Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.youtube_reader import YouTubeReader
from agno.vectordb.pgvector import PgVector
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Create Knowledge Instance
knowledge = Knowledge(
name="YouTube Knowledge Base",
description="Knowledge base from YouTube video transcripts",
vector_db=PgVector(
table_name="youtube_vectors",
db_url=db_url
),
)
# Add YouTube video content synchronously
knowledge.insert(
metadata={"source": "youtube", "type": "educational"},
urls=[
"https://www.youtube.com/watch?v=dQw4w9WgXcQ", # Replace with actual educational video
"https://www.youtube.com/watch?v=example123" # Replace with actual video URL
],
reader=YouTubeReader(),
)
# Create an agent with the knowledge
agent = Agent(
knowledge=knowledge,
search_knowledge=True,
)
# Query the knowledge base
agent.print_response(
"What are the main topics discussed in the videos?",
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 youtube-transcript-api pytube 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/youtube_reader_sync.py
python examples/basics/knowledge/concepts/readers/overview/youtube_reader_sync.py
| Parameter | Type | Default | Description |
|---|---|---|---|
video_url | str | Required | URL of the YouTube video to extract transcript from |