Model Integration Guide¶

This guide explains how to integrate different AI models into the AI Assistant System.

Overview¶

The AI Assistant System supports integration with various AI models, from cloud-based APIs to locally deployed models. This flexibility allows you to choose the best model for your specific use case.

Supported Model Types¶

Language Models: Text generation, understanding, and completion
Embedding Models: Text vectorization for semantic search
Image Generation Models: Text-to-image generation
Code Generation Models: Specialized for code completion and generation
Multimodal Models: Handling text, images, and other modalities

Model Integration Architecture¶

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Application   │───▶│  Model Manager   │───▶│   AI Models     │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌──────────────────┐
                       │  Model Registry  │
                       └──────────────────┘

Language Model Integration¶

OpenAI Models¶

from app.core.model_integration import ModelManager, OpenAIModel

# Initialize model manager
model_manager = ModelManager()

# Register OpenAI model
openai_model = OpenAIModel(
    name="gpt-4",
    api_key="your-api-key",
    model_params={
        "temperature": 0.7,
        "max_tokens": 1000,
        "top_p": 0.9
    }
)
model_manager.register_model(openai_model)

# Use the model
response = await model_manager.generate(
    model_name="gpt-4",
    prompt="Explain quantum computing",
    context="You are a physics teacher"
)

Anthropic Models¶

from app.core.model_integration import AnthropicModel

claude_model = AnthropicModel(
    name="claude-3-opus",
    api_key="your-api-key",
    model_params={
        "temperature": 0.5,
        "max_tokens": 2000
    }
)
model_manager.register_model(claude_model)

Local Models (Ollama)¶

from app.core.model_integration import OllamaModel

ollama_model = OllamaModel(
    name="llama2",
    base_url="http://localhost:11434",
    model_params={
        "temperature": 0.8,
        "num_predict": 1000
    }
)
model_manager.register_model(ollama_model)

Embedding Model Integration¶

OpenAI Embeddings¶

from app.core.model_integration import OpenAIEmbeddingModel

embedding_model = OpenAIEmbeddingModel(
    name="text-embedding-ada-002",
    api_key="your-api-key"
)
model_manager.register_embedding_model(embedding_model)

# Generate embeddings
text = "The AI Assistant System is powerful"
embeddings = await model_manager.generate_embeddings(
    model_name="text-embedding-ada-002",
    text=text
)

Hugging Face Embeddings¶

from app.core.model_integration import HuggingFaceEmbeddingModel

hf_embedding_model = HuggingFaceEmbeddingModel(
    name="sentence-transformers/all-MiniLM-L6-v2",
    model_path="/path/to/model"
)
model_manager.register_embedding_model(hf_embedding_model)

Image Generation Model Integration¶

DALL-E Integration¶

from app.core.model_integration import DALLEModel

dalle_model = DALLEModel(
    name="dall-e-3",
    api_key="your-api-key",
    model_params={
        "size": "1024x1024",
        "quality": "standard",
        "style": "vivid"
    }
)
model_manager.register_image_model(dalle_model)

# Generate image
image_url = await model_manager.generate_image(
    model_name="dall-e-3",
    prompt="A futuristic AI assistant robot",
    n=1
)

Stable Diffusion Integration¶

from app.core.model_integration import StableDiffusionModel

sd_model = StableDiffusionModel(
    name="stable-diffusion-v1-5",
    model_path="/path/to/stable-diffusion",
    model_params={
        "height": 512,
        "width": 512,
        "num_inference_steps": 50
    }
)
model_manager.register_image_model(sd_model)

Code Generation Model Integration¶

GitHub Copilot Integration¶

from app.core.model_integration import CopilotModel

copilot_model = CopilotModel(
    name="copilot",
    api_key="your-github-token",
    model_params={
        "temperature": 0.1,
        "max_tokens": 500
    }
)
model_manager.register_code_model(copilot_model)

# Generate code
code = await model_manager.generate_code(
    model_name="copilot",
    prompt="Create a Python function to calculate factorial",
    language="python"
)

Model Routing and Selection¶

Intelligent Model Selection¶

from app.core.model_integration import ModelRouter

router = ModelRouter()

# Define routing rules
router.add_rule(
    name="complex_reasoning",
    condition=lambda ctx: ctx.complexity > 0.8,
    model="claude-3-opus"
)

router.add_rule(
    name="code_generation",
    condition=lambda ctx: ctx.task_type == "code",
    model="gpt-4"
)

router.add_rule(
    name="simple_tasks",
    condition=lambda ctx: ctx.complexity < 0.5,
    model="gpt-3.5-turbo"
)

# Use router
context = RequestContext(
    task_type="code",
    complexity=0.9
)
selected_model = router.select_model(context)

Load Balancing¶

from app.core.model_integration import LoadBalancer

load_balancer = LoadBalancer(
    strategy="round_robin",
    models=["gpt-4", "claude-3-opus", "gpt-3.5-turbo"]
)

# Get next available model
model = load_balancer.get_next_model()
response = await model_manager.generate(
    model_name=model.name,
    prompt= prompt
)

Model Performance Optimization¶

Caching¶

from app.core.model_integration import CachedModel

# Wrap model with caching
cached_model = CachedModel(
    base_model=openai_model,
    cache_ttl=3600,  # 1 hour
    cache_key_generator=lambda prompt, **kwargs: f"{prompt[:100]}_{hash(str(kwargs))}"
)
model_manager.register_model(cached_model)

Batch Processing¶

from app.core.model_integration import BatchProcessor

batch_processor = BatchProcessor(
    model_name="gpt-4",
    batch_size=10,
    timeout=30
)

# Process multiple prompts
prompts = [
    "What is machine learning?",
    "Explain neural networks",
    "How does AI work?"
]
responses = await batch_processor.process_batch(prompts)

Model Quantization (for local models)¶

from app.core.model_integration import QuantizedModel

quantized_model = QuantizedModel(
    base_model=ollama_model,
    quantization_bits=8,  # 8-bit quantization
    device="cuda"
)
model_manager.register_model(quantized_model)

Model Monitoring and Metrics¶

Performance Metrics¶

from app.core.model_integration import ModelMetrics

metrics = ModelMetrics()

@metrics.track
async def generate_with_metrics(prompt, model_name):
    start_time = time.time()
    response = await model_manager.generate(
        model_name=model_name,
        prompt=prompt
    )

    metrics.record_execution(
        model_name=model_name,
        execution_time=time.time() - start_time,
        token_usage=response.usage
    )

    return response

Quality Metrics¶

from app.core.model_integration import QualityMetrics

quality_metrics = QualityMetrics()

# Evaluate response quality
def evaluate_response(prompt, response, expected):
    score = quality_metrics.calculate_score(
        prompt=prompt,
        response=response,
        expected=expected
    )
    return score

Model Security and Safety¶

Content Filtering¶

from app.core.model_integration import ContentFilter

content_filter = ContentFilter()

@content_filter.filter
async def safe_generate(prompt, model_name):
    # Check input for harmful content
    if content_filter.is_harmful(prompt):
        raise ValueError("Harmful content detected")

    # Generate response
    response = await model_manager.generate(
        model_name=model_name,
        prompt=prompt
    )

    # Check output for harmful content
    if content_filter.is_harmful(response.text):
        return "I cannot provide a response to this request."

    return response

Model Access Control¶

from app.core.model_integration import AccessControl

access_control = AccessControl()

# Define access policies
access_control.add_policy(
    name="premium_models",
    models=["gpt-4", "claude-3-opus"],
    required_permissions=["premium"]
)

access_control.add_policy(
    name="free_models",
    models=["gpt-3.5-turbo"],
    required_permissions=["basic"]
)

# Check access before model use
if access_control.check_access(user_permissions, model_name):
    response = await model_manager.generate(
        model_name=model_name,
        prompt=prompt
    )

Model Versioning¶

Model Version Management¶

from app.core.model_integration import ModelVersionManager

version_manager = ModelVersionManager()

# Register model versions
version_manager.register_version(
    model_name="gpt-4",
    version="v1",
    model=gpt4_v1_model
)

version_manager.register_version(
    model_name="gpt-4",
    version="v2",
    model=gpt4_v2_model
)

# Use specific version
response = await model_manager.generate(
    model_name="gpt-4",
    model_version="v1",
    prompt=prompt
)

A/B Testing¶

from app.core.model_integration import ABTestManager

ab_test = ABTestManager()

# Set up A/B test
ab_test.create_test(
    name="gpt4_vs_claude",
    model_a="gpt-4",
    model_b="claude-3-opus",
    traffic_split=0.5  # 50% traffic to each
)

# Get model for request
model_name = ab_test.get_model_for_request(user_id)
response = await model_manager.generate(
    model_name=model_name,
    prompt=prompt
)

Best Practices¶

Choose the Right Model: Select models based on your specific needs
Implement Caching: Cache responses to improve performance and reduce costs
Monitor Performance: Track metrics to optimize model usage
Implement Fallbacks: Have backup models for reliability
Security First: Implement proper content filtering and access control
Version Control: Manage model versions for consistency
Cost Optimization: Balance quality with cost considerations
Regular Updates: Keep models updated for better performance

Troubleshooting¶

Common Issues¶

Model Not Responding: Check API keys and network connectivity
Slow Response Times: Consider caching or using faster models
Poor Quality: Adjust model parameters or try different models
High Costs: Implement caching and use cost-effective models
Rate Limits: Implement proper rate limiting and fallbacks

Debug Mode¶

Enable debug mode for detailed logging:

```python import logging logging.getLogger("app.core.model_integration").setLevel(logging.DEBUG)