from __future__ import annotations
import copy
import logging
import re
from abc import ABC, abstractmethod
from collections.abc import Callable, Collection, Iterable, Sequence, Set
from dataclasses import dataclass
from typing import (
Any,
Literal,
Optional,
TypedDict,
TypeVar,
Union,
)
from core.rag.models.document import BaseDocumentTransformer, Document
logger = logging.getLogger(__name__)
TS = TypeVar("TS", bound="TextSplitter")
def _split_text_with_regex(text: str, separator: str, keep_separator: bool) -> list[str]:
# Now that we have the separator, split the text
if separator:
if keep_separator:
# The parentheses in the pattern keep the delimiters in the result.
_splits = re.split(f"({re.escape(separator)})", text)
splits = [_splits[i - 1] + _splits[i] for i in range(1, len(_splits), 2)]
if len(_splits) % 2 != 0:
splits += _splits[-1:]
else:
splits = re.split(separator, text)
else:
splits = list(text)
return [s for s in splits if (s not in {"", "\n"})]
class TextSplitter(BaseDocumentTransformer, ABC):
"""Interface for splitting text into chunks."""
def __init__(
self,
chunk_size: int = 4000,
chunk_overlap: int = 200,
length_function: Callable[[list[str]], list[int]] = lambda x: [len(x) for x in x],
keep_separator: bool = False,
add_start_index: bool = False,
) -> None:
"""Create a new TextSplitter.
Args:
chunk_size: Maximum size of chunks to return
chunk_overlap: Overlap in characters between chunks
length_function: Function that measures the length of given chunks
keep_separator: Whether to keep the separator in the chunks
add_start_index: If `True`, includes chunk's start index in metadata
"""
if chunk_overlap > chunk_size:
raise ValueError(
f"Got a larger chunk overlap ({chunk_overlap}) than chunk size ({chunk_size}), should be smaller."
)
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
self._length_function = length_function
self._keep_separator = keep_separator
self._add_start_index = add_start_index
@abstractmethod
def split_text(self, text: str) -> list[str]:
"""Split text into multiple components."""
def create_documents(self, texts: list[str], metadatas: Optional[list[dict]] = None) -> list[Document]:
"""Create documents from a list of texts."""
_metadatas = metadatas or [{}] * len(texts)
documents = []
for i, text in enumerate(texts):
index = -1
for chunk in self.split_text(text):
metadata = copy.deepcopy(_metadatas[i])
if self._add_start_index:
index = text.find(chunk, index + 1)
metadata["start_index"] = index
new_doc = Document(page_content=chunk, metadata=metadata)
documents.append(new_doc)
return documents
def split_documents(self, documents: Iterable[Document]) -> list[Document]:
"""Split documents."""
texts, metadatas = [], []
for doc in documents:
texts.append(doc.page_content)
metadatas.append(doc.metadata or {})
return self.create_documents(texts, metadatas=metadatas)
def _join_docs(self, docs: list[str], separator: str) -> Optional[str]:
text = separator.join(docs)
text = text.strip()
if text == "":
return None
else:
return text
def _merge_splits(self, splits: Iterable[str], separator: str, lengths: list[int]) -> list[str]:
# We now want to combine these smaller pieces into medium size
# chunks to send to the LLM.
separator_len = self._length_function([separator])[0]
docs = []
current_doc: list[str] = []
total = 0
index = 0
for d in splits:
_len = lengths[index]
if total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size:
if total > self._chunk_size:
logger.warning(
f"Created a chunk of size {total}, which is longer than the specified {self._chunk_size}"
)
if len(current_doc) > 0:
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
while total > self._chunk_overlap or (
total + _len + (separator_len if len(current_doc) > 0 else 0) > self._chunk_size and total > 0
):
total -= self._length_function([current_doc[0]])[0] + (
separator_len if len(current_doc) > 1 else 0
)
current_doc = current_doc[1:]
current_doc.append(d)
total += _len + (separator_len if len(current_doc) > 1 else 0)
index += 1
doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
return docs
@classmethod
def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
"""Text splitter that uses HuggingFace tokenizer to count length."""
try:
from transformers import PreTrainedTokenizerBase # type: ignore
if not isinstance(tokenizer, PreTrainedTokenizerBase):
raise ValueError("Tokenizer received was not an instance of PreTrainedTokenizerBase")
def _huggingface_tokenizer_length(text: str) -> int:
return len(tokenizer.encode(text))
except ImportError:
raise ValueError(
"Could not import transformers python package. Please install it with `pip install transformers`."
)
return cls(length_function=lambda x: [_huggingface_tokenizer_length(text) for text in x], **kwargs)
@classmethod
def from_tiktoken_encoder(
cls: type[TS],
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], Set[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> TS:
"""Text splitter that uses tiktoken encoder to count length."""
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to calculate max_tokens_for_prompt. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
def _tiktoken_encoder(text: str) -> int:
return len(
enc.encode(
text,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
)
)
if issubclass(cls, TokenTextSplitter):
extra_kwargs = {
"encoding_name": encoding_name,
"model_name": model_name,
"allowed_special": allowed_special,
"disallowed_special": disallowed_special,
}
kwargs = {**kwargs, **extra_kwargs}
return cls(length_function=lambda x: [_tiktoken_encoder(text) for text in x], **kwargs)
def transform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
"""Transform sequence of documents by splitting them."""
return self.split_documents(list(documents))
async def atransform_documents(self, documents: Sequence[Document], **kwargs: Any) -> Sequence[Document]:
"""Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
class CharacterTextSplitter(TextSplitter):
"""Splitting text that looks at characters."""
def __init__(self, separator: str = "\n\n", **kwargs: Any) -> None:
"""Create a new TextSplitter."""
super().__init__(**kwargs)
self._separator = separator
def split_text(self, text: str) -> list[str]:
"""Split incoming text and return chunks."""
# First we naively split the large input into a bunch of smaller ones.
splits = _split_text_with_regex(text, self._separator, self._keep_separator)
_separator = "" if self._keep_separator else self._separator
_good_splits_lengths = [] # cache the lengths of the splits
if splits:
_good_splits_lengths.extend(self._length_function(splits))
return self._merge_splits(splits, _separator, _good_splits_lengths)
class LineType(TypedDict):
"""Line type as typed dict."""
metadata: dict[str, str]
content: str
class HeaderType(TypedDict):
"""Header type as typed dict."""
level: int
name: str
data: str
class MarkdownHeaderTextSplitter:
"""Splitting markdown files based on specified headers."""
def __init__(self, headers_to_split_on: list[tuple[str, str]], return_each_line: bool = False):
"""Create a new MarkdownHeaderTextSplitter.
Args:
headers_to_split_on: Headers we want to track
return_each_line: Return each line w/ associated headers
"""
# Output line-by-line or aggregated into chunks w/ common headers
self.return_each_line = return_each_line
# Given the headers we want to split on,
# (e.g., "#, ##, etc") order by length
self.headers_to_split_on = sorted(headers_to_split_on, key=lambda split: len(split[0]), reverse=True)
def aggregate_lines_to_chunks(self, lines: list[LineType]) -> list[Document]:
"""Combine lines with common metadata into chunks
Args:
lines: Line of text / associated header metadata
"""
aggregated_chunks: list[LineType] = []
for line in lines:
if aggregated_chunks and aggregated_chunks[-1]["metadata"] == line["metadata"]:
# If the last line in the aggregated list
# has the same metadata as the current line,
# append the current content to the last lines's content
aggregated_chunks[-1]["content"] += " \n" + line["content"]
else:
# Otherwise, append the current line to the aggregated list
aggregated_chunks.append(line)
return [Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in aggregated_chunks]
def split_text(self, text: str) -> list[Document]:
"""Split markdown file
Args:
text: Markdown file"""
# Split the input text by newline character ("\n").
lines = text.split("\n")
# Final output
lines_with_metadata: list[LineType] = []
# Content and metadata of the chunk currently being processed
current_content: list[str] = []
current_metadata: dict[str, str] = {}
# Keep track of the nested header structure
# header_stack: List[Dict[str, Union[int, str]]] = []
header_stack: list[HeaderType] = []
initial_metadata: dict[str, str] = {}
for line in lines:
stripped_line = line.strip()
# Check each line against each of the header types (e.g., #, ##)
for sep, name in self.headers_to_split_on:
# Check if line starts with a header that we intend to split on
if stripped_line.startswith(sep) and (
# Header with no text OR header is followed by space
# Both are valid conditions that sep is being used a header
len(stripped_line) == len(sep) or stripped_line[len(sep)] == " "
):
# Ensure we are tracking the header as metadata
if name is not None:
# Get the current header level
current_header_level = sep.count("#")
# Pop out headers of lower or same level from the stack
while header_stack and header_stack[-1]["level"] >= current_header_level:
# We have encountered a new header
# at the same or higher level
popped_header = header_stack.pop()
# Clear the metadata for the
# popped header in initial_metadata
if popped_header["name"] in initial_metadata:
initial_metadata.pop(popped_header["name"])
# Push the current header to the stack
header: HeaderType = {
"level": current_header_level,
"name": name,
"data": stripped_line[len(sep) :].strip(),
}
header_stack.append(header)
# Update initial_metadata with the current header
initial_metadata[name] = header["data"]
# Add the previous line to the lines_with_metadata
# only if current_content is not empty
if current_content:
lines_with_metadata.append(
{
"content": "\n".join(current_content),
"metadata": current_metadata.copy(),
}
)
current_content.clear()
break
else:
if stripped_line:
current_content.append(stripped_line)
elif current_content:
lines_with_metadata.append(
{
"content": "\n".join(current_content),
"metadata": current_metadata.copy(),
}
)
current_content.clear()
current_metadata = initial_metadata.copy()
if current_content:
lines_with_metadata.append({"content": "\n".join(current_content), "metadata": current_metadata})
# lines_with_metadata has each line with associated header metadata
# aggregate these into chunks based on common metadata
if not self.return_each_line:
return self.aggregate_lines_to_chunks(lines_with_metadata)
else:
return [
Document(page_content=chunk["content"], metadata=chunk["metadata"]) for chunk in lines_with_metadata
]
# should be in newer Python versions (3.10+)
# @dataclass(frozen=True, kw_only=True, slots=True)
@dataclass(frozen=True)
class Tokenizer:
chunk_overlap: int
tokens_per_chunk: int
decode: Callable[[list[int]], str]
encode: Callable[[str], list[int]]
def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> list[str]:
"""Split incoming text and return chunks using tokenizer."""
splits: list[str] = []
input_ids = tokenizer.encode(text)
start_idx = 0
cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
while start_idx < len(input_ids):
splits.append(tokenizer.decode(chunk_ids))
start_idx += tokenizer.tokens_per_chunk - tokenizer.chunk_overlap
cur_idx = min(start_idx + tokenizer.tokens_per_chunk, len(input_ids))
chunk_ids = input_ids[start_idx:cur_idx]
return splits
class TokenTextSplitter(TextSplitter):
"""Splitting text to tokens using model tokenizer."""
def __init__(
self,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], Set[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(**kwargs)
try:
import tiktoken
except ImportError:
raise ImportError(
"Could not import tiktoken python package. "
"This is needed in order to for TokenTextSplitter. "
"Please install it with `pip install tiktoken`."
)
if model_name is not None:
enc = tiktoken.encoding_for_model(model_name)
else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
def split_text(self, text: str) -> list[str]:
def _encode(_text: str) -> list[int]:
return self._tokenizer.encode(
_text,
allowed_special=self._allowed_special,
disallowed_special=self._disallowed_special,
)
tokenizer = Tokenizer(
chunk_overlap=self._chunk_overlap,
tokens_per_chunk=self._chunk_size,
decode=self._tokenizer.decode,
encode=_encode,
)
return split_text_on_tokens(text=text, tokenizer=tokenizer)
class RecursiveCharacterTextSplitter(TextSplitter):
"""Splitting text by recursively look at characters.
Recursively tries to split by different characters to find one
that works.
"""
def __init__(
self,
separators: Optional[list[str]] = None,
keep_separator: bool = True,
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(keep_separator=keep_separator, **kwargs)
self._separators = separators or ["\n\n", "\n", " ", ""]
def _split_text(self, text: str, separators: list[str]) -> list[str]:
final_chunks = []
separator = separators[-1]
new_separators = []
for i, _s in enumerate(separators):
if _s == "":
separator = _s
break
if re.search(_s, text):
separator = _s
new_separators = separators[i + 1 :]
break
splits = _split_text_with_regex(text, separator, self._keep_separator)
_good_splits = []
_good_splits_lengths = [] # cache the lengths of the splits
_separator = "" if self._keep_separator else separator
s_lens = self._length_function(splits)
for s, s_len in zip(splits, s_lens):
if s_len < self._chunk_size:
_good_splits.append(s)
_good_splits_lengths.append(s_len)
else:
if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator, _good_splits_lengths)
final_chunks.extend(merged_text)
_good_splits = []
_good_splits_lengths = []
if not new_separators:
final_chunks.append(s)
else:
other_info = self._split_text(s, new_separators)
final_chunks.extend(other_info)
if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator, _good_splits_lengths)
final_chunks.extend(merged_text)
return final_chunks
def split_text(self, text: str) -> list[str]:
return self._split_text(text, self._separators)