YDB 公司
YDB 是一种多功能的开源分布式 SQL 数据库，它将高可用性和可扩展性与强一致性和 ACID 事务相结合。它可以同时容纳事务 （OLTP）、分析 （OLAP） 和流式工作负载。
此笔记本展示了如何使用与YDB向量存储。
设置
首先，使用 Docker 设置本地 YDB：
! docker run -d -p 2136:2136 --name ydb-langchain -e YDB_USE_IN_MEMORY_PDISKS=true -h localhost ydbplatform/local-ydb:trunk
您需要安装langchain-ydb使用此集成
! pip install -qU langchain-ydb
凭据
此笔记本没有凭据，只需确保您已安装如上所示的软件包。
如果您想获得一流的模型调用自动跟踪，您还可以通过取消下面的注释来设置 LangSmith API 密钥：
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
初始化
选择嵌入模型：
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
from langchain_ydb.vectorstores import YDB, YDBSearchStrategy, YDBSettings
settings = YDBSettings(
table="ydb_example",
strategy=YDBSearchStrategy.COSINE_SIMILARITY,
)
vector_store = YDB(embeddings, config=settings)
管理矢量存储
创建矢量存储后，您可以通过添加和删除不同的项目来与之交互。
将项目添加到向量存储
准备要使用的文档：
from uuid import uuid4
from langchain_core.documents import Document
document_1 = Document(
page_content="I had chocolate 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))]
API 参考：文档
您可以使用add_documents功能。
vector_store.add_documents(documents=documents, ids=uuids)
Inserting data...: 100%|██████████| 10/10 [00:00<00:00, 14.67it/s]
['947be6aa-d489-44c5-910e-62e4d58d2ffb',
'7a62904d-9db3-412b-83b6-f01b34dd7de3',
'e5a49c64-c985-4ed7-ac58-5ffa31ade699',
'99cf4104-36ab-4bd5-b0da-e210d260e512',
'5810bcd0-b46e-443e-a663-e888c9e028d1',
'190c193d-844e-4dbb-9a4b-b8f5f16cfae6',
'f8912944-f80a-4178-954e-4595bf59e341',
'34fc7b09-6000-42c9-95f7-7d49f430b904',
'0f6b6783-f300-4a4d-bb04-8025c4dfd409',
'46c37ba9-7cf2-4ac8-9bd1-d84e2cb1155c']
从 vector store 中删除项目
您可以使用 ID 从 vector store 中删除delete功能。
vector_store.delete(ids=[uuids[-1]])
True
查询向量存储
创建 vector store 并添加相关文档后，您可能希望在执行 chain 或 agent 期间对其进行查询。
直接查询
相似性搜索
可以按如下方式执行简单的相似性搜索：
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}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
带分数的相似性搜索
您还可以使用分数执行搜索：
results = vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=3)
for res, score in results:
print(f"* [SIM={score:.3f}] {res.page_content} [{res.metadata}]")
* [SIM=0.595] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]
* [SIM=0.212] I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]
* [SIM=0.118] Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]
滤波
您可以使用过滤条件进行搜索，如下所述：
results = vector_store.similarity_search_with_score(
"What did I eat for breakfast?",
k=4,
filter={"source": "tweet"},
)
for res, _ in results:
print(f"* {res.page_content} [{res.metadata}]")
* I had chocolate chip pancakes and scrambled eggs for breakfast this morning. [{'source': 'tweet'}]
* Wow! That was an amazing movie. I can't wait to see it again. [{'source': 'tweet'}]
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]
通过转换为 retriever 进行查询
您还可以将 vector store 转换为检索器，以便在您的链中更轻松地使用。
下面介绍如何将 vector store 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_kwargs={"k": 2},
)
results = retriever.invoke(
"Stealing from the bank is a crime", filter={"source": "news"}
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
* Robbers broke into the city bank and stole $1 million in cash. [{'source': 'news'}]
* The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]
用于检索增强生成
有关如何使用此向量存储进行检索增强生成 （RAG） 的指南，请参阅以下部分：
API 参考
有关所有YDB功能和配置可参考 API 参考 https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.ydb.YDB.html