bc-al-chunker 0.1.3

Static analysis and RAG-optimized chunking for Business Central AL files

bc-al-chunker

RAG-optimized chunking for Business Central AL files.

bc-al-chunker statically parses .al files and produces semantically-aware chunks optimized for embedding and retrieval-augmented generation (RAG). It understands the structure of every AL object type — tables, pages, codeunits, reports, queries, enums, interfaces, and all extension types — and splits large objects at natural semantic boundaries (sections, procedures, triggers) while keeping small objects whole.

Features

• Hierarchical chunking — small objects stay whole; large objects split at procedure/trigger/section boundaries
• Context headers — every sub-chunk gets a synthetic context comment so each chunk is self-contained for embedding
• All AL object types — table, page, codeunit, report, query, xmlport, enum, interface, permissionset, profile, controladdin, entitlement, and all extension variants
• Multiple data sources — local filesystem, GitHub API, Azure DevOps API
• Structured output — Python dataclasses with JSON and JSONL export
• Zero dependencies for core usage — httpx only needed for remote adapters
• Fully typed — strict mypy, PEP 561 py.typed marker

Installation

# Core (local filesystem only)
pip install bc-al-chunker

# With GitHub adapter
pip install bc-al-chunker[github]

# With Azure DevOps adapter
pip install bc-al-chunker[azure]

# Everything
pip install bc-al-chunker[all]

Quick Start

from bc_al_chunker import chunk

# Chunk all .al files in a directory
chunks = chunk("/path/to/al-repo")

# Multiple repositories
chunks = chunk(["/repo1", "/repo2"])

# Each chunk has content + rich metadata
for c in chunks:
    print(c.metadata.object_type, c.metadata.object_name, c.metadata.chunk_type)
    print(c.content[:100])
    print(c.token_estimate)

Configuration

from bc_al_chunker import chunk, ChunkingConfig

chunks = chunk(
    "/path/to/repo",
    config=ChunkingConfig(
        max_chunk_chars=2000,      # Max characters per chunk (default: 1500)
        min_chunk_chars=100,       # Min characters per chunk (default: 100)
        include_context_header=True,  # Prepend object context to sub-chunks
        estimate_tokens=True,      # Include token estimate on each chunk
    ),
)

Remote Sources

from bc_al_chunker import chunk_source
from bc_al_chunker.adapters.github import GitHubAdapter
from bc_al_chunker.adapters.azure_devops import AzureDevOpsAdapter

# GitHub
chunks = chunk_source(
    GitHubAdapter("microsoft/BCApps", token="ghp_...", paths=["src/"])
)

# Azure DevOps
chunks = chunk_source(
    AzureDevOpsAdapter("myorg", "myproject", "myrepo", token="pat...")
)

Export

from bc_al_chunker import chunk, chunks_to_json, chunks_to_jsonl, chunks_to_dicts

chunks = chunk("/path/to/repo")

# JSON array
chunks_to_json(chunks, "output.json")

# JSONL (streaming-friendly)
chunks_to_jsonl(chunks, "output.jsonl")

# Python dicts (for programmatic use)
dicts = chunks_to_dicts(chunks)

Chunking Strategy

The chunker uses a hierarchical, AST-aware strategy:

1. Parse — Each .al file is parsed into an ALObject AST with sections, procedures, triggers, and properties identified
2. Size check — If the object's source is ≤ max_chunk_chars, it becomes one WholeObject chunk
3. Split — Large objects are split:
   • Header chunk — object declaration + top-level properties
   • Section chunks — fields, keys, layout, actions, views, dataset, etc.
   • Procedure/Trigger chunks — each procedure or trigger as its own chunk
4. Context injection — Sub-chunks get a context header prepended:

   // Object: codeunit 50100 "Address Management"
   // File: src/Codeunits/AddressManagement.al
   procedure ValidateAddress(var CustAddr: Record "Customer Address")
   begin
       ...
   end;
   

This ensures every chunk is self-contained and produces high-quality embeddings for code search.

Chunk Schema

Each Chunk contains:

• content — the text to embed
• token_estimate — approximate token count (chars / 4)
• metadata:
  • file_path, object_type, object_id, object_name
  • chunk_type — whole_object, header, section, procedure, trigger
  • section_name, procedure_name
  • extends — for extension objects
  • source_table — extracted from page/codeunit properties
  • attributes — e.g., [EventSubscriber(...)]
  • line_start, line_end
  • file_hash — 16-character BLAKE2b hex digest of the source file content; use this to detect whether a file has changed before re-embedding

Detecting file changes with file_hash

Every chunk carries a file_hash computed from the file's source text using BLAKE2b (8-byte digest — the fastest built-in hash algorithm in Python's hashlib). Because the hash is derived from the normalized (BOM-stripped) UTF-8 content, it is stable regardless of encoding variants.

from bc_al_chunker import chunk, hash_source

chunks = chunk("/path/to/al-repo")

# Group chunks by file; all chunks from the same file share the same hash
file_hashes = {c.metadata.file_path: c.metadata.file_hash for c in chunks}

# Later — re-read a file and compare before doing expensive work
with open("MyTable.al", encoding="utf-8-sig") as f:
    current_hash = hash_source(f.read())

if current_hash != file_hashes.get("MyTable.al"):
    # File changed — re-chunk and re-embed
    ...

Development

# Clone and install
git clone https://github.com/andrijantasevski/bc-al-chunker.git
cd bc-al-chunker
uv sync --all-extras --group dev

# Run tests
uv run pytest tests/ -v

# Lint + format
uv run ruff check src/ tests/
uv run ruff format src/ tests/

# Type check
uv run mypy src/

License

MIT