Elsai Embeddings#

The Elsai Embeddings module enables seamless embedding generation using Azure OpenAI models.

Prerequisites#

  • Python >= 3.9

  • .env file with appropriate API keys and configuration variables

Installation#

To install the elsai-embeddings package:

pip install --index-url https://elsai-core-package.optisolbusiness.com/root/elsai-embeddings/ elsai-embeddings==0.1.0

Components#

1. Azure Embeddings#

AzureOpenAIEmbeddingModel is a class that connects to the Azure OpenAI embedding service to generate vector representations of text queries and documents.

from elsai_embeddings.azure_embeddings import AzureOpenAIEmbeddingModel

embeddings = AzureOpenAIEmbeddingModel(
    model="your_model_name",
    azure_api_key="your_azure_api_key",
    azure_api_version="your_azure_api_version",
    azure_deployment="your_azure_deployment_name",
    azure_endpoint="your_azure_endpoint"
)  # Or set in environment variables AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, OPENAI_API_VERSION, AZURE_EMBEDDING_DEPLOYMENT_NAME

embed_text = embeddings.embed_query("Your text to embed")

embed_docs = embeddings.embed_documents(["Document 1 text", "Document 2 text"])

embedding_model = embeddings.get_embedding_model() # Returns the underlying embedding model instance

Required Environment Variables:

  • AZURE_OPENAI_API_KEY – API key for accessing the Azure OpenAI service.

  • AZURE_OPENAI_ENDPOINT – Endpoint URL of your Azure OpenAI resource.

  • OPENAI_API_VERSION – API version used for the embedding requests.

  • AZURE_EMBEDDING_DEPLOYMENT_NAME – Deployment name for the embedding model on Azure.