---
title: UpstageEmbeddings
---

이 노트북은 Upstage embedding model을 시작하는 방법을 다룹니다.

## Installation

`langchain-upstage` package를 설치합니다.

```bash
pip install -U langchain-upstage

​
Environment Setup

다음 environment variable을 설정해야 합니다: 
import os

os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"

​
Usage

UpstageEmbeddings class를 초기화합니다. 
from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
embed_documents를 사용하여 text 또는 document list를 embedding합니다. 
doc_result = embeddings.embed_documents(
    ["Sung is a professor.", "This is another document"]
)
print(doc_result)
embed_query를 사용하여 query string을 embedding합니다. 
query_result = embeddings.embed_query("What does Sung do?")
print(query_result)
비동기 작업을 위해 aembed_documents와 aembed_query를 사용합니다. 
# async embed query
await embeddings.aembed_query("My query to look up")

# async embed documents
await embeddings.aembed_documents(
    ["This is a content of the document", "This is another document"]
)

​
Using with vector store

UpstageEmbeddings를 vector store component와 함께 사용할 수 있습니다. 다음은 간단한 예제입니다. 
from langchain_community.vectorstores import DocArrayInMemorySearch

vectorstore = DocArrayInMemorySearch.from_texts(
    ["harrison worked at kensho", "bears like to eat honey"],
    embedding=UpstageEmbeddings(model="solar-embedding-1-large"),
)
retriever = vectorstore.as_retriever()
docs = retriever.invoke("Where did Harrison work?")
print(docs)


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