Async PostgreSQL

Use PostgreSQL asynchronously for agent session storage.

Kern supports using PostgreSQL asynchronously, with the AsyncPostgresDb class.

Usage

1from kern.agent import Agent
2from kern.db.postgres import AsyncPostgresDb
3
4# Replace with your own connection string, and notice the `async_` prefix
5db_url = "postgresql+psycopg_async://ai:ai@localhost:5532/ai"
6
7# Setup your Database
8db = AsyncPostgresDb(db_url=db_url)
9
10# Setup your Agent with the Database
11agent = Agent(db=db)

Run Postgres (with PgVector)

Install docker desktop and run PgVector on port 5532 using:

1docker run -d \
2  -e POSTGRES_DB=ai \
3  -e POSTGRES_USER=ai \
4  -e POSTGRES_PASSWORD=ai \
5  -e PGDATA=/var/lib/postgresql/data/pgdata \
6  -v pgvolume:/var/lib/postgresql/data \
7  -p 5532:5432 \
8  --name pgvector \
9  agnohq/pgvector:16

Params

Parameter	Type	Default	Description
`id`	`Optional[str]`	-	The ID of the database instance. UUID by default.
`db_url`	`Optional[str]`	-	The database URL to connect to.
`db_engine`	`Optional[AsyncEngine]`	-	The SQLAlchemy asyncdatabase engine to use.
`db_schema`	`Optional[str]`	-	The database schema to use.
`session_table`	`Optional[str]`	-	Name of the table to store Agent, Team and Workflow sessions.
`memory_table`	`Optional[str]`	-	Name of the table to store memories.
`metrics_table`	`Optional[str]`	-	Name of the table to store metrics.
`eval_table`	`Optional[str]`	-	Name of the table to store evaluation runs data.
`knowledge_table`	`Optional[str]`	-	Name of the table to store knowledge content.
`traces_table`	`Optional[str]`	-	Name of the table to store traces.
`spans_table`	`Optional[str]`	-	Name of the table to store spans.