knowledge_base_example.py
from dotenv import load_dotenv
load_dotenv()

from xpander_sdk import Backend
from agno.agent import Agent
from agno.document.chunking.fixed import FixedSizeChunking
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector

# Initialize backend and agent
backend = Backend()
agno_agent = Agent(**backend.get_args())

# Get database connection from agent storage
db_url = agno_agent.storage.db_url

# Create knowledge base with PDF documents
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=PgVector(
        table_name="recipes_fixed_size_chunking", 
        db_url=db_url, 
        auto_upgrade_schema=True, 
        schema=agno_agent.storage.schema
    ),
    chunking_strategy=FixedSizeChunking(),
)

# Load documents into vector database
knowledge_base.load(recreate=True)

# Configure agent with knowledge base
agno_agent.knowledge = knowledge_base
agno_agent.search_knowledge = True

# Query the knowledge base
agno_agent.print_response("How to make Thai curry?", markdown=True)

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How to Run

knowledge_base_example.py
python knowledge_base_example.py
This example shows automatic PDF document parsing and text extraction, vector embeddings creation for semantic search, and Retrieval-Augmented Generation where the agent searches the knowledge base for relevant information and integrates retrieved content seamlessly into LLM responses for enhanced accuracy.

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Production Deployment

For creating and deploying agents to production, see the Setup and Deployment Guide.