langchain-ydb

---

LangChain's YDB integration (langchain-ydb) provides vector capabilities for working with YDB.

Getting Started

Setting Up YDB

Launch a YDB Docker container with:

docker run -d -p 2136:2136 --name ydb-langchain -e YDB_USE_IN_MEMORY_PDISKS=true -h localhost ydbplatform/local-ydb:trunk

Installing the Package

Install langchain-ydb package with:

pip install -U langchain-ydb

VectorStore works along with an embedding model, here using langchain-openai as example.

pip install langchain-openai
export OPENAI_API_KEY=...

Work with YDB Vector Store

Creating a Vector Store

from langchain_openai import OpenAIEmbeddings
from langchain_ydb.vectorstores import YDB, YDBSearchStrategy, YDBSettings


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

Add items to vector store

Once you have created your vector store, you can interact with it by adding and deleting different items.

Prepare documents to work with:

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))]

You can add items to your vector store by using the add_documents function.

vector_store.add_documents(documents=documents, ids=uuids)

Delete items from vector store

You can delete items from your vector store by ID using the delete function.

vector_store.delete(ids=[uuids[-1]])

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 res in results:
    print(f"* {res.page_content} [{res.metadata}]")

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, score in results:
    print(f"* [SIM={score:.3f}] {res.page_content} [{res.metadata}]")

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}]")

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 res in results:
    print(f"* {res.page_content} [{res.metadata}]")