Examples
- Examples
- Getting Started
- Agents
- Teams
- Workflows
- Applications
- Streamlit Apps
- Evals
Agent Concepts
- Reasoning
- Multimodal
- RAG
- User Control Flows
- Knowledge
- Memory
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Context
- Embedders
- Agent State
- Observability
- Miscellaneous
Models
- Anthropic
- AWS Bedrock
- AWS Bedrock Claude
- Azure AI Foundry
- Azure OpenAI
- Cerebras
- Cerebras OpenAI
- Cohere
- DeepInfra
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- IBM
- LM Studio
- LiteLLM
- LiteLLM OpenAI
- Meta
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Perplexity
- Together
- XAI
- Vercel
Vector Databases
LanceDB Integration
Code
cookbook/agent_concepts/vector_dbs/lance_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb
vector_db = LanceDb(
table_name="recipes",
uri="/tmp/lancedb", # You can change this path to store data elsewhere
)
knowledge_base = PDFUrlKnowledgeBase(
urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
vector_db=vector_db,
)
knowledge_base.load(recreate=False) # Comment out after first run
agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
agent.print_response("How to make Tom Kha Gai", markdown=True)
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2
Install libraries
pip install -U lancedb pypdf openai agno
3
Run Agent
python cookbook/agent_concepts/vector_dbs/lance_db.py
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