| name | spice-setup |
| description | Get started with Spice.ai — install the runtime, initialize a project, run the runtime, and use the CLI. Use this skill whenever the user mentions installing Spice, setting up a new Spice project, running `spice run`, looking up CLI commands or API endpoints, deployment models, or getting started with Spice. Also use when the user asks "how do I install Spice", "how do I start Spice", "what CLI commands does Spice have", or any question about Spice runtime setup and configuration basics. |
Getting Started with Spice
Spice is an open-source SQL query, search, and LLM-inference engine written in Rust. It federates queries across 30+ data sources, accelerates data locally, and integrates search and AI — all configured declaratively in YAML.
Spice is not a replacement for PostgreSQL/MySQL (use those for transactional workloads) or a data warehouse (use Snowflake/Databricks for centralized analytics). Think of it as the operational data & AI layer between your applications and your data infrastructure.
Install
macOS / Linux / WSL
curl https://install.spiceai.org | /bin/bash
Homebrew
brew install spiceai/spiceai/spice
Windows (PowerShell)
iex ((New-Object System.Net.WebClient).DownloadString("https://install.spiceai.org/Install.ps1"))
Verify & Upgrade
spice version
spice upgrade
If command not found, add to PATH: export PATH="$PATH:$HOME/.spice/bin"
Quick Start
spice init my_app
cd my_app
spice run
In another terminal:
spice sql
sql> show tables;
Spicepod Configuration (spicepod.yaml)
The Spicepod manifest defines all components for a Spice application:
version: v1
kind: Spicepod
name: my_app
secrets:
- from: env
name: env
datasets:
- from: <connector>:<path>
name: <dataset_name>
models:
- from: <provider>:<model>
name: <model_name>
embeddings:
- from: <provider>:<model>
name: <embedding_name>
All Sections
| Section | Purpose | Skill |
|---|
datasets | Data sources for SQL queries | spice-connect-data |
models | LLM/ML models for inference | spice-ai |
embeddings | Embedding models for vector search | spice-search |
secrets | Secure credential management | spice-secrets |
catalogs | External data catalog connections | spice-connect-data |
views | Virtual tables from SQL queries | spice-connect-data |
tools | LLM function calling capabilities | spice-ai |
workers | Model load balancing and routing | spice-ai |
runtime | Server ports, caching, telemetry | spice-caching |
snapshots | Acceleration snapshot management | spice-acceleration |
evals | Model evaluation definitions | spice-ai |
dependencies | Dependent Spicepods | (below) |
Dependencies
dependencies:
- lukekim/demo
- spiceai/quickstart
CLI Commands
| Command | Description |
|---|
spice init <name> | Initialize a new Spicepod |
spice run | Start the Spice runtime |
spice sql | Start interactive SQL REPL |
spice chat | Start chat REPL (requires model) |
spice search | Perform embeddings-based search |
spice add <spicepod> | Add a Spicepod dependency |
spice datasets | List loaded datasets |
spice models | List loaded models |
spice catalogs | List loaded catalogs |
spice status | Show runtime status |
spice refresh <dataset> | Refresh an accelerated dataset |
spice login | Login to the Spice.ai Platform |
spice version | Show CLI and runtime version |
spice upgrade | Upgrade CLI to latest version |
Runtime Endpoints
| Service | Default Address | Protocol |
|---|
| HTTP API | http://127.0.0.1:8090 | REST, OpenAI-compatible |
| Arrow Flight | 127.0.0.1:50051 | Arrow Flight / Flight SQL |
| Metrics | 127.0.0.1:9090 | Prometheus |
| OpenTelemetry | 127.0.0.1:50052 | OTLP gRPC |
HTTP API Paths
| Path | Description |
|---|
POST /v1/sql | Execute SQL query |
POST /v1/search | Embeddings-based search |
POST /v1/nsql | Natural language to SQL |
POST /v1/chat/completions | OpenAI-compatible chat |
POST /v1/embeddings | Generate embeddings |
GET /v1/datasets | List datasets |
GET /v1/models | List models |
GET /health | Health check |
Deployment Models
Spice ships as a single ~140MB binary with no external dependencies.
| Model | Best For |
|---|
| Standalone | Development, edge devices, simple workloads |
| Sidecar | Low-latency access, microservices |
| Microservice | Heavy or varying traffic behind a load balancer |
| Cluster | Large-scale data, horizontal scaling |
| Cloud | Auto-scaling, built-in observability (Spice.ai Cloud) |
Use Cases
| Use Case | How Spice Helps |
|---|
| Operational Data Lakehouse | Serve real-time workloads from Iceberg/Delta/Parquet with sub-second latency |
| Data Lake Accelerator | Accelerate queries from seconds to milliseconds locally |
| Enterprise Search | Combine semantic and full-text search across data |
| RAG Pipelines | Federated data + vector search + LLMs |
| Agentic AI | Tool-augmented LLMs with fast data access |
| Real-Time Analytics | Stream from Kafka/DynamoDB with sub-second latency |
Full Example
version: v1
kind: Spicepod
name: ai_app
secrets:
- from: env
name: env
embeddings:
- from: openai:text-embedding-3-small
name: embed
params:
openai_api_key: ${ secrets:OPENAI_API_KEY }
datasets:
- from: postgres:public.users
name: users
params:
pg_host: localhost
pg_user: ${ env:PG_USER }
pg_pass: ${ env:PG_PASS }
acceleration:
enabled: true
engine: duckdb
refresh_check_interval: 5m
- from: memory:store
name: llm_memory
access: read_write
models:
- from: openai:gpt-4o
name: assistant
params:
openai_api_key: ${ secrets:OPENAI_API_KEY }
tools: auto, memory, search
Documentation
