Supabase CLI

与Supabase项目交互：查询、增删改查、向量搜索和表管理。

设置

# Required
export SUPABASE_URL="https://yourproject.supabase.co"
export SUPABASE_SERVICE_KEY="eyJhbGciOiJIUzI1NiIs..."

# Optional: for management API
export SUPABASE_ACCESS_TOKEN="sbp_xxxxx"

快速命令

# 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

命令参考

query - 运行原始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 - 使用筛选器查询表

{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 - 插入行

{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 - 更新行

{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 - 插入或更新

{baseDir}/scripts/supabase.sh upsert <table> '<json>'

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

delete - 删除行

{baseDir}/scripts/supabase.sh delete <table> --eq <col:val>

# Example
{baseDir}/scripts/supabase.sh delete sessions --lt "expires_at:2024-01-01"

vector-search - 使用pgvector进行相似性搜索

{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 - 列出所有表

{baseDir}/scripts/supabase.sh tables

describe - 显示表结构

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

rpc - 调用存储过程

{baseDir}/scripts/supabase.sh rpc <function_name> '<json_params>'

# Example
{baseDir}/scripts/supabase.sh rpc get_user_stats '{"user_id": 123}'

向量搜索设置

1. 启用pgvector扩展

CREATE EXTENSION IF NOT EXISTS vector;

2. 创建包含嵌入列的表

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

3. 创建相似性搜索函数

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. 为性能创建索引

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

环境变量

备注

• 服务角色密钥绕过行级安全策略
• 客户端/受限访问请使用匿名密钥
• 向量搜索需要pgvector扩展
• 嵌入向量默认使用OpenAI的text-embedding-ada-002模型（1536维度）