local.py
"""
Local Vector Databases: ChromaDB and LanceDB
==============================================
For local development and prototyping, you can use embedded vector databases
that don't require a server.
ChromaDB:
- In-memory or persistent storage
- Simple setup, good for prototyping
- pip install chromadb
LanceDB:
- File-based storage (no server needed)
- Supports hybrid search
- pip install lancedb
See also: 01_qdrant.py for production, 03_managed.py for Pinecone, 04_pgvector.py for PostgreSQL.
"""
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
# ---------------------------------------------------------------------------
# ChromaDB Setup
# ---------------------------------------------------------------------------
try:
from agno.vectordb.chroma import ChromaDb
knowledge_chroma = Knowledge(
vector_db=ChromaDb(
collection="local_demo",
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
except ImportError:
knowledge_chroma = None
print("ChromaDB not installed. Run: pip install chromadb")
# ---------------------------------------------------------------------------
# LanceDB Setup
# ---------------------------------------------------------------------------
try:
from agno.vectordb.lancedb import LanceDb, SearchType
knowledge_lance = Knowledge(
vector_db=LanceDb(
uri="tmp/lancedb",
table_name="local_demo",
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
except ImportError:
knowledge_lance = None
print("LanceDB not installed. Run: pip install lancedb")
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
pdf_url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
if knowledge_chroma:
print("\n" + "=" * 60)
print("ChromaDB: in-memory vector database")
print("=" * 60 + "\n")
knowledge_chroma.insert(url=pdf_url)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_chroma,
search_knowledge=True,
markdown=True,
)
agent.print_response("What Thai recipes do you know?", stream=True)
if knowledge_lance:
print("\n" + "=" * 60)
print("LanceDB: file-based vector database with hybrid search")
print("=" * 60 + "\n")
knowledge_lance.insert(url=pdf_url)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_lance,
search_knowledge=True,
markdown=True,
)
agent.print_response("What Thai desserts are available?", stream=True)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno chromadb lancedb openai pyarrow pypdf
3
Export your API keys
export LANCEDB_API_KEY="your_lancedb_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:LANCEDB_API_KEY="your_lancedb_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
local.py, then run:python local.py