The YouTubeKnowledgeBase iterates over a list of Youtube URLs, extracts the video transcripts, converts them into vector embeddings and loads them to a vector database.

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Usage

We are using a local PgVector database for this example. Make sure it’s running

pip install bs4
knowledge_base.py
from agno.knowledge.youtube import YouTubeKnowledgeBase
from agno.vectordb.pgvector import PgVector

knowledge_base = YouTubeKnowledgeBase(
    urls=["https://www.youtube.com/watch?v=CDC3GOuJyZ0"],
    # Table name: ai.website_documents
    vector_db=PgVector(
        table_name="youtube_documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)

Then use the knowledge_base with an Agent:

agent.py
from agno.agent import Agent
from knowledge_base import knowledge_base

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)
agent.knowledge.load(recreate=False)

agent.print_response("Ask me about something from the knowledge base")

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Params

ParameterTypeDefaultDescription
urlsList[str][]URLs of the videos to read
readerOptional[YouTubeReader]NoneA YouTubeReader that reads transcripts of the videos at the urls and converts them into Documents for the vector database.

YouTubeKnowledgeBase is a subclass of the AgentKnowledge class and has access to the same params.

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Developer Resources

WikipediaFixed Size Chunking