Open In Colab

MyScale Vector Store#

In this notebook we are going to show a quick demo of using the MyScaleVectorStore.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-vector-stores-myscale
!pip install llama-index

Creating a MyScale Client#

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
from os import environ
import clickhouse_connect

environ["OPENAI_API_KEY"] = "sk-*"

# initialize client
client = clickhouse_connect.get_client(

Load documents, build and store the VectorStoreIndex with MyScaleVectorStore#

Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a MyScaleVectorStore and query to find context for our LLM QnA loop.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.myscale import MyScaleVectorStore
from IPython.display import Markdown, display
# load documents
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))
Document ID: a5f2737c-ed18-4e5d-ab9a-75955edb816d
Number of Documents:  1

Download Data

!mkdir -p 'data/paul_graham/'
!wget '' -O 'data/paul_graham/paul_graham_essay.txt'

You can process your files individually using SimpleDirectoryReader:

loader = SimpleDirectoryReader("./data/paul_graham/")
documents = loader.load_data()
for file in loader.input_files:
    # Here is where you would do any preprocessing
# initialize with metadata filter and store indexes
from llama_index.core import StorageContext

for document in documents:
    document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = MyScaleVectorStore(myscale_client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context

Query Index#

Now MyScale vector store supports filter search and hybrid search

You can learn more about query_engine and retriever.

import textwrap

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(
            ExactMatchFilter(key="user_id", value="123"),
response = query_engine.query("What did the author learn?")
print(textwrap.fill(str(response), 100))

Clear All Indexes#

for document in documents: