Fireworks provider support for OpenAI API endpoints

This wires up the openai_completion and openai_chat_completion API
methods for the remote Fireworks inference provider.

Signed-off-by: Ben Browning <bbrownin@redhat.com>

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -4,9 +4,10 @@
 # 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, List, Optional, Union
+from typing import Any, AsyncGenerator, Dict, List, Optional, Union
 
 from fireworks.client import Fireworks
+from openai import AsyncOpenAI
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -31,6 +32,7 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import OpenAIChatCompletion, OpenAICompletion, OpenAIMessageParam
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import (
@@ -39,6 +41,7 @@ from llama_stack.providers.utils.inference.model_registry import (
 from llama_stack.providers.utils.inference.openai_compat import (
     convert_message_to_openai_dict,
     get_sampling_options,
+    prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
@@ -81,10 +84,16 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
                 )
             return provider_data.fireworks_api_key
 
+    def _get_base_url(self) -> str:
+        return "https://api.fireworks.ai/inference/v1"
+
     def _get_client(self) -> Fireworks:
         fireworks_api_key = self._get_api_key()
         return Fireworks(api_key=fireworks_api_key)
 
+    def _get_openai_client(self) -> AsyncOpenAI:
+        return AsyncOpenAI(base_url=self._get_base_url(), api_key=self._get_api_key())
+
     async def completion(
         self,
         model_id: str,
@@ -268,3 +277,101 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProv
 
         embeddings = [data.embedding for data in response.data]
         return EmbeddingsResponse(embeddings=embeddings)
+
+    async def openai_completion(
+        self,
+        model: str,
+        prompt: Union[str, List[str], List[int], List[List[int]]],
+        best_of: Optional[int] = None,
+        echo: Optional[bool] = None,
+        frequency_penalty: Optional[float] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        logprobs: Optional[bool] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        seed: Optional[int] = None,
+        stop: Optional[Union[str, List[str]]] = None,
+        stream: Optional[bool] = None,
+        stream_options: Optional[Dict[str, Any]] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        user: Optional[str] = None,
+        guided_choice: Optional[List[str]] = None,
+        prompt_logprobs: Optional[int] = None,
+    ) -> OpenAICompletion:
+        model_obj = await self.model_store.get_model(model)
+        params = await prepare_openai_completion_params(
+            model=model_obj.provider_resource_id,
+            prompt=prompt,
+            best_of=best_of,
+            echo=echo,
+            frequency_penalty=frequency_penalty,
+            logit_bias=logit_bias,
+            logprobs=logprobs,
+            max_tokens=max_tokens,
+            n=n,
+            presence_penalty=presence_penalty,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            top_p=top_p,
+            user=user,
+        )
+        return await self._get_openai_client().completions.create(**params)
+
+    async def openai_chat_completion(
+        self,
+        model: str,
+        messages: List[OpenAIMessageParam],
+        frequency_penalty: Optional[float] = None,
+        function_call: Optional[Union[str, Dict[str, Any]]] = None,
+        functions: Optional[List[Dict[str, Any]]] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        logprobs: Optional[bool] = None,
+        max_completion_tokens: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        parallel_tool_calls: Optional[bool] = None,
+        presence_penalty: Optional[float] = None,
+        response_format: Optional[Dict[str, str]] = None,
+        seed: Optional[int] = None,
+        stop: Optional[Union[str, List[str]]] = None,
+        stream: Optional[bool] = None,
+        stream_options: Optional[Dict[str, Any]] = None,
+        temperature: Optional[float] = None,
+        tool_choice: Optional[Union[str, Dict[str, Any]]] = None,
+        tools: Optional[List[Dict[str, Any]]] = None,
+        top_logprobs: Optional[int] = None,
+        top_p: Optional[float] = None,
+        user: Optional[str] = None,
+    ) -> OpenAIChatCompletion:
+        model_obj = await self.model_store.get_model(model)
+        params = await prepare_openai_completion_params(
+            model=model_obj.provider_resource_id,
+            messages=messages,
+            frequency_penalty=frequency_penalty,
+            function_call=function_call,
+            functions=functions,
+            logit_bias=logit_bias,
+            logprobs=logprobs,
+            max_completion_tokens=max_completion_tokens,
+            max_tokens=max_tokens,
+            n=n,
+            parallel_tool_calls=parallel_tool_calls,
+            presence_penalty=presence_penalty,
+            response_format=response_format,
+            seed=seed,
+            stop=stop,
+            stream=stream,
+            stream_options=stream_options,
+            temperature=temperature,
+            tool_choice=tool_choice,
+            tools=tools,
+            top_logprobs=top_logprobs,
+            top_p=top_p,
+            user=user,
+        )
+        return await self._get_openai_client().chat.completions.create(**params)

tests/integration/inference/test_openai_completion.py

@@ -208,6 +208,9 @@ def test_openai_chat_completion_streaming(openai_client, client_with_models, tex
         stream=True,
         timeout=120,  # Increase timeout to 2 minutes for large conversation history
     )
-    streamed_content = [str(chunk.choices[0].delta.content.lower().strip()) for chunk in response]
+    streamed_content = []
+    for chunk in response:
+        if chunk.choices[0].delta.content:
+            streamed_content.append(chunk.choices[0].delta.content.lower().strip())
     assert len(streamed_content) > 0
     assert expected.lower() in "".join(streamed_content)