LucidNova RF1 API Documentation

Complete reference for integrating with our advanced multimodal AI system

Introduction

Welcome to the official documentation for the LucidNova RF1 API. This guide will help you integrate our advanced multimodal AI system into your applications, products, and services.

LucidNova RF1 is a state-of-the-art, unified AI model with 100 billion parameters that combines multiple capabilities into a single powerful system. Unlike traditional AI services that separate different capabilities into distinct models, LucidNova RF1 provides a seamless experience by intelligently activating appropriate capabilities based on the context of your query.

Core Features

LucidNova RF1 is a unified AI that offers several powerful capabilities:

Getting Started

To begin using the LucidNova RF1 API, follow these simple steps:

1. Create an account on the LucidQuery platform if you don't already have one.
2. Generate an API key from the API management page in your dashboard.
3. Install the client library for your preferred programming language, or use standard HTTP request libraries.
4. Make your first API call following the examples in this documentation.

Authentication

All requests to the LucidNova RF1 API require authentication using an API key. Your API key should be included in the HTTP headers of each request.

Add your API key to requests using the Authorization header:

Authorization: Bearer YOUR_API_KEY

API Endpoints

{
  "model": "lucidnova-rf1-100b",
  "messages": [
    {
      "role": "user",
      "content": "Hello World!"
    }
  ],
  "stream": false
}

In this example, the system will process the query and respond appropriately. LucidNova RF1 automatically adjusts its internal parameters based on the query context, providing an optimal response without requiring any special configuration.

Request Parameters

The API accepts the following parameters in your request:

Response Format

The API returns responses in JSON format, with a structure similar to other AI APIs for easy integration:

Standard Response (Non-Streaming)

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1681841142,
  "model": "lucidnova-rf1-100b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "\nAnalyzing the greeting 'Hello World!'...\nThis is a simple greeting that doesn't require any specialized knowledge or real-time information.\nI'll respond with an appropriate friendly greeting.\n\n\nHello! How can I assist you today? I'm LucidNova RF1, a multimodal AI assistant ready to help with a wide range of tasks. Feel free to ask me anything!"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 74,
    "total_tokens": 84
  }
}

Streaming Response

When using streaming mode (stream: true), the API will send partial responses as they are generated, following the same format as OpenAI's streaming API:

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1681841142,"model":"lucidnova-rf1-100b","choices":[{"delta":{"content":""},"index":0,"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1681841142,"model":"lucidnova-rf1-100b","choices":[{"delta":{"content":"Analyzing the greeting 'Hello World!'..."},"index":0,"finish_reason":null}]}

... more chunks ...

data: [DONE]

Standard Request Examples

Below are examples of how to make standard (non-streaming) requests to the LucidNova RF1 API in various programming languages:

# Using the OpenAI Python client library
from openai import OpenAI

# Initialize the client with your API key and base URL
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://lucidquery.com/api/swiftapi.php"
)

# Standard request example
response = client.chat.completions.create(
    model="lucidnova-rf1-100b",
    messages=[
        {"role": "user", "content": "Hello World!"}
    ]
)

print(response.choices[0].message.content)

// Using Node.js with the OpenAI npm package
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://lucidquery.com/api/swiftapi.php'
});

async function main() {
  // Standard request example
  const response = await openai.chat.completions.create({
    model: 'lucidnova-rf1-100b',
    messages: [
      { role: 'user', content: 'Hello World!' }
    ]
  });
  
  console.log(response.choices[0].message.content);
}

main();

# Standard request
curl -X POST https://lucidquery.com/api/swiftapi.php/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "lucidnova-rf1-100b",
    "messages": [
      {
        "role": "user",
        "content": "Hello World!"
      }
    ]
  }'

Streaming Request Examples

Below are examples of how to make streaming requests to the LucidNova RF1 API in various programming languages. Streaming provides a better user experience for longer responses as content appears progressively:

# Using the OpenAI Python client library
from openai import OpenAI

# Initialize the client with your API key and base URL
client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://lucidquery.com/api/swiftapi.php"
)

# Streaming example
stream = client.chat.completions.create(
    model="lucidnova-rf1-100b",
    messages=[
        {"role": "user", "content": "Hello World!"}
    ],
    stream=True
)

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

// Using Node.js with the OpenAI npm package
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'YOUR_API_KEY',
  baseURL: 'https://lucidquery.com/api/swiftapi.php'
});

async function main() {
  // Streaming example
  const stream = await openai.chat.completions.create({
    model: 'lucidnova-rf1-100b',
    messages: [
      { role: 'user', content: 'Hello World!' }
    ],
    stream: true
  });
  
  for await (const chunk of stream) {
    if (chunk.choices[0]?.delta?.content) {
      process.stdout.write(chunk.choices[0].delta.content);
    }
  }
}

main();

# Streaming request
curl -X POST https://lucidquery.com/api/swiftapi.php/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  --no-buffer \
  -d '{
    "model": "lucidnova-rf1-100b",
    "messages": [
      {
        "role": "user",
        "content": "Hello World!"
      }
    ],
    "stream": true
  }'

Pricing

LucidNova RF1 uses a token-based pricing model similar to other AI APIs. Tokens are the basic text units processed by our AI (roughly 4 characters or 0.75 words in English). We charge different rates for input and output tokens:

Rate Limits

To ensure system stability and fair resource allocation, the LucidNova RF1 API implements the following rate limits:

If you exceed these limits, the API will return a 429 error with a message indicating which limit was reached. Enterprise customers can request higher rate limits.

Error Handling

The API uses standard HTTP status codes and returns detailed error messages:

Error response example:

{
  "error": {
    "message": "Token rate limit exceeded. Maximum 10000 tokens per minute.",
    "type": "rate_limit_exceeded",
    "param": null,
    "code": "rate_limit_exceeded"
  }
}

Best Practices

Follow these recommendations to get the most out of the LucidNova RF1 API:

1. Be Specific and Clear

   The more specific and clear your input, the better the response will be. While LucidNova RF1 has strong contextual understanding, explicitly stating what you want often yields the best results.

2. Manage Token Usage

   Keep your inputs concise to conserve tokens. Remember that both input and output tokens count toward your usage limits. If you don't need the reasoning pathway in responses, you can strip out the <think> sections programmatically.

3. Use Streaming for Long Responses

   For queries that might generate lengthy responses, use streaming to provide a better user experience with progressive content display.

4. Implement Retry Logic

   Implement exponential backoff retry logic for rate limit errors and transient server issues.

5. Leverage Conversation History

   For context-dependent tasks, include relevant previous messages in the conversation history to maintain context without repeating information.

Frequently Asked Questions

LucidNova RF1 stands out with its hybrid architecture that combines a diffusion-based reasoning layer with an autoregressive transformer. This approach enables fast, transparent reasoning without the performance penalty typical of other reasoning models. The system also features real-time web access and self-tuning parameters that automatically optimize for different types of queries.

No. Unlike many other AI models, LucidNova RF1 has real-time web access capabilities that allow it to retrieve current information when needed, without requiring explicit instructions to do so.

LucidNova RF1 features self-tuning parameters that automatically adjust based on the query context. The system determines the optimal approach for each type of request without requiring manual parameter tuning.

LucidNova RF1 understands and can generate code in a wide range of programming languages, including but not limited to: Python, JavaScript, TypeScript, Java, C#, C++, PHP, Ruby, Go, Rust, Swift, Kotlin, and SQL.

Support

If you need assistance with the LucidNova RF1 API, you can contact our support team:

• Email Support: [email protected]

For Enterprise customers, please contact your dedicated account manager for priority support.