chore(package): migrate to src/ layout (#3920)

Migrates package structure to src/ layout following Python packaging
best practices.

All code moved from `llama_stack/` to `src/llama_stack/`. Public API
unchanged - imports remain `import llama_stack.*`.

Updated build configs, pre-commit hooks, scripts, and GitHub workflows
accordingly. All hooks pass, package builds cleanly.

**Developer note**: Reinstall after pulling: `pip install -e .`

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -1,102 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the terms described in the LICENSE file in
-# the root directory of this source tree.
-
-import asyncio
-import base64
-import platform
-import struct
-from typing import TYPE_CHECKING
-
-import torch
-
-from llama_stack.log import get_logger
-
-if TYPE_CHECKING:
-    from sentence_transformers import SentenceTransformer
-
-from llama_stack.apis.inference import (
-    ModelStore,
-    OpenAIEmbeddingData,
-    OpenAIEmbeddingsRequestWithExtraBody,
-    OpenAIEmbeddingsResponse,
-    OpenAIEmbeddingUsage,
-)
-
-EMBEDDING_MODELS = {}
-
-DARWIN = "Darwin"
-
-
-log = get_logger(name=__name__, category="providers::utils")
-
-
-class SentenceTransformerEmbeddingMixin:
-    model_store: ModelStore
-
-    async def openai_embeddings(
-        self,
-        params: OpenAIEmbeddingsRequestWithExtraBody,
-    ) -> OpenAIEmbeddingsResponse:
-        # Convert input to list format if it's a single string
-        input_list = [params.input] if isinstance(params.input, str) else params.input
-        if not input_list:
-            raise ValueError("Empty list not supported")
-
-        # Get the model and generate embeddings
-        model_obj = await self.model_store.get_model(params.model)
-        embedding_model = await self._load_sentence_transformer_model(model_obj.provider_resource_id)
-        embeddings = await asyncio.to_thread(embedding_model.encode, input_list, show_progress_bar=False)
-
-        # Convert embeddings to the requested format
-        data = []
-        for i, embedding in enumerate(embeddings):
-            if params.encoding_format == "base64":
-                # Convert float array to base64 string
-                float_bytes = struct.pack(f"{len(embedding)}f", *embedding)
-                embedding_value = base64.b64encode(float_bytes).decode("ascii")
-            else:
-                # Default to float format
-                embedding_value = embedding.tolist()
-
-            data.append(
-                OpenAIEmbeddingData(
-                    embedding=embedding_value,
-                    index=i,
-                )
-            )
-
-        # Not returning actual token usage
-        usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
-        return OpenAIEmbeddingsResponse(
-            data=data,
-            model=params.model,
-            usage=usage,
-        )
-
-    async def _load_sentence_transformer_model(self, model: str) -> "SentenceTransformer":
-        global EMBEDDING_MODELS
-
-        loaded_model = EMBEDDING_MODELS.get(model)
-        if loaded_model is not None:
-            return loaded_model
-
-        log.info(f"Loading sentence transformer for {model}...")
-
-        def _load_model():
-            from sentence_transformers import SentenceTransformer
-
-            platform_name = platform.system()
-            if platform_name == DARWIN:
-                # PyTorch's OpenMP kernels can segfault on macOS when spawned from background
-                # threads with the default parallel settings, so force a single-threaded CPU run.
-                log.debug(f"Constraining torch threads on {platform_name} to a single worker")
-                torch.set_num_threads(1)
-
-            return SentenceTransformer(model, trust_remote_code=True)
-
-        loaded_model = await asyncio.to_thread(_load_model)
-        EMBEDDING_MODELS[model] = loaded_model
-        return loaded_model