spice-ai

name

spice-ai

description

Add AI and LLM capabilities to Spice — tools, NSQL (text-to-SQL), memory, model routing/workers, and evals. Use this skill whenever the user wants to enable LLM tools (SQL, search, memory, MCP, web search), set up text-to-SQL via /v1/nsql, add persistent conversational memory, configure model routing with workers (load balancing, fallback, weighted distribution), set up evals, or use the OpenAI-compatible chat API. This skill covers AI features and orchestration. For configuring individual model providers (OpenAI, Anthropic, etc.), see spice-models.

Add AI Capabilities

Spice integrates AI as a first-class runtime capability. Connect to hosted LLM providers or serve models locally, with an OpenAI-compatible API, tool use, text-to-SQL, and model routing — all configured in YAML.

Configure a Model

models:
  - from: <provider>:<model_id>
    name: <model_name>
    params:
      <provider>_api_key: ${ secrets:API_KEY }
      tools: auto # optional: enable runtime tools
      system_prompt: | # optional: default system prompt
        You are a helpful assistant.

Supported Providers

Provider	From Format	Status
OpenAI (or compatible)	openai:gpt-4o	Stable
Anthropic	anthropic:claude-sonnet-4-5	Alpha
Azure OpenAI	azure:my-deployment	Alpha
Google AI	google:gemini-pro	Alpha
xAI	xai:grok-beta	Alpha
Perplexity	perplexity:sonar-pro	Alpha
Amazon Bedrock	bedrock:anthropic.claude-3	Alpha
Databricks	databricks:llama-3-70b	Alpha
Spice.ai	spiceai:llama3	Release Candidate
HuggingFace	hf:meta-llama/Llama-3-8B-Instruct	Release Candidate
Local file	file:./models/llama.gguf	Release Candidate

Using Models

Chat API (OpenAI-compatible)

Existing applications using OpenAI SDKs can swap endpoints without code changes:

curl http://localhost:8090/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt4",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

CLI

spice chat
chat> How many orders were placed last month?

Text-to-SQL (NSQL)

The /v1/nsql endpoint converts natural language to SQL and executes it. Spice uses tools like table_schema, random_sample, and sample_distinct_columns to help models write accurate SQL:

curl -XPOST "http://localhost:8090/v1/nsql" \
  -H "Content-Type: application/json" \
  -d '{"query": "What was the highest tip any passenger gave?"}'

Tools (Function Calling)

Tools extend LLM capabilities with runtime functions:

Built-in Tools

Tool	Description	Group
list_datasets	List available datasets	auto
sql	Execute SQL queries	auto
table_schema	Get table schema	auto
search	Vector similarity search	auto
sample_distinct_columns	Sample distinct column values	auto
random_sample	Random row sampling	auto
top_n_sample	Top N rows by ordering	auto
memory:load	Load stored memories	memory
memory:store	Store new memories	memory
websearch	Search the web	—

Enable Tools

models:
  - from: openai:gpt-4o
    name: analyst
    params:
      openai_api_key: ${ secrets:OPENAI_API_KEY }
      tools: auto # all default tools
      # tools: sql, search      # or specific tools only

Memory (Persistent Context)

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

models:
  - from: openai:gpt-4o
    name: assistant
    params:
      tools: auto, memory

Web Search

tools:
  - name: web
    from: websearch
    description: 'Search the web for information.'
    params:
      engine: perplexity
      perplexity_auth_token: ${ secrets:PERPLEXITY_TOKEN }

models:
  - from: openai:gpt-4o
    name: researcher
    params:
      tools: auto, web

MCP Server Integration

tools:
  - name: external_tools
    from: mcp
    params:
      mcp_endpoint: http://localhost:3000/mcp

Tool Recursion Limit

models:
  - from: openai:gpt-4o
    name: my_model
    params:
      tool_recursion_limit: 3 # default: 10

Model Routing (Workers)

Workers coordinate traffic across multiple models for load balancing, fallback, and weighted routing. Workers are called with the same API as models.

Round Robin

workers:
  - name: balanced
    type: load_balance
    description: Distribute requests evenly.
    load_balance:
      routing:
        - from: model_a
        - from: model_b

Fallback (Priority Order)

workers:
  - name: fallback
    type: load_balance
    description: Try GPT-4o first, fall back to Claude.
    load_balance:
      routing:
        - from: gpt4
          order: 1
        - from: claude
          order: 2

Weighted Distribution

workers:
  - name: weighted
    type: load_balance
    description: Route 80% to fast model.
    load_balance:
      routing:
        - from: fast_model
          weight: 4 # 80%
        - from: slow_model
          weight: 1 # 20%

Model Examples

OpenAI with Tools

models:
  - from: openai:gpt-4o
    name: gpt4
    params:
      openai_api_key: ${ secrets:OPENAI_API_KEY }
      tools: auto

OpenAI-Compatible Provider (e.g., Groq)

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

With System Prompt and Parameter Overrides

models:
  - from: openai:gpt-4o
    name: pirate_haikus
    params:
      system_prompt: |
        Write everything in Haiku like a pirate.
      openai_temperature: 0.1
      openai_response_format: "{ 'type': 'json_object' }"

Local Model (GGUF)

models:
  - from: file:./models/llama-3.gguf
    name: local_llama

Evals

Evaluate model performance:

evals:
  - name: accuracy_test
    description: Verify model understands the data.
    dataset: test_data
    scorers:
      - Match

Documentation

• Model Providers
• LLM Tools
• Workers
• Memory
• Parameter Overrides
• Evals
• MCP Integration
• Model Grades Report