Add JSON structured outputs to Ollama Provider (#680)

# What does this PR do?

Addresses issue #679

- Adds support for the response_format field for chat completions and
completions so users can get their outputs in JSON

## Test Plan

<details>

<summary>Integration tests</summary>

`pytest
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output
-k ollama -s -v`

```python
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_8b-ollama] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_3b-ollama] PASSED

================================== 2 passed, 18 deselected, 3 warnings in 41.41s ==================================
```

</details>

<details>
<summary>Manual Tests</summary>

```
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export OLLAMA_INFERENCE_MODEL=llama3.2:3b-instruct-fp16
export LLAMA_STACK_PORT=5000

ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m
llama stack build --template ollama --image-type conda
llama stack run ./run.yaml \
  --port $LLAMA_STACK_PORT \
  --env INFERENCE_MODEL=$INFERENCE_MODEL \
  --env OLLAMA_URL=http://localhost:11434
```

```python
    client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}")

    MODEL_ID=meta-llama/Llama-3.2-3B-Instruct
    prompt =f"""
        Create a step by step plan to complete the task of creating a codebase that is a web server that has an API endpoint that translates text from English to French.
        You have 3 different operations you can perform. You can create a file, update a file, or delete a file.
        Limit your step by step plan to only these operations per step.
        Don't create more than 10 steps.

        Please ensure there's a README.md file in the root of the codebase that describes the codebase and how to run it.
        Please ensure there's a requirements.txt file in the root of the codebase that describes the dependencies of the codebase.
        """
    response = client.inference.chat_completion(
        model_id=MODEL_ID,
        messages=[
            {"role": "user", "content": prompt},
        ],
        sampling_params={
            "max_tokens": 200000,
        },
        response_format={
            "type": "json_schema",
            "json_schema": {
                "$schema": "http://json-schema.org/draft-07/schema#",
                "title": "Plan",
                "description": f"A plan to complete the task of creating a codebase that is a web server that has an API endpoint that translates text from English to French.",
                "type": "object",
                "properties": {
                    "steps": {
                        "type": "array",
                        "items": {
                            "type": "string"
                        }
                    }
                },
                "required": ["steps"],
                "additionalProperties": False,
            }
        },
        stream=True,
    )

    content = ""
    for chunk in response:
        if chunk.event.delta:
            print(chunk.event.delta, end="", flush=True)
            content += chunk.event.delta

    try:
        plan = json.loads(content)
        print(plan)
    except Exception as e:
        print(f"Error parsing plan into JSON: {e}")
        plan = {"steps": []}
```

Outputs:

```json
{
    "steps": [
        "Update the requirements.txt file to include the updated dependencies specified in the peer's feedback, including the Google Cloud Translation API key.",
        "Update the app.py file to address the code smells and incorporate the suggested improvements, such as handling errors and exceptions, initializing the Translator object correctly, adding input validation, using type hints and docstrings, and removing unnecessary logging statements.",
        "Create a README.md file that describes the codebase and how to run it.",
        "Ensure the README.md file is up-to-date and accurate.",
        "Update the requirements.txt file to reflect any additional dependencies specified by the peer's feedback.",
        "Add documentation for each function in the app.py file using docstrings.",
        "Implement logging statements throughout the app.py file to monitor application execution.",
        "Test the API endpoint to ensure it correctly translates text from English to French and handles errors properly.",
        "Refactor the code to follow PEP 8 style guidelines and ensure consistency in naming conventions, indentation, and spacing.",
        "Create a new folder for logs and add a logging configuration file (e.g., logconfig.json) that specifies the logging level and output destination.",
        "Deploy the web server on a production environment (e.g., AWS Elastic Beanstalk or Google Cloud Platform) to make it accessible to external users."
    ]
}
```


</details>

## Sources

- Ollama api docs:
https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion
- Ollama structured output docs:
https://github.com/ollama/ollama/blob/main/docs/api.md#request-structured-outputs

## Before submitting

- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
      Pull Request section?
- [ ] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -236,6 +236,7 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
             tool_prompt_format=tool_prompt_format,
             stream=stream,
             logprobs=logprobs,
+            response_format=response_format,
         )
         if stream:
             return self._stream_chat_completion(request)
@@ -279,6 +280,14 @@ class OllamaInferenceAdapter(Inference, ModelsProtocolPrivate):
             )
             input_dict["raw"] = True
 
+        if fmt := request.response_format:
+            if fmt.type == "json_schema":
+                input_dict["format"] = fmt.json_schema
+            elif fmt.type == "grammar":
+                raise NotImplementedError("Grammar response format is not supported")
+            else:
+                raise ValueError(f"Unknown response format type: {fmt.type}")
+
         return {
             "model": request.model,
             **input_dict,

llama_stack/providers/tests/inference/test_text_inference.py

@@ -210,6 +210,7 @@ class TestInference:
         provider = inference_impl.routing_table.get_provider_impl(inference_model)
         if provider.__provider_spec__.provider_type not in (
             "inline::meta-reference",
+            "remote::ollama",
             "remote::tgi",
             "remote::together",
             "remote::fireworks",
@@ -272,6 +273,7 @@ class TestInference:
         provider = inference_impl.routing_table.get_provider_impl(inference_model)
         if provider.__provider_spec__.provider_type not in (
             "inline::meta-reference",
+            "remote::ollama",
             "remote::fireworks",
             "remote::tgi",
             "remote::together",