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Chat Integration Guide

Seamless Integration with OpenAI Client Libraries

Kitten Stack's API is designed as a drop-in replacement for OpenAI's Chat Completions API.
This means you can use your existing OpenAI client code with minimal changes to start
building applications with enhanced context capabilities.

Basic Integration Steps

  1. Obtain API Key
    Sign up at www.kittenstack.com/signup and get your API key from the dashboard.

  2. Update Base URL
    Change your OpenAI client's base URL to https://api.kittenstack.com/v1

  3. Use Your API Key
    Replace your OpenAI API key with your Kitten Stack API key.

  4. Start Making Requests
    Your chat completions will now automatically include context from your uploaded documents and can be integrated into any application you build.

Code Examples

JavaScript

import OpenAI from 'openai';

// Initialize the client with Kitten Stack's base URL and your API key
const openai = new OpenAI({
  baseURL: 'https://api.kittenstack.com/v1',
  apiKey: 'your_kitten_stack_api_key'
});

// Use the client as you normally would with OpenAI
async function getCompletion() {
  const response = await openai.chat.completions.create({
    model: 'openai/gpt-4-turbo',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What does our Q4 report say about revenue growth?' }
    ]
  });
  
  console.log(response.choices[0].message.content);
}

getCompletion();

Python

from openai import OpenAI

# Initialize with Kitten Stack's base URL and your API key
client = OpenAI(
    base_url="https://api.kittenstack.com/v1",
    api_key="your_kitten_stack_api_key"
)

# Use the client as you would with OpenAI
response = client.chat.completions.create(
    model="openai/gpt-4-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What does our Q4 report say about revenue growth?"}
    ]
)

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

Advanced Context Control

Kitten Stack automatically retrieves relevant context from your document repository, but
you can customize how context is selected and used in your applications.

Include search options in your request to fine-tune how context is retrieved and integrated

// JavaScript example with search options
const response = await openai.chat.completions.create({
  model: 'openai/gpt-4-turbo',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What does our Q4 report say about revenue growth?' }
  ],
  // Customize context retrieval
  search_options: {
    use_documents: true,          // Include document context (default true)
    similarity_threshold: 0.8,    // Minimum similarity score (0-1)
    document_types: ["pdf", "docx"], // Filter by file types
    max_documents: 5              // Maximum documents to include
  }
});

Supported Models

Kitten Stack supports models from various providers through a unified interface. Specify the provider and model
in the format: provider/model-name

Provider Model Examples
openai openai/gpt-4-turbo, openai/gpt-3.5-turbo
anthropic anthropic/claude-3-opus, anthropic/claude-3-sonnet, anthropic/claude-3-haiku
google google/gemini-pro, google/gemini-ultra

Handling Streaming Responses

Kitten Stack supports streaming responses for a better user experience with long-form content:

// JavaScript streaming example
const stream = await openai.chat.completions.create({
  model: 'openai/gpt-4-turbo',
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'Write a detailed summary of our Q1 performance.' }
  ],
  stream: true
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || '');
}

Error Handling

Kitten Stack uses standard HTTP status codes. Here's how to handle common errors:

// JavaScript error handling example
try {
  const response = await openai.chat.completions.create({
    model: 'openai/gpt-4-turbo',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What does our annual report say?' }
    ]
  });
  
  console.log(response.choices[0].message.content);
} catch (error) {
  if (error.status === 401) {
    console.error('Authentication failed. Check your API key.');
  } else if (error.status === 402) {
    console.error('Insufficient credits. Please top up your account.');
  } else if (error.status === 429) {
    console.error('Rate limit exceeded. Please slow down requests.');
  } else {
    console.error('An error occurred:', error.message);
  }
}

Building Complete Applications

Once you've integrated the chat API, explore these additional components to build complete LLM applications: