Nexent Model Architecture

Nexent provides a comprehensive model architecture supporting multiple AI model types through OpenAI-compatible interfaces. The SDK supports large language models, multimodal models, embedding models, and speech processing capabilities.

📋 Overview

The models module provides standardized interfaces for various AI model providers and types:

🎯 Supported Model Categories

🤖 Large Language Models (LLM)

• OpenAI-compatible models: Any provider following OpenAI API specification
• Long context models: Support for extended context windows
• Multimodal language models: Text + image processing capabilities
• Local deployment: Ollama, vLLM, and other self-hosted solutions

🎭 Vision Language Models (VLM)

• Multimodal understanding: Process text, images, and documents simultaneously
• OpenAI-compatible VLMs: GPT-4V, Claude-3, and compatible models
• Document analysis: OCR, table extraction, and visual reasoning

🔤 Embedding Models

• Universal compatibility: All OpenAI-compatible embedding services
• Multilingual support: International language processing
• Specialized embeddings: Document, code, and domain-specific embeddings
• Vector database integration: Seamless integration with vector stores

🎤 Speech Processing Models

• Text-to-Speech (TTS): Multiple provider support
• Speech-to-Text (STT): Real-time and batch processing
• Voice cloning: Advanced voice synthesis capabilities
• Multilingual speech: Support for multiple languages and accents

🏗️ Model Implementation Classes

💡 Usage

from nexent.core.models import OpenAIModel

# Initialize OpenAI model
model = OpenAIModel(
    api_key="your-api-key",
    model_name="gpt-4"
)

# Use model for completion
response = model.complete("Hello, world!")

⚙️ Configuration

Models can be configured through:

• Environment variables
• Configuration files
• Direct parameter passing

For detailed usage examples and API reference, see the SDK documentation.