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.
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Nothing withdrawn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 2026Releases
latest 3-
0.2.025 Jun 2026Release notes
Open source →- Typed exceptions —
ModelDownloadOfflineException,InsufficientStorageException, andEngineNotInitializedException(all extendCodesparkException), thrown at the right points so you can write precise try/catch aroundinitialize()andembed(). - AI-agent-friendly docs — added "AI Agent Context" guidance to the primary
methods (
initialize,embed) and anllms.txtdescribing intent + minimal syntax for LLM tooling. - Example — added
example/lib/download_progress_ui.dartshowing a real first-run download UI with progress + error/retry handling.
- Typed exceptions —
-
0.1.124 Jun 2026Release notes
Open source →- 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.
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0.1.023 Jun 2026Release notes
Open source →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) inModelCatalog.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.