Add Groq provider - chat completions

llama_stack/providers/remote/inference/groq/groq.py

@@ -0,0 +1,142 @@
+# 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 warnings
+from typing import AsyncIterator, List, Optional, Union
+
+from groq import Groq
+from llama_models.datatypes import SamplingParams
+from llama_models.llama3.api.datatypes import (
+    InterleavedTextMedia,
+    Message,
+    ToolChoice,
+    ToolDefinition,
+    ToolPromptFormat,
+)
+from llama_models.sku_list import CoreModelId
+
+from llama_stack.apis.inference import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChunk,
+    CompletionResponse,
+    CompletionResponseStreamChunk,
+    EmbeddingsResponse,
+    Inference,
+    LogProbConfig,
+    ResponseFormat,
+)
+from llama_stack.providers.remote.inference.groq.config import GroqConfig
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    build_model_alias_with_just_provider_model_id,
+    ModelRegistryHelper,
+)
+from .groq_utils import (
+    convert_chat_completion_request,
+    convert_chat_completion_response,
+    convert_chat_completion_response_stream,
+)
+
+_MODEL_ALIASES = [
+    build_model_alias(
+        "llama3-8b-8192",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias_with_just_provider_model_id(
+        "llama-3.1-8b-instant",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "llama3-70b-8192",
+        CoreModelId.llama3_70b_instruct.value,
+    ),
+    build_model_alias(
+        "llama-3.3-70b-versatile",
+        CoreModelId.llama3_3_70b_instruct.value,
+    ),
+    # Groq only contains a preview version for llama-3.2-3b
+    # Preview models aren't recommended for production use, but we include this one
+    # to pass the test fixture
+    # TODO(aidand): Replace this with a stable model once Groq supports it
+    build_model_alias(
+        "llama-3.2-3b-preview",
+        CoreModelId.llama3_2_3b_instruct.value,
+    ),
+]
+
+
+class GroqInferenceAdapter(Inference, ModelRegistryHelper):
+    _client: Groq
+
+    def __init__(self, config: GroqConfig):
+        ModelRegistryHelper.__init__(self, model_aliases=_MODEL_ALIASES)
+        self._client = Groq(api_key=config.api_key)
+
+    def completion(
+        self,
+        model_id: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[CompletionResponse, AsyncIterator[CompletionResponseStreamChunk]]:
+        # Groq doesn't support non-chat completion as of time of writing
+        raise NotImplementedError()
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[
+            ToolPromptFormat
+        ] = None,  # API default is ToolPromptFormat.json, we default to None to detect user input
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> Union[
+        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
+    ]:
+
+        if model_id == "llama-3.2-3b-preview":
+            warnings.warn(
+                "Groq only contains a preview version for llama-3.2-3b-instruct. "
+                "Preview models aren't recommended for production use. "
+                "They can be discontinued on short notice."
+            )
+
+        model_id = self.get_provider_model_id(model_id)
+        request = convert_chat_completion_request(
+            request=ChatCompletionRequest(
+                model=model_id,
+                messages=messages,
+                sampling_params=sampling_params,
+                response_format=response_format,
+                tools=tools,
+                tool_choice=tool_choice,
+                tool_prompt_format=tool_prompt_format,
+                stream=stream,
+                logprobs=logprobs,
+            )
+        )
+
+        response = self._client.chat.completions.create(**request)
+
+        if stream:
+            return convert_chat_completion_response_stream(response)
+        else:
+            return convert_chat_completion_response(response)
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()