Supabase CLI

Interact with Supabase projects: queries, CRUD, vector search, and table management.

Setup

# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."
Optional: for management API

export SUPABASE_ACCESS_TOKEN="sbp_xxxxx"

Quick Commands

# SQL query
{baseDir}/scripts/supabase.sh query "SELECT * FROM users LIMIT 5"
Insert data
{baseDir}/scripts/supabase.sh insert users '{"name": "John", "email": "john@example.com"}'
Select with filters
{baseDir}/scripts/supabase.sh select users --eq "status:active" --limit 10
Update
{baseDir}/scripts/supabase.sh update users '{"status": "inactive"}' --eq "id:123"
Delete
{baseDir}/scripts/supabase.sh delete users --eq "id:123"
Vector similarity search
{baseDir}/scripts/supabase.sh vector-search documents "search query" --match-fn match_documents --limit 5
List tables
{baseDir}/scripts/supabase.sh tables
Describe table
{baseDir}/scripts/supabase.sh describe users

Commands Reference

query - Run raw SQL

{baseDir}/scripts/supabase.sh query "<SQL>"
Examples
{baseDir}/scripts/supabase.sh query "SELECT COUNT(*) FROM users"
{baseDir}/scripts/supabase.sh query "CREATE TABLE items (id serial primary key, name text)"
{baseDir}/scripts/supabase.sh query "SELECT * FROM users WHERE created_at > '2024-01-01'"

select - Query table with filters

{baseDir}/scripts/supabase.sh select <table> [options]
Options:
--columns <cols>    Comma-separated columns (default: *)
--eq <col:val>      Equal filter (can use multiple)
--neq <col:val>     Not equal filter
--gt <col:val>      Greater than
--lt <col:val>      Less than
--like <col:val>    Pattern match (use % for wildcard)
--limit <n>         Limit results
--offset <n>        Offset results
--order <col>       Order by column
--desc              Descending order
Examples
{baseDir}/scripts/supabase.sh select users --eq "status:active" --limit 10
{baseDir}/scripts/supabase.sh select posts --columns "id,title,created_at" --order created_at --desc
{baseDir}/scripts/supabase.sh select products --gt "price:100" --lt "price:500"

insert - Insert row(s)

{baseDir}/scripts/supabase.sh insert <table> '<json>'
Single row
{baseDir}/scripts/supabase.sh insert users '{"name": "Alice", "email": "alice@test.com"}'
Multiple rows
{baseDir}/scripts/supabase.sh insert users '[{"name": "Bob"}, {"name": "Carol"}]'

update - Update rows

{baseDir}/scripts/supabase.sh update <table> '<json>' --eq <col:val>
Example
{baseDir}/scripts/supabase.sh update users '{"status": "inactive"}' --eq "id:123"
{baseDir}/scripts/supabase.sh update posts '{"published": true}' --eq "author_id:5"

upsert - Insert or update

{baseDir}/scripts/supabase.sh upsert <table> '<json>'
Example (requires unique constraint)
{baseDir}/scripts/supabase.sh upsert users '{"id": 1, "name": "Updated Name"}'

delete - Delete rows

{baseDir}/scripts/supabase.sh delete <table> --eq <col:val>
Example
{baseDir}/scripts/supabase.sh delete sessions --lt "expires_at:2024-01-01"

vector-search - Similarity search with pgvector

{baseDir}/scripts/supabase.sh vector-search <table> "<query>" [options]
Options:
--match-fn <name>     RPC function name (default: match_<table>)
--limit <n>           Number of results (default: 5)
--threshold <n>       Similarity threshold 0-1 (default: 0.5)
--embedding-model <m> Model for query embedding (default: uses OpenAI)
Example
{baseDir}/scripts/supabase.sh vector-search documents "How to set up authentication" --limit 10
Requires a match function like:
CREATE FUNCTION match_documents(query_embedding vector(1536), match_threshold float, match_count int)

tables - List all tables

{baseDir}/scripts/supabase.sh tables

describe - Show table schema

{baseDir}/scripts/supabase.sh describe <table>

rpc - Call stored procedure

{baseDir}/scripts/supabase.sh rpc <function_name> '<json_params>'
Example
{baseDir}/scripts/supabase.sh rpc get_user_stats '{"user_id": 123}'

Vector Search Setup

1. Enable pgvector extension

CREATE EXTENSION IF NOT EXISTS vector;

2. Create table with embedding column

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  content text,
  metadata jsonb,
  embedding vector(1536)
);

3. Create similarity search function

CREATE OR REPLACE FUNCTION match_documents(
  query_embedding vector(1536),
  match_threshold float DEFAULT 0.5,
  match_count int DEFAULT 5
)
RETURNS TABLE (
  id bigint,
  content text,
  metadata jsonb,
  similarity float
)
LANGUAGE plpgsql
AS $$
BEGIN
  RETURN QUERY
  SELECT
    documents.id,
    documents.content,
    documents.metadata,
    1 - (documents.embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (documents.embedding <=> query_embedding) > match_threshold
  ORDER BY documents.embedding <=> query_embedding
  LIMIT match_count;
END;
$$;

4. Create index for performance

CREATE INDEX ON documents 
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);

Environment Variables

Variable	Required	Description
SUPABASE_URL	Yes	Project URL (https://xxx.supabase.co)
SUPABASE_SERVICE_KEY	Yes	Service role key (full access)
SUPABASE_ANON_KEY	No	Anon key (restricted access)
SUPABASE_ACCESS_TOKEN	No	Management API token
OPENAI_API_KEY	No	For generating embeddings

Notes

Service role key bypasses RLS (Row Level Security)
Use anon key for client-side/restricted access
Vector search requires pgvector extension
Embeddings default to OpenAI text-embedding-ada-002 (1536 dimensions)

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Supabase CLI

Setup

Optional: for management API

Quick Commands

Insert data

Select with filters

Update

Delete

Vector similarity search

List tables

Describe table

Commands Reference

query - Run raw SQL

Examples

select - Query table with filters

Examples

insert - Insert row(s)

Single row

Multiple rows

update - Update rows

Example

upsert - Insert or update

Example (requires unique constraint)

delete - Delete rows

Example

vector-search - Similarity search with pgvector

Example

Requires a match function like:

CREATE FUNCTION match_documents(query_embedding vector(1536), match_threshold float, match_count int)

tables - List all tables

describe - Show table schema

rpc - Call stored procedure

Example

Vector Search Setup

1. Enable pgvector extension

2. Create table with embedding column

3. Create similarity search function

4. Create index for performance

Environment Variables

Notes

Quick Start

Manual Installation

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