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awesome-llm-apps/rag_tutorials/agentic_rag/rag_agent.py
2024-11-04 20:59:39 -06:00

30 lines
1019 B
Python

from phi.agent import Agent
from phi.model.openai import OpenAIChat
from phi.knowledge.pdf import PDFUrlKnowledgeBase
from phi.vectordb.lancedb import LanceDb, SearchType
from phi.playground import Playground, serve_playground_app
from phi.tools.duckduckgo import DuckDuckGo
db_uri = "tmp/lancedb"
# 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=db_uri, search_type=SearchType.vector),
)
# Load the knowledge base: Comment out after first run
knowledge_base.load(upsert=True)
rag_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
agent_id="rag-agent",
knowledge=knowledge_base, # Add the knowledge base to the agent
tools=[DuckDuckGo()],
show_tool_calls=True,
markdown=True,
)
app = Playground(agents=[rag_agent]).get_app()
if __name__ == "__main__":
serve_playground_app("rag_agent:app", reload=True)