OpenHands/openhands/utils/chunk_localizer.py

"""Chunk localizer to help localize the most relevant chunks in a file.

This is primarily used to localize the most relevant chunks in a file
for a given query (e.g. edit draft produced by the agent).
"""

import pylcs
from pydantic import BaseModel
from tree_sitter_languages import get_parser

from openhands.core.logger import openhands_logger as logger


class Chunk(BaseModel):
    text: str
    line_range: tuple[int, int]  # (start_line, end_line), 1-index, inclusive
    normalized_lcs: float | None = None

    def visualize(self) -> str:
        lines = self.text.split('\n')
        assert len(lines) == self.line_range[1] - self.line_range[0] + 1
        ret = ''
        for i, line in enumerate(lines):
            ret += f'{self.line_range[0] + i}|{line}\n'
        return ret


def _create_chunks_from_raw_string(content: str, size: int):
    lines = content.split('\n')
    ret = []
    for i in range(0, len(lines), size):
        _cur_lines = lines[i : i + size]
        ret.append(
            Chunk(
                text='\n'.join(_cur_lines),
                line_range=(i + 1, i + len(_cur_lines)),
            )
        )
    return ret


def create_chunks(
    text: str, size: int = 100, language: str | None = None
) -> list[Chunk]:
    try:
        parser = get_parser(language) if language is not None else None
    except AttributeError:
        logger.debug(f'Language {language} not supported. Falling back to raw string.')
        parser = None

    if parser is None:
        # fallback to raw string
        return _create_chunks_from_raw_string(text, size)

    # TODO: implement tree-sitter chunking
    # return _create_chunks_from_tree_sitter(parser.parse(bytes(text, 'utf-8')), max_chunk_lines=size)
    raise NotImplementedError('Tree-sitter chunking not implemented yet.')


def normalized_lcs(chunk: str, query: str) -> float:
    """Calculate the normalized Longest Common Subsequence (LCS) to compare file chunk with the query (e.g. edit draft).

    We normalize Longest Common Subsequence (LCS) by the length of the chunk
    to check how **much** of the chunk is covered by the query.
    """
    if len(chunk) == 0:
        return 0.0
    _score = pylcs.lcs_sequence_length(chunk, query)
    return _score / len(chunk)


def get_top_k_chunk_matches(
    text: str, query: str, k: int = 3, max_chunk_size: int = 100
) -> list[Chunk]:
    """Get the top k chunks in the text that match the query.

    The query could be a string of draft code edits.

    Args:
        text: The text to search for the query.
        query: The query to search for in the text.
        k: The number of top chunks to return.
        max_chunk_size: The maximum number of lines in a chunk.
    """
    raw_chunks = create_chunks(text, max_chunk_size)
    chunks_with_lcs: list[Chunk] = [
        Chunk(
            text=chunk.text,
            line_range=chunk.line_range,
            normalized_lcs=normalized_lcs(chunk.text, query),
        )
        for chunk in raw_chunks
    ]
    sorted_chunks = sorted(
        chunks_with_lcs,
        key=lambda x: x.normalized_lcs,  # type: ignore
        reverse=True,
    )
    return sorted_chunks[:k]