VoyageAI Embedder

VoyageAIEmbedder defaults to voyage-2 with 1024 dimensions.

voyageai_embedder.py

from agno.knowledge.embedder.voyageai import VoyageAIEmbedder

document_embedder = VoyageAIEmbedder(
    request_params={"input_type": "document"},
)
query_embedder = VoyageAIEmbedder(
    request_params={"input_type": "query"},
)

document_vector = document_embedder.get_embedding(
    "The quick brown fox jumps over the lazy dog."
)
query_vector = query_embedder.get_embedding("Which animal jumps?")

print(len(document_vector), len(query_vector))

VoyageAIEmbedder applies one request_params dictionary to every call. A single instance used by a vector database therefore applies the same input_type to document insertion and query search. VoyageAI retrieval models distinguish document from query inputs.

For a nondefault vector size, set dimensions and request_params={"output_dimension": ...} to the same value.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

uv venv --python 3.12
.venv\Scripts\activate

Export the API key

export VOYAGE_API_KEY=your_voyage_api_key_here

Install dependencies

uv pip install -U agno voyageai

Run the example

python voyageai_embedder.py

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VoyageAI Embedder

Run the Example

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Run the Example

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