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Data connectors let you pull rows from a database and index them in Moss with a single ingest() call. Each connector is a self-contained pip package — install only what you need.
PackageSourceExtra dependency
moss-connector-sqliteSQLite(stdlib sqlite3)
moss-connector-mongodbMongoDBpymongo
moss-connector-mysqlMySQL / MariaDB / PlanetScalepymysql
moss-connector-supabaseSupabase (PostgREST)supabase

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How it works

You define a connector (the data source + a mapper function) and call ingest(). The mapper converts each row into a DocumentInfo object you control: choose which column becomes the searchable text and which become filterable metadata. 
import asyncio
from moss import DocumentInfo
from moss_connector_sqlite import SQLiteConnector, ingest

async def main():
    source = SQLiteConnector(
        database="my.db",
        query="SELECT id, title, body FROM articles",
        mapper=lambda r: DocumentInfo(
            id=str(r["id"]),
            text=r["body"],
            metadata={"title": r["title"]},
        ),
    )

    result = await ingest(
        source,
        project_id="...",
        project_key="...",
        index_name="articles",
    )
    print(f"Indexed {result.doc_count} rows")

asyncio.run(main())
Use auto_id=True in ingest() when your mapper doesn’t produce a stable primary key and you want Moss to generate UUID document IDs.

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SQLite

pip install moss-connector-sqlite

import asyncio
from moss import DocumentInfo
from moss_connector_sqlite import SQLiteConnector, ingest

async def main():
    source = SQLiteConnector(
        database="./my.db",
        query="SELECT id, title, body FROM articles",
        mapper=lambda r: DocumentInfo(
            id=str(r["id"]),
            text=r["body"],
            metadata={"title": r["title"]},
        ),
    )
    result = await ingest(source, project_id="...", project_key="...", index_name="articles")
    print(f"copied {result.doc_count} rows")

asyncio.run(main())

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MongoDB

pip install moss-connector-mongodb

import asyncio
from moss import DocumentInfo
from moss_connector_mongodb import MongoDBConnector, ingest

async def main():
    source = MongoDBConnector(
        uri="mongodb://localhost:27017",
        database="shop",
        collection="articles",
        mapper=lambda r: DocumentInfo(
            id=str(r["_id"]),   # bson.ObjectId → hex string
            text=r["body"],
            metadata={"title": r["title"]},
        ),
        filter={"status": "published"},                 # optional
        projection={"_id": 1, "title": 1, "body": 1},  # optional
    )
    result = await ingest(source, project_id="...", project_key="...", index_name="articles")
    print(f"copied {result.doc_count} rows")

asyncio.run(main())

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MySQL / MariaDB

pip install moss-connector-mysql

import asyncio
from moss import DocumentInfo
from moss_connector_mysql import MySQLConnector, ingest

async def main():
    source = MySQLConnector(
        host="localhost",
        user="root",
        password="secret",
        database="mydb",
        query="SELECT id, title, body FROM articles",
        mapper=lambda row: DocumentInfo(
            id=str(row["id"]),
            text=row["body"],
            metadata={"title": row["title"]},
        ),
        port=3306,
    )
    result = await ingest(source, project_id="...", project_key="...", index_name="articles")
    print(f"copied {result.doc_count} rows")

asyncio.run(main())

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Supabase

pip install moss-connector-supabase

import asyncio
from moss import DocumentInfo
from moss_connector_supabase import SupabaseConnector, ingest

async def main():
    source = SupabaseConnector(
        url="https://xxx.supabase.co",
        key="your-anon-or-service-key",
        table="articles",
        mapper=lambda row: DocumentInfo(
            id=str(row["id"]),
            text=row["body"],
            metadata={"title": row["title"]},
        ),
    )
    result = await ingest(source, project_id="...", project_key="...", index_name="articles")
    print(f"copied {result.doc_count} rows")

asyncio.run(main())
The Supabase connector reads rows as dicts via PostgREST. Your table needs at least one stringifiable column for id and one text column for text. All other columns are optional metadata.