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Code Issues Actions 4 Packages Projects Releases Wiki Activity

add mutil-thread document embedding (#3016)

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Co-authored-by: jyong <jyong@dify.ai>
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Jyong 2024-03-28 17:02:35 +08:00 committed by GitHub
parent 20d16d7b31
commit b0b0cc045f
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7 changed files with 39 additions and 22 deletions
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49
api/core/indexing_runner.py
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@ -1,3 +1,4 @@
import concurrent.futures
import datetime
import json
import logging
@ -650,17 +651,44 @@ class IndexingRunner:
# chunk nodes by chunk size
indexing_start_at = time.perf_counter()
tokens = 0
chunk_size = 100
chunk_size = 10
embedding_model_type_instance = None
if embedding_model_instance:
embedding_model_type_instance = embedding_model_instance.model_type_instance
embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
futures = []
for i in range(0, len(documents), chunk_size):
chunk_documents = documents[i:i + chunk_size]
futures.append(executor.submit(self._process_chunk, current_app._get_current_object(), index_processor,
chunk_documents, dataset,
dataset_document, embedding_model_instance,
embedding_model_type_instance))
for i in range(0, len(documents), chunk_size):
for future in futures:
tokens += future.result()
indexing_end_at = time.perf_counter()
# update document status to completed
self._update_document_index_status(
document_id=dataset_document.id,
after_indexing_status="completed",
extra_update_params={
DatasetDocument.tokens: tokens,
DatasetDocument.completed_at: datetime.datetime.utcnow(),
DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
}
)
def _process_chunk(self, flask_app, index_processor, chunk_documents, dataset, dataset_document,
embedding_model_instance, embedding_model_type_instance):
with flask_app.app_context():
# check document is paused
self._check_document_paused_status(dataset_document.id)
chunk_documents = documents[i:i + chunk_size]
tokens = 0
if dataset.indexing_technique == 'high_quality' or embedding_model_type_instance:
tokens += sum(
embedding_model_type_instance.get_num_tokens(
@ -670,9 +698,9 @@ class IndexingRunner:
)
for document in chunk_documents
)
# load index
index_processor.load(dataset, chunk_documents)
db.session.add(dataset)
document_ids = [document.metadata['doc_id'] for document in chunk_documents]
db.session.query(DocumentSegment).filter(
@ -687,18 +715,7 @@ class IndexingRunner:
db.session.commit()
indexing_end_at = time.perf_counter()
# update document status to completed
self._update_document_index_status(
document_id=dataset_document.id,
after_indexing_status="completed",
extra_update_params={
DatasetDocument.tokens: tokens,
DatasetDocument.completed_at: datetime.datetime.utcnow(),
DatasetDocument.indexing_latency: indexing_end_at - indexing_start_at,
}
)
return tokens
def _check_document_paused_status(self, document_id: str):
indexing_cache_key = 'document_{}_is_paused'.format(document_id)

2
api/core/rag/extractor/unstructured/unstructured_doc_extractor.py
View File

@ -53,7 +53,7 @@ class UnstructuredWordExtractor(BaseExtractor):
elements = partition_docx(filename=self._file_path)
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()

2
api/core/rag/extractor/unstructured/unstructured_eml_extractor.py
View File

@ -43,7 +43,7 @@ class UnstructuredEmailExtractor(BaseExtractor):
pass
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()

2
api/core/rag/extractor/unstructured/unstructured_markdown_extractor.py
View File

@ -38,7 +38,7 @@ class UnstructuredMarkdownExtractor(BaseExtractor):
elements = partition_md(filename=self._file_path, api_url=self._api_url)
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()

2
api/core/rag/extractor/unstructured/unstructured_msg_extractor.py
View File

@ -28,7 +28,7 @@ class UnstructuredMsgExtractor(BaseExtractor):
elements = partition_msg(filename=self._file_path, api_url=self._api_url)
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()

2
api/core/rag/extractor/unstructured/unstructured_text_extractor.py
View File

@ -28,7 +28,7 @@ class UnstructuredTextExtractor(BaseExtractor):
elements = partition_text(filename=self._file_path, api_url=self._api_url)
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()

2
api/core/rag/extractor/unstructured/unstructured_xml_extractor.py
View File

@ -28,7 +28,7 @@ class UnstructuredXmlExtractor(BaseExtractor):
elements = partition_xml(filename=self._file_path, xml_keep_tags=True, api_url=self._api_url)
from unstructured.chunking.title import chunk_by_title
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=0)
chunks = chunk_by_title(elements, max_characters=2000, combine_text_under_n_chars=2000)
documents = []
for chunk in chunks:
text = chunk.text.strip()