Add AST-aware code chunking for better code understanding (#58)

* feat(core): Add AST-aware code chunking with astchunk integration

This PR introduces intelligent code chunking that preserves semantic boundaries
(functions, classes, methods) for better code understanding in RAG applications.

Key Features:
- AST-aware chunking for Python, Java, C#, TypeScript files
- Graceful fallback to traditional chunking for unsupported languages
- New specialized code RAG application for repositories
- Enhanced CLI with --use-ast-chunking flag
- Comprehensive test suite with integration tests

Technical Implementation:
- New chunking_utils.py module with enhanced chunking logic
- Extended base RAG framework with AST chunking arguments
- Updated document RAG with --enable-code-chunking flag
- CLI integration with proper error handling and fallback

Benefits:
- Better semantic understanding of code structure
- Improved search quality for code-related queries
- Maintains backward compatibility with existing workflows
- Supports mixed content (code + documentation) seamlessly

Dependencies:
- Added astchunk and tree-sitter parsers to pyproject.toml
- All dependencies are optional - fallback works without them

Testing:
- Comprehensive test suite in test_astchunk_integration.py
- Integration tests with document RAG
- Error handling and edge case coverage

Documentation:
- Updated README.md with AST chunking highlights
- Added ASTCHUNK_INTEGRATION.md with complete guide
- Updated features.md with new capabilities

* Refactored chunk utils

* Remove useless import

* Update README.md

* Update apps/chunking/utils.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update apps/code_rag.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Fix issue

* apply suggestion from @Copilot

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Fixes after pr review

* Fix tests not passing

* Fix linter error for documentation files

* Update .gitignore with unwanted files

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Andy Lee <andylizf@outlook.com>

apps/base_rag_example.py

@@ -11,7 +11,6 @@ from typing import Any
 import dotenv
 from leann.api import LeannBuilder, LeannChat
 from leann.registry import register_project_directory
-from llama_index.core.node_parser import SentenceSplitter
 
 dotenv.load_dotenv()
 
@@ -109,6 +108,38 @@ class BaseRAGExample(ABC):
             help="Thinking budget for reasoning models (low/medium/high). Supported by GPT-Oss:20b and other reasoning models.",
         )
 
+        # AST Chunking parameters
+        ast_group = parser.add_argument_group("AST Chunking Parameters")
+        ast_group.add_argument(
+            "--use-ast-chunking",
+            action="store_true",
+            help="Enable AST-aware chunking for code files (requires astchunk)",
+        )
+        ast_group.add_argument(
+            "--ast-chunk-size",
+            type=int,
+            default=512,
+            help="Maximum characters per AST chunk (default: 512)",
+        )
+        ast_group.add_argument(
+            "--ast-chunk-overlap",
+            type=int,
+            default=64,
+            help="Overlap between AST chunks (default: 64)",
+        )
+        ast_group.add_argument(
+            "--code-file-extensions",
+            nargs="+",
+            default=None,
+            help="Additional code file extensions to process with AST chunking (e.g., .py .java .cs .ts)",
+        )
+        ast_group.add_argument(
+            "--ast-fallback-traditional",
+            action="store_true",
+            default=True,
+            help="Fall back to traditional chunking if AST chunking fails (default: True)",
+        )
+
         # Search parameters
         search_group = parser.add_argument_group("Search Parameters")
         search_group.add_argument(
@@ -310,21 +341,3 @@ class BaseRAGExample(ABC):
             await self.run_single_query(args, index_path, args.query)
         else:
             await self.run_interactive_chat(args, index_path)
-
-
-def create_text_chunks(documents, chunk_size=256, chunk_overlap=25) -> list[str]:
-    """Helper function to create text chunks from documents."""
-    node_parser = SentenceSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        separator=" ",
-        paragraph_separator="\n\n",
-    )
-
-    all_texts = []
-    for doc in documents:
-        nodes = node_parser.get_nodes_from_documents([doc])
-        if nodes:
-            all_texts.extend(node.get_content() for node in nodes)
-
-    return all_texts