from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.docling_reader import DoclingReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Create a knowledge base with docling reader
knowledge = Knowledge(
vector_db=PgVector(
table_name="docling_documents",
db_url=db_url,
)
)
reader = DoclingReader()
# Add docx document
knowledge.insert(
path="cookbook/07_knowledge/testing_resources/project_proposal.docx",
reader=reader,
)
# Add pptx presentation
knowledge.insert(
path="cookbook/07_knowledge/testing_resources/ai_presentation.pptx",
reader=reader,
)
# Add xlsx spreadsheet
knowledge.insert(
path="cookbook/07_knowledge/testing_resources/sample_products.xlsx",
reader=reader,
)
# Add JPEG file
knowledge.insert(
path="cookbook/07_knowledge/testing_resources/restaurant_invoice.jpeg",
reader=reader,
)
# Add MP4 file (requires FFmpeg and openai-whisper)
knowledge.insert(
path="cookbook/07_knowledge/testing_resources/agno_description.mp4",
reader=DoclingReader(output_format="vtt"),
)
# Create an agent with the knowledge base
agent = Agent(
knowledge=knowledge,
search_knowledge=True,
)
# Query across all document types
agent.print_response(
"Summarize the key information from all documents",
markdown=True,
)
Run the Example
# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/05_integrations/readers
# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate
# Install additional dependencies for audio/video processing
uv pip install -U openai-whisper
# Optional: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh
python docling_reader_multi_format.py
ffmpeg (required for audio/video processing):
- macOS:
brew install ffmpeg - Ubuntu:
sudo apt-get install ffmpeg - Windows: Download from https://ffmpeg.org/download.html