From 87472104706eaef3d2a2de69cc6d50c7dd5e27b2 Mon Sep 17 00:00:00 2001
From: Ben Browning <bbrownin@redhat.com>
Date: Thu, 8 May 2025 18:12:51 -0400
Subject: [PATCH] fix: ollama openai completion and chat completion params

The ollama provider was using an older variant of the code to convert
incoming parameters from the OpenAI API completions and chat
completion endpoints into requests that get sent to the backend
provider over its own OpenAI client. This updates it to use the common
`prepare_openai_completion_params` method used elsewhere, which takes
care of removing stray `None` values even for nested structures.

Without this, some other parameters - even if they have values of None
- make their way to ollama and actually influence its inference output
as opposed to when those parameters are not sent at all.

This passes tests/integration/inference/test_openai_completion.py and
fixes the issue found in #2098, which was tested via manual curl
requests crafted a particular way.

Fixes #2098

Signed-off-by: Ben Browning <bbrownin@redhat.com>
---
 .../remote/inference/ollama/ollama.py         | 97 +++++++++----------
 1 file changed, 45 insertions(+), 52 deletions(-)

diff --git a/llama_stack/providers/remote/inference/ollama/ollama.py b/llama_stack/providers/remote/inference/ollama/ollama.py
index 32e2b17d0..72cf0d129 100644
--- a/llama_stack/providers/remote/inference/ollama/ollama.py
+++ b/llama_stack/providers/remote/inference/ollama/ollama.py
@@ -61,6 +61,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
     get_sampling_options,
+    prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
@@ -395,29 +396,25 @@ class OllamaInferenceAdapter(
             raise ValueError("Ollama does not support non-string prompts for completion")
 
         model_obj = await self._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
-        }
+        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.openai_client.completions.create(**params)  # type: ignore
 
     async def openai_chat_completion(
@@ -447,35 +444,31 @@ class OllamaInferenceAdapter(
         user: str | None = None,
     ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
         model_obj = await self._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
-        }
+        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.openai_client.chat.completions.create(**params)  # type: ignore
 
     async def batch_completion(