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 from the model cards and start using them in your code.

Installation

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

Initialization

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

_ = load_dotenv(find_dotenv())

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
)

Synchronous Requests

Get Text Embedding

response = model.get_text_embedding("hello world")
print(response)

Batch Requests

texts = ["hello world", "goodbye world"]
response_batch = model.get_text_embedding_batch(texts)
print(response_batch)

Query Requests

query_response = model.get_query_embedding("hello world")
print(query_response)

Asynchronous Requests

Get Text Embedding

async def main():
    text = "hello world"
    async_response = await model.aget_text_embedding(text)
    print(async_response)


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

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For any questions or feedback, please contact us at feedback@deepinfra.com.