Source code for langchain_text_splitters.markdown

from __future__ import annotations

from typing import Any, Dict, List, Tuple, TypedDict

from langchain_core.documents import Document

from langchain_text_splitters.base import Language
from langchain_text_splitters.character import RecursiveCharacterTextSplitter


[docs]class MarkdownTextSplitter(RecursiveCharacterTextSplitter): """Attempts to split the text along Markdown-formatted headings."""
[docs] def __init__(self, **kwargs: Any) -> None: """Initialize a MarkdownTextSplitter.""" separators = self.get_separators_for_language(Language.MARKDOWN) super().__init__(separators=separators, **kwargs)
[docs]class MarkdownHeaderTextSplitter: """Splitting markdown files based on specified headers."""
[docs] def __init__( self, headers_to_split_on: List[Tuple[str, str]], return_each_line: bool = False, strip_headers: bool = True, ): """Create a new MarkdownHeaderTextSplitter. Args: headers_to_split_on: Headers we want to track return_each_line: Return each line w/ associated headers strip_headers: Strip split headers from the content of the chunk """ # 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 ) # Strip headers split headers from the content of the chunk self.strip_headers = strip_headers
[docs] 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"] elif ( aggregated_chunks and aggregated_chunks[-1]["metadata"] != line["metadata"] # may be issues if other metadata is present and len(aggregated_chunks[-1]["metadata"]) < len(line["metadata"]) and aggregated_chunks[-1]["content"].split("\n")[-1][0] == "#" and not self.strip_headers ): # If the last line in the aggregated list # has different metadata as the current line, # and has shallower header level than the current line, # and the last line is a header, # and we are not stripping headers, # append the current content to the last line's content aggregated_chunks[-1]["content"] += " \n" + line["content"] # and update the last line's metadata aggregated_chunks[-1]["metadata"] = line["metadata"] 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 ]
[docs] 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] = {} in_code_block = False opening_fence = "" for line in lines: stripped_line = line.strip() # Remove all non-printable characters from the string, keeping only visible # text. stripped_line = "".join(filter(str.isprintable, stripped_line)) if not in_code_block: # Exclude inline code spans if stripped_line.startswith("```") and stripped_line.count("```") == 1: in_code_block = True opening_fence = "```" elif stripped_line.startswith("~~~"): in_code_block = True opening_fence = "~~~" else: if stripped_line.startswith(opening_fence): in_code_block = False opening_fence = "" if in_code_block: current_content.append(stripped_line) continue # 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() if not self.strip_headers: current_content.append(stripped_line) 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 ]
[docs]class LineType(TypedDict): """Line type as typed dict.""" metadata: Dict[str, str] content: str
[docs]class HeaderType(TypedDict): """Header type as typed dict.""" level: int name: str data: str