dropped impls for hf serverless and hf endpoint

llama_stack/providers/remote/inference/tgi/__init__.py

@@ -4,23 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Union
+from .config import TGIImplConfig
-
-from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
 
 
-async def get_adapter_impl(
+async def get_adapter_impl(config: TGIImplConfig, _deps):
-    config: Union[InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig],
+    from .tgi import TGIAdapter
-    _deps,
-):
-    from .tgi import InferenceAPIAdapter, InferenceEndpointAdapter, TGIAdapter
 
     if isinstance(config, TGIImplConfig):
         impl = TGIAdapter()
-    elif isinstance(config, InferenceAPIImplConfig):
-        impl = InferenceAPIAdapter()
-    elif isinstance(config, InferenceEndpointImplConfig):
-        impl = InferenceEndpointAdapter()
     else:
         raise ValueError(
             f"Invalid configuration. Expected 'TGIAdapter', 'InferenceAPIImplConfig' or 'InferenceEndpointImplConfig'. Got {type(config)}."

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -7,7 +7,7 @@
 
 from typing import AsyncGenerator, List, Optional
 
-from huggingface_hub import AsyncInferenceClient, HfApi
+from huggingface_hub import AsyncInferenceClient
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -52,7 +52,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     completion_request_to_prompt_model_input_info,
 )
 
-from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
+from .config import TGIImplConfig
 
 logger = get_logger(name=__name__, category="inference")
 
@@ -250,33 +250,8 @@ class _HfAdapter(Inference, ModelsProtocolPrivate):
 class TGIAdapter(_HfAdapter):
     async def initialize(self, config: TGIImplConfig) -> None:
         logger.info(f"Initializing TGI client with url={config.url}")
-        # unfortunately, the TGI async client does not work well with proxies
-        # so using sync client for now instead
         self.client = AsyncInferenceClient(model=f"{config.url}")
 
         endpoint_info = await self.client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
-
-
-class InferenceAPIAdapter(_HfAdapter):
-    async def initialize(self, config: InferenceAPIImplConfig) -> None:
-        self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
-        endpoint_info = await self.client.get_endpoint_info()
-        self.max_tokens = endpoint_info["max_total_tokens"]
-        self.model_id = endpoint_info["model_id"]
-
-
-class InferenceEndpointAdapter(_HfAdapter):
-    async def initialize(self, config: InferenceEndpointImplConfig) -> None:
-        # Get the inference endpoint details
-        api = HfApi(token=config.api_token.get_secret_value())
-        endpoint = api.get_inference_endpoint(config.endpoint_name)
-
-        # Wait for the endpoint to be ready (if not already)
-        endpoint.wait(timeout=60)
-
-        # Initialize the adapter
-        self.client = endpoint.async_client
-        self.model_id = endpoint.repository
-        self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])

llama_stack/providers/utils/inference/openai_compat.py

@@ -1157,10 +1157,6 @@ async def convert_chat_completion_request_to_openai_params(
         # Apply additionalProperties: False recursively to all objects
         fmt = _add_additional_properties_recursive(fmt)
 
-        from rich.pretty import pprint
-
-        pprint(fmt)
-
         input_dict["response_format"] = {
             "type": "json_schema",
             "json_schema": {

tests/integration/inference/test_vision_inference.py

@@ -87,7 +87,7 @@ def test_image_chat_completion_streaming(client_with_models, vision_model_id):
     assert any(expected in streamed_content for expected in {"dog", "puppy", "pup"})
 
 
-@pytest.mark.parametrize("type_", ["url"])
+@pytest.mark.parametrize("type_", ["url", "data"])
 def test_image_chat_completion_base64(client_with_models, vision_model_id, base64_image_data, base64_image_url, type_):
     image_spec = {
         "url": {