LLM Integration Module

from django_cfg.modules.django_llm import (
    LLMClient, LLMCache, LLMProvider,
    DjangoTranslator,
    chat_completion, translate_text, translate_json,
    get_available_models,
)
 
# Text completion
response = chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    model="openai/gpt-4o-mini"
)
 
# Translation
translated = translate_text("Привет мир", target_language="en")
 
# JSON translation
data = translate_json({"title": "Привет"}, target_language="en")
 
# Available models
models = get_available_models()

from django_cfg.modules.django_llm.features.vision import VisionClient
from django_cfg.modules.django_llm.features.image_gen import ImageGenClient
 
# Vision analysis (auto-resize enabled by default)
vision = VisionClient()
result = vision.analyze(
    image_source="https://example.com/image.jpg",
    query="Describe this image"
)
print(f"Cost: ${result.cost_usd:.4f}")
 
# Image generation
gen = ImageGenClient()
image = gen.generate(prompt="A sunset over mountains")
print(image.first_url)

django_llm/
├── client/             # LLMClient for text completions
├── providers/          # Provider management (OpenAI, OpenRouter)
├── registry/           # Model catalogue, pricing, cost calculation
├── storage/            # Response caching with TTL
├── structured/         # Structured output + JSON extraction
├── tokenizer.py        # Token counting
├── monitoring/         # Provider balance monitoring
├── features/
│   ├── vision/         # VisionClient, OCR, image resizing
│   ├── image_gen/      # ImageGenClient
│   └── translator/     # DjangoTranslator
└── __init__.py

# Default: 512x512 resize = 85 tokens per image
client = VisionClient()  # auto_resize=True, default_detail="low"
 
# OCR extraction
response = client.ocr(
    image_url="https://example.com/document.jpg",
    mode="base"
)
print(response.text)

client = ImageGenClient()
response = client.generate(
    prompt="A futuristic city",
    model_quality="best",
    size="1024x1024"
)

client = LLMClient()
 
# Chat
response = client.chat_completion(
    messages=[{"role": "user", "content": "Explain AI"}],
    model="openai/gpt-4o-mini"
)
 
# Embeddings
embedding = client.generate_embedding(
    text="Sample text",
    model="text-embedding-ada-002"
)

from django_cfg.modules.django_llm.registry import calculate_chat_cost
 
cost = calculate_chat_cost(
    model="openai/gpt-4o-mini",
    input_tokens=100,
    output_tokens=50,
    models_cache=models_cache
)

# Auto-detection
client = VisionClient()  # Uses django_config.api_keys.get_openrouter_key()
 
# Explicit
client = VisionClient(api_key="sk-or-v1-...")