How AI Assistant Integration Works

1. The Problem

Different AI providers (OpenAI, Google Gemini, Anthropic Claude) have different APIs, rate limits, and capabilities. Switching between providers or handling failover requires significant code changes.

Solution: A unified bridge that abstracts provider differences and routes requests to the healthiest available provider automatically.

2. Architecture Overview

3. Intent Mapping

The Intent Mapper converts natural language task descriptions into capability requirements. It uses keyword patterns to identify what capabilities are needed.

# Example intent mapping patterns
PATTERNS = {
    "code": ["code_generation", "coding"],
    "analyze": ["code_analysis", "text_analysis"],
    "summarize": ["text_summarization"],
    "translate": ["translation"],
}

# Input: "Write code to sort a list"
# Output: ["code_generation", "coding"]

4. Provider Health Monitoring

The Provider Monitor continuously tracks the health of each provider:

• Latency: Average response time (lower is better)
• Success Rate: Percentage of successful requests
• Availability: Whether the provider is currently responding
• Rate Limits: Remaining quota and reset time

5. Provider Selection

When a request comes in, the bridge selects the best provider based on:

1. Capability support (does the provider support the required capability?)
2. Current health score (latency + success rate)
3. Rate limit status (has quota remaining?)
4. User preference (if specified)

def select_provider(capabilities, preference=None):
    candidates = []
    for provider in available_providers:
        if provider.supports(capabilities):
            score = provider.health_score()
            if preference == provider.name:
                score *= 1.5  # Boost preferred
            candidates.append((provider, score))
    return max(candidates, key=lambda x: x[1])

6. Tool Format Conversion

Different providers expect different tool/function schemas. The Tool Converter handles this:

# OpenAI format
{"type": "function", "function": {"name": "search", ...}}

# Anthropic format
{"name": "search", "input_schema": {...}}

# Gemini format
{"name": "search", "parameters": {...}}

7. Feedback Loop

After each request, feedback is collected and sent to the Learning Layer (M5):

Successful requests boost the provider's score; failures reduce it. This creates a self-improving system that learns which providers work best for which tasks.