semantic_search_codespark 0.1.0

Offline semantic search for Flutter — match by meaning, not spelling. Finds 'physician' when users type 'doctor'. On-device MiniLM, no API keys, tiny API.

semantic_search_codespark

Offline semantic search for Flutter — match by meaning, not spelling. No API keys. No cloud. No OpenAI/Gemini/Ollama. A tiny API over a local model.

It finds matches that keyword and fuzzy search cannot — a user typing "I forgot my login details" matches "Reset your password" despite sharing zero words.

If this saves you a backend, please star the repo and like it on pub.dev — it helps others find it.

Why it's different

Most "search" packages are one of: fuzzy-only, cloud-based (API key), make you bring your own vectors, or bundle a model that bloats your app. This one is on-device, zero-key, full-pipeline, and zero-setup:

Package	Semantic	Offline	No API key	Model setup
semantic_search_codespark	✓	✓	✓	auto-downloads + verifies, cached
mobile_rag_engine	✓	✓	✓	manual curl into assets
pocket_brain	✓	✓	✓	bundled (bloats every build)
mk_smart_search	✗ fuzzy only	✓	✓	—
zir_semantic_search	✓	✗	✗ needs key	cloud API

The ~23 MB MiniLM model downloads once on first run and is cached (via ai_core_codespark) — so your app binary stays small instead of shipping the model to every user up front.

Why not just use ai_core_codespark?

You can — the engine exposes embed, VectorStore, and cosine directly. This package is the ergonomic layer on top:

• One line to index, one to search — no manual VectorStore + embedBatch + VectorRecord wiring.
• Typed results — searchObjects<T> and SemanticIndex<T> hand back your objects (SemanticResult<T>.item), instead of stuffing fields into a metadata map and reading them back untyped.
• Embed once, query many — createIndex embeds the corpus a single time; each search only embeds the query.

Reach for ai_core_codespark when you want the raw primitives (custom pipelines, classification, diversity/MMR). Reach for this when you just want to rank things by meaning.

Install

dependencies:
  semantic_search_codespark: ^0.1.0

Quick start

import 'package:semantic_search_codespark/semantic_search_codespark.dart';

final search = SemanticSearch();
await search.initialize();            // downloads the model once (~23 MB), then cached

final hits = await search.search(
  query: 'doctor',
  items: ['physician', 'car', 'engineer'],
);
print(hits.first.item);               // physician

These results are probed against the real model, so expectations are honest:

Query	Finds
car	automobile, vehicle	strong
doctor	physician, nurse	strong
"I forgot my login details"	"Reset your password"	zero-word-overlap match
flutter state management	riverpod	no — niche jargon, see below

The model knows general language, not niche jargon. Search works best over descriptive text (notes, FAQs, product names, support topics). For domain terms, search over richer text (descriptions/tags), not bare labels.

Search typed objects

final hits = await search.searchObjects<Product>(
  query: 'comfy running shoes',
  items: products,
  textOf: (p) => '${p.name} ${p.description}',
);
hits.first.item;   // your Product, ranked by meaning

Repeated queries — embed once, search many

final index = await search.createIndex<Faq>(
  items: faqs,
  textOf: (f) => f.title,
);
final a = await index.search('where is my parcel');   // only the query is embedded
final b = await index.search('when will it arrive');

Use createIndex whenever you query the same data more than once — it embeds the corpus a single time. search() re-embeds items on every call. Both accept topK and an optional cosine threshold; the index also offers searchDiverse() (MMR) to avoid near-duplicate results.

How it works

Text to a local MiniLM embedding (via ai_core_codespark) to a vector to cosine ranking. Inference runs on a background isolate so the UI stays smooth.

Platform support

Android	iOS	macOS	Windows	Linux	Web
✓	✓	✓	✓	✓	experimental

Roadmap

• v0.1 — semantic-only search (this release).
• v0.2 — Hybrid search: fuse semantic + fuzzy matching (Reciprocal Rank Fusion) so word-level and meaning-level queries both rank well.

More from ksaikiran.dev

Other Flutter packages for text, search, and input:

Package	What it does
ai_core_codespark	The on-device embedding engine this package is built on.
text_comparison_score_codespark	String similarity — Levenshtein, Damerau-Levenshtein, Jaro-Winkler.
animated_dropdown_search_codespark	Dropdown widget with built-in search and highlighting.
text_highlight_codespark	Highlight rich text — queries, regex, per-term colors, tappable spans.

Browse the full list on pub.dev/publishers/ksaikiran.dev.

License

MIT © Sai Kiran Katayath — part of the codespark on-device AI ecosystem · ksaikiran.dev