lokum-engine 0.1.2

LokumAI engines: RAG + MLX LoRA fine-tuning utilities

lokum-engine

Developer-first building blocks extracted from LokumAI:

• RAGEngine: persistent retrieval (FAISS + sentence-transformers) over local files
• FinetuneEngine: MLX LoRA runner utilities + ChatML-safe dataset presplitting

Repo: https://github.com/fox7524/lokum-engine

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Install

pip install lokum-engine

This package intentionally includes “heavy” deps (FAISS, sentence-transformers, PyMuPDF, MLX, …).

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Quickstart (RAG)

from lokum_engine import RAGEngineMid

rag = RAGEngineMid()  # base | mid | fab
rag.ingest_folder("/path/to/your/docs", recursive=True)

ctx = rag.query("What is this project about?", k=5)
print(ctx)

Quality profiles (RAG)

RAG has 3 preset profiles:

• base: faster / lighter defaults
• mid: balanced defaults (matches the original engine behavior)
• fab: quality-oriented (more aggressive retrieval + heavier embedding model)

Use whichever style you prefer:

from lokum_engine import RAGEngineBase, RAGEngineMid, RAGEngineFab

rag_fast = RAGEngineBase()
rag_balanced = RAGEngineMid()
rag_best = RAGEngineFab()

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Persistence & storage paths

By default RAG state is stored under ~/.lokumai/rag.

Override with env vars:

• LOKUMAI_HOME — base app folder (default: ~/.lokumai)
• LOKUMAI_RAG_DIR — RAG store dir (default: ~/.lokumai/rag)

Tip: If you change the embedding model later, old FAISS indexes might not be compatible (different vector dim).

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Configuration (RAG)

• LOKUMAI_RAG_QUALITY — base|mid|fab (if you don’t pass quality= in code)
• LOKUMAI_EMBED_MODEL — override embedding model name (HuggingFace / sentence-transformers)
• LOKUMAI_EMBED_DEVICE — cpu or mps (auto-detects MPS if available)
• LOKUMAI_EMBED_BATCH — embedding batch size
• LOKUMAI_RAG_CHECKPOINT_CHUNKS — periodic save threshold (chunk count)
• LOKUMAI_RAG_CHECKPOINT_SECS — periodic save threshold (seconds)

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Quickstart (MLX LoRA fine-tuning)

from lokum_engine import FinetuneEngineMid

ft = FinetuneEngineMid(model_path="/path/to/mlx/model")

# (Optional) build a basic dataset from raw text chunks
train_fp, valid_fp = ft.prepare_dataset(["some text chunk", "another chunk"])

# Recommended: presplit to avoid OOM & never cut ChatML tags
ft.presplit_dataset(ft.dataset_dir, max_seq_length=512, batch_size=2)

proc = ft.start_training(
    dataset_path=ft.dataset_dir,
    batch_size=2,
    num_layers=16,
    iters=100,
)
print("PID:", proc.pid)

Quality profiles (Fine-tune)

Fine-tune also has 3 preset profiles:

from lokum_engine import FinetuneEngineBase, FinetuneEngineMid, FinetuneEngineFab

ft_fast = FinetuneEngineBase(model_path="...")
ft_balanced = FinetuneEngineMid(model_path="...")
ft_best = FinetuneEngineFab(model_path="...")

Notes:

• fab is more aggressive and can OOM depending on model + hardware. Use env overrides to dial it down.

Configuration (Fine-tune)

• LOKUMAI_FT_QUALITY — base|mid|fab
• LOKUMAI_FT_PRESPLIT — 1|0
• LOKUMAI_FT_PRESPLIT_CHARS_PER_TOKEN — presplit aggressiveness (lower = more splitting)
• LOKUMAI_FT_MAX_SEQ_LENGTH
• LOKUMAI_FT_CLEAR_CACHE_THRESHOLD
• LOKUMAI_FT_STEPS_PER_EVAL
• LOKUMAI_FT_VAL_BATCHES
• LOKUMAI_FT_GRAD_CHECKPOINT — 1|0

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Troubleshooting

RAGEngine.enabled == False

RAG requires (at minimum):

• sentence-transformers
• faiss-cpu

OCR returns empty text

pytesseract needs the system tesseract binary installed.

macOS:

brew install tesseract

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License

MIT