Upstage

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

API	Description	Import	Example usage
Chat	Build assistants using Solar Mini Chat	from langchain_upstage import ChatUpstage	Go
Text Embedding	Embed strings to vectors	from langchain_upstage import UpstageEmbeddings	Go
Groundedness Check	Verify groundedness of assistant's response	from langchain_upstage import UpstageGroundednessCheck	Go
Layout Analysis	Serialize documents with tables and figures	from langchain_upstage import UpstageLayoutAnalysisLoader	Go

See documentations for more details about the features.

Quick Examples

Environment Setup

import os

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

Chat

from langchain_upstage import ChatUpstage

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

Text embedding

from langchain_upstage import UpstageEmbeddings

embeddings = UpstageEmbeddings()
doc_result = embeddings.embed_documents(
    ["Sam is a teacher.", "This is another document"]
)
print(doc_result)

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

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)
print(response)

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]:
    print(doc)