🌟 MyScale VectorStore Support (openai#377)

* add myscale notebook * add myscale to vector database notebook
orasept77 · May 1, 2023 · 7fcba40 · 7fcba40
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diff --git a/examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb b/examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb
@@ -50,7 +50,10 @@
  " - *Setup*: Set up the Typesense Python client. For more details go [here](https://typesense.org/docs/0.24.0/api/)\n",
  " - *Index Data*: We'll create a collection and index it for both __titles__ and __content__.\n",
  " - *Search Data*: Run a few example queries with various goals in mind.\n",
- "\n",
+ "- **MyScale**\n",
+ " - *Setup*: Set up the MyScale Python client. For more details go [here](https://docs.myscale.com/en/python-client/)\n",
+ " - *Index Data*: We'll create a table and index it for __content__.\n",
+ " - *Search Data*: Run a few example queries with various goals in mind.\n",
  "\n",
  "Once you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings."
  ]
@@ -80,6 +83,7 @@
  "!pip install qdrant-client\n",
  "!pip install redis\n",
  "!pip install typesense\n",
+ "!pip install clickhouse-connect\n",
  "\n",
  "#Install wget to pull zip file\n",
  "!pip install wget"
@@ -119,6 +123,9 @@
  "# Typesense's client library for Python\n",
  "import typesense\n",
  "\n",
+ "# MyScale's client library for Python\n",
+ "import clickhouse-connect\n",
+ "\n",
  "\n",
  "# I've set this to our new embeddings model, this can be changed to the embedding model of your choice\n",
  "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
@@ -2249,6 +2256,166 @@
  "source": [
  "Thanks for following along, you're now equipped to set up your own vector databases and use embeddings to do all kinds of cool things - enjoy! For more complex use cases please continue to work through other cookbook examples in this repo."
  ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "56a02772",
+ "metadata": {},
+ "source": [
+ "# MyScale\n",
+ "The next vector database we'll consider is [MyScale](https://myscale.com).\n",
+ "\n",
+ "[MyScale](https://myscale.com) 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.\n",
+ "\n",
+ "Deploy and execute vector search with SQL on your cluster within two minutes by using [MyScale Console](https://console.myscale.com)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3e1f96b",
+ "metadata": {},
+ "source": [
+ "## Connect to MyScale\n",
+ "\n",
+ "Follow the [connections details](https://docs.myscale.com/en/cluster-management/) 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:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "024243cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import clickhouse_connect\n",
+ "\n",
+ "# initialize client\n",
+ "client = clickhouse_connect.get_client(host='YOUR_CLUSTER_HOST', port=8443, username='YOUR_USERNAME', password='YOUR_CLUSTER_PASSWORD')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "067009db",
+ "metadata": {},
+ "source": [
+ "## Index data\n",
+ "\n",
+ "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:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "685cba13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create articles table with vector index\n",
+ "embedding_len=len(article_df['content_vector'][0]) # 1536\n",
+ "\n",
+ "client.command(f\"\"\"\n",
+ "CREATE TABLE IF NOT EXISTS default.articles\n",
+ "(\n",
+ " id UInt64,\n",
+ " url String,\n",
+ " title String,\n",
+ " text String,\n",
+ " content_vector Array(Float32),\n",
+ " CONSTRAINT cons_vector_len CHECK length(content_vector) = {embedding_len},\n",
+ " VECTOR INDEX article_content_index content_vector TYPE HNSWFLAT('metric_type=Cosine')\n",
+ ")\n",
+ "ENGINE = MergeTree ORDER BY id\n",
+ "\"\"\")\n",
+ "\n",
+ "# insert data into the table in batches\n",
+ "from tqdm.auto import tqdm\n",
+ "\n",
+ "batch_size = 100\n",
+ "total_records = len(article_df)\n",
+ "\n",
+ "# we only need subset of columns\n",
+ "article_df = article_df[['id', 'url', 'title', 'text', 'content_vector']]\n",
+ "\n",
+ "# upload data in batches\n",
+ "data = article_df.to_records(index=False).tolist()\n",
+ "column_names = article_df.columns.tolist()\n",
+ "\n",
+ "for i in tqdm(range(0, total_records, batch_size)):\n",
+ " i_end = min(i + batch_size, total_records)\n",
+ " client.insert(\"default.articles\", data[i:i_end], column_names=column_names)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b0f0e591",
+ "metadata": {},
+ "source": [
+ "We need to check the build status of the vector index before proceeding with the search, as it is automatically built in the background."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9251bdf1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check count of inserted data\n",
+ "print(f\"articles count: {client.command('SELECT count(*) FROM default.articles')}\")\n",
+ "\n",
+ "# check the status of the vector index, make sure vector index is ready with 'Built' status\n",
+ "get_index_status=\"SELECT status FROM system.vector_indices WHERE name='article_content_index'\"\n",
+ "print(f\"index build status: {client.command(get_index_status)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fe55234a",
+ "metadata": {},
+ "source": [
+ "## Search data\n",
+ "\n",
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd5f03c6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import openai\n",
+ "\n",
+ "query = \"Famous battles in Scottish history\"\n",
+ "\n",
+ "# creates embedding vector from user query\n",
+ "embed = openai.Embedding.create(\n",
+ " input=query,\n",
+ " model=\"text-embedding-ada-002\",\n",
+ ")[\"data\"][0][\"embedding\"]\n",
+ "\n",
+ "# query the database to find the top K similar content to the given query\n",
+ "top_k = 10\n",
+ "results = client.query(f\"\"\"\n",
+ "SELECT id, url, title, distance(content_vector, {embed}) as dist\n",
+ "FROM default.articles\n",
+ "ORDER BY dist\n",
+ "LIMIT {top_k}\n",
+ "\"\"\")\n",
+ "\n",
+ "# display results\n",
+ "for i, r in enumerate(results.named_results()):\n",
+ " print(i+1, r['title'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0119d87a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
  }
  ],
  "metadata": {
@@ -2267,7 +2434,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.9.9"
+ "version": "3.9.16"
  },
  "vscode": {
  "interpreter": {