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text spliter length method use default embedding model tokenizer (#2011)

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Co-authored-by: jyong <jyong@dify.ai>
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Jyong 2024-01-12 18:45:34 +08:00 committed by GitHub
parent 1779cea6e3
commit a63a9c7d45
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2 changed files with 69 additions and 24 deletions
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58
api/core/indexing_runner.py
View File

@ -13,7 +13,7 @@ from core.docstore.dataset_docstore import DatasetDocumentStore
from core.errors.error import ProviderTokenNotInitError
from core.generator.llm_generator import LLMGenerator
from core.index.index import IndexBuilder
from core.model_manager import ModelManager
from core.model_manager import ModelManager, ModelInstance
from core.model_runtime.entities.model_entities import ModelType, PriceType
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
@ -61,8 +61,24 @@ class IndexingRunner:
# load file
text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
# get embedding model instance
embedding_model_instance = None
if dataset.indexing_technique == 'high_quality':
if dataset.embedding_model_provider:
embedding_model_instance = self.model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
else:
embedding_model_instance = self.model_manager.get_default_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
# get splitter
splitter = self._get_splitter(processing_rule)
splitter = self._get_splitter(processing_rule, embedding_model_instance)
# split to documents
documents = self._step_split(
@ -121,8 +137,24 @@ class IndexingRunner:
# load file
text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
# get embedding model instance
embedding_model_instance = None
if dataset.indexing_technique == 'high_quality':
if dataset.embedding_model_provider:
embedding_model_instance = self.model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
else:
embedding_model_instance = self.model_manager.get_default_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
# get splitter
splitter = self._get_splitter(processing_rule)
splitter = self._get_splitter(processing_rule, embedding_model_instance)
# split to documents
documents = self._step_split(
@ -253,7 +285,7 @@ class IndexingRunner:
text_docs = FileExtractor.load(file_detail, is_automatic=processing_rule.mode == 'automatic')
# get splitter
splitter = self._get_splitter(processing_rule)
splitter = self._get_splitter(processing_rule, embedding_model_instance)
# split to documents
documents = self._split_to_documents_for_estimate(
@ -384,7 +416,7 @@ class IndexingRunner:
)
# get splitter
splitter = self._get_splitter(processing_rule)
splitter = self._get_splitter(processing_rule, embedding_model_instance)
# split to documents
documents = self._split_to_documents_for_estimate(
@ -502,7 +534,8 @@ class IndexingRunner:
text = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\x80-\xFF]', '', text)
return text
def _get_splitter(self, processing_rule: DatasetProcessRule) -> TextSplitter:
def _get_splitter(self, processing_rule: DatasetProcessRule,
embedding_model_instance: Optional[ModelInstance]) -> TextSplitter:
"""
Get the NodeParser object according to the processing rule.
"""
@ -517,19 +550,20 @@ class IndexingRunner:
if separator:
separator = separator.replace('\\n', '\n')
character_splitter = FixedRecursiveCharacterTextSplitter.from_gpt2_encoder(
character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder(
chunk_size=segmentation["max_tokens"],
chunk_overlap=0,
fixed_separator=separator,
separators=["\n\n", "。", ".", " ", ""]
separators=["\n\n", "。", ".", " ", ""],
embedding_model_instance=embedding_model_instance
)
else:
# Automatic segmentation
character_splitter = EnhanceRecursiveCharacterTextSplitter.from_gpt2_encoder(
character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder(
chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
chunk_overlap=0,
separators=["\n\n", "。", ".", " ", ""]
separators=["\n\n", "。", ".", " ", ""],
embedding_model_instance=embedding_model_instance
)
return character_splitter
@ -714,7 +748,7 @@ class IndexingRunner:
return text
def format_split_text(self, text):
regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q\d+:|$)"
regex = r"Q\d+:\s*(.*?)\s*A\d+:\s*([\s\S]*?)(?=Q\d+:|$)"
matches = re.findall(regex, text, re.UNICODE)
return [

35
api/core/spiltter/fixed_text_splitter.py
View File

@ -1,8 +1,10 @@
"""Functionality for splitting text."""
from __future__ import annotations
from typing import Any, List, Optional
from typing import Any, List, Optional, cast
from core.model_manager import ModelInstance
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
from langchain.text_splitter import (TS, AbstractSet, Collection, Literal, RecursiveCharacterTextSplitter,
TokenTextSplitter, Type, Union)
@ -12,22 +14,30 @@ class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
"""
This class is used to implement from_gpt2_encoder, to prevent using of tiktoken
"""
@classmethod
def from_gpt2_encoder(
cls: Type[TS],
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
def from_encoder(
cls: Type[TS],
embedding_model_instance: Optional[ModelInstance],
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
):
def _token_encoder(text: str) -> int:
return GPT2Tokenizer.get_num_tokens(text)
if embedding_model_instance:
embedding_model_type_instance = embedding_model_instance.model_type_instance
embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
return embedding_model_type_instance.get_num_tokens(
model=embedding_model_instance.model,
credentials=embedding_model_instance.credentials,
texts=[text]
)
else:
return GPT2Tokenizer.get_num_tokens(text)
if issubclass(cls, TokenTextSplitter):
extra_kwargs = {
"encoding_name": encoding_name,
"model_name": model_name,
"model_name": embedding_model_instance.model if embedding_model_instance else 'gpt2',
"allowed_special": allowed_special,
"disallowed_special": disallowed_special,
}
@ -35,6 +45,7 @@ class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
return cls(length_function=_token_encoder, **kwargs)
class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter):
def __init__(self, fixed_separator: str = "\n\n", separators: Optional[List[str]] = None, **kwargs: Any):
"""Create a new TextSplitter."""
@ -90,4 +101,4 @@ class FixedRecursiveCharacterTextSplitter(EnhanceRecursiveCharacterTextSplitter)
if _good_splits:
merged_text = self._merge_splits(_good_splits, separator)
final_chunks.extend(merged_text)
return final_chunks
return final_chunks