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Upstage is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.

Solar LLM​

Solar Mini Chat is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.

Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as Groundedness Check and Layout Analysis.

Installation and Setup​

Install langchain-upstage package:

pip install -qU langchain-core langchain-upstage

Get API Keys and set environment variable UPSTAGE_API_KEY.

Upstage LangChain integrations​

APIDescriptionImportExample usage
ChatBuild assistants using Solar Mini Chatfrom langchain_upstage import ChatUpstageGo
Text EmbeddingEmbed strings to vectorsfrom langchain_upstage import UpstageEmbeddingsGo
Groundedness CheckVerify groundedness of assistant's responsefrom langchain_upstage import UpstageGroundednessCheckGo
Layout AnalysisSerialize documents with tables and figuresfrom langchain_upstage import UpstageLayoutAnalysisLoaderGo

See documentations for more details about the features.

Quick Examples​

Environment Setup​

import os

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


from langchain_upstage import ChatUpstage

chat = ChatUpstage()
response = chat.invoke("Hello, how are you?")

Text embedding​

from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
doc_result = embeddings.embed_documents(
["Sung is a professor.", "This is another document"]

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

Groundedness Check​

from langchain_upstage import UpstageGroundednessCheck

groundedness_check = UpstageGroundednessCheck()

request_input = {
"context": "Mauna Kea is an inactive volcano on the island of Hawaii. Its peak is 4,207.3 m above sea level, making it the highest point in Hawaii and second-highest peak of an island on Earth.",
"answer": "Mauna Kea is 5,207.3 meters tall.",
response = groundedness_check.invoke(request_input)

Layout Analysis​

from langchain_upstage import UpstageLayoutAnalysisLoader

file_path = "/PATH/TO/YOUR/FILE.pdf"
layzer = UpstageLayoutAnalysisLoader(file_path, split="page")

# For improved memory efficiency, consider using the lazy_load method to load documents page by page.
docs = layzer.load() # or layzer.lazy_load()

for doc in docs[:3]:

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