> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tokenlab.sh/llms.txt
> Use this file to discover all available pages before exploring further.

# Generate Content

> Generates content using Google Gemini API format

TokenLab supports the native Google Gemini API format for Gemini models. This allows direct compatibility with Google AI SDKs.

## Path Parameters

<ParamField path="model" type="string" required>
  Model name (e.g., `gemini-2.5-pro`, `gemini-3.5-flash`).
</ParamField>

## Query Parameters

<ParamField query="key" type="string">
  API key (alternative to header authentication).
</ParamField>

## Authentication

Gemini endpoints support multiple authentication methods:

* `?key=YOUR_API_KEY` query parameter
* `x-goog-api-key: YOUR_API_KEY` header
* `Authorization: Bearer YOUR_API_KEY` header

## Request Body

<ParamField body="contents" type="array" required>
  Conversation contents.

  Each content object contains:

  * `role` (string): `user` or `model`
  * `parts` (array): Content parts. TokenLab supports:
    * text parts: `{ "text": "..." }`
    * inline media parts: `inlineData` / `inline_data`
    * URL-based file parts: `fileData` / `file_data`

  For media parts, TokenLab currently accepts image, audio, and video MIME types and forwards them through the Gemini-compatible model details.
  For production integrations, prefer URL-based `fileData` / `file_data` media parts with a public `https` URL.
  TokenLab uses the native Gemini path when available and switches to a compatible public path when native handling is unavailable for that multimodal request.

  Role values `user` and `model` are normalized case-insensitively. `inlineData` / `inline_data` with `application/octet-stream` is accepted only when TokenLab can identify supported image or video bytes; otherwise the request fails before routing. For native image-output requests, only the Google search/maps tool family is accepted, and unsupported tool combinations fail before upstream retries.
</ParamField>

<ParamField body="systemInstruction" type="object">
  System instruction for the model.
</ParamField>

<ParamField body="generationConfig" type="object">
  Generation configuration:

  * `temperature` (number): Sampling temperature
  * `topP` (number): Nucleus sampling probability
  * `topK` (integer): Top-K sampling
  * `maxOutputTokens` (integer): Maximum output tokens
  * `stopSequences` (array): Stop sequences
  * `candidateCount` (integer): Candidate count for non-streaming generation. Streaming requests must omit it or keep it at `1`.
  * `responseModalities` (array): Requested output modalities for compatible native routes.
  * `responseMimeType` (string): Output MIME type such as `text/plain` or `application/json`.
  * `responseSchema` (object): JSON schema for structured output when `responseMimeType` requests JSON.
  * `thinkingConfig` / `thinking_config` (object): Thinking budget options for compatible models.
</ParamField>

<ParamField body="safetySettings" type="array">
  Safety filter settings.
</ParamField>

## Response

<ResponseField name="candidates" type="array">
  Generated content candidates.
</ResponseField>

<ResponseField name="usageMetadata" type="object">
  Token usage information.
</ResponseField>

<RequestExample>
  ```bash cURL theme={null}
  curl -X POST "https://api.tokenlab.sh/v1beta/models/gemini-2.5-pro:generateContent?key=sk-your-api-key" \
    -H "Content-Type: application/json" \
    -d '{
      "contents": [
        {
          "parts": [{"text": "Hello, Gemini!"}]
        }
      ],
      "generationConfig": {
        "temperature": 0.7,
        "maxOutputTokens": 1024
      }
    }'
  ```

  ```python Python theme={null}
  import google.generativeai as genai

  genai.configure(
      api_key="sk-your-api-key",
      transport="rest",
      client_options={"api_endpoint": "api.tokenlab.sh"}
  )

  model = genai.GenerativeModel("gemini-2.5-pro")
  response = model.generate_content("Hello, Gemini!")

  print(response.text)
  ```

  ```javascript JavaScript theme={null}
  import { GoogleGenerativeAI } from "@google/generative-ai";

  const genAI = new GoogleGenerativeAI("sk-your-api-key", {
    baseUrl: "https://api.tokenlab.sh"
  });

  const model = genAI.getGenerativeModel({ model: "gemini-2.5-pro" });
  const result = await model.generateContent("Hello, Gemini!");

  console.log(result.response.text());
  ```

  ```go Go theme={null}
  package main

  import (
      "bytes"
      "encoding/json"
      "fmt"
      "io"
      "net/http"
  )

  func main() {
      payload := map[string]interface{}{
          "contents": []map[string]interface{}{
              {
                  "parts": []map[string]string{
                      {"text": "Hello, Gemini!"},
                  },
              },
          },
          "generationConfig": map[string]interface{}{
              "temperature":    0.7,
              "maxOutputTokens": 1024,
          },
      }

      jsonData, _ := json.Marshal(payload)
      req, _ := http.NewRequest("POST",
          "https://api.tokenlab.sh/v1beta/models/gemini-2.5-pro:generateContent?key=sk-your-api-key",
          bytes.NewBuffer(jsonData))
      req.Header.Set("Content-Type", "application/json")

      client := &http.Client{}
      resp, _ := client.Do(req)
      defer resp.Body.Close()

      body, _ := io.ReadAll(resp.Body)
      fmt.Println(string(body))
  }
  ```

  ```php PHP theme={null}
  <?php
  $payload = [
      'contents' => [
          [
              'parts' => [
                  ['text' => 'Hello, Gemini!']
              ]
          ]
      ],
      'generationConfig' => [
          'temperature' => 0.7,
          'maxOutputTokens' => 1024
      ]
  ];

  $ch = curl_init('https://api.tokenlab.sh/v1beta/models/gemini-2.5-pro:generateContent?key=sk-your-api-key');

  curl_setopt_array($ch, [
      CURLOPT_RETURNTRANSFER => true,
      CURLOPT_POST => true,
      CURLOPT_HTTPHEADER => [
          'Content-Type: application/json'
      ],
      CURLOPT_POSTFIELDS => json_encode($payload)
  ]);

  $response = curl_exec($ch);
  curl_close($ch);

  $data = json_decode($response, true);
  echo $data['candidates'][0]['content']['parts'][0]['text'];
  ```
</RequestExample>

## Vision Input Example

For Gemini multimodal requests, place media inside `contents[].parts[]` using either inline bytes or URL-based file references.

Supported media categories in the public Gemini contract:

* image
* audio
* video

For inline media, use either `inlineData` or `inline_data` and pass Base64-encoded file bytes.

For URL-based media, use either `fileData` or `file_data` and pass a public `https` URL.

## Video Input Example

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Please describe this video." },
        {
          "fileData": {
            "mimeType": "video/mp4",
            "fileUri": "https://example.com/demo.mp4"
          }
        }
      ]
    }
  ]
}
```

## Audio Input Example

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Please describe this audio." },
        {
          "fileData": {
            "mimeType": "audio/mpeg",
            "fileUri": "https://example.com/demo.mp3"
          }
        }
      ]
    }
  ]
}
```

## Image Input Example

Use inline image bytes:

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Please describe this image." },
        {
          "inlineData": {
            "mimeType": "image/jpeg",
            "data": "/9j/4AAQSkZJRgABAQ..."
          }
        }
      ]
    }
  ]
}
```

Use an image URL:

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Please describe this image." },
        {
          "fileData": {
            "mimeType": "image/jpeg",
            "fileUri": "https://example.com/demo.jpg"
          }
        }
      ]
    }
  ]
}
```

## Audio Input Example

Use an audio URL:

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Transcribe and summarize this audio." },
        {
          "file_data": {
            "mime_type": "audio/mpeg",
            "file_uri": "https://example.com/sample.mp3"
          }
        }
      ]
    }
  ]
}
```

## Video Input Example

Use a video URL:

```json theme={null}
{
  "contents": [
    {
      "role": "user",
      "parts": [
        { "text": "Describe this video briefly." },
        {
          "fileData": {
            "mimeType": "video/mp4",
            "fileUri": "https://example.com/sample.mp4"
          }
        }
      ]
    }
  ]
}
```

<ResponseExample>
  ```json Response theme={null}
  {
    "candidates": [
      {
        "content": {
          "role": "model",
          "parts": [
            {"text": "Hello! How can I assist you today?"}
          ]
        },
        "finishReason": "STOP",
        "safetyRatings": [
          {"category": "HARM_CATEGORY_HARASSMENT", "probability": "NEGLIGIBLE"}
        ]
      }
    ],
    "usageMetadata": {
      "promptTokenCount": 5,
      "candidatesTokenCount": 10,
      "totalTokenCount": 15
    }
  }
  ```
</ResponseExample>