Ollama Embedder

The OllamaEmbedder can be used to embed text data into vectors locally using Ollama.

The model used for generating embeddings needs to run locally. In this case it is openhermes so you have to install ollama and run ollama pull openhermes in your terminal.

Usage

ollama_embedder.py

from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector
from agno.knowledge.embedder.ollama import OllamaEmbedder

# Embed sentence in database
embeddings = OllamaEmbedder(id="openhermes").get_embedding("The quick brown fox jumps over the lazy dog.")

# Print the embeddings and their dimensions
print(f"Embeddings: {embeddings[:5]}")
print(f"Dimensions: {len(embeddings)}")

# Use an embedder in a knowledge base
knowledge = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="ollama_embeddings",
        embedder=OllamaEmbedder(),
    ),
    max_results=2,
)

Params

Parameter	Type	Default	Description
model	str	"openhermes"	The name of the model used for generating embeddings.
dimensions	int	4096	The dimensionality of the embeddings generated by the model.
host	str	-	The host address for the API endpoint.
timeout	Any	-	The timeout duration for API requests.
options	Any	-	Additional options for configuring the API request.
client_kwargs	Optional[Dict[str, Any]]	-	Additional keyword arguments for configuring the API client. Optional.
ollama_client	Optional[OllamaClient]	-	An instance of the OllamaClient to use for making API requests. Optional.

Developer Resources

• View Cookbook