Model Providers

Overview of supported model providers for ML and LLMs in Spice.

Spice supports various model providers for traditional machine learning (ML) models and large language models (LLMs).

Name	Description	Status	ML Format(s)	LLM Format(s)*
openai	OpenAI (or compatible) LLM endpoint	Release Candidate	-	OpenAI-compatible HTTP endpoint
file	Local filesystem	Release Candidate	ONNX	GGUF, GGML, SafeTensor
huggingface	Models hosted on HuggingFace	Release Candidate	ONNX	GGUF, GGML, SafeTensor
spice.ai	Models hosted on the Spice.ai Cloud Platform	Alpha	ONNX	OpenAI-compatible HTTP endpoint
azure	Azure OpenAI	Alpha	-	OpenAI-compatible HTTP endpoint
anthropic	Models hosted on Anthropic	Alpha	-	OpenAI-compatible HTTP endpoint
xai	Models hosted on xAI	Alpha	-	OpenAI-compatible HTTP endpoint
databricks	Models deployed to Databricks Mosaic AI	Alpha	-	OpenAI-compatible HTTP endpoint

• LLM Format(s) may require additional files (e.g. tokenizer_config.json).

The model type is inferred based on the model source and files. For more detail, refer to the model reference specification.

Features

Spice supports a variety of features for large language models (LLMs):

• Custom Tools: Provide models with tools to interact with the Spice runtime. See Tools.

• System Prompts: Customize system prompts and override defaults for v1/chat/completion. See Parameter Overrides.

• Memory: Provide LLMs with memory persistence tools to store and retrieve information across conversations. See Memory.

• Vector Search: Perform advanced vector-based searches using embeddings. See Vector Search.

• Evals: Evaluate, track, compare, and improve language model performance for specific tasks. See Evals.

• Local Models: Load and serve models locally from various sources, including local filesystems and Hugging Face. See Local Models.

For more details, refer to the Large Language Models documentation.

Model Examples

The following examples demonstrate how to configure and use various models or model features with Spice. Each example provides a specific use case to help you understand the configuration options available.

Example: Configuring an OpenAI Model

To use a language model hosted on OpenAI (or compatible), specify the openai path and model ID in from. For more details, see OpenAI Model Provider.

Example spicepod.yml:

models:
  - from: openai:gpt-4o-mini
    name: openai
    params:
      openai_api_key: ${ secrets:SPICE_OPENAI_API_KEY }

  - from: openai:llama3-groq-70b-8192-tool-use-preview
    name: groq-llama
    params:
      endpoint: https://api.groq.com/openai/v1
      openai_api_key: ${ secrets:SPICE_GROQ_API_KEY }

Example: Using an OpenAI Model with Tools

To specify tools for an OpenAI model, include them in the params.tools field. For more details, see the Tools documentation.

models:
  - name: sql-model
    from: openai:gpt-4o
    params:
      tools: list_datasets, sql, table_schema

Example: Adding Memory to a Model

To enable memory tools for a model, define a store memory dataset and specify memory in the model's tools parameter. For more details, see the Memory documentation.

datasets:
  - from: memory:store
    name: llm_memory
    mode: read_write

models:
  - name: memory-enabled-model
    from: openai:gpt-4o
    params:
      tools: memory, sql

Example: Setting Default Parameter Overrides

To set default overrides for parameters, use the openai_ prefix followed by the parameter name. For more details, see the Parameter Overrides documentation.

models:
  - name: pirate-haikus
    from: openai:gpt-4o
    params:
      openai_temperature: 0.1
      openai_response_format: { 'type': 'json_object' }

Example: Configuring a System Prompt

To configure an additional system prompt, use the system_prompt parameter. For more details, see the Parameter Overrides documentation.

models:
  - name: pirate-haikus
    from: openai:gpt-4o
    params:
      system_prompt: |
        Write everything in Haiku like a pirate

Example: Serving a Local Model

To serve a model from the local filesystem, specify the from path as file and provide the local path. For more details, see Filesystem Model Provider.

models:
  - from: file://absolute/path/to/my/model.onnx
    name: local_fs_model

Example: Analyzing GitHub Issues with a Chat Model

This example demonstrates how to pull GitHub issue data from the last 14 days, accelerate the data, create a chat model with memory and tools to access the accelerated data, and use Spice to ask the chat model about the general themes of new issues.

Step 1: Pull GitHub Issue Data

First, configure a dataset to pull GitHub issue data from the last 14 days.

datasets:
  - from: github:github.com/<owner>/<repo>/issues
    name: github_issues
    params:
      github_token: ${secrets:GITHUB_TOKEN}
    acceleration:
      enabled: true
      refresh_mode: append
      refresh_check_interval: 24h
      refresh_data_window: 14d

Step 2: Create a Chat Model with Memory and Tools

Next, create a chat model that includes memory and tools to access the accelerated GitHub issue data.

datasets:
  - from: memory:store
    name: llm_memory
    mode: read_write

models:
  - name: github-issues-analyzer
    from: openai:gpt-4o
    params:
      tools: memory, sql

Step 3: Query the Chat Model

At this step, the spicepod.yaml should look like:

datasets:
  - from: github:github.com/<owner>/<repo>/issues
    name: github_issues
    params:
      github_token: ${secrets:GITHUB_TOKEN}
    acceleration:
      enabled: true
      refresh_mode: append
      refresh_check_interval: 24h
      refresh_data_window: 14d

  - from: memory:store
    name: llm_memory
    mode: read_write

models:
  - name: github-issues-analyzer
    from: openai:gpt-4o
    params:
      openai_api_key: ${ secrets:SPICE_OPENAI_API_KEY }
      tools: memory, sql

Finally, use Spice to ask the chat model about the general themes of new issues in the last 14 days. The following curl command demonstrates how to make this request using the OpenAI-compatible API.

curl -X POST https://data.spiceai.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H 'X-API-KEY: <spiceai_api_key>' \
  -d '{
    "model": "github-issues-analyzer",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What are the general themes of new issues in the last 14 days?"}
    ]
  }'

Refer to the Create Chat Completion API documentation for more details on making chat completion requests.