13 docs tagged with "Models"

Machine learning models and AI inference engines.

Embedding Models

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

Evaluating Language Models

Learn how Spice evaluates, tracks, compares, and improves language model performance for specific tasks

Language Model Memory

Learn how to provide LLMs with memory

Language Model Overrides

Learn how to override default LLM hyperparameters in Spice.

Language Models Tools

Learn how LLMs interact with the Spice runtime.

Large Language Models

Learn how to configure large language models (LLMs)

Load and Serve Models Locally

Learn how to load and serve large learning models.

Machine Learning Models

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

Model Context Protocol (MCP)

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

Search Functionality

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

System Prompt parameterization

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

Vector-Based Search

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

Web Search

Learn how Spice can perform web search