Veo-3.1 Troubleshooting

{
  "error": {
    "message": "Incorrect API key provided",
    "type": "invalid_request_error",
    "code": "invalid_api_key"
  }
}

client = OpenAI(
    api_key="sk-YOUR_VALID_API_KEY",  # Must start with sk-
    base_url="https://api.yelinai.com/v1"
)

{
  "error": {
    "message": "Insufficient balance",
    "type": "insufficient_quota",
    "code": "insufficient_balance"
  }
}

{
  "error": {
    "message": "The model 'veo-31' does not exist",
    "type": "invalid_request_error",
    "code": "model_not_found"
  }
}

model="veo-31"       # ❌ Wrong: should be veo-3.1
model="veo_3_1"      # ❌ Wrong: should use hyphens
model="veo3.1"       # ❌ Wrong: missing hyphen

model="veo-3.1"              # ✅ Correct
model="veo-3.1-fast"         # ✅ Correct
model="veo-3.1-fl"           # ✅ Correct
model="veo-3.1-landscape"    # ✅ Correct

{
  "error": {
    "message": "Invalid message format",
    "type": "invalid_request_error"
  }
}

# ❌ Wrong: content should be an array
messages=[{
    "role": "user",
    "content": "Generate video"
}]

# ❌ Wrong: missing type field
messages=[{
    "role": "user",
    "content": [{
        "text": "Generate video"
    }]
}]

# ✅ Correct: content is an array containing objects
messages=[{
    "role": "user",
    "content": [
        {
            "type": "text",
            "text": "Generate video"
        }
    ]
}]

{
  "error": {
    "message": "Failed to fetch image",
    "code": "image_fetch_failed"
  }
}

import base64

def encode_image(image_path):
    with open(image_path, "rb") as f:
        return base64.b64encode(f.read()).decode()

image_base64 = encode_image("./image.jpg")

content = [
    {"type": "text", "text": "Generate video"},
    {
        "type": "image_url",
        "image_url": {
            "url": f"data:image/jpeg;base64,{image_base64}"
        }
    }
]

{
  "error": {
    "message": "Unsupported image format",
    "code": "invalid_image_format"
  }
}

from PIL import Image
import io
import base64

# Open image
img = Image.open("image.bmp")

# Convert to JPEG
buffer = io.BytesIO()
img.convert('RGB').save(buffer, format='JPEG')
img_base64 = base64.b64encode(buffer.getvalue()).decode()

# Use converted image
url = f"data:image/jpeg;base64,{img_base64}"

{
  "error": {
    "message": "Image size exceeds limit",
    "code": "image_too_large"
  }
}

from PIL import Image

def compress_image(input_path, output_path, max_size_mb=10):
    img = Image.open(input_path)

    # If image too large, scale proportionally
    max_dimension = 2048
    if max(img.size) > max_dimension:
        img.thumbnail((max_dimension, max_dimension), Image.Resampling.LANCZOS)

    # Save and adjust quality
    quality = 95
    while True:
        img.save(output_path, 'JPEG', quality=quality, optimize=True)
        size_mb = os.path.getsize(output_path) / (1024 * 1024)

        if size_mb <= max_size_mb or quality <= 50:
            break

        quality -= 5

compress_image("large_image.jpg", "compressed.jpg")

{
  "error": {
    "message": "This model does not support image input",
    "code": "model_not_support_image"
  }
}

# ❌ Wrong: veo-3.1 does not support images
response = client.chat.completions.create(
    model="veo-3.1",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Generate video"},
            {"type": "image_url", "image_url": {"url": "..."}}
        ]
    }]
)

# ✅ Correct: use veo-3.1-fl
response = client.chat.completions.create(
    model="veo-3.1-fl",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Generate video"},
            {"type": "image_url", "image_url": {"url": "..."}}
        ]
    }]
)

ReadTimeout: The read operation timed out

import httpx
from openai import OpenAI

client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://api.yelinai.com/v1",
    http_client=httpx.Client(
        timeout=httpx.Timeout(
            connect=30.0,   # Connection timeout: 30s
            read=300.0,     # Read timeout: 5 minutes
            write=30.0,     # Write timeout: 30s
            pool=30.0       # Pool timeout: 30s
        )
    )
)

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-YOUR_API_KEY',
  baseURL: 'https://api.yelinai.com/v1',
  timeout: 300000,  # 5 minutes
  maxRetries: 3
});

Stream interrupted: connection closed

from openai import OpenAI
import time

def generate_with_retry(client, **kwargs):
    max_retries = 3
    retry_delay = 5

    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(**kwargs)

            for chunk in response:
                if chunk.choices[0].delta.content:
                    yield chunk.choices[0].delta.content

            break  # Success, exit loop

        except Exception as e:
            if attempt < max_retries - 1:
                print(f"Attempt {attempt + 1} failed, retrying in {retry_delay} seconds...")
                time.sleep(retry_delay)
                retry_delay *= 2  # Exponential backoff
            else:
                raise e

# Usage
client = OpenAI(
    api_key="sk-YOUR_API_KEY",
    base_url="https://api.yelinai.com/v1"
)

for content in generate_with_retry(
    client,
    model="veo-3.1",
    messages=[...],
    stream=True
):
    print(content, end='')

# ❌ Insufficient detail
prompt = "cat walking"

# ✅ Detailed description
prompt = "An orange Persian cat elegantly walking on a forest path covered with fallen leaves, sunlight filtering through trees creating dappled shadows, autumn breeze, cinematic quality, shallow depth of field"

# Testing: veo-3.1-fast ($0.15)
# Production: veo-3.1 ($0.25)

# ❌ Too complex
prompt = "A flying cat chasing a glowing mechanical butterfly underwater while rainbow rain falls"

# ✅ Simplified reasonable
prompt = "A cat chasing a butterfly in a garden, sunny weather, flowers swaying"

# ❌ Contradiction: can't have fire underwater
prompt = "Burning flames underwater"

# ✅ Reasonable
prompt = "Bubbles slowly rising underwater, light penetrating water surface"

# ❌ No transition guidance
prompt = "Two images"

# ✅ Clear transition
prompt = "Smooth transition from first image to second using fade effect, maintaining continuity"

# Common error 1: Version incompatibility
# Solution: Upgrade to latest version
pip install --upgrade openai

# Common error 2: Import error
# Wrong: from openai import Client
# Correct: from openai import OpenAI

# Common error 3: Async client usage
from openai import AsyncOpenAI  # Async requires AsyncOpenAI

# Common error 4: Streaming processing
# Must set stream=True
response = client.chat.completions.create(
    model="veo-3.1",
    messages=[...],
    stream=True  # Don't forget
)

// Common error 1: Outdated version
// Solution: npm install --save openai@latest

// Common error 2: Import method
// Wrong: const openai = require('openai')
// Correct: import OpenAI from 'openai'

// Common error 3: Async handling
// Must use await or .then()
const stream = await client.chat.completions.create({...});

// Common error 4: Stream processing
for await (const chunk of stream) {  // Don't forget await
  console.log(chunk.choices[0]?.delta?.content);
}

# Note: n=4 generates 4 videos, charged for 4 requests
response = client.chat.completions.create(
    model="veo-3.1",
    messages=[...],
    n=4  # Cost: $0.25 × 4 = $1.00
)

# Testing phase use fast model
model = "veo-3.1-fast"  # $0.15/request

# Production phase use standard model
model = "veo-3.1"  # $0.25/request