Generate Content

AnyPath supports the native Google Gemini API format for Gemini models.

Path Parameters

Parameter model

Type: string

Location: path

Required: yes

Model name (e.g., gemini-2.5-pro, gemini-2.5-flash).

Query Parameters

Parameter key

Type: string

Location: query

API key (alternative to header authentication).

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

Parameter contents

Type: array

Location: body

Required: yes

Conversation contents.

Each content object contains:

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

For media parts, AnyPath currently accepts image MIME types. For production traffic, prefer URL-based fileData / file_data media parts with a public https URL.

Parameter systemInstruction

Type: object

Location: body

System instruction for the model.

Parameter generationConfig

Type: object

Location: body

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

Parameter safetySettings

Type: array

Location: body

Safety filter settings.

Response

Response Field candidates

Type: array

Generated content candidates.

Response Field usageMetadata

Type: object

Token usage information.

Request Example

curl -X POST "https://api.anypath.dev/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
    }
  }'

import google.generativeai as genai

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

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

print(response.text)

import { GoogleGenerativeAI } from "@google/generative-ai";

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

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

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

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.anypath.dev/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
$payload = [
    'contents' => [
        [
            'parts' => [
                ['text' => 'Hello, Gemini!']
            ]
        ]
    ],
    'generationConfig' => [
        'temperature' => 0.7,
        'maxOutputTokens' => 1024
    ]
];

$ch = curl_init('https://api.anypath.dev/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'];

Vision Input Example

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

Supported media categories:

• image

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.

Image Input Example

Use inline image bytes:

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

Use an image URL:

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

Response Example

{
  "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
  }
}

Error Response Example

{
  "error": {
    "message": "API key spending limit exceeded.",
    "type": "spending_limit_exceeded",
    "code": "spending_limit_exceeded"
  }
}

{
  "error": {
    "message": "The requested model does not exist or is not available.",
    "type": "model_not_found",
    "code": "model_not_found"
  }
}