YDB
YDB is a versatile open source Distributed SQL Database that combines high availability and scalability with strong consistency and ACID transactions. It accommodates transactional (OLTP), analytical (OLAP), and streaming workloads simultaneously.
This notebook shows how to use functionality related to theYDB vector store.
Setup
First, set up a local YDB with Docker:
! docker run-d-p2136:2136--name ydb-langchain-e YDB_USE_IN_MEMORY_PDISKS=true-h localhost ydbplatform/local-ydb:trunk
You'll need to installlangchain-ydb to use this integration
! pip install-qU langchain-ydb
Credentials
There are no credentials for this notebook, just make sure you have installed the packages as shown above.
If you want to get best in-class automated tracing of your model calls you can also set yourLangSmith API key by uncommenting below:
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
Initialization
pip install -qU langchain-openai
import getpass
import os
ifnot os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"]= getpass.getpass("Enter API key for OpenAI: ")
from langchain_openaiimport OpenAIEmbeddings
embeddings= OpenAIEmbeddings(model="text-embedding-3-large")
from langchain_ydb.vectorstoresimport YDB, YDBSearchStrategy, YDBSettings
settings= YDBSettings(
table="ydb_example",
strategy=YDBSearchStrategy.COSINE_SIMILARITY,
)
vector_store= YDB(embeddings, config=settings)
Manage vector store
Once you have created your vector store, you can interact with it by adding and deleting different items.
Add items to vector store
Prepare documents to work with:
from uuidimport uuid4
from langchain_core.documentsimport 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 _inrange(len(documents))]
You can add items to your vector store by using theadd_documents function.
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
You can delete items from your vector store by ID using thedelete function.
vector_store.delete(ids=[uuids[-1]])
True
Query vector store
Once your vector store has been created and relevant documents have been added, you will likely want to query it during the execution of your chain or agent.
Query directly
Similarity search
A simple similarity search can be performed as follows:
results= vector_store.similarity_search(
"LangChain provides abstractions to make working with LLMs easy", k=2
)
for resin 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
You can also perform a search with a score:
results= vector_store.similarity_search_with_score("Will it be hot tomorrow?", k=3)
for res, scorein 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
You can search with filters as described below:
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
You can also transform the vector store into a retriever for easier usage in your chains.
Here's how to transform your vector store into a retriever and then invoke the retriever with a simple query and filter.
retriever= vector_store.as_retriever(
search_kwargs={"k":2},
)
results= retriever.invoke(
"Stealing from the bank is a crime",filter={"source":"news"}
)
for resin 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
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
API reference
For detailed documentation of allYDB features and configurations head to the API reference:https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.ydb.YDB.html
Related
- Vector storeconceptual guide
- Vector storehow-to guides