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Removed docling-related code.

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ilya-kolchinsky 2025-03-11 16:54:55 +01:00
parent 9305b4157e
commit 8ca56484dc
4 changed files with 0 additions and 150 deletions
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18
llama_stack/providers/inline/preprocessing/docling/__init__.py
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@ -1,18 +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 .config import InlineDoclingConfig
async def get_provider_impl(
config: InlineDoclingConfig,
_deps,
):
from .docling import InclineDoclingPreprocessorImpl
impl = InclineDoclingPreprocessorImpl(config)
await impl.initialize()
return impl

10
llama_stack/providers/inline/preprocessing/docling/config.py
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@ -1,10 +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 pydantic import BaseModel
class InlineDoclingConfig(BaseModel):
chunk: bool

114
llama_stack/providers/inline/preprocessing/docling/docling.py
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@ -1,114 +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 logging
from typing import List, Optional
from docling.document_converter import DocumentConverter
from docling_core.transforms.chunker.hybrid_chunker import HybridChunker
from llama_stack.apis.common.content_types import URL
from llama_stack.apis.preprocessing import (
Preprocessing,
PreprocessingDataElement,
PreprocessingDataFormat,
PreprocessingDataType,
Preprocessor,
PreprocessorChain,
PreprocessorOptions,
PreprocessorResponse,
)
from llama_stack.apis.vector_io import Chunk
from llama_stack.providers.datatypes import PreprocessorsProtocolPrivate
from llama_stack.providers.inline.preprocessing.docling import InlineDoclingConfig
log = logging.getLogger(__name__)
class InclineDoclingPreprocessorImpl(Preprocessing, PreprocessorsProtocolPrivate):
# this preprocessor receives URLs / paths to documents as input
input_types = [PreprocessingDataType.document_uri]
# this preprocessor either only converts the documents into a text format, or also chunks them
output_types = [PreprocessingDataType.raw_text_document, PreprocessingDataType.chunks]
def __init__(self, config: InlineDoclingConfig) -> None:
self.config = config
self.converter = None
self.chunker = None
async def initialize(self) -> None: ...
async def shutdown(self) -> None: ...
async def register_preprocessor(self, preprocessor: Preprocessor) -> None: ...
async def unregister_preprocessor(self, preprocessor_id: str) -> None: ...
async def do_preprocess(
self,
preprocessor_id: str,
preprocessor_inputs: List[PreprocessingDataElement],
options: Optional[PreprocessorOptions] = None,
) -> PreprocessorResponse:
if self.converter is None:
# this is the first time this method is called
self.converter = DocumentConverter()
if self.config.chunk and self.chunker is None:
# TODO: docling should use Llama Stack's inference API instead of handling tokenization by itself
self.chunker = HybridChunker()
results = []
for inp in preprocessor_inputs:
if isinstance(inp.data_element_path_or_content, str):
url = inp.data_element_path_or_content
elif isinstance(inp.data_element_path_or_content, URL):
url = inp.data_element_path_or_content.uri
else:
log.error(
f"Unexpected type {type(inp.data_element_path_or_content)} for input {inp.data_element_path_or_content}, skipping this input."
)
continue
converted_document = self.converter.convert(url).document
if self.config.chunk:
result = self.chunker.chunk(converted_document)
for i, chunk in enumerate(result):
metadata = chunk.meta.dict()
# TODO: some vector DB adapters rely on a hard-coded header 'document_id'. This should be fixed.
metadata["document_id"] = inp.data_element_id
# TODO: the RAG tool implementation relies in a hard-coded header 'token_count'
metadata["token_count"] = self.chunker._count_chunk_tokens(chunk)
raw_chunk = Chunk(content=chunk.text, metadata=metadata)
chunk_data_element = PreprocessingDataElement(
data_element_id=f"{inp.data_element_id}_chunk_{i}",
data_element_type=PreprocessingDataType.chunks,
data_element_format=PreprocessingDataFormat.txt,
data_element_path_or_content=raw_chunk,
)
results.append(chunk_data_element)
else:
result = PreprocessingDataElement(
data_element_id=inp.data_element_id,
data_element_type=PreprocessingDataType.raw_text_document,
data_element_format=PreprocessingDataFormat.txt,
data_element_path_or_content=converted_document.export_to_markdown(),
)
results.append(result)
output_data_type = (
PreprocessingDataType.chunks if self.config.chunk else PreprocessingDataType.raw_text_document
)
return PreprocessorResponse(success=True, output_data_type=output_data_type, results=results)
async def preprocess(
self,
preprocessors: PreprocessorChain,
preprocessor_inputs: List[PreprocessingDataElement],
) -> PreprocessorResponse:
return await self.do_preprocess(preprocessor_id="", preprocessor_inputs=preprocessor_inputs)

8
llama_stack/providers/registry/preprocessing.py
View file

@ -15,14 +15,6 @@ from llama_stack.providers.datatypes import (
def available_providers() -> List[ProviderSpec]:
return [
InlineProviderSpec(
api=Api.preprocessing,
provider_type="inline::docling",
pip_packages=["docling"],
module="llama_stack.providers.inline.preprocessing.docling",
config_class="llama_stack.providers.inline.preprocessing.docling.InlineDoclingConfig",
api_dependencies=[],
),
InlineProviderSpec(
api=Api.preprocessing,
provider_type="inline::basic",