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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

The DocumentKnowledgeBase reads local docs files, 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 textract

from agno.knowledge.document import DocumentKnowledgeBase
from agno.vectordb.pgvector import PgVector

knowledge_base = DocumentKnowledgeBase(
    path="data/docs",
    # Table name: ai.documents
    vector_db=PgVector(
        table_name="documents",
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    ),
)
Then use the knowledge_base with an Agent: 
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
documentsList[Document]-List of Document objects to be used as the knowledge base
DocumentKnowledgeBase is a subclass of the AgentKnowledge class and has access to the same params.

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Developer Resources