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.

What Jammi does

• Query local data with SQL — register Parquet, CSV, and JSON files, run full SQL via DataFusion
• Federate external databases — query PostgreSQL and MySQL alongside local files
• Generate embeddings — load any BERT-family model from HuggingFace Hub (or local safetensors / ONNX), persist results to Parquet with sidecar ANN indexes
• Vector search — ANN similarity search over embedding tables with automatic fallback to brute-force
• Search builder — fluent API for .join(), .annotate(), .filter(), .sort(), .limit(), .select(), .run()
• Evidence provenance — retrieved_by and annotated_by tracking on every search result
• Fine-tuning — LoRA adapters with contrastive loss to improve embeddings for your domain
• Evaluation — retrieval metrics (recall@k, precision@k, MRR, nDCG), classification (accuracy, F1), and A/B model comparison
• Per-row error handling — null or invalid text produces error status per row, not a batch failure
• Model caching — LRU eviction, ref-counted guards, single-flight loading
• GPU scheduling — memory-budget admission control with RAII permits
• Crash recovery — on restart, recovers result tables stuck in “building” state
• Inference observability — attach observers to hook into every output batch

Three ways to use Jammi

All three interfaces share the same engine, configuration, and storage format. Embeddings generated from Python are queryable from the CLI, and vice versa.

For multi-language access or BI tool integration, jammi serve starts an Arrow Flight SQL server — any Arrow client can connect and query via standard SQL.

Crates

jammi-db has no dependency on jammi-ai. You can use it standalone for SQL queries over local data without pulling in the AI layer.