Using MyScale as a vector database for OpenAI embeddings

May 1, 2023
Open in Github

This notebook provides a step-by-step guide on using MyScale as a vector database for OpenAI embeddings. The process includes:

  1. Utilizing precomputed embeddings generated by OpenAI API.
  2. Storing these embeddings in a cloud instance of MyScale.
  3. Converting raw text query to an embedding using OpenAI API.
  4. Leveraging MyScale to perform nearest neighbor search within the created collection.

What is MyScale

MyScale is a database built on Clickhouse that combines vector search and SQL analytics to offer a high-performance, streamlined, and fully managed experience. It's designed to facilitate joint queries and analyses on both structured and vector data, with comprehensive SQL support for all data processing.

Deployment options

  • Deploy and execute vector search with SQL on your cluster within two minutes by using MyScale Console.


To follow this guide, you will need to have the following:

  1. A MyScale cluster deployed by following the quickstart guide.
  2. The 'clickhouse-connect' library to interact with MyScale.
  3. An OpenAI API key for vectorization of queries.

Install requirements

This notebook requires the openai, clickhouse-connect, as well as some other dependencies. Use the following command to install them:

! pip install openai clickhouse-connect wget pandas
import openai

# get API key from on OpenAI website
openai.api_key = "OPENAI_API_KEY"

# check we have authenticated

Connect to MyScale

Follow the connections details section to retrieve the cluster host, username, and password information from the MyScale console, and use it to create a connection to your cluster as shown below:

import clickhouse_connect

# initialize client
client = clickhouse_connect.get_client(host='YOUR_CLUSTER_HOST', port=8443, username='YOUR_USERNAME', password='YOUR_CLUSTER_PASSWORD')

We need to load the dataset of precomputed vector embeddings for Wikipedia articles provided by OpenAI. Use the wget package to download the dataset.

import wget

embeddings_url = ""

# The file is ~700 MB so this will take some time

After the download is complete, extract the file using the zipfile package:

import zipfile

with zipfile.ZipFile("", "r") as zip_ref:

Now, we can load the data from vector_database_wikipedia_articles_embedded.csv into a Pandas DataFrame:

import pandas as pd

from ast import literal_eval

# read data from csv
article_df = pd.read_csv('../data/vector_database_wikipedia_articles_embedded.csv')
article_df = article_df[['id', 'url', 'title', 'text', 'content_vector']]

# read vectors from strings back into a list
article_df["content_vector"] = article_df.content_vector.apply(literal_eval)

Index data

We will create an SQL table called articles in MyScale to store the embeddings data. The table will include a vector index with a cosine distance metric and a constraint for the length of the embeddings. Use the following code to create and insert data into the articles table:

# create articles table with vector index
embedding_len=len(article_df['content_vector'][0]) # 1536

    id UInt64,
    url String,
    title String,
    text String,
    content_vector Array(Float32),
    CONSTRAINT cons_vector_len CHECK length(content_vector) = {embedding_len},
    VECTOR INDEX article_content_index content_vector TYPE HNSWFLAT('metric_type=Cosine')
ENGINE = MergeTree ORDER BY id

# insert data into the table in batches
from import tqdm

batch_size = 100
total_records = len(article_df)

# upload data in batches
data = article_df.to_records(index=False).tolist()
column_names = article_df.columns.tolist() 

for i in tqdm(range(0, total_records, batch_size)):
    i_end = min(i + batch_size, total_records)
    client.insert("default.articles", data[i:i_end], column_names=column_names)

We need to check the build status of the vector index before proceeding with the search, as it is automatically built in the background.

# check count of inserted data
print(f"articles count: {client.command('SELECT count(*) FROM default.articles')}")

# check the status of the vector index, make sure vector index is ready with 'Built' status
get_index_status="SELECT status FROM system.vector_indices WHERE name='article_content_index'"
print(f"index build status: {client.command(get_index_status)}")
articles count: 25000
index build status: Built

Search data

Once indexed in MyScale, we can perform vector search to find similar content. First, we will use the OpenAI API to generate embeddings for our query. Then, we will perform the vector search using MyScale.

import openai

query = "Famous battles in Scottish history"

# creates embedding vector from user query
embed = openai.Embedding.create(

# query the database to find the top K similar content to the given query
top_k = 10
results = client.query(f"""
SELECT id, url, title, distance(content_vector, {embed}) as dist
FROM default.articles
LIMIT {top_k}

# display results
for i, r in enumerate(results.named_results()):
    print(i+1, r['title'])
1 Battle of Bannockburn
2 Wars of Scottish Independence
3 1651
4 First War of Scottish Independence
5 Robert I of Scotland
6 841
7 1716
8 1314
9 1263
10 William Wallace