16 docs tagged with "Models"

Describes how embedding models are used in Spice to convert text into numerical vectors for machine learning and search applications.

Operating guide for filesystem-loaded models in production: formats, device selection, memory footprint, and observability.

Operating guide for the Hugging Face model in production: tokens, download cache, device selection, local inference footprint, and observability.

Learn how to provide LLMs with memory

Learn how to override default LLM hyperparameters in Spice.

Learn how LLMs interact with the Spice runtime.

Learn how to configure large language models (LLMs)

Learn how to load and serve large learning models.

Spice supports loading and serving ONNX models for inference, from sources including local filesystems, Hugging Face, and the Spice.ai Cloud platform.

Learn how to use the Model Context Protocol (MCP) with Spice.

Embed list-of-strings columns as a column of vectors and use ColBERT-style late-interaction search in Spice.

Operating guide for the OpenAI model in production: API keys, usage tiers, rate limiting, Responses API, metrics, and observability.

Learn how Spice can search across datasets using database-native and vector-search methods.

Learn how to update system prompts for each request with Jinja-styled templating.

Learn how Spice can perform searches using vector-based methods.

Learn how Spice can perform web search