embedders.py
"""
Embedders: Choosing and Configuring Embedding Models
=====================================================
Embedders convert text into vectors for semantic search. The choice of
embedder affects search quality, cost, and privacy.
This example shows two common configurations:
1. OpenAI (cloud, recommended default)
2. Ollama (local, private, no API calls)
For a full comparison of all 17+ supported providers, see:
../reference/embedder_comparison.md
"""
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
qdrant_url = "http://localhost:6333"
pdf_url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
async def main():
# --- 1. OpenAI embedder (cloud, recommended default) ---
print("\n" + "=" * 60)
print("EMBEDDER 1: OpenAI text-embedding-3-small")
print("=" * 60 + "\n")
knowledge_openai = Knowledge(
vector_db=Qdrant(
collection="embedder_openai",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
await knowledge_openai.ainsert(url=pdf_url, skip_if_exists=True)
agent_openai = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_openai,
search_knowledge=True,
markdown=True,
)
agent_openai.print_response("How do I make pad thai?", stream=True)
# --- 2. Ollama embedder (local, private) ---
# Requires: ollama pull nomic-embed-text
print("\n" + "=" * 60)
print("EMBEDDER 2: Ollama nomic-embed-text (local)")
print("=" * 60 + "\n")
try:
from agno.knowledge.embedder.ollama import OllamaEmbedder
knowledge_ollama = Knowledge(
vector_db=Qdrant(
collection="embedder_ollama",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OllamaEmbedder(
id="nomic-embed-text",
dimensions=768,
),
),
)
await knowledge_ollama.ainsert(url=pdf_url, skip_if_exists=True)
agent_ollama = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge_ollama,
search_knowledge=True,
markdown=True,
)
agent_ollama.print_response("How do I make pad thai?", stream=True)
except ImportError:
print("Ollama not installed. Run: pip install ollama")
except Exception as e:
print("Ollama embedder failed (is Ollama running?): %s" % e)
asyncio.run(main())
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 fastembed importlib-metadata ollama openai pypdf qdrant-client
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run Qdrant
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
5
Run the example
Save the code above as
embedders.py, then run:python embedders.py