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Showing content from https://python.langchain.com/docs/integrations/vectorstores/lindorm below:

LindormVectorStore | 🦜️🔗 LangChain

This notebook covers how to get started with the Lindorm vector store.

To access Lindorm vector stores you'll need to create a Lindorm account, get the ak/sk, and install the langchain-lindorm-integration integration package.

%pip install -qU "langchain-lindorm-integration"

Head to here to sign up to Lindorm and generate the ak/sk.

import os


class Config:
    SEARCH_ENDPOINT = os.environ.get("SEARCH_ENDPOINT", "SEARCH_ENDPOINT")
    SEARCH_USERNAME = os.environ.get("SEARCH_USERNAME", "root")
    SEARCH_PWD = os.environ.get("SEARCH_PASSWORD", "<PASSWORD>")
    AI_LLM_ENDPOINT = os.environ.get("AI_ENDPOINT", "<AI_ENDPOINT>")
    AI_USERNAME = os.environ.get("AI_USERNAME", "root")
    AI_PWD = os.environ.get("AI_PASSWORD", "<PASSWORD>")
    AI_DEFAULT_EMBEDDING_MODEL = "bge_m3_model"  

here we use the embedding model deployed on Lindorm AI Service.

from langchain_lindorm_integration.embeddings import LindormAIEmbeddings
from langchain_lindorm_integration.vectorstores import LindormVectorStore

embeddings = LindormAIEmbeddings(
    endpoint=Config.AI_LLM_ENDPOINT,
    username=Config.AI_USERNAME,
    password=Config.AI_PWD,
    model_name=Config.AI_DEFAULT_EMBEDDING_MODEL,
)

index = "test_index"
vector = embeddings.embed_query("hello word")
dimension = len(vector)
vector_store = LindormVectorStore(
    lindorm_search_url=Config.SEARCH_ENDPOINT,
    embedding=embeddings,
    http_auth=(Config.SEARCH_USERNAME, Config.SEARCH_PWD),
    dimension=dimension,
    embeddings=embeddings,
    index_name=index,
)

from langchain_core.documents import Document

document_1 = Document(page_content="foo", metadata={"source": "https://example.com"})

document_2 = Document(page_content="bar", metadata={"source": "https://example.com"})

document_3 = Document(page_content="baz", metadata={"source": "https://example.com"})

documents = [document_1, document_2, document_3]

vector_store.add_documents(documents=documents, ids=["1", "2", "3"])

vector_store.delete(ids=["3"])

{'took': 400,
 'timed_out': False,
 'total': 1,
 'deleted': 1,
 'batches': 1,
 'version_conflicts': 0,
 'noops': 0,
 'retries': {'bulk': 0, 'search': 0},
 'throttled_millis': 0,
 'requests_per_second': -1.0,
 'throttled_until_millis': 0,
 'failures': []}

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Performing a simple similarity search can be done as follows:

results = vector_store.similarity_search(query="thud", k=1)
for doc in results:
    print(f"* {doc.page_content} [{doc.metadata}]")

* foo [{'source': 'https://example.com'}]

If you want to execute a similarity search and receive the corresponding scores you can run:

results = vector_store.similarity_search_with_score(query="thud", k=1)
for doc, score in results:
    print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")

* [SIM=0.671268] foo [{'source': 'https://example.com'}]

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

• Tutorials
• How-to: Question and answer with RAG
• Retrieval conceptual docs

For detailed documentation of all LindormVectorStore features and configurations head to the API reference.

• Vector store conceptual guide
• Vector store how-to guides

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