AI Models

Access multiple AI models through unified endpoints. SolCognia aggregates leading AI models including GPT, Claude, and Gemini.

Available Models

List Models

Get information about all available AI models:

GET /api/v1/models

Response:

{
  "models": [
    {
      "id": "gpt-4",
      "name": "GPT-4",
      "provider": "OpenAI",
      "capabilities": ["chat", "completion", "code"],
      "max_tokens": 8192,
      "cost_per_token": 0.03,
      "available": true
    },
    {
      "id": "claude-3",
      "name": "Claude 3",
      "provider": "Anthropic",
      "capabilities": ["chat", "analysis", "writing"],
      "max_tokens": 100000,
      "cost_per_token": 0.025,
      "available": true
    }
  ]
}

Chat Completions

Create Chat Completion

Send messages to an AI model and receive responses:

POST /api/v1/models/{model_id}/chat

Request Body:

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"}
  ],
  "temperature": 0.7,
  "max_tokens": 150,
  "stream": false
}

Response:

{
  "id": "chat-abc123",
  "model": "gpt-4",
  "created": 1677652288,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The capital of France is Paris."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 8,
    "total_tokens": 28
  },
  "tokens_earned": 5
}

Streaming Responses

For real-time responses, use streaming:

POST /api/v1/models/gpt-4/chat
Content-Type: application/json

{
  "messages": [{"role": "user", "content": "Tell me a story"}],
  "stream": true
}

Streaming Response:

data: {"id":"chat-123","choices":[{"delta":{"content":"Once"}}]}

data: {"id":"chat-123","choices":[{"delta":{"content":" upon"}}]}

data: {"id":"chat-123","choices":[{"delta":{"content":" a"}}]}

data: [DONE]

Text Completions

Create Completion

Generate text completions for prompts:

POST /api/v1/models/{model_id}/completion

Request Body:

{
  "prompt": "The future of AI is",
  "max_tokens": 100,
  "temperature": 0.8,
  "top_p": 1.0,
  "frequency_penalty": 0.0,
  "presence_penalty": 0.0
}

Response:

{
  "id": "cmpl-abc123",
  "model": "gpt-4",
  "choices": [
    {
      "text": " bright and full of possibilities. As we continue to advance...",
      "index": 0,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 100,
    "total_tokens": 105
  },
  "tokens_earned": 8
}

Model-Specific Features

GPT Models

POST /api/v1/models/gpt-4/chat
{
  "messages": [...],
  "functions": [
    {
      "name": "get_weather",
      "description": "Get current weather",
      "parameters": {
        "type": "object",
        "properties": {
          "location": {"type": "string"}
        }
      }
    }
  ]
}

Claude Models

POST /api/v1/models/claude-3/chat
{
  "messages": [...],
  "system": "You are an expert analyst",
  "max_tokens": 4000
}

Gemini Models

POST /api/v1/models/gemini-pro/chat
{
  "messages": [...],
  "safety_settings": [
    {
      "category": "HARM_CATEGORY_HARASSMENT",
      "threshold": "BLOCK_MEDIUM_AND_ABOVE"
    }
  ]
}

Model Comparison

Compare Responses

Get responses from multiple models for the same prompt:

POST /api/v1/models/compare

Request Body:

{
  "models": ["gpt-4", "claude-3", "gemini-pro"],
  "messages": [
    {"role": "user", "content": "Explain quantum computing"}
  ]
}

Response:

{
  "comparisons": [
    {
      "model": "gpt-4",
      "response": {...},
      "tokens_earned": 5
    },
    {
      "model": "claude-3",
      "response": {...},
      "tokens_earned": 5
    },
    {
      "model": "gemini-pro",
      "response": {...},
      "tokens_earned": 5
    }
  ],
  "total_tokens_earned": 15
}

Usage Analytics

Get Model Usage

Track your usage across different models:

GET /api/v1/models/{model_id}/usage?period=7d

Response:

{
  "model": "gpt-4",
  "period": "7d",
  "total_requests": 150,
  "total_tokens": 45000,
  "tokens_earned": 225,
  "daily_breakdown": [
    {
      "date": "2024-01-01",
      "requests": 25,
      "tokens": 7500,
      "tokens_earned": 37
    }
  ]
}

Error Handling

Common Errors

Model Not Available (503)

{
  "error": {
    "code": "model_unavailable",
    "message": "The requested model is temporarily unavailable",
    "retry_after": 300
  }
}

Token Limit Exceeded (400)

{
  "error": {
    "code": "token_limit_exceeded",
    "message": "Request exceeds maximum token limit",
    "max_tokens": 8192,
    "requested_tokens": 10000
  }
}

Rate Limit Exceeded (429)

{
  "error": {
    "code": "rate_limit_exceeded",
    "message": "Too many requests",
    "retry_after": 60,
    "limit": 1000,
    "remaining": 0
  }
}

Best Practices

Optimize Token Usage

• Use appropriate max_tokens limits

• Implement response caching for repeated queries

• Choose the right model for your use case

Handle Streaming

const response = await fetch('/api/v1/models/gpt-4/chat', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    messages: [...],
    stream: true
  })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  
  const chunk = decoder.decode(value);
  const lines = chunk.split('\n');
  
  for (const line of lines) {
    if (line.startsWith('data: ')) {
      const data = line.slice(6);
      if (data === '[DONE]') return;
      
      try {
        const parsed = JSON.parse(data);
        console.log(parsed.choices[0].delta.content);
      } catch (e) {
        // Handle parsing errors
      }
    }
  }
}

Monitor Performance

• Track response times and success rates

• Monitor token usage and costs

• Set up alerts for unusual patterns

The Models API will be available with our Q4 2025 developer ecosystem launch.