PDF URL Password Reader

The PDF URL Password Reader processes password-protected PDF files directly from URLs, allowing you to handle secure remote documents.

Code

examples/concepts/knowledge/readers/pdf_reader_url_password.py

from agno.agent import Agent
from agno.knowledge.content import ContentAuth
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

# 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(
    url="https://agno-public.s3.us-east-1.amazonaws.com/recipes/ThaiRecipes_protected.pdf",
    auth=ContentAuth(password="ThaiRecipes"),
)

# Create an agent with the knowledge
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.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U pypdf requests 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

   Mac

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

Params

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