Qdrant on Databricks

Time: 30 minLevel: IntermediateComplete Notebook

Databricks is a unified analytics platform for working with big data and AI. It’s built around Apache Spark, a powerful open-source distributed computing system well-suited for processing large-scale datasets and performing complex analytics tasks.

Apache Spark is designed to scale horizontally, meaning it can handle expensive operations like generating vector embeddings by distributing computation across a cluster of machines. This scalability is crucial when dealing with large datasets.

In this example, we will demonstrate how to vectorize a dataset with dense and sparse embeddings using Qdrant’s FastEmbed library. We will then load this vectorized data into a Qdrant cluster using the Qdrant Spark connector on Databricks.

Setting up a Databricks project

  • Set up a Databricks cluster following the official documentation guidelines.

  • Install the Qdrant Spark connector as a library:

    • Navigate to the Libraries section in your cluster dashboard.

    • Click on Install New at the top-right to open the library installation modal.

    • Search for io.qdrant:spark:VERSION in the Maven packages and click on Install.

      Install the library

  • Create a new Databricks notebook on your cluster to begin working with your data and libraries.

Download a dataset

  • Install the required dependencies:
%pip install fastembed datasets
  • Download the dataset:
from datasets import load_dataset

dataset_name = "tasksource/med"
dataset = load_dataset(dataset_name, split="train")
# We'll use the first 100 entries from this dataset and exclude some unused columns.
dataset = dataset.select(range(100)).remove_columns(["gold_label", "genre"])
  • Convert the dataset into a Spark dataframe:
dataset_df = spark.read.parquet("file:/dbfs/pq.pq")

Vectorizing the data

In this section, we’ll be generating both dense and sparse vectors for our rows using FastEmbed. We’ll create a user-defined function (UDF) to handle this step.

Creating the vectorization function

from fastembed import TextEmbedding, SparseTextEmbedding

def vectorize(partition_data):
    # Initialize dense and sparse models
    dense_model = TextEmbedding(model_name="BAAI/bge-small-en-v1.5")
    sparse_model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")

    for row in partition_data:
        # Generate dense and sparse vectors
        dense_vector = next(dense_model.embed(row.sentence1))
        sparse_vector = next(sparse_model.embed(row.sentence2))

        yield [
            row.sentence1,  # 1st column: original text
            row.sentence2,  # 2nd column: original text
            dense_vector.tolist(),  # 3rd column: dense vector
            sparse_vector.indices.tolist(),  # 4th column: sparse vector indices
            sparse_vector.values.tolist(),  # 5th column: sparse vector values

We’re using the BAAI/bge-small-en-v1.5 model for dense embeddings and prithivida/Splade_PP_en_v1 for sparse embeddings.

Applying the UDF on our dataframe

Next, let’s apply our vectorize UDF on our Spark dataframe to generate embeddings.

embeddings = dataset_df.rdd.mapPartitions(vectorize)

The mapPartitions() method returns a Resilient Distributed Dataset (RDD) which should then be converted back to a Spark dataframe.

Building the new Spark dataframe with the vectorized data

We’ll now create a new Spark dataframe (embeddings_df) with the vectorized data using the specified schema.

from pyspark.sql.types import StructType, StructField, StringType, ArrayType, FloatType, IntegerType

# Define the schema for the new dataframe
schema = StructType([
    StructField("sentence1", StringType()),
    StructField("sentence2", StringType()),
    StructField("dense_vector", ArrayType(FloatType())),
    StructField("sparse_vector_indices", ArrayType(IntegerType())),
    StructField("sparse_vector_values", ArrayType(FloatType()))

# Create the new dataframe with the vectorized data
embeddings_df = spark.createDataFrame(data=embeddings, schema=schema)

Uploading the data to Qdrant

  • Create a Qdrant collection:

    • Follow the documentation to create a collection with the appropriate configurations. Here’s an example request to support both dense and sparse vectors:
    PUT /collections/{collection_name}
      "vectors": {
        "dense": {
          "size": 384,
          "distance": "Cosine"
      "sparse_vectors": {
        "sparse": {}
  • Upload the dataframe to Qdrant:

options = {
    "qdrant_url": "<QDRANT_GRPC_URL>",
    "api_key": "<QDRANT_API_KEY>",
    "collection_name": "<QDRANT_COLLECTION_NAME>",
    "vector_fields": "dense_vector",
    "vector_names": "dense",
    "sparse_vector_value_fields": "sparse_vector_values",
    "sparse_vector_index_fields": "sparse_vector_indices",
    "sparse_vector_names": "sparse",
    "schema": embeddings_df.schema.json(),


Ensure to replace the placeholder values (<QDRANT_GRPC_URL>, <QDRANT_API_KEY>, <QDRANT_COLLECTION_NAME>) with your actual values. If the id_field option is not specified, Qdrant Spark connector generates random UUIDs for each point.

The command output you should see is similar to:

Command took 40.37 seconds -- by xxxxx90@xxxxxx.com at 4/17/2024, 12:13:28 PM on fastembed


That wraps up our tutorial! Feel free to explore more functionalities and experiments with different models, parameters, and features available in Databricks, Spark, and Qdrant.

Happy data engineering!

Qdrant on Databricks