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"""Code chunking with Tree-sitter and text fallback."""
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator, Optional
from coderag.config import get_settings
from coderag.logging import get_logger
from coderag.models.chunk import Chunk, ChunkMetadata, ChunkType
from coderag.models.document import Document
logger = get_logger(__name__)
@dataclass
class ChunkerConfig:
"""Chunker configuration."""
chunk_size: int = 1500
chunk_overlap: int = 200
min_chunk_size: int = 50
class CodeChunker:
"""Chunks code files into semantic units."""
def __init__(self, config: Optional[ChunkerConfig] = None) -> None:
settings = get_settings()
self.config = config or ChunkerConfig(
chunk_size=settings.ingestion.chunk_size,
chunk_overlap=settings.ingestion.chunk_overlap,
)
self._tree_sitter_available = self._check_tree_sitter()
def _check_tree_sitter(self) -> bool:
try:
import tree_sitter_python
return True
except ImportError:
logger.warning("Tree-sitter not available, using text chunking")
return False
def chunk_document(self, document: Document) -> Iterator[Chunk]:
if document.language == "python" and self._tree_sitter_available:
yield from self._chunk_python(document)
else:
yield from self._chunk_text(document)
def _chunk_python(self, document: Document) -> Iterator[Chunk]:
try:
import tree_sitter_python as tspython
from tree_sitter import Language, Parser
PY_LANGUAGE = Language(tspython.language())
parser = Parser(PY_LANGUAGE)
tree = parser.parse(bytes(document.content, "utf-8"))
yield from self._extract_python_chunks(tree.root_node, document)
except Exception as e:
logger.warning("Tree-sitter parsing failed, falling back to text", error=str(e))
yield from self._chunk_text(document)
def _extract_python_chunks(self, node, document: Document) -> Iterator[Chunk]:
lines = document.content.split("\n")
for child in node.children:
if child.type in ("function_definition", "async_function_definition"):
yield self._create_chunk_from_node(child, document, lines, ChunkType.FUNCTION)
elif child.type == "class_definition":
yield self._create_chunk_from_node(child, document, lines, ChunkType.CLASS)
# Also extract methods
for class_child in child.children:
if class_child.type == "block":
for block_child in class_child.children:
if block_child.type in ("function_definition", "async_function_definition"):
yield self._create_chunk_from_node(
block_child, document, lines, ChunkType.METHOD,
parent_name=self._get_node_name(child)
)
# If no semantic chunks found, fall back to text chunking
if not any(child.type in ("function_definition", "class_definition", "async_function_definition")
for child in node.children):
yield from self._chunk_text(document)
def _create_chunk_from_node(
self,
node,
document: Document,
lines: list[str],
chunk_type: ChunkType,
parent_name: Optional[str] = None,
) -> Chunk:
start_line = node.start_point[0] + 1
end_line = node.end_point[0] + 1
content = "\n".join(lines[start_line - 1:end_line])
name = self._get_node_name(node)
signature = self._get_signature(node, lines)
docstring = self._get_docstring(node, lines)
metadata = ChunkMetadata(
file_path=document.file_path,
start_line=start_line,
end_line=end_line,
chunk_type=chunk_type,
language=document.language,
name=name,
signature=signature,
docstring=docstring,
parent_name=parent_name,
)
return Chunk(content=content, metadata=metadata, repo_id=document.repo_id)
def _get_node_name(self, node) -> Optional[str]:
for child in node.children:
if child.type == "identifier":
return child.text.decode("utf-8")
return None
def _get_signature(self, node, lines: list[str]) -> Optional[str]:
if node.type in ("function_definition", "async_function_definition"):
start_line = node.start_point[0]
return lines[start_line].strip()
return None
def _get_docstring(self, node, lines: list[str]) -> Optional[str]:
for child in node.children:
if child.type == "block":
for block_child in child.children:
if block_child.type == "expression_statement":
for expr_child in block_child.children:
if expr_child.type == "string":
return expr_child.text.decode("utf-8").strip('"""\'\'\'')
return None
def _chunk_text(self, document: Document) -> Iterator[Chunk]:
lines = document.content.split("\n")
chunk_size = self.config.chunk_size
overlap = self.config.chunk_overlap
current_start = 0
while current_start < len(lines):
# Calculate chunk boundaries
char_count = 0
end_line = current_start
while end_line < len(lines) and char_count < chunk_size:
char_count += len(lines[end_line]) + 1
end_line += 1
content = "\n".join(lines[current_start:end_line])
if len(content.strip()) >= self.config.min_chunk_size:
metadata = ChunkMetadata(
file_path=document.file_path,
start_line=current_start + 1,
end_line=end_line,
chunk_type=ChunkType.TEXT,
language=document.language,
)
yield Chunk(content=content, metadata=metadata, repo_id=document.repo_id)
# Move start with overlap
overlap_lines = 0
overlap_chars = 0
while overlap_lines < end_line - current_start and overlap_chars < overlap:
overlap_chars += len(lines[end_line - 1 - overlap_lines]) + 1
overlap_lines += 1
current_start = end_line - overlap_lines
if current_start <= 0 or end_line >= len(lines):
break
def chunk_files(self, documents: Iterator[Document]) -> Iterator[Chunk]:
total_chunks = 0
for doc in documents:
doc_chunks = 0
for chunk in self.chunk_document(doc):
doc_chunks += 1
total_chunks += 1
yield chunk
logger.debug("Document chunked", file=doc.file_path, chunks=doc_chunks)
logger.info("Chunking complete", total_chunks=total_chunks)
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