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": "[email protected]"}'

# 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 "\x3CSQL>"

# 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 \x3Ctable> [options]

Options:
  --columns \x3Ccols>    Comma-separated columns (default: *)
  --eq \x3Ccol:val>      Equal filter (can use multiple)
  --neq \x3Ccol:val>     Not equal filter
  --gt \x3Ccol:val>      Greater than
  --lt \x3Ccol:val>      Less than
  --like \x3Ccol:val>    Pattern match (use % for wildcard)
  --limit \x3Cn>         Limit results
  --offset \x3Cn>        Offset results
  --order \x3Ccol>       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 \x3Ctable> '\x3Cjson>'

# Single row
{baseDir}/scripts/supabase.sh insert users '{"name": "Alice", "email": "[email protected]"}'

# Multiple rows
{baseDir}/scripts/supabase.sh insert users '[{"name": "Bob"}, {"name": "Carol"}]'

update - Update rows

{baseDir}/scripts/supabase.sh update \x3Ctable> '\x3Cjson>' --eq \x3Ccol: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 \x3Ctable> '\x3Cjson>'

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

delete - Delete rows

{baseDir}/scripts/supabase.sh delete \x3Ctable> --eq \x3Ccol: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 \x3Ctable> "\x3Cquery>" [options]

Options:
  --match-fn \x3Cname>     RPC function name (default: match_\x3Ctable>)
  --limit \x3Cn>           Number of results (default: 5)
  --threshold \x3Cn>       Similarity threshold 0-1 (default: 0.5)
  --embedding-model \x3Cm> 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 \x3Ctable>

rpc - Call stored procedure

{baseDir}/scripts/supabase.sh rpc \x3Cfunction_name> '\x3Cjson_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 $$
BEGIN
  RETURN QUERY
  SELECT
    documents.id,
    documents.content,
    documents.metadata,
    1 - (documents.embedding \x3C=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (documents.embedding \x3C=> query_embedding) > match_threshold
  ORDER BY documents.embedding \x3C=> query_embedding
  LIMIT match_count;
END;
$$;

4. Create index for performance

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

Environment Variables

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)