fix: address first round of reviews

Signed-off-by: Alina Ryan <aliryan@redhat.com>

src/llama_stack_api/file_processor.py

@@ -1,96 +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.
-
-from typing import Any, Protocol, runtime_checkable
-
-from pydantic import BaseModel
-
-from .common.tracing import telemetry_traceable
-from .schema_utils import json_schema_type, webmethod
-from .vector_io import Chunk, VectorStoreChunkingStrategy
-from .version import LLAMA_STACK_API_V1ALPHA
-
-
-@json_schema_type
-class ProcessFileRequest(BaseModel):
-    """Request for processing a file into structured content."""
-
-    file_data: bytes
-    """Raw file data to process."""
-
-    filename: str
-    """Original filename for format detection and processing hints."""
-
-    options: dict[str, Any] | None = None
-    """Optional processing options. Provider-specific parameters."""
-
-    chunking_strategy: VectorStoreChunkingStrategy | None = None
-    """Optional chunking strategy for splitting content into chunks."""
-
-    include_embeddings: bool = False
-    """Whether to generate embeddings for chunks."""
-
-
-@json_schema_type
-class ProcessedContent(BaseModel):
-    """Result of file processing operation."""
-
-    content: str
-    """Extracted text content from the file."""
-
-    chunks: list[Chunk] | None = None
-    """Optional chunks if chunking strategy was provided."""
-
-    embeddings: list[list[float]] | None = None
-    """Optional embeddings for chunks if requested."""
-
-    metadata: dict[str, Any]
-    """Processing metadata including processor name, timing, and provider-specific data."""
-
-
-@telemetry_traceable
-@runtime_checkable
-class FileProcessor(Protocol):
-    """
-    File Processor API for converting files into structured, processable content.
-
-    This API provides a flexible interface for processing various file formats
-    (PDFs, documents, images, etc.) into text content that can be used for
-    vector store ingestion, RAG applications, or standalone content extraction.
-
-    The API supports:
-    - Multiple file formats through extensible provider architecture
-    - Configurable processing options per provider
-    - Integration with vector store chunking strategies
-    - Optional embedding generation for chunks
-    - Rich metadata about processing results
-
-    Future providers can extend this interface to support additional formats,
-    processing capabilities, and optimization strategies.
-    """
-
-    @webmethod(route="/file-processor/process", method="POST", level=LLAMA_STACK_API_V1ALPHA)
-    async def process_file(
-        self,
-        file_data: bytes,
-        filename: str,
-        options: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
-        include_embeddings: bool = False,
-    ) -> ProcessedContent:
-        """
-        Process a file into structured content with optional chunking and embeddings.
-
-        This method processes raw file data and converts it into text content for applications such as vector store ingestion.
-
-        :param file_data: Raw bytes of the file to process.
-        :param filename: Original filename for format detection.
-        :param options: Provider-specific processing options (e.g., OCR settings, output format).
-        :param chunking_strategy: Optional strategy for splitting content into chunks.
-        :param include_embeddings: Whether to generate embeddings for chunks.
-        :returns: ProcessedContent with extracted text, optional chunks, and metadata.
-        """
-        ...