retrieval_agent.py

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.embedder.openai import OpenAIEmbedder
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb, SearchType
from phi.playground import Playground, serve_playground_app

# Create a knowledge base from a PDF
knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    # Use LanceDB as the vector database
    vector_db=LanceDb(
        table_name="recipes",
        uri="tmp/lancedb",
        search_type=SearchType.vector,
        embedder=OpenAIEmbedder(model="text-embedding-3-small"),
    ),
)

knowledge_base.load()

retrieval_agent = Agent(
    model=OpenAIChat(id="gpt-4o-mini"),
    # Add the knowledge base to the agent
    knowledge=knowledge_base,
    show_tool_calls=True,
    markdown=True,
)
# retrieval_agent.print_response(
# "How do I make chicken and galangal in coconut milk soup", stream=True
# )

# Create playground with both agents
app = Playground(agents=[retrieval_agent]).get_app()

if __name__ == "__main__":
    serve_playground_app("retrieval_agent:app", reload=True, port=7777)