> For the complete documentation index, see [llms.txt](https://docs.float16.cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.float16.cloud/getting-started/llm-as-a-service/quick-start.md).

# Quick Start

## Setting up API Key

After accessing LLM as a service, you need to set up your API key. Learn how to set your API key [here](/getting-started/llm-as-a-service/quick-start/set-the-credentials.md).

## Quickly test API

To quickly try the API using cURL, use the following command:

```bash
curl -X POST https://api.float16.cloud/v1/chat/completions -d 

  '{
    "model": "seallm-7b-v3",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "สวัสดี"
      }
    ]
   }'

  -H "Content-Type: application/json" 
  -H "Authorization: Bearer <float16-api-key>"
```

Paste this in your terminal to see the response.

## Using the Chat API

Our API is compatible with OpenAI, allowing integration with your chat UI using OpenAI or LangChain libraries.

### OpenAI

1. Install the OpenAI package:

```bash
pip install openai
```

2. Use this Python code snippet (example using SeaLLM-7B-v2.5 model):

```python
import httpx
import openai

FLOAT16_BASE_URL = "https://api.float16.cloud/v1/"
FLOAT16_API_KEY = "<your API key>"

client = openai.OpenAI(
    api_key=FLOAT16_API_KEY,
    base_url=FLOAT16_BASE_URL,
)
client._base_url = httpx.URL(FLOAT16_BASE_URL)

# Streaming chat:
messages = [{"role": "system", "content": "You are truly awesome."}]

while True:
    content = input(f"User:")
    messages.append({"role": "user", "content": content})
    print(f"Assistant:", sep="", end="", flush=True)
    content = ""

    for chunk in client.chat.completions.create(
        messages=messages,
        model="seallm-7b-v3",
        stream=True,
    ):
        delta_content = chunk.choices[0].delta.content
        if delta_content:
            print(delta_content, sep="", end="", flush=True)
            content += delta_content
    
    messages.append({"role": "assistant", "content": content})
    print("\n")
```

For more information on the OpenAI library, visit the [OpenAI docs](https://platform.openai.com/docs/libraries/python-library).

### LangChain

To use Float16.cloud with the LangChain, follow these steps:

1. Install the LangChain package:

```bash
pip install langchain langchain_community
```

or

```bash
conda install langchain langchain_community -c conda-forge
```

2. Use this Python code snippet (example using SeaLLM-7B-v2.5 model):

```python
from langchain_community.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

FLOAT16_BASE_URL = "https://api.float16.cloud/v1/"
FLOAT16_API_KEY = "<your API key>"

chat = ChatOpenAI(
    model="seallm-7b-v3",
    api_key=FLOAT16_API_KEY,
    base_url=FLOAT16_BASE_URL,
    streaming=True,
)

# Simple invocation:
print(chat.invoke([HumanMessage(content="Hello")]))

# Streaming invocation:
for chunk in chat.stream("Write me a blog about how to start to raise cats"):
    print(chunk.content, end="", flush=True)
```

For more information on the LangChain library, visit the [LangChain docs](https://python.langchain.com/v0.2/docs/integrations/chat/openai/).

{% hint style="info" %}
**For Further Assistance:**

If you need additional help, feel free to contact us at <support@float16.cloud>.
{% endhint %}