This notebook covers how to get started with Upstage embedding models.

​
Installation

Install langchain-upstage package. 
pip install -U langchain-upstage

​
Environment Setup

Make sure to set the following environment variables: 
import os

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

​
Usage

Initialize UpstageEmbeddings class. 
from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
Use embed_documents to embed list of texts or documents. 
doc_result = embeddings.embed_documents(
    ["Sung is a professor.", "This is another document"]
)
print(doc_result)
Use embed_query to embed query string. 
query_result = embeddings.embed_query("What does Sung do?")
print(query_result)
Use aembed_documents and aembed_query for async operations. 
# 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

You can use UpstageEmbeddings with vector store component. The following demonstrates a simple example. 
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)