test(core): remove provider data context leak integration

tests/integration/fixtures/context_echo_provider.py

@@ -1,153 +0,0 @@
-"""
-Test-only inference provider that echoes PROVIDER_DATA_VAR in responses.
-
-This provider is used to test context isolation between requests in end-to-end
-scenarios with a real server.
-"""
-
-import json
-from typing import AsyncIterator
-from pydantic import BaseModel
-
-from llama_stack.apis.inference import (
-    Inference,
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAIChatCompletionRequestWithExtraBody,
-    OpenAICompletion,
-    OpenAICompletionRequestWithExtraBody,
-    OpenAIEmbeddingsRequestWithExtraBody,
-    OpenAIEmbeddingsResponse,
-)
-from llama_stack.apis.models import Model
-from llama_stack.core.request_headers import PROVIDER_DATA_VAR
-from llama_stack_client.types.inference_chat_completion_chunk import (
-    ChatCompletionChunkChoice,
-    ChatCompletionChunkChoiceDelta,
-)
-
-
-class ContextEchoConfig(BaseModel):
-    """Minimal config for the test provider."""
-    pass
-
-
-class ContextEchoInferenceProvider(Inference):
-    """
-    Test-only provider that echoes the current PROVIDER_DATA_VAR value.
-
-    Used to detect context leaks between streaming requests in end-to-end tests.
-    """
-
-    def __init__(self, config: ContextEchoConfig) -> None:
-        self.config = config
-
-    async def initialize(self) -> None:
-        pass
-
-    async def shutdown(self) -> None:
-        pass
-
-    async def register_model(self, model: Model) -> Model:
-        return model
-
-    async def unregister_model(self, model_id: str) -> None:
-        pass
-
-    async def list_models(self) -> list[Model]:
-        return []
-
-    async def openai_embeddings(
-        self,
-        params: OpenAIEmbeddingsRequestWithExtraBody,
-    ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError("Embeddings not supported by test provider")
-
-    async def openai_completion(
-        self,
-        params: OpenAICompletionRequestWithExtraBody,
-    ) -> OpenAICompletion:
-        raise NotImplementedError("Use openai_chat_completion instead")
-
-    async def openai_chat_completion(
-        self,
-        params: OpenAIChatCompletionRequestWithExtraBody,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        """Echo the provider data context back in streaming chunks."""
-
-        async def stream_with_context():
-            # Read the current provider data from context
-            # This is the KEY part - if context leaks, this will show old data
-            provider_data = PROVIDER_DATA_VAR.get()
-
-            # Create a JSON message with the provider data
-            # The test will parse this to verify correct isolation
-            message = json.dumps({
-                "provider_data": provider_data,
-                "test_marker": "context_echo"
-            })
-
-            # Yield a chunk with the provider data
-            yield OpenAIChatCompletionChunk(
-                id="context-echo-1",
-                choices=[
-                    ChatCompletionChunkChoice(
-                        delta=ChatCompletionChunkChoiceDelta(
-                            content=message,
-                            role="assistant",
-                        ),
-                        index=0,
-                        finish_reason=None,
-                    )
-                ],
-                created=0,
-                model=params.model,
-                object="chat.completion.chunk",
-            )
-
-            # Final chunk with finish_reason
-            yield OpenAIChatCompletionChunk(
-                id="context-echo-2",
-                choices=[
-                    ChatCompletionChunkChoice(
-                        delta=ChatCompletionChunkChoiceDelta(),
-                        index=0,
-                        finish_reason="stop",
-                    )
-                ],
-                created=0,
-                model=params.model,
-                object="chat.completion.chunk",
-            )
-
-        if params.stream:
-            return stream_with_context()
-        else:
-            # Non-streaming fallback
-            provider_data = PROVIDER_DATA_VAR.get()
-            message_content = json.dumps({
-                "provider_data": provider_data,
-                "test_marker": "context_echo"
-            })
-
-            from llama_stack_client.types.inference_chat_completion import (
-                ChatCompletionChoice,
-                ChatCompletionMessage,
-            )
-
-            return OpenAIChatCompletion(
-                id="context-echo",
-                choices=[
-                    ChatCompletionChoice(
-                        finish_reason="stop",
-                        index=0,
-                        message=ChatCompletionMessage(
-                            content=message_content,
-                            role="assistant",
-                        ),
-                    )
-                ],
-                created=0,
-                model=params.model,
-                object="chat.completion",
-            )