# 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 %}


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.float16.cloud/getting-started/llm-as-a-service/quick-start.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.