from __future__ import annotations
from typing import Any, Dict, Iterator, Literal, Optional
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers.language.c import CSegmenter
from langchain_community.document_loaders.parsers.language.cobol import CobolSegmenter
from langchain_community.document_loaders.parsers.language.cpp import CPPSegmenter
from langchain_community.document_loaders.parsers.language.csharp import CSharpSegmenter
from langchain_community.document_loaders.parsers.language.elixir import ElixirSegmenter
from langchain_community.document_loaders.parsers.language.go import GoSegmenter
from langchain_community.document_loaders.parsers.language.java import JavaSegmenter
from langchain_community.document_loaders.parsers.language.javascript import (
JavaScriptSegmenter,
)
from langchain_community.document_loaders.parsers.language.kotlin import KotlinSegmenter
from langchain_community.document_loaders.parsers.language.lua import LuaSegmenter
from langchain_community.document_loaders.parsers.language.perl import PerlSegmenter
from langchain_community.document_loaders.parsers.language.php import PHPSegmenter
from langchain_community.document_loaders.parsers.language.python import PythonSegmenter
from langchain_community.document_loaders.parsers.language.ruby import RubySegmenter
from langchain_community.document_loaders.parsers.language.rust import RustSegmenter
from langchain_community.document_loaders.parsers.language.scala import ScalaSegmenter
from langchain_community.document_loaders.parsers.language.typescript import (
TypeScriptSegmenter,
)
LANGUAGE_EXTENSIONS: Dict[str, str] = {
"py": "python",
"js": "js",
"cobol": "cobol",
"c": "c",
"cpp": "cpp",
"cs": "csharp",
"rb": "ruby",
"scala": "scala",
"rs": "rust",
"go": "go",
"kt": "kotlin",
"lua": "lua",
"pl": "perl",
"ts": "ts",
"java": "java",
"php": "php",
"ex": "elixir",
"exs": "elixir",
}
LANGUAGE_SEGMENTERS: Dict[str, Any] = {
"python": PythonSegmenter,
"js": JavaScriptSegmenter,
"cobol": CobolSegmenter,
"c": CSegmenter,
"cpp": CPPSegmenter,
"csharp": CSharpSegmenter,
"ruby": RubySegmenter,
"rust": RustSegmenter,
"scala": ScalaSegmenter,
"go": GoSegmenter,
"kotlin": KotlinSegmenter,
"lua": LuaSegmenter,
"perl": PerlSegmenter,
"ts": TypeScriptSegmenter,
"java": JavaSegmenter,
"php": PHPSegmenter,
"elixir": ElixirSegmenter,
}
Language = Literal[
"cpp",
"go",
"java",
"kotlin",
"js",
"ts",
"php",
"proto",
"python",
"rst",
"ruby",
"rust",
"scala",
"swift",
"markdown",
"latex",
"html",
"sol",
"csharp",
"cobol",
"c",
"lua",
"perl",
"elixir",
]
[docs]class LanguageParser(BaseBlobParser):
"""Parse using the respective programming language syntax.
Each top-level function and class in the code is loaded into separate documents.
Furthermore, an extra document is generated, containing the remaining top-level code
that excludes the already segmented functions and classes.
This approach can potentially improve the accuracy of QA models over source code.
The supported languages for code parsing are:
- C: "c" (*)
- C++: "cpp" (*)
- C#: "csharp" (*)
- COBOL: "cobol"
- Elixir: "elixir"
- Go: "go" (*)
- Java: "java" (*)
- JavaScript: "js" (requires package `esprima`)
- Kotlin: "kotlin" (*)
- Lua: "lua" (*)
- Perl: "perl" (*)
- Python: "python"
- Ruby: "ruby" (*)
- Rust: "rust" (*)
- Scala: "scala" (*)
- TypeScript: "ts" (*)
Items marked with (*) require the packages `tree_sitter` and
`tree_sitter_languages`. It is straightforward to add support for additional
languages using `tree_sitter`, although this currently requires modifying LangChain.
The language used for parsing can be configured, along with the minimum number of
lines required to activate the splitting based on syntax.
If a language is not explicitly specified, `LanguageParser` will infer one from
filename extensions, if present.
Examples:
.. code-block:: python
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import LanguageParser
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py", ".js"],
parser=LanguageParser()
)
docs = loader.load()
Example instantiations to manually select the language:
.. code-block:: python
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py"],
parser=LanguageParser(language="python")
)
Example instantiations to set number of lines threshold:
.. code-block:: python
loader = GenericLoader.from_filesystem(
"./code",
glob="**/*",
suffixes=[".py"],
parser=LanguageParser(parser_threshold=200)
)
"""
[docs] def __init__(self, language: Optional[Language] = None, parser_threshold: int = 0):
"""
Language parser that split code using the respective language syntax.
Args:
language: If None (default), it will try to infer language from source.
parser_threshold: Minimum lines needed to activate parsing (0 by default).
"""
if language and language not in LANGUAGE_SEGMENTERS:
raise Exception(f"No parser available for {language}")
self.language = language
self.parser_threshold = parser_threshold
[docs] def lazy_parse(self, blob: Blob) -> Iterator[Document]:
code = blob.as_string()
language = self.language or (
LANGUAGE_EXTENSIONS.get(blob.source.rsplit(".", 1)[-1])
if isinstance(blob.source, str)
else None
)
if language is None:
yield Document(
page_content=code,
metadata={
"source": blob.source,
},
)
return
if self.parser_threshold >= len(code.splitlines()):
yield Document(
page_content=code,
metadata={
"source": blob.source,
"language": language,
},
)
return
self.Segmenter = LANGUAGE_SEGMENTERS[language]
segmenter = self.Segmenter(blob.as_string())
if not segmenter.is_valid():
yield Document(
page_content=code,
metadata={
"source": blob.source,
},
)
return
for functions_classes in segmenter.extract_functions_classes():
yield Document(
page_content=functions_classes,
metadata={
"source": blob.source,
"content_type": "functions_classes",
"language": language,
},
)
yield Document(
page_content=segmenter.simplify_code(),
metadata={
"source": blob.source,
"content_type": "simplified_code",
"language": language,
},
)
