FastEmbed for lightweight local embeddings

How it works

FastEmbed handles the inference steps around supported models and supplies embedding interfaces for retrieval workflows. It can run independently of the Qdrant database.

What you need

Select a supported model and ensure the runtime can obtain its files in the deployment environment. Test without a network connection if the application is expected to work offline.

What to try first

Encode a small set of sentences on a CPU and use them in a nearest-neighbor test. Record initial model download time separately from steady-state inference.

Benchmark a warm process with a fixed batch size, and log model cache behavior separately. This distinguishes CPU inference time from artifact retrieval.

Where to look for failures

Model choice determines vector dimension and behavior. Confirm query formatting and rebuild incompatible indexes when changing the model.

Related Franklin pages

Check whether the task needs a library inside the app or a separate model endpoint. Preserve query formatting, checkpoint and vector dimensions when comparing either deployment.

• Evaluate and use embedding and reranking models from the BGE family. FlagEmbedding for BGE retrieval models
• Encode inputs for semantic search or score query-document pairs with a cross-encoder. Sentence Transformers for embeddings and reranking

For the role of the encoder in a search stack, read Franklin’s model guide. Embedding models

Project documentation

• Current project README
• README at the reviewed revision