Hybrid Search
LanceDB Hybrid Search
Examples
- Introduction
- Getting Started
- Agents
- Teams
- Workflows
- Applications
Agent Concepts
- Multimodal
- RAG
- Knowledge
- Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Embedders
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- xAI
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
Hybrid Search
LanceDB Hybrid Search
Code
cookbook/agent_concepts/hybrid_search/lancedb/agent.py
from typing import Optional
import typer
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb
from agno.vectordb.search import SearchType
from rich.prompt import Prompt
vector_db = LanceDb(
table_name="recipes",
uri="tmp/lancedb",
search_type=SearchType.hybrid,
)
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=vector_db,
)
def lancedb_agent(user: str = "user"):
agent = Agent(
user_id=user,
knowledge=knowledge_base,
search_knowledge=True,
show_tool_calls=True,
debug_mode=True,
)
while True:
message = Prompt.ask(f"[bold] :sunglasses: {user} [/bold]")
if message in ("exit", "bye"):
break
agent.print_response(message)
if __name__ == "__main__":
# Comment out after first run
knowledge_base.load(recreate=False)
typer.run(lancedb_agent)
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2
Set your API key
export OPENAI_API_KEY=xxx
3
Install libraries
pip install -U lancedb tantivy pypdf openai agno
4
Run Agent
python cookbook/agent_concepts/hybrid_search/lancedb/agent.py