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ai_core_codespark

Local AI model for Flutter — on-device text embeddings & vector search, fully offline, no API keys or cloud. The engine behind semantic search & RAG.

0.2.0 Katayath-Sai-Kiran/ai_core_codespark

What this package is like to depend on

Last release 1 months ago

25 Jun 2026

Too new to tell

only 1 release windows

Nearly every release is documented

notes for 3 of 3 stable releases

Nothing withdrawn

no release was ever pulled

2 months old

3 releases · first in 2026

3 releases in the last 12 months

see the full history below

Release timeline

3 releases · Jun 2026 to Jun 2026
Release Pre-release

Releases

latest 3
  1. 0.2.0 25 Jun 2026
    Release notes
    • Typed exceptionsModelDownloadOfflineException, InsufficientStorageException, and EngineNotInitializedException (all extend CodesparkException), thrown at the right points so you can write precise try/catch around initialize() and embed().
    • AI-agent-friendly docs — added "AI Agent Context" guidance to the primary methods (initialize, embed) and an llms.txt describing intent + minimal syntax for LLM tooling.
    • Example — added example/lib/download_progress_ui.dart showing a real first-run download UI with progress + error/retry handling.
    Open source →
  2. 0.1.1 24 Jun 2026
    Release notes
    • Docs: removed decorative emoji from the README for a cleaner, plain-text read.
    • Docs: added a "More from ksaikiran.dev" section linking related text, search, and input packages.
    Open source →
  3. 0.1.0 23 Jun 2026
    Release notes

    Initial release — the on-device embedding foundation for the codespark family.

    • CodesparkEngine: one-call API to download, verify, load and run an embedding model entirely on-device (offline, no API keys).
    • ModelManager: streamed model download with SHA-256 verification and an atomic, cached, resumable-safe install. Ships a pinned, verified all-MiniLM-L6-v2 (int8, 384-dim) in ModelCatalog.
    • BertTokenizer: pure-Dart WordPiece tokenizer, verified byte-for-byte against the HuggingFace reference tokenizer (accents, CJK, Cyrillic, punctuation).
    • Embedder + EmbedderIsolate: ONNX inference with mean/cls/max pooling and L2 normalization, run on a background isolate to keep the UI smooth.
    • Similarity: cosine, dot product, top-k and MMR (diversity-aware ranking).
    • VectorStore: in-memory brute-force search with JSON persistence.
    Open source →

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