jammi-ai-cu12 0.3.0

Embeddable AI engine for inference, embeddings, vector search, and fine-tuning (CUDA 12)

Jammi AI

Jammi is an embeddable AI engine that brings model inference into your data pipeline. Register data sources, run SQL queries, generate embeddings, search with vector similarity, fine-tune models on your domain, and evaluate results — all without leaving your application.

Install

pip install jammi-ai

For CUDA/GPU support:

pip install jammi-ai-cu12

Quickstart

import jammi

# Connect (pass gpu_device=-1 to force CPU)
db = jammi.connect()

# Register a local data source
db.add_source("patents", path="patents.parquet", format="parquet")

# Query with SQL — returns a pyarrow.Table
table = db.sql("SELECT id, title, year FROM patents.public.patents WHERE year > 2020 LIMIT 5")
print(table.to_pandas())

# Generate and persist embeddings (with an ANN index)
db.generate_text_embeddings(
    source="patents",
    model="sentence-transformers/all-MiniLM-L6-v2",
    columns=["title"],
    key="id",
)

# Semantic search
query_vec = db.encode_text_query("sentence-transformers/all-MiniLM-L6-v2", "quantum computing applications")

search = db.search("patents", query=query_vec, k=5)
search.sort("similarity", descending=True)
results = search.run()   # pyarrow.Table

print(results.to_pandas())

Features

• SQL over local files — query Parquet, CSV, and JSON via DataFusion
• Federated queries — join local files with PostgreSQL or MySQL
• Text embeddings — load any BERT-family model from Hugging Face Hub (or local safetensors / ONNX) and persist results to Parquet with ANN indexes
• Image embeddings — CLIP-style vision encoders
• Vector search — ANN similarity search with automatic brute-force fallback
• SearchBuilder — fluent API for .filter(), .sort(), .join(), .annotate(), .limit(), .select(), .run()
• Evidence provenance — retrieved_by and annotated_by tracking on every search result
• Fine-tuning — LoRA / deep LoRA adapters with contrastive loss to improve embeddings for your domain
• Evaluation — recall@k, precision@k, MRR, nDCG, accuracy, F1, and A/B model comparison
• Model caching — LRU eviction, ref-counted guards, single-flight loading
• GPU scheduling — memory-budget admission control with RAII permits
• Crash recovery — recovers embedding tables stuck in "building" state on restart

SearchBuilder

search = db.search("patents", query=query_vec, k=20)
search.filter("year >= 2020")
search.sort("similarity", descending=True)
search.limit(5)
search.select(["id", "title", "similarity"])
results = search.run()   # pyarrow.Table

All results are returned as pyarrow.Table — zero-copy from the Rust engine.

Fine-tuning

job = db.fine_tune(
    source="patents",
    model="sentence-transformers/all-MiniLM-L6-v2",
    triplets="triplets_train.parquet",
)
job.wait()

Requirements

• Python 3.9+
• Linux (x86_64) or macOS (Apple Silicon or Intel)

Windows is not yet supported due to a dependency on POSIX memory-mapping APIs.

Documentation

Full documentation, including guides for SQL queries, embeddings, search, fine-tuning, and evaluation:

https://f-inverse.github.io/jammi-ai/

License

Apache-2.0