OpenAI API Compatibility

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Introduction

Aikeedo supports any API provider that implements the OpenAI API specification. This means you can integrate any service that follows OpenAI’s API format, whether it’s a cloud service, self-hosted model, or custom implementation.

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Common Providers

While you can use any OpenAI-compatible provider, here are some popular options:

• Together AI
• OpenRouter
• Groq
• DeepSeek
• Google AI (Gemini)
• Perplexity AI
• Hugging Face
• Nebius
• DeepInfra
• AI/ML API
• Azure OpenAI Service
• v0 by Vercel
• Self-hosted models (via compatible servers)
• Custom LLM implementations

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Adding a New Provider

1. Go to Settings → Integrations
2. Scroll to “Custom LLM Servers” section
3. Click “New server”

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Configuration Fields

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API Server

• Name: A descriptive name for the provider
• Server address: The API endpoint URL
• API Key/Authorization token: Your authentication token for the service

Dynamic Variables

You can use the following variable in your server address:

• {model}: Will be dynamically replaced with the current model name

Example for Hugging Face:

https://api-inference.huggingface.co/models/{model}

When using model “mistralai/Mistral-7B-Instruct-v0.1”, the actual request will go to:

https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1

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Headers

Aikeedo automatically adds these default headers:

• Content-Type: application/json
• Authorization: Bearer YOUR_API_KEY (when API key field is filled)

Adding Custom Headers

1. Click “Add header”
2. Enter the header key (e.g., HTTP-Referer)
3. Enter the header value
4. Repeat for additional headers

Examples

Override default Authorization header:

Key: Authorization
Value: Basic YOUR_BASE64_CREDENTIALS

Add provider-specific headers:

Key: X-Title
Value: Your Application Name

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Models Configuration

For each model you want to use:

• Key: Unique identifier for the model (e.g., gpt-3.5-turbo)
• Name: Display name shown to users
• Provider: The model provider’s name
• Vision: Toggle if the model supports image analysis
• Tools: Toggle if the model supports function calling

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Provider-Specific Setup

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Tools Compatibility

When configuring models for your custom LLM server, you can enable them for:

1. Chat: Interactive conversations and assistance
2. Writer: Content generation and writing tasks
3. Coder: Programming help and code generation
4. Title Generation: Automatic title creation for content

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Best Practices

1. Testing:

   • Test each model after configuration
   • Verify response formats
   • Check token limits and pricing

2. Security:

   • Keep API keys secure
   • Use HTTPS for external providers
   • Regularly rotate API keys

3. Monitoring:

   • Track API usage
   • Monitor response times
   • Check for error rates

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Troubleshooting

Common issues and solutions:

1. Authentication Errors:

   • Verify API key format
   • Check header configuration
   • Confirm server address is correct

2. Model Issues:

   • Ensure model names match provider’s specifications
   • Verify model availability in your subscription
   • Check provider’s status page

3. Connection Problems:

   • Verify network connectivity
   • Check for firewall restrictions
   • Confirm server address format

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Rate Limits and Quotas

• Monitor your provider’s rate limits
• Check quota usage regularly
• Set up alerts for quota thresholds
• Consider implementing retry logic for rate limit errors

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Need Help?

For additional support:

1. Check the provider’s documentation
2. Contact support@aikeedo.com