跳到主要内容
MyScale 是一个基于开源 ClickHouse 构建的云数据库，专为 AI 应用和解决方案优化。
本笔记本演示了如何使用 MyScale 向量数据库的相关功能。

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环境设置

pip install -qU  clickhouse-connect langchain-community
我们想使用 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:")
设置 MyScale 索引参数有两种方式。
  1. 环境变量 在您运行应用程序之前，请使用 export 设置环境变量： export MYSCALE_HOST='<您的端点网址>' MYSCALE_PORT=<您的端点端口> MYSCALE_USERNAME=<您的用户名> MYSCALE_PASSWORD=<您的密码> ... 您可以在我们的 SaaS 上轻松找到您的帐户、密码和其他信息。详情请参阅 此文档 MyScaleSettings 下的所有属性都可以通过前缀 MYSCALE_ 设置，并且不区分大小写。
  2. 使用参数创建 MyScaleSettings 对象 
    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()

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.

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获取连接信息和数据模式

print(str(docsearch))

​
过滤

您可以直接访问 MyScale SQL 的 WHERE 语句。您可以按照标准 SQL 编写 WHERE 子句。 注意：请注意 SQL 注入，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，您可以通过以下方式使用过滤器进行搜索：
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)

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

您可以使用 .drop() 方法删除整个表，或使用 .delete() 方法部分删除您的数据。 
# 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()
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
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