example

Model2Vec examples

Runnable examples for the model2vec package. Each is a standalone script that imports the package with package:model2vec/model2vec.dart, just like your own code would.

Each example tells one story. Start with main.dart, then reach for the other two when you need retrieval or scale.

Running

dart run example/main.dart            # quickstart
dart run example/scaling_example.dart # batch, parallel, streaming
dart run example/rag_example.dart     # local retrieval (RAG)

The native Rust library is compiled automatically on first run via the package's build hook, so there is nothing to set up. The first run also downloads the embedding model from Hugging Face (a few MB), so it needs network access; later runs use the local cache.

What's here

• main.dart — quickstart. Load a model, embed a few sentences, and compare them with cosine similarity. The shortest path from zero to a result.

• scaling_example.dart — production, at scale: loading with a live download progress bar (loadModelWithProgress), batch embedding, parallel embedding across CPU cores with an EmbeddingPool, and the streaming API for datasets too large to hold in memory.

• rag_example.dart — a local retrieval (RAG) pipeline: chunkText splits documents, EmbeddingIndex stores each passage with its text as payload, then it answers questions with nearest-neighbour search, threshold filtering, and MMR reranking for diverse results — and persists the index to disk with toBytes / fromBytes.