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MyScale

MyScale 是一个为AI应用和解决方案优化的云数据库，基于开源的 ClickHouse 构建。

本笔记本展示了如何使用与MyScale向量数据库相关的功能。

设置环境​

%pip install --upgrade --quiet  clickhouse-connect langchain-community

我们想要使用OpenAIEmbeddings，所以我们必须获取OpenAI API密钥。

import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
if "OPENAI_API_BASE" not in os.environ:
    os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
if "MYSCALE_HOST" not in os.environ:
    os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
if "MYSCALE_PORT" not in os.environ:
    os.environ["MYSCALE_PORT"] = getpass.getpass("MyScale Port:")
if "MYSCALE_USERNAME" not in os.environ:
    os.environ["MYSCALE_USERNAME"] = getpass.getpass("MyScale Username:")
if "MYSCALE_PASSWORD" not in os.environ:
    os.environ["MYSCALE_PASSWORD"] = getpass.getpass("MyScale Password:")

有两种方法可以为myscale索引设置参数。

  1. 环境变量

    在运行应用程序之前，请使用export设置环境变量： export MYSCALE_HOST='' MYSCALE_PORT= MYSCALE_USERNAME= MYSCALE_PASSWORD= ...

    您可以在我们的SaaS上轻松找到您的账户、密码和其他信息。详情请参阅此文档

    MyScaleSettings下的每个属性都可以使用前缀MYSCALE_进行设置，并且不区分大小写。

  2. Create MyScaleSettings object with parameters

    from langchain_community.vectorstores import MyScale, MyScaleSettings
    config = MyScaleSetting(host="<your-backend-url>", port=8443, ...)
    index = MyScale(embedding_function, config)
    index.add_documents(...)
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import MyScale
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import 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()
API Reference:TextLoader
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 [00:15<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.

获取连接信息和数据模式​

print(str(docsearch))

过滤​

你可以直接访问myscale SQL的where语句。你可以按照标准SQL编写WHERE子句。

注意: 请注意SQL注入问题，此接口不得由最终用户直接调用。

如果您在设置中自定义了column_map，您可以使用如下过滤器进行搜索：

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import 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, d in enumerate(docs):
    d.metadata = {"doc_id": i}

docsearch = MyScale.from_documents(docs, embeddings)
API Reference:TextLoader | MyScale
Inserting data...: 100%|██████████| 42/42 [00:15<00:00,  2.68it/s]

带分数的相似性搜索​

返回的距离分数是余弦距离。因此，分数越低越好。

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] + "...")
0.229655921459198 {'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...

删除您的数据​

你可以使用.drop()方法删除表，或者使用.delete()方法部分删除你的数据。

# 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, dist in 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()

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