mirror of
https://github.com/tiennm99/DocsGPT.git
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116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
import re
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from typing import List, Tuple
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import logging
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from application.parser.chunking_creator import ChunkerCreator
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from application.parser.schema.base import Document
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from application.parser.tokenization import get_token_counter
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logger = logging.getLogger(__name__)
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class Chunker:
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"""Classic token-window chunker (registered as ``classic_chunk``).
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Strategy dispatch lives in ``ChunkerCreator``; this class is one
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registered implementation. The ``chunking_strategy`` arg is retained for
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backward-compatible construction and is not used for dispatch here.
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"""
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def __init__(
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self,
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chunking_strategy: str = "classic_chunk",
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max_tokens: int = 2000,
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min_tokens: int = 150,
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duplicate_headers: bool = False,
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):
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self.chunking_strategy = chunking_strategy
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self.max_tokens = max_tokens
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self.min_tokens = min_tokens
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self.duplicate_headers = duplicate_headers
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# Counted in the embedding model's tokenizer, not cl100k: ``max_tokens``
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# is compared against a limit the embedding server enforces in its own
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# units, so counting in any other unit is a guess.
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self.counter = get_token_counter()
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def separate_header_and_body(self, text: str) -> Tuple[str, str]:
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header_pattern = r"^(.*?\n){3}"
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match = re.match(header_pattern, text)
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if match:
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header = match.group(0)
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body = text[len(header):]
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else:
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header, body = "", text # No header, treat entire text as body
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return header, body
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def split_document(self, doc: Document) -> List[Document]:
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"""Split one oversized document into ``max_tokens``-sized chunks.
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Pieces are sliced out of the original text rather than decoded back
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from token ids. WordPiece tokenizers normalise as they decode --
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all-mpnet-base-v2 lowercases -- so a decode round-trip would rewrite
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every stored document.
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"""
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header, body = self.separate_header_and_body(doc.text)
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header_tokens = self.counter.count(header) if header else 0
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# A chunk carrying the header has that much less room for body text.
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with_header_budget = max(1, self.max_tokens - header_tokens)
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if self.duplicate_headers:
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body_pieces = self.counter.split(body, with_header_budget)
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else:
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body_pieces = self.counter.split(
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body, self.max_tokens, first_max_tokens=with_header_budget
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)
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split_docs = []
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for part_index, piece in enumerate(body_pieces):
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include_header = bool(header) and (self.duplicate_headers or part_index == 0)
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chunk_text = f"{header}{piece}" if include_header else piece
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split_docs.append(
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Document(
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text=chunk_text,
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doc_id=f"{doc.doc_id}-{part_index}",
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embedding=doc.embedding,
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extra_info={
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**(doc.extra_info or {}),
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"token_count": self.counter.count(chunk_text),
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},
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)
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)
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return split_docs
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def classic_chunk(self, documents: List[Document]) -> List[Document]:
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processed_docs = []
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i = 0
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while i < len(documents):
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doc = documents[i]
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token_count = self.counter.count(doc.text)
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if self.min_tokens <= token_count <= self.max_tokens:
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doc.extra_info = doc.extra_info or {}
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doc.extra_info["token_count"] = token_count
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processed_docs.append(doc)
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i += 1
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elif token_count < self.min_tokens:
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doc.extra_info = doc.extra_info or {}
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doc.extra_info["token_count"] = token_count
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processed_docs.append(doc)
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i += 1
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else:
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# Split large documents
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processed_docs.extend(self.split_document(doc))
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i += 1
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return processed_docs
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def chunk(
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self,
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documents: List[Document]
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) -> List[Document]:
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return self.classic_chunk(documents)
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ChunkerCreator.register("classic_chunk", Chunker)
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