Knowledge Base Integration (RAG)

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
db = agno_agent.get_db()

# 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.db_url, 
        auto_upgrade_schema=True, 
        schema=db.db_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)

python knowledge_base_example.py