seekdb — AI-Agent Database CLI with Documentation

This skill lets AI Agents operate seekdb via seekdb-cli commands and look up seekdb documentation when needed.

Quick orientation: Start with seekdb ai-guide to get a full JSON self-description of the CLI. Then run commands directly — no setup needed.

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Part 1: seekdb-cli Operations

seekdb-cli is purpose-built for AI Agents. All output is structured JSON, all operations are stateless, and built-in safety guardrails prevent accidental data loss.

seekdb Deployment

seekdb supports two modes. Use this section to guide users to the right deployment path based on their OS and scenario.

Embedded Mode

seekdb runs as a library inside the application — no server process required. Install via pyseekdb:

pip install -U pyseekdb

Supported platforms: Linux (glibc ≥ 2.28), macOS 15+ · Architectures: x86_64, aarch64

Windows and older macOS versions do not support embedded mode. Use server mode instead.

Server Mode

A persistent seekdb process, connectable via MySQL client or seekdb-cli (remote DSN). Choose by OS:

OS	Recommended method	Quick start
Linux (CentOS/RHEL/Anolis/openEuler)	Package manager (RPM)	curl -fsSL https://obportal.s3.ap-southeast-1.amazonaws.com/download-center/opensource/seekdb/seekdb_install.sh | sudo bash
Linux (Debian/Ubuntu)	Package manager (DEB)	sudo apt update && sudo apt install seekdb
macOS 15+	Homebrew	brew tap oceanbase/seekdb && brew install seekdb
Any OS with Docker	Docker	docker run -d -p 2881:2881 oceanbase/seekdb
Windows / macOS (GUI)	OceanBase Desktop	Download from oceanbase.ai/download

Connect after server mode deployment (default port 2881, user root, empty password):

mysql -h127.0.0.1 -uroot -P2881 -A -Dtest
# or via seekdb-cli:
seekdb --dsn "seekdb://root:@127.0.0.1:2881/test" status

Minimum requirements: 1 CPU core, 2 GB RAM, SSD storage.

For full deployment details, see the deployment docs.

Prerequisites

Check if seekdb-cli is installed:

seekdb --version

If not installed, choose the method that matches your environment:

Recommended — pipx (works globally without polluting system Python):

# Install pipx first if needed (Ubuntu/Debian)
sudo apt install pipx && pipx ensurepath
# Then install seekdb-cli
pipx install seekdb-cli

Alternative — pip (when inside a project venv or on systems without PEP 668):

pip install seekdb-cli

Note for Ubuntu 23.04+ / Debian 12+: Direct pip install at the system level is blocked by PEP 668. Use pipx instead — it creates an isolated environment while keeping the seekdb command globally available on your PATH.

Connection

seekdb-cli auto-discovers the connection (env var, .env, ~/.seekdb/config.env, or default ~/.seekdb/seekdb.db). No setup needed — just run commands directly.

If the user provides a specific DSN, pass it via --dsn (must appear before the subcommand):

# Remote mode
seekdb --dsn "seekdb://user:pass@host:port/db" schema tables

# Embedded mode (local database file)
seekdb --dsn "embedded:./seekdb.db" status
seekdb --dsn "embedded:~/.seekdb/seekdb.db?database=mydb" sql "SELECT 1"

DSN formats:

• Remote: seekdb://user:pass@host:port/db
• Embedded: embedded:<path>[?database=<db>] (default database: test)

Get a Full CLI Guide (run first)

seekdb ai-guide

Returns a structured JSON document with every command, parameter, workflow, safety rules, and output format. Run this once at the start of any seekdb task to orient yourself.

Recommended Workflows

SQL Database Exploration

1. seekdb schema tables              → list all tables (name, column count, row count)
2. seekdb schema describe <table>    → get column names, types, indexes, comments
3. seekdb table profile <table>      → data statistics (null ratios, distinct, min/max, top values)
4. seekdb relations infer            → infer JOIN relationships between tables
5. seekdb sql "SELECT ... LIMIT N"   → execute SQL with explicit LIMIT

Vector Collection Workflow

1. seekdb collection list            → list all collections
2. seekdb collection info <name>     → collection details and document preview
3. seekdb query <collection> --text "..." → hybrid search (default: semantic + fulltext)
4. seekdb add <collection> --data '...'  → add new documents

AI Model Setup Workflow

1. seekdb ai model list                                    → check registered models
2. seekdb ai model create <name> --type <type> --model <model_name>
3. seekdb ai model endpoint create <ep> <model> --url <url> --access-key <key>
4. seekdb ai complete "<prompt>" --model <name>           → test completion

Command Reference

seekdb sql

Execute SQL statements. Default is read-only mode.

# Read query
seekdb sql "SELECT id, name FROM users LIMIT 10"

# Read from file
seekdb sql --file query.sql

# Read from stdin
echo "SELECT 1" | seekdb sql --stdin

# Include table schema in output
seekdb sql "SELECT * FROM orders LIMIT 5" --with-schema

# Disable large-field truncation
seekdb sql "SELECT content FROM articles LIMIT 1" --no-truncate

# Write operation (requires --write flag)
seekdb sql --write "INSERT INTO users (name) VALUES ('Alice')"
seekdb sql --write "UPDATE users SET name = 'Bob' WHERE id = 1"
seekdb sql --write "DELETE FROM users WHERE id = 3"

Output format:

{"ok": true, "columns": ["id", "name"], "rows": [{"id": 1, "name": "Alice"}], "affected": 0, "time_ms": 12}

seekdb schema tables

seekdb schema tables

{"ok": true, "data": [{"name": "users", "columns": 5, "rows": 1200}, {"name": "orders", "columns": 8, "rows": 50000}]}

seekdb schema describe

seekdb schema describe orders

{"ok": true, "data": {"table": "orders", "comment": "Order table", "columns": [{"name": "id", "type": "int", "comment": "Order ID"}, {"name": "status", "type": "varchar(20)", "comment": "0=pending, 1=paid"}], "indexes": ["PRIMARY(id)", "idx_status(status)"]}}

seekdb schema dump

seekdb schema dump

Returns all CREATE TABLE DDL statements.

seekdb table profile

Generate statistical summary of a table without returning raw data. Helps understand data distribution before writing SQL.

seekdb table profile <table>

{"ok": true, "data": {
  "table": "orders",
  "row_count": 50000,
  "columns": [
    {"name": "id", "type": "int", "null_ratio": 0, "distinct": 50000, "min": 1, "max": 50000},
    {"name": "user_id", "type": "int", "null_ratio": 0, "distinct": 1200, "min": 1, "max": 1500},
    {"name": "amount", "type": "decimal(10,2)", "null_ratio": 0.02, "min": 0.5, "max": 9999.99},
    {"name": "status", "type": "varchar(20)", "null_ratio": 0, "distinct": 4, "top_values": ["paid", "pending", "refunded", "cancelled"]},
    {"name": "created_at", "type": "datetime", "null_ratio": 0, "min": "2024-01-01", "max": "2026-03-10"}
  ],
  "candidate_join_keys": ["user_id"],
  "candidate_time_columns": ["created_at"]
}}

seekdb relations infer

Infer JOIN relationships between tables by analyzing column name patterns (e.g., user_id → users.id) and type compatibility.

# Infer all table relationships
seekdb relations infer

# Infer for a specific table only
seekdb relations infer --table orders

{"ok": true, "data": [
  {"from": "orders.user_id", "to": "users.id", "confidence": "high"},
  {"from": "orders.product_id", "to": "products.id", "confidence": "high"},
  {"from": "order_items.order_id", "to": "orders.id", "confidence": "high"}
]}

seekdb collection list

seekdb collection list

{"ok": true, "data": [{"name": "docs", "count": 1500}, {"name": "faq", "count": 200}]}

seekdb collection create

seekdb collection create my_docs --dimension 384 --distance cosine
seekdb collection create my_docs -d 768 --distance l2

Options: --dimension / -d (default: 384), --distance cosine | l2 | ip (default: cosine).

seekdb collection delete

seekdb collection delete my_docs

seekdb collection info

seekdb collection info my_docs

{"ok": true, "data": {"name": "my_docs", "count": 1500, "dimension": 384, "distance": "cosine", "preview": {"ids": ["doc1", "doc2"], "documents": ["Hello world", "Test doc"], "metadatas": [{"category": "test"}, {}]}}}

dimension and distance are included when available from the collection metadata.

seekdb query

Search a collection using hybrid (default), semantic (vector), or fulltext mode.

# Hybrid search (default: semantic + fulltext, RRF ranking)
seekdb query my_docs --text "how to deploy seekdb"

# Semantic (vector) only
seekdb query my_docs --text "how to deploy seekdb" --mode semantic

# Fulltext search
seekdb query my_docs --text "deployment guide" --mode fulltext

# With metadata filter
seekdb query my_docs --text "performance tuning" --where '{"category": "tech"}'

# Limit results (--limit or -n, default: 10)
seekdb query my_docs --text "seekdb" -n 5

{"ok": true, "data": {"results": [
  {"id": "doc1", "score": 0.92, "document": "How to deploy seekdb...", "metadata": {"category": "tech"}},
  {"id": "doc2", "score": 0.85, "document": "seekdb performance tuning...", "metadata": {"category": "tech"}}
], "count": 2}, "time_ms": 35}

seekdb get

Retrieve documents from a collection by IDs or metadata filter.

# Get by IDs
seekdb get my_docs --ids "doc1,doc2"

# Get by metadata filter (--limit or -n, default: 10)
seekdb get my_docs --where '{"category": "tech"}' -n 20

seekdb add

Add data to a collection. Exactly one source is required: --file, --stdin, or --data. The collection is auto-created if it does not exist.

# From file (JSON array, JSONL, or CSV)
seekdb add my_docs --file data.jsonl
seekdb add my_docs --file articles.csv --vectorize-column content

# Inline: single object or array
seekdb add my_docs --data '{"id":"1","document":"Hello world","metadata":{"source":"cli"}}'
seekdb add my_docs --data '[{"id":"a","document":"Doc A"},{"id":"b","document":"Doc B"}]'

# From stdin (JSON array or JSONL; use with pipes)
echo '{"id":"1","document":"from pipe"}' | seekdb add my_docs --stdin
some_script | seekdb add my_docs --stdin

Record format: Each record may have id (optional), document/text/content (text to vectorize), and any other fields become metadata. If embedding is present, it is used directly.

seekdb export

Export collection data to a file.

seekdb export my_docs --output backup.json
seekdb export my_docs --output backup.jsonl -n 5000

Options: --output (required), --limit / -n (default: 10000).

seekdb ai model list

List AI models registered in the database (from DBA_OB_AI_MODELS / DBMS_AI_SERVICE). Works in both remote and embedded mode.

seekdb ai model list

{"ok": true, "data": [{"name": "my_llm", "type": "completion", "model_name": "THUDM/GLM-4-9B-0414", "model_id": 1}]}

seekdb ai model create

Register an AI model via DBMS_AI_SERVICE.CREATE_AI_MODEL. Create an endpoint separately to use it for completion.

seekdb ai model create my_llm --type completion --model "THUDM/GLM-4-9B-0414"
seekdb ai model create my_embed --type dense_embedding --model "BAAI/bge-m3"
seekdb ai model create my_rerank --type rerank --model "<rerank_model>"

Types: completion, dense_embedding, rerank.

seekdb ai model delete

Drop an AI model. Drop any endpoints that use it first.

seekdb ai model delete my_llm

seekdb ai model endpoint create / delete

Create or drop an endpoint that binds an AI model to a URL and API key (so the database can call the model).

seekdb ai model endpoint create my_ep my_llm \
  --url "https://api.siliconflow.cn/v1/chat/completions" \
  --access-key "<YOUR_API_KEY>" \
  --provider siliconflow

seekdb ai model endpoint delete my_ep

Supported --provider values:

Provider	Vendor
siliconflow	SiliconFlow (OpenAI-compatible)
openAI	OpenAI
deepseek	DeepSeek (OpenAI-compatible)
aliyun-openAI	Alibaba Cloud (OpenAI-compatible)
aliyun-dashscope	Alibaba Cloud DashScope
hunyuan-openAI	Tencent Hunyuan (OpenAI-compatible)

Common --url values (use the specific interface URL, not the base URL):

Vendor	completion	embedding	rerank
SiliconFlow	https://api.siliconflow.cn/v1/chat/completions	https://api.siliconflow.cn/v1/embeddings	https://api.siliconflow.cn/v1/rerank
DeepSeek	https://api.deepseek.com/chat/completions	—	—
Alibaba (OpenAI)	https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions	https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings	—
Tencent Hunyuan	https://api.hunyuan.cloud.tencent.com/v1/chat/completions	https://api.hunyuan.cloud.tencent.com/v1/embeddings	—

seekdb ai complete

Run text completion using the database AI_COMPLETE function. Requires a registered completion model and an endpoint. Supported in both remote and embedded mode.

seekdb ai complete "Summarize this table structure" --model my_llm

{"ok": true, "data": {"model": "my_llm", "response": "The table has..."}, "time_ms": 1200}

seekdb ai-guide

Output a structured JSON guide for AI Agents containing all commands, parameters, workflow, and safety rules. Execute once to learn the full CLI.

seekdb ai-guide

seekdb status

seekdb status

Returns CLI version, server version, database name, and connectivity.

Safety Features

Row Protection

Queries without LIMIT are automatically probed. If result exceeds 100 rows, execution is blocked:

{"ok": false, "error": {"code": "LIMIT_REQUIRED", "message": "Query returns more than 100 rows. Please add LIMIT to your SQL."}}

Action: Add an explicit LIMIT clause and retry.

Write Protection

Write operations (INSERT/UPDATE/DELETE) are blocked by default:

{"ok": false, "error": {"code": "WRITE_NOT_ALLOWED", "message": "Write operations require --write flag."}}

Action: Add --write flag to enable write operations.

Even with --write, these are always blocked:

• DELETE / UPDATE without WHERE clause
• DROP / TRUNCATE statements

Error Auto-Correction

On SQL errors, the CLI automatically attaches schema hints:

Column not found → returns the table's column list and indexes:

{"ok": false, "error": {"code": "SQL_ERROR", "message": "Unknown column 'username'"}, "schema": {"table": "users", "columns": ["id", "name", "email"], "indexes": ["PRIMARY(id)"]}}

Table not found → returns available table names:

{"ok": false, "error": {"code": "SQL_ERROR", "message": "Table 'user' does not exist"}, "schema": {"tables": ["users", "orders", "products"]}}

Action: Use the schema info to correct the SQL and retry.

Large Field Truncation

TEXT/BLOB fields are truncated to 200 characters by default, with original length noted:

{"content": "First 200 characters of content...(truncated, 8520 chars)"}

Use --no-truncate to get full content when needed.

Sensitive Field Masking

Columns matching sensitive patterns are automatically masked:

Pattern	Example Output
phone/mobile/tel	138****5678
email	z***@gmail.com
password/secret/token	******
id_card/ssn	110***********1234

Output Formats

Default is JSON. Switch with --format / -f:

seekdb -f table sql "SELECT id, name FROM users LIMIT 5"
seekdb --format csv sql "SELECT id, name FROM users LIMIT 5"
seekdb -f jsonl sql "SELECT id, name FROM users LIMIT 5"

All formats now work with non-row data (e.g., schema tables, collection list). CSV and JSONL will auto-detect list-of-dict data in the data field.

Exit Codes

Code	Meaning
0	Success
1	Business error (SQL error, connection error, etc.)
2	Usage error (missing arguments, invalid options)

Operation Logging

All commands are logged to ~/.seekdb/sql-history.jsonl (seekdb-cli SQL execution history) for audit:

{"ts": "2026-03-12T14:23:01", "command": "sql", "sql": "SELECT id FROM users LIMIT 10", "ok": true, "rows": 10, "time_ms": 12}

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Part 2: Documentation Lookup

Use documentation lookup when you need to understand a seekdb concept, look up SQL syntax, find configuration details, or answer user questions about seekdb.

Tip: For live database state (tables, schemas, data), run CLI commands directly. For concepts, syntax, and how-to guidance, search the docs.

When to Use Documentation Lookup

• Need to understand a seekdb concept (vector search, hybrid search, HNSW index, etc.)
• Need SQL or PL syntax reference not answerable by seekdb schema commands
• Need deployment, configuration, or integration guidance
• CLI returned an error and you need to understand why from the docs

Path Resolution

1. Read this SKILL.md to get its absolute path and extract the parent directory as <skill_dir>
2. Catalog: <skill_dir>references/seekdb-docs-catalog.jsonl If missing locally, load from: https://raw.githubusercontent.com/oceanbase/seekdb-ecology-plugins/main/agent-skills/skills/seekdb/references/seekdb-docs-catalog.jsonl

Documentation Workflow

Step 1: Search Catalog

Search the catalog file for lines containing the query keywords. Each line is one JSON object with path, description, and branch.

# Example: search for "vector search" in the catalog
grep -i "vector search" <skill_dir>references/seekdb-docs-catalog.jsonl

Step 2: Match Query

• Extract path, description, and branch from search results
• Select entries whose descriptions best match the query semantically
• Consider multiple matches for comprehensive answers

Step 3: Fetch Document

Fetch the document from the public docs URL:

• URL: https://raw.githubusercontent.com/oceanbase/seekdb-doc/[branch]/en-US/[path]
• [branch] and [path] come from the catalog entry (e.g., V1.0.0, V1.1.0)

Example

Query: "How to integrate with Claude Code?"

1. Search catalog for "claude code"
2. Found: {"path": "300.integrations/300.developer-tools/700.claude-code.md",
           "description": "This guide explains how to use the seekdb plugin with Claude Code...",
           "branch": "V1.0.0"}
3. Fetch: https://raw.githubusercontent.com/oceanbase/seekdb-doc/V1.0.0/en-US/300.integrations/300.developer-tools/700.claude-code.md

See references/doc-examples.md for more workflow examples.

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If the information you need cannot be found in the catalog or the CLI output, visit the official seekdb documentation at oceanbase.ai/docs for the most complete and up-to-date reference.