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Documentation Index

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The Docling Reader processes multiple document formats using IBM’s Docling library. It handles PDFs, documents, presentations, spreadsheets, images, audio, video and markup files.

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Code

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,
    )
)

# Add documents using DoclingReader
knowledge.insert(
    path="documents/report.pdf",
    reader=DoclingReader(),
)

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

# Query the knowledge base
agent.print_response(
    "Summarize the key findings from the report",
    markdown=True,
)

​
Usage

1

Set up your virtual environment

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

Install dependencies

# Base dependencies
uv pip install -U docling sqlalchemy psycopg pgvector agno openai

# For audio/video processing
uv pip install -U openai-whisper
Install ffmpeg (required for audio/video processing):
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/basics/knowledge/concepts/readers/overview/docling_reader_sync.py

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Params

ParameterTypeDefaultDescription
output_formatstr"markdown"Export format ("markdown", "text", "json", "yaml", "html", "html_split_page", "doctags", "vtt")
converterOptional[DocumentConverter]NoneCustom Docling converter configuration
format_optionsOptional[dict]NoneFormat options dictionary for DocumentConverter
chunking_strategyOptional[ChunkingStrategy]DocumentChunking()Strategy for chunking the document