Lindorm

import os


class Config:
    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"  # set to your deployed model

pip install -qU langchain-lindorm-integration

Note: you may need to restart the kernel to use updated packages.

from langchain_lindorm_integration import LindormAIEmbeddings

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

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector

[-0.016254117712378502, -0.01154549140483141, 0.0042558759450912476, -0.011416379362344742, -0.01770

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector

[-0.016254086047410965, -0.011545476503670216, 0.0042558712884783745, -0.011416426859796047, -0.0177
[-0.07268096506595612, -3.236892371205613e-05, -0.0019329536007717252, -0.030644644051790237, -0.018