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


Install langchain-upstage package.

pip install -U langchain-upstage

Environment Setupโ€‹

Make sure to set the following environment variables:

import os

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


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"]

Use embed_query to embed query string.

query_result = embeddings.embed_query("What does Sung do?")

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 storeโ€‹

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"],
retriever = vectorstore.as_retriever()
docs = retriever.invoke("Where did Harrison work?")

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