Chat API Reference

Complete reference for the BrilliantAI Chat Completions API.

Create Chat Completion

Endpoint

POST https://api.brilliantai.co/chat/completions

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	ID of the model to use
messages	array	Yes	Array of messages in the conversation
temperature	number	No	Sampling temperature (0-2)
max_tokens	integer	No	Maximum tokens to generate
stream	boolean	No	Stream responses back
top_p	number	No	Nucleus sampling parameter
frequency_penalty	number	No	Frequency penalty (-2 to 2)
presence_penalty	number	No	Presence penalty (-2 to 2)

Example Request

from openai import OpenAI

client = OpenAI(
    base_url="https://api.brilliantai.co",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is quantum computing?"}
    ],
    temperature=0.7,
    max_tokens=500
)

Response Format

{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "llama-3.3-70b",
    "choices": [{
        "index": 0,
        "message": {
            "role": "assistant",
            "content": "Quantum computing is..."
        },
        "finish_reason": "stop"
    }],
    "usage": {
        "prompt_tokens": 25,
        "completion_tokens": 32,
        "total_tokens": 57
    }
}

Streaming Responses

Example

stream = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Error Handling

try:
    response = client.chat.completions.create(
        model="llama-3.3-70b",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Explain quantum entanglement."}
        ]
    )
except openai.APIError as e:
    print(f"API Error: {e}")
except openai.RateLimitError as e:
    print(f"Rate Limit Error: {e}")
except openai.APIConnectionError as e:
    print(f"Connection Error: {e}")

Best Practices

1. Prompt Engineering

   • Clear, specific prompts
   • Proper system messages
   • Context management

2. Performance

   • Use streaming for long responses
   • Implement retry logic
   • Handle rate limits

3. Cost Management

   • Monitor token usage
   • Set appropriate max_tokens
   • Use caching when possible

Next Steps

• Learn about Embeddings API
• Check out example applications
• Explore available models