MyScale

MyScale is a cloud-based database optimized for AI applications and solutions, built on the open-source ClickHouse.

This notebook shows how to use functionality related to the MyScale vector database.

Setting up envrionments

!pip install clickhouse-connect

We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.

import os
import getpass

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')

There are two ways to set up parameters for myscale index.

1. Environment Variables

   Before you run the app, please set the environment variable with export: export MYSCALE_URL='<your-endpoints-url>' MYSCALE_PORT=<your-endpoints-port> MYSCALE_USERNAME=<your-username> MYSCALE_PASSWORD=<your-password> ...

   You can easily find your account, password and other info on our SaaS. For details please refer to this document

   Every attributes under MyScaleSettings can be set with prefix MYSCALE_ and is case insensitive.

2. Create MyScaleSettings object with parameters

   from langchain.vectorstores import MyScale, MyScaleSettings
   config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
   index = MyScale(embedding_function, config)
   index.add_documents(...)
   

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import MyScale
from langchain.document_loaders import TextLoader

from langchain.document_loaders import TextLoader
loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

for d in docs:
    d.metadata = {'some': 'metadata'}
docsearch = MyScale.from_documents(docs, embeddings)

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

Inserting data...: 100%|██████████| 42/42 [00:18<00:00,  2.21it/s]

print(docs[0].page_content)

As Frances Haugen, who is here with us tonight, has shown, we must hold social media platforms accountable for the national experiment they’re conducting on our children for profit. 

It’s time to strengthen privacy protections, ban targeted advertising to children, demand tech companies stop collecting personal data on our children. 

And let’s get all Americans the mental health services they need. More people they can turn to for help, and full parity between physical and mental health care. 

Third, support our veterans. 

Veterans are the best of us. 

I’ve always believed that we have a sacred obligation to equip all those we send to war and care for them and their families when they come home. 

My administration is providing assistance with job training and housing, and now helping lower-income veterans get VA care debt-free.  

Our troops in Iraq and Afghanistan faced many dangers.

Get connection info and data schema

print(str(docsearch))

Filtering

You can have direct access to myscale SQL where statement. You can write WHERE clause following standard SQL.

NOTE: Please be aware of SQL injection, this interface must not be directly called by end-user.

If you custimized your column_map under your setting, you search with filter like this:

from langchain.vectorstores import MyScale, MyScaleSettings
from langchain.document_loaders import TextLoader

loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

for i, d in enumerate(docs):
    d.metadata = {'doc_id': i}

docsearch = MyScale.from_documents(docs, embeddings)

Inserting data...: 100%|██████████| 42/42 [00:15<00:00,  2.69it/s]

meta = docsearch.metadata_column
output = docsearch.similarity_search_with_relevance_scores('What did the president say about Ketanji Brown Jackson?', 
                                                           k=4, where_str=f"{meta}.doc_id<10")
for d, dist in output:
    print(dist, d.metadata, d.page_content[:20] + '...')

0.252379834651947 {'doc_id': 6, 'some': ''} And I’m taking robus...
0.25022566318511963 {'doc_id': 1, 'some': ''} Groups of citizens b...
0.2469480037689209 {'doc_id': 8, 'some': ''} And so many families...
0.2428302764892578 {'doc_id': 0, 'some': 'metadata'} As Frances Haugen, w...

Deleting your data

docsearch.drop()