+
+from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem
+from llama_stack.apis.inference import Inference, Message, ToolChoice, ResponseFormat, LogProbConfig, ToolConfig, \
+    ChatCompletionResponse, ChatCompletionResponseStreamChunk, EmbeddingsResponse, TextTruncation, EmbeddingTaskType
+from llama_stack.models.llama.datatypes import SamplingParams, ToolDefinition, ToolPromptFormat
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+
+from . import WatsonXConfig
+
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+
+from .models import MODEL_ENTRIES
+
+
+
+class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
+    def __init__(self, config: WatsonXConfig) -> None:
+        ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
+
+        print(f"Initializing WatsonXInferenceAdapter({config.url})...")
+
+        self._config = config
+        self._credential = {
+            "url": self._config.url,
+            "apikey": self._config.api_key
+        }
+
+        self._project_id = self._config.project_id
+        self.params = {
+            GenParams.MAX_NEW_TOKENS: 4096,
+            GenParams.STOP_SEQUENCES: ["<|endoftext|>"]
+        }
+
+    async def completion(
+            self,
+            model_id: str,
+            content: InterleavedContent,
+            sampling_params: Optional[SamplingParams] = None,
+            response_format: Optional[ResponseFormat] = None,
+            stream: Optional[bool] = False,
+            logprobs: Optional[LogProbConfig] = None,
+    ):
+        pass
+
+    async def embeddings(
+            self,
+            model_id: str,
+            contents: List[str] | List[InterleavedContentItem],
+            text_truncation: Optional[TextTruncation] = TextTruncation.none,
+            output_dimension: Optional[int] = None,
+            task_type: Optional[EmbeddingTaskType] = None,
+    ) -> EmbeddingsResponse:
+        pass
+
+    async def chat_completion(
+            self,
+            model_id: str,
+            messages: List[Message],
+            sampling_params: Optional[SamplingParams] = None,
+            response_format: Optional[ResponseFormat] = None,
+            tools: Optional[List[ToolDefinition]] = None,
+            tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+            tool_prompt_format: Optional[ToolPromptFormat] = None,
+            stream: Optional[bool] = False,
+            logprobs: Optional[LogProbConfig] = None,
+            tool_config: Optional[ToolConfig] = None,
+    ):
+        # Language model
+        model = Model(
+            model_id=model_id,
+            credentials=self._credential,
+            project_id=self._project_id,
+        )
+        prompt = "\n".join(messages) + "\nAI: "
+
+        response = model.generate_text(prompt=prompt, params=self.params)
+
+        return response
+