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The OllamaEmbedder can be used to embed text data into vectors locally using Ollama.
The model used for generating embeddings needs to run locally.

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Usage

cookbook/embedders/ollama_embedder.py
from phi.agent import AgentKnowledge
from phi.vectordb.pgvector import PgVector
from phi.embedder.ollama import OllamaEmbedder

embeddings = OllamaEmbedder().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)}")

# Example usage:
knowledge_base = AgentKnowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="ollama_embeddings",
        embedder=OllamaEmbedder(),
    ),
    num_documents=2,
)

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Params

ParameterTypeDefaultDescription
modelstr"openhermes"The name of the model used for generating embeddings.
dimensionsint4096The dimensionality of the embeddings generated by the model.
hoststr-The host address for the API endpoint.
timeoutAny-The timeout duration for API requests.
optionsAny-Additional options for configuring the API request.
client_kwargsOptional[Dict[str, Any]]-Additional keyword arguments for configuring the API client. Optional.
ollama_clientOptional[OllamaClient]-An instance of the OllamaClient to use for making API requests. Optional.