LlamaIndex Embeddings Integration: Deepinfra

With this integration, you can use the Deepinfra embeddings model to get embeddings for your text data. Here is the link to the embeddings models.

First, you need to sign up on the Deepinfra website and get the API token. You can copy model_ids over the model cards and start using them in your code.

Installation

pip install llama-index llama-index-embeddings-deepinfra

Usage

from dotenv import load_dotenv, find_dotenv
from llama_index.embeddings.deepinfra import DeepInfraEmbeddingModel

# Load environment variables
_ = load_dotenv(find_dotenv())

# Initialize model with optional configuration
model = DeepInfraEmbeddingModel(
    model_id="BAAI/bge-large-en-v1.5",  # Use custom model ID
    api_token="YOUR_API_TOKEN",  # Optionally provide token here
    normalize=True,  # Optional normalization
    text_prefix="text: ",  # Optional text prefix
    query_prefix="query: ",  # Optional query prefix
)

# Example usage
response = model.get_text_embedding("hello world")

# Batch requests
texts = ["hello world", "goodbye world"]
response = model.get_text_embedding_batch(texts)

# Query requests
response = model.get_query_embedding("hello world")


# Asynchronous requests
async def main():
    text = "hello world"
    response = await model.aget_text_embedding(text)


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())