MyScale
MyScale is a cloud-based database optimized for AI applications and solutions, built on the open-sourceClickHouse.
This notebook shows how to use functionality related to theMyScale vector database.
Setting up environments
%pip install--upgrade--quiet clickhouse-connect langchain-community
We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.
import getpass
import os
if"OPENAI_API_KEY"notin os.environ:
os.environ["OPENAI_API_KEY"]= getpass.getpass("OpenAI API Key:")
if"OPENAI_API_BASE"notin os.environ:
os.environ["OPENAI_API_BASE"]= getpass.getpass("OpenAI Base:")
if"MYSCALE_HOST"notin os.environ:
os.environ["MYSCALE_HOST"]= getpass.getpass("MyScale Host:")
if"MYSCALE_PORT"notin os.environ:
os.environ["MYSCALE_PORT"]= getpass.getpass("MyScale Port:")
if"MYSCALE_USERNAME"notin os.environ:
os.environ["MYSCALE_USERNAME"]= getpass.getpass("MyScale Username:")
if"MYSCALE_PASSWORD"notin os.environ:
os.environ["MYSCALE_PASSWORD"]= getpass.getpass("MyScale Password:")
There are two ways to set up parameters for myscale index.
Environment Variables
Before you run the app, please set the environment variable with
export:export MYSCALE_HOST='<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 tothis document
Every attributes under
MyScaleSettingscan be set with prefixMYSCALE_and is case insensitive.Create
MyScaleSettingsobject with parametersfrom langchain_community.vectorstoresimport MyScale, MyScaleSettings
config= MyScaleSetting(host="<your-backend-url>", port=8443,...)
index= MyScale(embedding_function, config)
index.add_documents(...)
from langchain_community.document_loadersimport TextLoader
from langchain_community.vectorstoresimport MyScale
from langchain_openaiimport OpenAIEmbeddings
from langchain_text_splittersimport CharacterTextSplitter
from langchain_community.document_loadersimport TextLoader
loader= TextLoader("../../how_to/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 din 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 [00:15<00:00, 2.66it/s]
print(docs[0].page_content)
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
Get connection info and data schema
print(str(docsearch))
Filtering
You can have direct access to myscale SQL where statement. You can writeWHERE clause following standard SQL.
NOTE: Please be aware of SQL injection, this interface must not be directly called by end-user.
If you customized yourcolumn_map under your setting, you search with filter like this:
from langchain_community.document_loadersimport TextLoader
from langchain_community.vectorstoresimport MyScale
loader= TextLoader("../../how_to/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, dinenumerate(docs):
d.metadata={"doc_id": i}
docsearch= MyScale.from_documents(docs, embeddings)
Inserting data...: 100%|██████████| 42/42 [00:15<00:00, 2.68it/s]
Similarity search with score
The returned distance score is cosine distance. Therefore, a lower score is better.
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, distin output:
print(dist, d.metadata, d.page_content[:20]+"...")
0.229655921459198 {'doc_id': 0} Madam Speaker, Madam...
0.24506962299346924 {'doc_id': 8} And so many families...
0.24786919355392456 {'doc_id': 1} Groups of citizens b...
0.24875116348266602 {'doc_id': 6} And I’m taking robus...
Deleting your data
You can either drop the table with.drop() method or partially delete your data with.delete() method.
# use directly a `where_str` to delete
docsearch.delete(where_str=f"{docsearch.metadata_column}.doc_id < 5")
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, distin output:
print(dist, d.metadata, d.page_content[:20]+"...")
0.24506962299346924 {'doc_id': 8} And so many families...
0.24875116348266602 {'doc_id': 6} And I’m taking robus...
0.26027143001556396 {'doc_id': 7} We see the unity amo...
0.26390212774276733 {'doc_id': 9} And unlike the $2 Tr...
docsearch.drop()
Related
- Vector storeconceptual guide
- Vector storehow-to guides