feat: initial implementation of snowflake provider + distro

distributions/snowflake/build.yaml

@@ -0,0 +1,9 @@
+name: snowflake
+distribution_spec:
+  description: Use Snowflake for running LLM inference
+  providers:
+    inference: remote::snowflake
+    memory: meta-reference
+    safety: meta-reference
+    agents: meta-reference
+    telemetry: meta-reference

llama_stack/providers/adapters/inference/snowflake/__init__.py

@@ -0,0 +1,17 @@
+# 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 .config import SnowflakeImplConfig
+from .snowflake import SnowflakeInferenceAdapter
+
+
+async def get_adapter_impl(config: SnowflakeImplConfig, _deps):
+    assert isinstance(
+        config, SnowflakeImplConfig
+    ), f"Unexpected config type: {type(config)}"
+    impl = SnowflakeInferenceAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/adapters/inference/snowflake/config.py

@@ -0,0 +1,21 @@
+# 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_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class SnowflakeImplConfig(BaseModel):
+    url: str = Field(
+        default=None,
+        description="The URL for the Snowflake Cortex model serving endpoint",
+    )
+    api_token: str = Field(
+        default=None,
+        description="The Snowflake Cortex API token",
+    )

llama_stack/providers/adapters/inference/snowflake/snowflake.py

@@ -0,0 +1,237 @@
+# 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 AsyncGenerator
+
+import httpx
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.inference import *  # noqa: F403
+from llama_stack.distribution.request_headers import NeedsRequestProviderData
+from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+    process_completion_response,
+    process_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+    completion_request_to_prompt,
+)
+
+from .config import SnowflakeImplConfig
+
+
+SNOWFLAKE_SUPPORTED_MODELS = {
+    "Llama3.1-8B-Instruct": "snowflake-meta-Llama-3.1-8B-Instruct-Turbo",
+    "Llama3.1-70B-Instruct": "snowflake-meta-Llama-3.1-70B-Instruct-Turbo",
+    "Llama3.1-405B-Instruct": "snowflake-meta-Llama-3.1-405B-Instruct-Turbo",
+}
+
+
+class SnowflakeInferenceAdapter(
+    ModelRegistryHelper, Inference, NeedsRequestProviderData
+):
+
+    def __init__(self, config: SnowflakeImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self, stack_to_provider_models_map=SNOWFLAKE_SUPPORTED_MODELS
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        request = CompletionRequest(
+            model=model,
+            content=content,
+            sampling_params=sampling_params,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_completion(request)
+        else:
+            return await self._nonstream_completion(request)
+
+    def _get_cortex_headers(
+        self,
+    ):
+        snowflake_api_key = None
+        if self.config.api_key is not None:
+            snowflake_api_key = self.config.api_key
+        else:
+            provider_data = self.get_request_provider_data()
+            if provider_data is None or not provider_data.snowflake_api_key:
+                raise ValueError(
+                    'Pass Snowflake API Key in the header X-LlamaStack-ProviderData as { "snowflake_api_key": <your api key>}'
+                )
+            snowflake_api_key = provider_data.snowflake_api_key
+
+        headers = {
+            "Accept": "text/stream",
+            "Content-Type": "application/json",
+            "Authorization": f'Snowflake Token="{snowflake_api_key}"',
+        }
+
+        return headers
+
+    def _get_cortex_client(self, timeout=None, concurrent_limit=1000):
+
+        client = httpx.Client(
+            timeout=timeout,
+            limits=httpx.Limits(
+                max_connections=concurrent_limit,
+                max_keepalive_connections=concurrent_limit,
+            ),
+        )
+
+        return client
+
+    async def _nonstream_completion(
+        self, request: CompletionRequest
+    ) -> ChatCompletionResponse:
+        params = self._get_params_for_completion(request)
+        r = self._get_cortex_client().post(**params)
+        return process_completion_response(
+            r, self.formatter
+        )  # TODO VALIDATE COMPLETION PROCESSOR
+
+    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
+        params = self._get_params_for_completion(request)
+
+        # if we shift to TogetherAsyncClient, we won't need this wrapper
+        async def _to_async_generator():
+            s = self._get_cortex_client().completions.create(**params)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_completion_stream_response(stream, self.formatter):
+            yield chunk
+
+    def _build_options(
+        self, sampling_params: Optional[SamplingParams], fmt: ResponseFormat
+    ) -> dict:
+        options = get_sampling_options(sampling_params)
+        if fmt:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                raise NotImplementedError("Grammar response format not supported yet")
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        return options
+
+    def _get_params_for_completion(self, request: CompletionRequest) -> dict:
+        return {
+            "model": self.map_to_provider_model(request.model),
+            "prompt": completion_request_to_prompt(request, self.formatter),
+            "stream": request.stream,
+            **self._build_options(request.sampling_params, request.response_format),
+        }
+
+    async def chat_completion(
+        self,
+        model: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+
+        request = ChatCompletionRequest(
+            model=model,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        if stream:
+            return self._stream_chat_completion(request)
+        else:
+            return await self._nonstream_chat_completion(request)
+
+    async def _nonstream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> ChatCompletionResponse:
+        params = self._get_params(request)
+        r = self._get_cortex_client().post(**params)
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: ChatCompletionRequest
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        # if we shift to TogetherAsyncClient, we won't need this wrapper
+        async def _to_async_generator():
+            s = self._get_cortex_client().post(**params)
+            for chunk in s:
+                yield chunk
+
+        stream = _to_async_generator()
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    # TODO UPDATE PARAM STRUCTURE
+    def _get_params(self, request: ChatCompletionRequest) -> dict:
+        return {
+            "headers": self._get_cortex_headers(),
+            "data": {
+                "model": self.map_to_provider_model(request.model),
+                "messages": [
+                    {
+                        "content": chat_completion_request_to_prompt(
+                            request, self.formatter
+                        )
+                    }
+                ],
+                "stream": request.stream,
+            },
+        }
+
+    async def embeddings(
+        self,
+        model: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()

llama_stack/providers/registry/inference.py

@@ -97,6 +97,15 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.adapters.inference.tgi.InferenceEndpointImplConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="snowflake",
+                pip_packages=["python-snowflake-snowpark"],
+                module="llama_stack.providers.adapters.inference.snowflake",
+                config_class="llama_stack.providers.adapters.inference.snowflake.SnowflakeImplConfig",
+            ),
+        ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(