The PDF Password Reader handles password-protected PDF files, allowing you to process secure documents and convert them into searchable knowledge bases.

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Code

examples/concepts/knowledge/readers/pdf_reader_password.py
from agno.agent import Agent
from agno.knowledge.content import ContentAuth
from agno.knowledge.knowledge import Knowledge
from agno.utils.media import download_file
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
download_file(
    "https://agno-public.s3.us-east-1.amazonaws.com/recipes/ThaiRecipes_protected.pdf",
    "ThaiRecipes_protected.pdf",
)

# Create a knowledge base with simplified password handling
knowledge = Knowledge(
    vector_db=PgVector(
        table_name="pdf_documents_password",
        db_url=db_url,
    ),
)

knowledge.add_content(
    path="ThaiRecipes_protected.pdf",
    auth=ContentAuth(password="ThaiRecipes"),
)

# Create an agent with the knowledge base
agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

agent.print_response("Give me the recipe for pad thai")

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U pypdf sqlalchemy psycopg pgvector agno openai
3

Set environment variables

export OPENAI_API_KEY=xxx
4

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
5

Run Agent

python examples/concepts/knowledge/readers/pdf_reader_password.py

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Params

ParameterTypeDefaultDescription
pathPathRequiredPath to PDF file or URL
split_on_pagesboolTrueSplit the PDF into pages
page_start_numbering_formatOptional[str]NoneFormat for page numbering
page_end_numbering_formatOptional[str]NoneFormat for page numbering
passwordOptional[str]NonePassword to unlock the PDF