LlamaIndex Embeddings Integration: Databricks

This integration adds support for embedding models hosted on the databricks platform via serving endpoints. The API follows the specifications of OpenAI, so this integration simply adapts the llama-index-embeddings-openai integration and internally uses the openai Python API library, too.

The signature furthermore aligns with the existing Databricks LLM integration with respect to the naming of the model, api_key and endpoint variables to ensure a smooth user experience.

Installation

pip install llama-index
pip install llama-index-embeddings-databricks

Usage

Passing the api_key and endpoint directly as arguments:

import os
from llama_index.core import Settings
from llama_index.embeddings.databricks import DatabricksEmbedding

# Set up the DatabricksEmbedding class with the required model, API key and serving endpoint
embed_model = DatabricksEmbedding(
    model="databricks-bge-large-en",
    api_key="<MY TOKEN>",
    endpoint="<MY ENDPOINT>",
)
Settings.embed_model = embed_model

# Embed some text
embeddings = embed_model.get_text_embedding(
    "The DatabricksEmbedding integration works great."
)

Using environment variables:

export DATABRICKS_TOKEN=<MY TOKEN>
export DATABRICKS_SERVING_ENDPOINT=<MY ENDPOINT>

import os
from dotenv import load_dotenv
from llama_index.core import Settings
from llama_index.embeddings.databricks import DatabricksEmbedding

load_dotenv()
# Set up the DatabricksEmbedding class with the required model, API key and serving endpoint
embed_model = DatabricksEmbedding(model="databricks-bge-large-en")
Settings.embed_model = embed_model

# Embed some text
embeddings = embed_model.get_text_embedding(
    "The DatabricksEmbedding integration works great."
)