Deepinfra

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<a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/embeddings/deepinfra.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

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¶

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

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

Initialization¶

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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
)

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¶

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

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

Batch Requests¶

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

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

Query Requests¶

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

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

Asynchronous Requests¶

Get Text Embedding¶

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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())

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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