YDB
YDB 是一个多功能的开源分布式 SQL 数据库，兼具高可用性、可扩展性以及强一致性与 ACID 事务。它能够同时支持事务型（OLTP）、分析型（OLAP）和流式工作负载。
此笔记本展示了如何使用与YDB向量存储相关的功能。
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
使用此集成需要安装 langchain-ydb
! pip install -qU langchain-ydb
Credentials
没有为此笔记本提供凭据，请确保已按上方所示安装了所需的包。
如果您想要获得最佳的模型调用自动化跟踪，您也可以通过取消注释下方代码来设置您的LangSmith API密钥。
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
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)
管理向量存储
一旦你创建了你的向量存储，你就可以通过添加和删除不同的项目来与它进行交互。
添加项到向量存储
准备文档以进行操作：
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
您可以通过使用 add_documents 函数将项目添加到您的向量存储中。
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']
删除向量存储中的项
您可以通过ID使用delete函数从向量存储中删除项目。
vector_store.delete(ids=[uuids[-1]])
True
查询向量存储
一旦创建了向量存储并添加了相关文档，您可能希望在链或代理的执行过程中查询它。
查询直接
相似度搜索
一个简单的相似性搜索可以按如下方式进行：
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}]")
* 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'}]
相似性搜索（带分数）
您还可以执行带分数的搜索：
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'}]
过滤
您可以通过如下所述的筛选器进行搜索：
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'}]
查询通过转换为检索器
您也可以将向量存储转换为检索器，以便在链条中更方便地使用。
以下是将您的向量存储转换为检索器，然后使用简单查询和过滤条件调用该检索器的方法。
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'}]
使用检索增强生成
对于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：
API 参考
有关所有 YDB 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.ydb.YDB.html