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 variables UPSTAGE_API_KEY and UPSTAGE_DOCUMENT_AI_API_KEY.

As of April 2024, you need separate API Keys for Solar and Document AI(Layout Analysis). The API Keys will be consolidated soon (hopefully in May) and you’ll need just one key for all features.

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"
os.environ["UPSTAGE_DOCUMENT_AI_API_KEY"] = "YOUR_DOCUMENT_AI_API_KEY"

Chat

from langchain_upstage import ChatUpstage

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

API Reference:

• ChatUpstage

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)

API Reference:

• UpstageEmbeddings

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)

API Reference:

• UpstageGroundednessCheck

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)

API Reference:

• UpstageLayoutAnalysisLoader