feat: add (openai, anthropic, gemini) providers via litellm (#1267)

# What does this PR do?

This PR introduces more non-llama model support to llama stack.
Providers introduced: openai, anthropic and gemini. All of these
providers use essentially the same piece of code -- the implementation
works via the `litellm` library.

We will expose only specific models for providers we enable making sure
they all work well and pass tests. This setup (instead of automatically
enabling _all_ providers and models allowed by LiteLLM) ensures we can
also perform any needed prompt tuning on a per-model basis as needed
(just like we do it for llama models.)

## Test Plan

```bash
#!/bin/bash

args=("$@")
for model in openai/gpt-4o anthropic/claude-3-5-sonnet-latest gemini/gemini-1.5-flash; do
    LLAMA_STACK_CONFIG=dev pytest -s -v tests/client-sdk/inference/test_text_inference.py \
        --embedding-model=all-MiniLM-L6-v2 \
        --vision-inference-model="" \
        --inference-model=$model "${args[@]}"
done
```

tests/client-sdk/conftest.py

@@ -116,12 +116,14 @@ def client_with_models(llama_stack_client, text_model_id, vision_model_id, embed
     providers = [p for p in client.providers.list() if p.api == "inference"]
     assert len(providers) > 0, "No inference providers found"
     inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
-    if text_model_id:
+
+    model_ids = [m.identifier for m in client.models.list()]
+    if text_model_id and text_model_id not in model_ids:
         client.models.register(model_id=text_model_id, provider_id=inference_providers[0])
-    if vision_model_id:
+    if vision_model_id and vision_model_id not in model_ids:
         client.models.register(model_id=vision_model_id, provider_id=inference_providers[0])
 
-    if embedding_model_id and embedding_dimension:
+    if embedding_model_id and embedding_dimension and embedding_model_id not in model_ids:
         # try to find a provider that supports embeddings, if sentence-transformers is not available
         selected_provider = None
         for p in providers:

tests/client-sdk/inference/test_text_inference.py

@@ -19,6 +19,16 @@ PROVIDER_TOOL_PROMPT_FORMAT = {
 PROVIDER_LOGPROBS_TOP_K = {"remote::together", "remote::fireworks", "remote::vllm"}
 
 
+def skip_if_model_doesnt_support_completion(client_with_models, model_id):
+    models = {m.identifier: m for m in client_with_models.models.list()}
+    provider_id = models[model_id].provider_id
+    providers = {p.provider_id: p for p in client_with_models.providers.list()}
+    provider = providers[provider_id]
+    print(f"Provider: {provider.provider_type} for model {model_id}")
+    if provider.provider_type in ("remote::openai", "remote::anthropic", "remote::gemini"):
+        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support completion")
+
+
 @pytest.fixture(scope="session")
 def provider_tool_format(inference_provider_type):
     return (
@@ -35,6 +45,7 @@ def provider_tool_format(inference_provider_type):
     ],
 )
 def test_text_completion_non_streaming(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     tc = TestCase(test_case)
 
     response = client_with_models.inference.completion(
@@ -56,6 +67,7 @@ def test_text_completion_non_streaming(client_with_models, text_model_id, test_c
     ],
 )
 def test_text_completion_streaming(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     tc = TestCase(test_case)
 
     response = client_with_models.inference.completion(
@@ -79,6 +91,7 @@ def test_text_completion_streaming(client_with_models, text_model_id, test_case)
     ],
 )
 def test_text_completion_log_probs_non_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
         pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
 
@@ -107,6 +120,7 @@ def test_text_completion_log_probs_non_streaming(client_with_models, text_model_
     ],
 )
 def test_text_completion_log_probs_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
     if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
         pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
 
@@ -139,6 +153,8 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id,
     ],
 )
 def test_text_completion_structured_output(client_with_models, text_model_id, test_case):
+    skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
+
     class AnswerFormat(BaseModel):
         name: str
         year_born: str
@@ -237,9 +253,7 @@ def test_text_chat_completion_with_tool_calling_and_non_streaming(
         tool_prompt_format=tool_prompt_format,
         stream=False,
     )
-    # No content is returned for the system message since we expect the
-    # response to be a tool call
-    assert response.completion_message.content == ""
+    # some models can return content for the response in addition to the tool call
     assert response.completion_message.role == "assistant"
 
     assert len(response.completion_message.tool_calls) == 1