api-docs

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.minimaxDocs = void 0;
exports.minimaxDocs = [
    {
        id: "minimax-chat",
        title: "MiniMax Chat API",
        category: "chat",
        provider: "minimax",
        keywords: ["minimax", "chat", "completions", "abab", "chat completion"],
        content: `# MiniMax Chat API

## Endpoint
POST https://api.minimax.chat/v1/text/chatcompletion_v2

## Headers
- Authorization: Bearer YOUR_API_KEY
- Content-Type: application/json

## Request Body
\`\`\`json
{
  "model": "MiniMax-M1" | "MiniMax-Text-01" | "abab6.5" | "abab6.5s" | "abab6.5g" | "abab5.5s",
  "messages": [
    {
      "role": "system" | "user" | "assistant" | "tool",
      "content": "string" | [
        {"type": "text", "text": "string"},
        {"type": "image", "image_url": "string (url or base64)"},
        {"type": "file", "file_url": "string"}
      ],
      "name": "string (optional, for named speakers)"
    }
  ],
  "max_tokens": number,
  "temperature": number (0-1, default 0.9),
  "top_p": number (0-1, default 0.95),
  "stream": boolean,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "string",
        "description": "string",
        "parameters": {JSON Schema}
      }
    }
  ],
  "tool_choice": "auto" | "none" | {"type": "function", "function": {"name": "string"}},
  "bot_setting": [
    {
      "bot_name": "string",
      "content": "string (personality)"
    }
  ]
}
\`\`\`

## Response
\`\`\`json
{
  "id": "xxx",
  "model": "MiniMax-M1",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "string",
        "tool_calls": [
          {
            "id": "xxx",
            "type": "function",
            "function": {"name": "string", "arguments": "JSON string"}
          }
        ]
      },
      "finish_reason": "stop" | "length" | "tool_calls"
    }
  ],
  "usage": {
    "total_tokens": 0,
    "prompt_tokens": 0,
    "completion_tokens": 0
  }
}
\`\`\`

## Available Models
- MiniMax-M1: Latest flagship model, strong reasoning
- MiniMax-Text-01: Optimized for text tasks
- abab6.5: Previous generation, balanced
- abab6.5s: Smaller, faster variant
- abab6.5g: Optimized for generation
- abab5.5s: Legacy model

## Python Example
\`\`\`python
import requests

url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

payload = {
    "model": "MiniMax-M1",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello!"}
    ],
    "max_tokens": 1024,
    "temperature": 0.7
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["choices"][0]["message"]["content"])
\`\`\`

## Node.js Example
\`\`\`javascript
const response = await fetch("https://api.minimax.chat/v1/text/chatcompletion_v2", {
  method: "POST",
  headers: {
    "Authorization": \`Bearer \${API_KEY}\`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "MiniMax-M1",
    messages: [
      { role: "system", content: "You are helpful" },
      { role: "user", content: "Hello!" }
    ],
    max_tokens: 1024,
    temperature: 0.7
  })
});

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

## cURL Example
\`\`\`bash
curl -X POST "https://api.minimax.chat/v1/text/chatcompletion_v2" \\
  -H "Authorization: Bearer $MINIMAX_API_KEY" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "MiniMax-M1",
    "messages": [
      {"role": "system", "content": "You are helpful"},
      {"role": "user", "content": "Hello!"}
    ],
    "max_tokens": 1024,
    "temperature": 0.7
  }'
\`\`\``
    },
    {
        id: "minimax-embedding",
        title: "MiniMax Embeddings API",
        category: "embeddings",
        provider: "minimax",
        keywords: ["minimax", "embeddings", "vector", "semantic"],
        content: `# MiniMax Embeddings API

## Endpoint
POST https://api.minimax.chat/v1/embeddings

## Request
\`\`\`json
{
  "model": "embo-01",
  "texts": ["text1", "text2"],
  "type": "query" | "db"
}
\`\`\`

## Response
\`\`\`json
{
  "vectors": [
    [0.01, 0.02, ...],
    [0.03, 0.04, ...]
  ],
  "base_resp": {
    "status_code": 0,
    "status_msg": "success"
  }
}
\`\`\`

## Model Details
- embo-01: 1024 dimensions
- type "query": For search queries
- type "db": For database documents

## Python Example
\`\`\`python
import requests

url = "https://api.minimax.chat/v1/embeddings"
headers = {"Authorization": f"Bearer {API_KEY}"}

response = requests.post(url, headers=headers, json={
    "model": "embo-01",
    "texts": ["Hello world"],
    "type": "db"
})

vectors = response.json()["vectors"]
\`\`\``
    },
    {
        id: "minimax-function-calling",
        title: "MiniMax Function Calling",
        category: "tools",
        provider: "minimax",
        keywords: ["minimax", "function", "tools", "tool calling"],
        content: `# MiniMax Function Calling

## Tool Definition
\`\`\`json
{
  "type": "function",
  "function": {
    "name": "get_weather",
    "description": "Get weather for location",
    "parameters": {
      "type": "object",
      "properties": {
        "location": {"type": "string"},
        "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
      },
      "required": ["location"]
    }
  }
}
\`\`\`

## Usage
Same pattern as OpenAI:
1. Send tools in request
2. Model returns tool_calls in response
3. Execute tool
4. Send result back with role "tool" and tool_call_id

## Python Example
\`\`\`python
response = requests.post(url, headers=headers, json={
    "model": "MiniMax-M1",
    "messages": [
        {"role": "user", "content": "What's weather in Tokyo?"}
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get weather",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {"type": "string"}
                    },
                    "required": ["location"]
                }
            }
        }
    ]
})
\`\`\``
    },
    {
        id: "minimax-rate-limits",
        title: "MiniMax Rate Limits & Pricing",
        category: "limits",
        provider: "minimax",
        keywords: ["minimax", "rate limit", "pricing", "cost"],
        content: `# MiniMax Rate Limits & Pricing

## Rate Limits
- Varies by plan and model
- Contact MiniMax for specific limits

## Pricing
- MiniMax-M1: ~¥0.01-0.02 per 1K tokens (varies)
- abab6.5: Lower cost per token
- Check official docs for current pricing

## Error Codes
- 1000: Invalid parameter
- 1001: Authentication failed
- 1004: Rate limit exceeded
- 1008: Model overloaded
- 1013: Context length exceeded

## Context Windows
- MiniMax-M1: Up to 1M tokens (with long context mode)
- abab6.5: 8K tokens
- abab6.5s/g: 8K tokens`
    }
];
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