Add openai completion/chat completion

llama_stack/providers/remote/inference/lmstudio/lmstudio.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import AsyncIterator, List, Optional, Union
+from typing import Any, AsyncIterator, Dict, List, Optional, Union
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -19,6 +19,11 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     LogProbConfig,
     Message,
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAICompletion,
+    OpenAIMessageParam,
+    OpenAIResponseFormatParam,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
@@ -51,17 +56,139 @@ class LMStudioInferenceAdapter(Inference, ModelsProtocolPrivate):
     def client(self) -> LMStudioClient:
         return LMStudioClient(url=self.url)
 
-    async def batch_chat_completion(self, *args, **kwargs):
-        raise NotImplementedError("Batch chat completion not supported by LM Studio Provider")
+    async def batch_completion(
+        self,
+        model_id: str,
+        content_batch: List[InterleavedContent],
+        sampling_params: Optional[SamplingParams] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = None,
+    ):
+        raise NotImplementedError("Batch completion is not supported by LM Studio Provider")
 
-    async def batch_completion(self, *args, **kwargs):
-        raise NotImplementedError("Batch completion not supported by LM Studio Provider")
+    async def batch_chat_completion(
+        self,
+        model_id: str,
+        messages_batch: List[List[Message]],
+        sampling_params: Optional[SamplingParams] = None,
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_config: Optional[ToolConfig] = None,
+        response_format: Optional[ResponseFormat] = None,
+        logprobs: Optional[LogProbConfig] = None,
+    ):
+        raise NotImplementedError("Batch completion is not supported by LM Studio Provider")
 
-    async def openai_chat_completion(self, *args, **kwargs):
-        raise NotImplementedError("OpenAI chat completion not supported by LM Studio Provider")
+    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[OpenAIResponseFormatParam] = 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,
+    ) -> Union[OpenAIChatCompletion, AsyncIterator[OpenAIChatCompletionChunk]]:
+        if self.model_store is None:
+            raise ValueError("ModelStore is not initialized")
+        model_obj = await self.model_store.get_model(model)
+        params = {
+            k: v
+            for k, v in {
+                "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,
+            }.items()
+            if v is not None
+        }
+        return await self.openai_client.chat.completions.create(**params)  # type: ignore
 
-    async def openai_completion(self, *args, **kwargs):
-        raise NotImplementedError("OpenAI completion not supported by LM Studio Provider")
+    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:
+        if not isinstance(prompt, str):
+            raise ValueError("LM Studio does not support non-string prompts for completion")
+        if self.model_store is None:
+            raise ValueError("ModelStore is not initialized")
+        model_obj = await self.model_store.get_model(model)
+        params = {
+            k: v
+            for k, v in {
+                "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,
+            }.items()
+            if v is not None
+        }
+        return await self.openai_client.completions.create(**params)  # type: ignore
 
     async def initialize(self) -> None:
         pass