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Fixed a few issues in the docling provider.

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This commit is contained in:
ilya-kolchinsky 2025-03-06 20:51:37 +01:00
parent f10a412898
commit 275fdbc23f
2 changed files with 10 additions and 3 deletions
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1
llama_stack/providers/inline/preprocessing/docling/config.py
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@ -8,4 +8,3 @@ from pydantic import BaseModel
class InlineDoclingConfig(BaseModel):
chunk: bool
tokenizer: str

12
llama_stack/providers/inline/preprocessing/docling/docling.py
View file

@ -41,7 +41,8 @@ class InclineDoclingPreprocessorImpl(Preprocessing, PreprocessorsProtocolPrivate
async def initialize(self) -> None:
if self.config.chunk:
self.chunker = HybridChunker(tokenizer=self.config.tokenizer)
# TODO: docling should use Llama Stack's inference API instead of handling tokenization by itself
self.chunker = HybridChunker()
async def shutdown(self) -> None: ...
@ -69,10 +70,16 @@ class InclineDoclingPreprocessorImpl(Preprocessing, PreprocessorsProtocolPrivate
continue
converted_document = self.converter.convert(url).document
if self.config.chunk:
result = self.chunker.chunk(converted_document)
for i, chunk in enumerate(result):
raw_chunk = Chunk(content=chunk.text, metadata=chunk.meta)
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,
@ -80,6 +87,7 @@ class InclineDoclingPreprocessorImpl(Preprocessing, PreprocessorsProtocolPrivate
data_element_path_or_content=raw_chunk,
)
results.append(chunk_data_element)
else:
result = PreprocessingDataElement(
data_element_id=inp.data_element_id,