Agent with Knowledge

Code

1from kern.agent import Agent
2from kern.knowledge.knowledge import Knowledge
3from kern.models.aws import AwsBedrock
4from kern.vectordb.pgvector import PgVector
5
6db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
7
8knowledge_base = Knowledge(
9    vector_db=PgVector(table_name="recipes", db_url=db_url),
10)
11knowledge_base.insert(
12  url="https://kern-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
13)
14
15agent = Agent(
16    model=AwsBedrock(id="mistral.mistral-large-2402-v1:0"), markdown=True
17    knowledge=knowledge_base,
18    )
19agent.print_response("How to make Thai curry?", markdown=True)

Usage

Set up your virtual environment

1uv venv --python 3.12
2source .venv/bin/activate

1uv venv --python 3.12
2.venv\Scripts\activate

Set your AWS Credentials

1export AWS_ACCESS_KEY_ID=***
2export AWS_SECRET_ACCESS_KEY=***
3export AWS_REGION=***

Install dependencies

1uv pip install -U boto3 sqlalchemy pgvector pypdf openai psycopg kern-ai

Run PgVector

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

Run Agent

1python knowledge.py