ClickHouse
ClickHouse 是最快且资源效率最高的开源数据库,适用于实时应用和分析,支持完整的SQL以及广泛的功能,帮助用户编写分析查询。最近添加的数据结构和距离搜索功能(如
L2Distance
)以及 近似最近邻搜索索引 使得ClickHouse可以作为高性能和可扩展的向量数据库,用于存储和搜索向量,支持SQL。
本笔记本展示了如何使用与 ClickHouse
向量存储相关的功能。
设置
首先使用docker设置一个本地的ClickHouse服务器:
! docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11
您需要安装 langchain-community
和 clickhouse-connect
才能使用此集成
pip install -qU langchain-community clickhouse-connect
凭证
此笔记本没有凭证,只需确保您已按照上述说明安装了软件包。
如果您想获得最佳的自动跟踪模型调用,您还可以通过取消注释以下内容来设置您的 LangSmith API 密钥:
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
实例化
- OpenAI
- HuggingFace
- Fake Embedding
pip install -qU langchain-openai
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
pip install -qU langchain-huggingface
from langchain_huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model="sentence-transformers/all-mpnet-base-v2")
pip install -qU langchain-core
from langchain_core.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=4096)
<!--IMPORTS:[{"imported": "Clickhouse", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html", "title": "ClickHouse"}, {"imported": "ClickhouseSettings", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.ClickhouseSettings.html", "title": "ClickHouse"}]-->
from langchain_community.vectorstores import Clickhouse, ClickhouseSettings
settings = ClickhouseSettings(table="clickhouse_example")
vector_store = Clickhouse(embeddings, config=settings)
管理向量存 储
一旦您创建了向量存储,我们可以通过添加和删除不同的项目与之交互。
向向量存储添加项目
我们可以通过使用 add_documents
函数向我们的向量存储添加项目。
<!--IMPORTS:[{"imported": "Document", "source": "langchain_core.documents", "docs": "https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html", "title": "ClickHouse"}]-->
from uuid import uuid4
from langchain_core.documents import Document
document_1 = Document(
page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source": "tweet"},
)
document_2 = Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source": "news"},
)
document_3 = Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source": "tweet"},
)
document_4 = Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source": "news"},
)
document_5 = Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source": "tweet"},
)
document_6 = Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source": "website"},
)
document_7 = Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source": "website"},
)
document_8 = Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source": "tweet"},
)
document_9 = Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source": "news"},
)
document_10 = Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source": "tweet"},
)
documents = [
document_1,
document_2,
document_3,
document_4,
document_5,
document_6,
document_7,
document_8,
document_9,
document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=uuids)
从向量存储删除项目
我们可以通过使用 delete
函数按 ID 从我们的向量存储中删除项目。
vector_store.delete(ids=uuids[-1])
查询向量存储
一旦您的向量存储被创建并且相关文档已添加,您很可能希望在运行链或代理时查询它。
直接查询
相似性搜索
执行简单的相似性搜索可以如下进行:
results = vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy", k=2
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
带分数的相似性搜索
您还可以按分数进行搜索:
results = vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=1)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
过滤
您可以直接访问 ClickHouse 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,此接口不得直接由最终用户调用。
如果您在设置中自定义了column_map
,可以使用如下过滤器进行搜索:
meta = vector_store.metadata_column
results = vector_store.similarity_search_with_relevance_scores(
"What did I eat for breakfast?",
k=4,
where_str=f"{meta}.source = 'tweet'",
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
其他搜索方法
还有多种其他搜索方法未在此笔记本中涵盖,例如MMR搜索或按向量搜索。有关Clickhouse
向量存储可用搜索能力的完整列表,请查看API参考。
通过转换为检索器进行查询
您还可以将向量存储转换为检索器,以便在您的链中更轻松地使用。
以下是如何将您的向量存储转换为检索器,然后使用简单的查询和过滤器调用检索器。
retriever = vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 1, "score_threshold": 0.5},
)
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
用于检索增强生成的用法
有关如何使用此向量存储进行检索增强生成(RAG)的指南,请参见以下部分:
更多信息,请查看使用Astra DB的完整RAG模板这里。
API参考
有关所有Clickhouse
功能和配置的详细文档,请访问API参考:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.clickhouse.Clickhouse.html