Text Generation

Animus’s text generation capabilities let you create human-like text for a wide range of applications. This guide covers the fundamentals of generating text with our REST API.

Quickstart

To generate text with Animus, you’ll make a request to our chat completions endpoint. Here’s a basic example using direct API calls:

// Using fetch API
const response = await fetch('https://api.animusai.co/v2/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': `Bearer ${process.env.ANIMUS_API_KEY}`
  },
  body: JSON.stringify({
    model: "vivian-llama3.1-70b-1.0-fp8",
    messages: [
      { role: "user", content: "What are the key features of deep learning?" }
    ]
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

import requests

url = "https://api.animusai.co/v2/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"  # Replace with your API key
}

payload = {
    "model": "vivian-llama3.1-70b-1.0-fp8",
    "messages": [
        {
            "role": "user",
            "content": "What are the key features of deep learning?"
        }
    ]
}

response = requests.post(url, headers=headers, json=payload)
data = response.json()
print(data['choices'][0]['message']['content'])

curl https://api.animusai.co/v2/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ANIMUS_API_KEY" \
  -d '{
    "model": "vivian-llama3.1-70b-1.0-fp8",
    "messages": [
      {"role": "user", "content": "What are the key features of deep learning?"}
    ]
  }'

Advanced Usage

Message Roles and Names

When sending messages to the chat completions API, you can use different roles to structure your conversation:

system: Provides guidance to the model on how to behave
user: Represents user inputs in the conversation
assistant: Represents previous responses from the model

Additionally, you can use the name parameter to give a specific identity to user or assistant messages. This is useful for multi-agent simulations or distinguishing between different participants in a conversation:

{
  "model": "vivian-llama3.1-70b-1.0-fp8",
  "messages": [
    {
      "role": "system",
      "content": "You are participating in a group discussion with users Alice and Bob."
    },
    {
      "role": "user",
      "name": "Alice",
      "content": "I think we should focus on improving user experience first."
    },
    {
      "role": "user",
      "name": "Bob",
      "content": "I disagree, I believe performance optimizations should be our priority."
    }
  ]
}

The name parameter must be a string that adheres to the following regex pattern: ^[a-zA-Z0-9_-]{1,64}$. That means it can contain alphanumeric characters, underscores, and hyphens, with a maximum length of 64 characters.

Controlling Response Format

You can control the format of the responses by providing specific instructions in the system or user messages:

{
  "model": "vivian-llama3.1-70b-1.0-fp8",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant that responds in JSON format."
    },
    {
      "role": "user",
      "content": "Provide information about planets in our solar system."
    }
  ]
}

Temperature and Top P

Control the randomness and creativity of responses with these parameters:

{
  "model": "vivian-llama3.1-70b-1.0-fp8",
  "messages": [
    { "role": "user", "content": "Write a short poem about AI." }
  ],
  "temperature": 0.7,
  "top_p": 0.9
}

temperature: (0-2) Lower values make output more deterministic; higher values make output more random
top_p: (0-1) Controls diversity via nucleus sampling

Maximum Tokens

Limit the length of the response by setting a maximum token count:

{
  "model": "vivian-llama3.1-70b-1.0-fp8",
  "messages": [
    { "role": "user", "content": "Explain how neural networks work." }
  ],
  "max_tokens": 150
}

Streaming Responses

For a more interactive experience, you can stream responses as they’re generated:

const response = await fetch('https://api.animusai.co/v2/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': `Bearer ${process.env.ANIMUS_API_KEY}`
  },
  body: JSON.stringify({
    model: "vivian-llama3.1-70b-1.0-fp8",
    messages: [
      { role: "user", content: "Tell me a story about a space explorer." }
    ],
    stream: true
  })
});

// Handle the stream
const reader = response.body.getReader();
const decoder = new TextDecoder("utf-8");

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  
  // Process the received chunk
  const chunk = decoder.decode(value);
  console.log(chunk);
}

import requests
import json

url = "https://api.animusai.co/v2/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

payload = {
    "model": "vivian-llama3.1-70b-1.0-fp8",
    "messages": [
        {"role": "user", "content": "Tell me a story about a space explorer."}
    ],
    "stream": True
}

response = requests.post(url, headers=headers, json=payload, stream=True)

for line in response.iter_lines():
    if line:
        # Parse the data line
        line_text = line.decode('utf-8')
        if line_text.startswith('data: '):
            data_json = line_text[6:]  # Remove 'data: ' prefix
            if data_json != "[DONE]":
                try:
                    chunk = json.loads(data_json)
                    content = chunk['choices'][0]['delta'].get('content', '')
                    if content:
                        print(content, end='', flush=True)
                except json.JSONDecodeError:
                    pass

curl https://api.animusai.co/v2/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ANIMUS_API_KEY" \
  -d '{
    "model": "vivian-llama3.1-70b-1.0-fp8",
    "messages": [
      {"role": "user", "content": "Tell me a story about a space explorer."}
    ],
    "stream": true
  }' \
  --no-buffer

Prompt Engineering Best Practices

For better results:

Be specific: Provide clear instructions with examples
Use system messages: Set the tone and behavior of the model
Structure your prompts: Break complex tasks into smaller steps
Context matters: Provide relevant background information
Iterate and refine: Test different prompts and learn what works best

Common Use Cases

Animus’s text generation can be used for:

Content Creation: Generate articles, blog posts, or marketing copy
Conversational AI: Build chatbots and virtual assistants
Code Generation: Get help with programming tasks
Summarization: Condense long texts into concise summaries
Translation: Convert text between languages
Creative Writing: Generate stories, poems, or scripts

Error Handling

Implement robust error handling in your applications:

try {
  const response = await fetch('https://api.animusai.co/v2/chat/completions', {
    // request configuration
  });
  
  if (!response.ok) {
    const error = await response.json();
    console.error('API Error:', error);
    // Handle specific error types
  } else {
    const data = await response.json();
    // Process successful response
  }
} catch (error) {
  console.error('Network or parsing error:', error);
  // Handle connection or parsing issues
}

Next Steps

Vision

Learn how to analyze images and videos with the REST API

Moderation

Implement content moderation in your applications

Webhooks

Set up webhooks for real-time notifications

API Reference

Complete API documentation and reference

Get Started

SDK Features

Advanced SDK

REST API Integration

Models

Quickstart

Advanced Usage

Message Roles and Names

Controlling Response Format

Temperature and Top P

Maximum Tokens

Streaming Responses

Prompt Engineering Best Practices

Common Use Cases

Error Handling

Next Steps

Vision

Moderation

Webhooks

API Reference

​Quickstart

​Advanced Usage

​Message Roles and Names

​Controlling Response Format

​Temperature and Top P

​Maximum Tokens

​Streaming Responses

​Prompt Engineering Best Practices

​Common Use Cases

​Error Handling

​Next Steps

Vision

Moderation

Webhooks

API Reference

Quickstart

Advanced Usage

Message Roles and Names

Controlling Response Format

Temperature and Top P

Maximum Tokens

Streaming Responses

Prompt Engineering Best Practices

Common Use Cases

Error Handling

Next Steps