YDB는 고가용성과 확장성을 강한 일관성과 ACID 트랜잭션과 결합한 다재다능한 오픈 소스 분산 SQL Database입니다. 트랜잭션(OLTP), 분석(OLAP), 스트리밍 워크로드를 동시에 처리할 수 있습니다.
이 노트북은 YDB vector store와 관련된 기능을 사용하는 방법을 보여줍니다.

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Setup

먼저, Docker로 로컬 YDB를 설정합니다: 
! docker run -d -p 2136:2136 --name ydb-langchain -e YDB_USE_IN_MEMORY_PDISKS=true -h localhost ydbplatform/local-ydb:trunk
이 통합을 사용하려면 langchain-ydb를 설치해야 합니다 
! pip install -qU langchain-ydb

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Credentials

이 노트북에는 별도의 자격 증명이 필요하지 않습니다. 위에 표시된 대로 패키지를 설치했는지 확인하세요. 모델 호출에 대한 최고 수준의 자동 트레이싱을 원한다면 아래 주석을 해제하여 LangSmith API 키를 설정할 수도 있습니다: 
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

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Initialization

# | output: false
# | echo: false
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

/Users/ovcharuk/opensource/langchain/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

from langchain_ydb.vectorstores import YDB, YDBSearchStrategy, YDBSettings

settings = YDBSettings(
    table="ydb_example",
    strategy=YDBSearchStrategy.COSINE_SIMILARITY,
)
vector_store = YDB(embeddings, config=settings)

​
Manage vector store

vector store를 생성한 후에는 항목을 추가하거나 삭제하여 상호작용할 수 있습니다.

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Add items to vector store

작업할 문서를 준비합니다: 
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))]
add_documents function을 사용하여 vector store에 항목을 추가할 수 있습니다. 
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']

​
Delete items from vector store

delete function을 사용하여 ID로 vector store에서 항목을 삭제할 수 있습니다. 
vector_store.delete(ids=[uuids[-1]])

True

​
Query vector store

vector store가 생성되고 관련 문서가 추가되면, chain이나 agent 실행 중에 이를 쿼리하고 싶을 것입니다.

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Query directly

간단한 유사도 검색은 다음과 같이 수행할 수 있습니다: 
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'}]

​
Similarity search with score

점수와 함께 검색을 수행할 수도 있습니다: 
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'}]

​
Filtering

아래와 같이 필터를 사용해 검색할 수 있습니다: 
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'}]

​
Query by turning into retriever

chain에서 더 쉽게 사용하기 위해 vector store를 retriever로 변환할 수도 있습니다. 다음은 vector store를 retriever로 변환한 뒤, 간단한 쿼리와 필터로 retriever를 호출하는 방법입니다. 
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'}]

​
Usage for retrieval-augmented generation

이 vector store를 retrieval-augmented generation(RAG)에 사용하는 방법에 대한 가이드는 다음 섹션을 참조하세요:

​
API reference

YDB의 모든 기능과 구성에 대한 자세한 문서는 API 레퍼런스를 확인하세요: python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.ydb.YDB.html
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