> ## Documentation Index
> Fetch the complete documentation index at: https://langchain.idochub.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Upstage

> [Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.
>
> **Solar Pro** is an enterprise-grade LLM optimized for single-GPU deployment, excelling in instruction-following and processing structured formats like HTML and Markdown. It supports English, Korean, and Japanese with top multilingual performance and offers domain expertise in finance, healthcare, and legal.

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

### Upstage LangChain integrations

| API                | Description                                 | Import                                                     | Example usage                                |
| ------------------ | ------------------------------------------- | ---------------------------------------------------------- | -------------------------------------------- |
| Chat               | Build assistants using Solar Chat           | `from langchain_upstage import ChatUpstage`                | [Go](../../chat/upstage)                     |
| Text Embedding     | Embed strings to vectors                    | `from langchain_upstage import UpstageEmbeddings`          | [Go](../../text_embedding/upstage)           |
| Groundedness Check | Verify groundedness of assistant's response | `from langchain_upstage import UpstageGroundednessCheck`   | [Go](../../tools/upstage_groundedness_check) |
| Document Parse     | Serialize documents with tables and figures | `from langchain_upstage import UpstageDocumentParseLoader` | [Go](../../document_loaders/upstage)         |

See [documentations](https://console.upstage.ai/docs/getting-started/overview) for more details about the models and features.

## Installation and Setup

Install `langchain-upstage` package:

```bash theme={null}
pip install -qU langchain-core langchain-upstage
```

Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`.

```python theme={null}
import os

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

## Chat models

### Solar LLM

See [a usage example](/oss/python/integrations/chat/upstage).

```python theme={null}
from langchain_upstage import ChatUpstage

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

## Embedding models

See [a usage example](/oss/python/integrations/text_embedding/upstage).

```python theme={null}
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"]
)
print(doc_result)

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

## Document loader

### Document Parse

See [a usage example](/oss/python/integrations/document_loaders/upstage).

```python theme={null}
from langchain_upstage import UpstageDocumentParseLoader

file_path = "/PATH/TO/YOUR/FILE.pdf"
layzer = UpstageDocumentParseLoader(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)
```

## Tools

### Groundedness Check

See [a usage example](/oss/python/integrations/tools/upstage_groundedness_check).

```python theme={null}
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)
```