Basic app example

Minimal Dart console apps for chat, embeddings, SQLite vector retrieval and decision models, the quickest way to see the core llamadart API.

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Path: example/basic_app · Platforms: Dart console on macOS, Linux and Windows · First-run download: Qwen3.5-0.8B-Q4_K_M.gguf (533 MB)

Dart console apps that show the core API: chat, embeddings, vector retrieval and decision models, without Flutter.

Run#

cd example/basic_app
dart pub get
dart run

Variants:

# Embeddings (downloads embeddinggemma-300M-Q8_0.gguf, 334 MB)
dart run bin/llamadart_embedding_example.dart -i "hello world" -i "rag"

# Retrieval: embeddings stored and searched in SQLite with sqlite_vector
dart run bin/llamadart_sqlite_vector_example.dart \
  -q "How do I improve embedding throughput?" \
  -d "Increase maxParallelSequences for wider embedding batches." \
  -d "Tune batchSize and ubatchSize together."

# Decision model: triage a support ticket
# (downloads laya-Q8_0.gguf, 421 MB, and laya-head.safetensors, 106 MB)
dart run bin/llamadart_decision_example.dart

What it demonstrates#

Loading a model from an hf:// source into the shared cache, streaming a chat reply, and disposing the engine (Generation and streaming).
LoRA adapters (--lora), GBNF-constrained output (--grammar) and a sample tool call (--tool-test) in the chat CLI (LoRA adapters, Tool calling).
Single and batched embeddings, and query-versus-candidate probes (Embeddings).
Local retrieval with SQLite vector search, exact or quantized, with a recall check against exact search (Embeddings).
Typed choice, score and yes/no answers from a decision model with ChoiceKey.enumOf, ScoreKey.of, NoulKey.of and answerOf (Decision models).

Test#

cd example/basic_app
dart test

Full options: every flag of the four CLIs, the retrieval result fields and the decision-model head options are in the example README.

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