refactor(passthrough): replace client with httpx, simplify implementation

- Use httpx directly instead of AsyncLlamaStackClient
- Remove ModelRegistryHelper dependency (unused)
- Consolidate to auth_credential field via RemoteInferenceProviderConfig
- Implement list_models() to fetch from downstream /v1/models
- Implement all inference methods (completion, chat, embeddings)
- Fix provider data validator field names
- Add SSE parsing for streaming responses

src/llama_stack/providers/remote/inference/passthrough/__init__.py

@@ -10,8 +10,8 @@ from .config import PassthroughImplConfig
 
 
 class PassthroughProviderDataValidator(BaseModel):
-    url: str
-    api_key: str
+    passthrough_url: str
+    passthrough_api_key: str
 
 
 async def get_adapter_impl(config: PassthroughImplConfig, _deps):

src/llama_stack/providers/remote/inference/passthrough/config.py

@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import Field, SecretStr
+from pydantic import Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
@@ -19,11 +19,6 @@ class PassthroughImplConfig(RemoteInferenceProviderConfig):
         description="The URL for the passthrough endpoint",
     )
 
-    api_key: SecretStr | None = Field(
-        default=None,
-        description="API Key for the passthrouth endpoint",
-    )
-
     @classmethod
     def sample_run_config(
         cls, url: str = "${env.PASSTHROUGH_URL}", api_key: str = "${env.PASSTHROUGH_API_KEY}", **kwargs

src/llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -4,10 +4,12 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import json
 from collections.abc import AsyncIterator
-from typing import Any
+from enum import Enum
 
-from llama_stack_client import AsyncLlamaStackClient
+import httpx
+from pydantic import BaseModel
 
 from llama_stack.apis.inference import (
     Inference,
@@ -20,15 +22,13 @@ from llama_stack.apis.inference import (
     OpenAIEmbeddingsResponse,
 )
 from llama_stack.apis.models import Model
-from llama_stack.core.library_client import convert_pydantic_to_json_value
-from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
+from llama_stack.core.request_headers import NeedsRequestProviderData
 
 from .config import PassthroughImplConfig
 
 
-class PassthroughInferenceAdapter(Inference):
+class PassthroughInferenceAdapter(NeedsRequestProviderData, Inference):
     def __init__(self, config: PassthroughImplConfig) -> None:
-        ModelRegistryHelper.__init__(self)
         self.config = config
 
     async def unregister_model(self, model_id: str) -> None:
@@ -37,86 +37,188 @@ class PassthroughInferenceAdapter(Inference):
     async def register_model(self, model: Model) -> Model:
         return model
 
-    def _get_client(self) -> AsyncLlamaStackClient:
-        passthrough_url = None
-        passthrough_api_key = None
-        provider_data = None
+    async def list_models(self) -> list[Model]:
+        """List models by calling the downstream /v1/models endpoint."""
+        base_url = self._get_passthrough_url().rstrip("/")
+        api_key = self._get_passthrough_api_key()
 
+        url = f"{base_url}/v1/models"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Accept": "application/json",
+        }
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url, headers=headers, timeout=30.0)
+            response.raise_for_status()
+
+            response_data = response.json()
+            # The response should be a list of Model objects
+            return [Model.model_validate(m) for m in response_data]
+
+    async def should_refresh_models(self) -> bool:
+        """Passthrough should refresh models since they come from downstream dynamically."""
+        return self.config.refresh_models
+
+    def _get_passthrough_url(self) -> str:
+        """Get the passthrough URL from config or provider data."""
         if self.config.url is not None:
-            passthrough_url = self.config.url
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.passthrough_url:
-                raise ValueError(
-                    'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
-                )
-            passthrough_url = provider_data.passthrough_url
+            return self.config.url
 
-        if self.config.api_key is not None:
-            passthrough_api_key = self.config.api_key.get_secret_value()
-        else:
-            provider_data = self.get_request_provider_data()
-            if provider_data is None or not provider_data.passthrough_api_key:
-                raise ValueError(
-                    'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
-                )
-            passthrough_api_key = provider_data.passthrough_api_key
+        provider_data = self.get_request_provider_data()
+        if provider_data is None:
+            raise ValueError(
+                'Pass url of the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_url": <your passthrough url>}'
+            )
+        return provider_data.passthrough_url
 
-        return AsyncLlamaStackClient(
-            base_url=passthrough_url,
-            api_key=passthrough_api_key,
-            provider_data=provider_data,
+    def _get_passthrough_api_key(self) -> str:
+        """Get the passthrough API key from config or provider data."""
+        if self.config.auth_credential is not None:
+            return self.config.auth_credential.get_secret_value()
+
+        provider_data = self.get_request_provider_data()
+        if provider_data is None:
+            raise ValueError(
+                'Pass API Key for the passthrough endpoint in the header X-LlamaStack-Provider-Data as { "passthrough_api_key": <your api key>}'
+            )
+        return provider_data.passthrough_api_key
+
+    def _serialize_value(self, value):
+        """Convert Pydantic models and enums to JSON-serializable values."""
+        if isinstance(value, BaseModel):
+            return json.loads(value.model_dump_json())
+        elif isinstance(value, Enum):
+            return value.value
+        elif isinstance(value, list):
+            return [self._serialize_value(item) for item in value]
+        elif isinstance(value, dict):
+            return {k: self._serialize_value(v) for k, v in value.items()}
+        else:
+            return value
+
+    async def _make_request(
+        self,
+        endpoint: str,
+        params: dict,
+        response_type: type,
+        stream: bool = False,
+    ):
+        """Make an HTTP request to the passthrough endpoint."""
+        base_url = self._get_passthrough_url().rstrip("/")
+        api_key = self._get_passthrough_api_key()
+
+        url = f"{base_url}/v1/{endpoint.lstrip('/')}"
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+        }
+
+        # Serialize the request body
+        json_body = self._serialize_value(params)
+
+        if stream:
+            return self._stream_request(url, headers, json_body, response_type)
+        else:
+            return await self._non_stream_request(url, headers, json_body, response_type)
+
+    async def _non_stream_request(
+        self,
+        url: str,
+        headers: dict,
+        json_body: dict,
+        response_type: type,
+    ):
+        """Make a non-streaming HTTP request."""
+        async with httpx.AsyncClient() as client:
+            response = await client.post(url, json=json_body, headers=headers, timeout=30.0)
+            response.raise_for_status()
+
+            response_data = response.json()
+            return response_type.model_validate(response_data)
+
+    async def _stream_request(
+        self,
+        url: str,
+        headers: dict,
+        json_body: dict,
+        response_type: type,
+    ) -> AsyncIterator:
+        """Make a streaming HTTP request with Server-Sent Events parsing."""
+        async with httpx.AsyncClient() as client:
+            async with client.stream("POST", url, json=json_body, headers=headers, timeout=30.0) as response:
+                response.raise_for_status()
+
+                async for line in response.aiter_lines():
+                    if line.startswith("data:"):
+                        # Extract JSON after "data: " prefix
+                        data_str = line[len("data:") :].strip()
+
+                        # Skip empty lines or "[DONE]" marker
+                        if not data_str or data_str == "[DONE]":
+                            continue
+
+                        try:
+                            data = json.loads(data_str)
+                            yield response_type.model_validate(data)
+                        except Exception:
+                            # Log and skip malformed chunks
+                            continue
+
+    async def openai_completion(
+        self,
+        params: OpenAICompletionRequestWithExtraBody,
+    ) -> OpenAICompletion:
+        model_obj = await self.model_store.get_model(params.model)
+
+        # Create a copy with the provider's model ID
+        params = params.model_copy()
+        params.model = model_obj.provider_resource_id
+
+        request_params = params.model_dump(exclude_none=True)
+
+        return await self._make_request(
+            endpoint="completions",
+            params=request_params,
+            response_type=OpenAICompletion,
+            stream=params.stream or False,
+        )
+
+    async def openai_chat_completion(
+        self,
+        params: OpenAIChatCompletionRequestWithExtraBody,
+    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
+        model_obj = await self.model_store.get_model(params.model)
+
+        # Create a copy with the provider's model ID
+        params = params.model_copy()
+        params.model = model_obj.provider_resource_id
+
+        request_params = params.model_dump(exclude_none=True)
+
+        return await self._make_request(
+            endpoint="chat/completions",
+            params=request_params,
+            response_type=OpenAIChatCompletionChunk if params.stream else OpenAIChatCompletion,
+            stream=params.stream or False,
         )
 
     async def openai_embeddings(
         self,
         params: OpenAIEmbeddingsRequestWithExtraBody,
     ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
-
-    async def openai_completion(
-        self,
-        params: OpenAICompletionRequestWithExtraBody,
-    ) -> OpenAICompletion:
-        client = self._get_client()
         model_obj = await self.model_store.get_model(params.model)
 
+        # Create a copy with the provider's model ID
         params = params.model_copy()
         params.model = model_obj.provider_resource_id
 
         request_params = params.model_dump(exclude_none=True)
 
-        return await client.inference.openai_completion(**request_params)
-
-    async def openai_chat_completion(
-        self,
-        params: OpenAIChatCompletionRequestWithExtraBody,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        client = self._get_client()
-        model_obj = await self.model_store.get_model(params.model)
-
-        params = params.model_copy()
-        params.model = model_obj.provider_resource_id
-
-        request_params = params.model_dump(exclude_none=True)
-
-        return await client.inference.openai_chat_completion(**request_params)
-
-    def cast_value_to_json_dict(self, request_params: dict[str, Any]) -> dict[str, Any]:
-        json_params = {}
-        for key, value in request_params.items():
-            json_input = convert_pydantic_to_json_value(value)
-            if isinstance(json_input, dict):
-                json_input = {k: v for k, v in json_input.items() if v is not None}
-            elif isinstance(json_input, list):
-                json_input = [x for x in json_input if x is not None]
-                new_input = []
-                for x in json_input:
-                    if isinstance(x, dict):
-                        x = {k: v for k, v in x.items() if v is not None}
-                    new_input.append(x)
-                json_input = new_input
-
-            json_params[key] = json_input
-
-        return json_params
+        return await self._make_request(
+            endpoint="embeddings",
+            params=request_params,
+            response_type=OpenAIEmbeddingsResponse,
+            stream=False,
+        )