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from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Create a knowledge base with PDF documents
knowledge = Knowledge(
vector_db=PgVector(
table_name="pdf_documents",
db_url=db_url,
)
)
# Add PDF content synchronously
knowledge.insert(
path="cookbook/08_knowledge/testing_resources/cv_1.pdf",
reader=PDFReader(),
)
# Create an agent with the knowledge base
agent = Agent(
knowledge=knowledge,
search_knowledge=True,
)
# Query the knowledge base
agent.print_response(
"What skills does an applicant require to apply for the Software Engineer position?",
markdown=True,
)
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Install dependencies
uv pip install -U pypdf sqlalchemy psycopg pgvector agno openai
Set environment variables
export OPENAI_API_KEY=xxx
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agno/pgvector:16
Run Agent
python examples/basics/knowledge/concepts/readers/overview/pdf_reader_sync.py
python examples/basics/knowledge/concepts/readers/overview/pdf_reader_sync.py
| Parameter | Type | Default | Description |
|---|---|---|---|
path | Path | Required | Path to PDF file or URL |
split_on_pages | bool | True | Split the PDF into pages |
page_start_numbering_format | Optional[str] | None | Format for page numbering |
page_end_numbering_format | Optional[str] | None | Format for page numbering |
password | Optional[str] | None | Password to unlock the PDF |
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