This section details on the original issue you should resolve

<issue_title>[Feat] Model Ensemble Support</issue_title>
<issue_description>## Introduction

Support a model ensemble orchestration service that can intelligently combine outputs from multiple LLM endpoints using configurable aggregation strategies, enabling improved reliability, accuracy, and flexible cost-performance trade-offs.

Use Case

Problem Statement

1. Single model limitations: Individual models have reliability and accuracy constraints that affect production deployments
2. No orchestration layer: Current router lacks the ability to coordinate multiple model inferences and combine results
3. Fixed routing: Requests are routed to a single model, missing opportunities for consensus-based decision making
4. Limited reliability options: No built-in mechanisms for fallback, voting, or ensemble strategies

Real-World Scenarios

Critical Applications

• Medical diagnosis assistance where consensus from multiple models increases confidence
• Legal document analysis requiring high accuracy verification
• Financial advisory systems where reliability directly impacts business outcomes
• Safety-critical AI systems (content moderation, fraud detection)

Cost Optimization

• Query multiple smaller models instead of one large expensive model
• Start with fast/cheap models, escalate to ensemble for uncertain cases
• Adaptive routing based on query complexity or confidence thresholds

Reliability & Accuracy

• Voting mechanisms to reduce hallucinations and errors
• Consensus-based outputs for higher confidence results
• Graceful degradation with fallback chains
• A/B testing and gradual rollout of new models

Model Diversity

• Combine outputs from different model architectures (e.g., GPT-style + Llama-style)
• Ensemble different model sizes for balanced performance
• Cross-validate responses from models with different training data

Architecture

graph TB
    Client[Client Request] --> Router[Semantic Router]
    Router --> Orchestrator[Ensemble Orchestrator]
    
    Orchestrator --> Strategy{Routing Strategy}
    
    Strategy -->|Parallel Query| M1[Model Endpoint 1]
    Strategy -->|Parallel Query| M2[Model Endpoint 2]
    Strategy -->|Parallel Query| M3[Model Endpoint N]
    
    M1 --> Aggregator[Aggregation Engine]
    M2 --> Aggregator
    M3 --> Aggregator
    
    Aggregator --> Voting[Voting Strategy]
    Aggregator --> Weighted[Weighted Consensus]
    Aggregator --> Ranking[Reranking]
    Aggregator --> Average[Score Averaging]
    Aggregator --> FirstSuccess[First Success]
    
    Voting --> Response[Final Response]
    Weighted --> Response
    Ranking --> Response
    Average --> Response
    FirstSuccess --> Response
    
    style Orchestrator fill:#e1f5ff
    style Aggregator fill:#fff4e1
    style Response fill:#e1ffe1

Core Components

1. Ensemble Orchestrator

Coordinates parallel or sequential requests to multiple model endpoints:

• Manages concurrent inference requests
• Handles timeouts and partial failures
• Tracks response metadata (latency, confidence scores)
• Supports both synchronous and streaming responses

2. Aggregation Engine

Combines multiple model outputs using configurable strategies:

• Voting: Majority consensus for classification/multiple choice
• Weighted Consensus: Confidence-weighted combination
• Score Averaging: Average numerical outputs or probabilities
• First Success: Return first valid response (latency optimization)
• Reranking: Use a separate model to rank and select best output
• Longest Common Subsequence: Find common patterns across responses

3. Configuration Interface

Flexible control mechanisms:

• Header-based routing (e.g., X-Ensemble-Models, X-Ensemble-Strategy)
• JSON configuration for ensemble policies
• Per-request override capabilities
• Global defaults with request-level customization

4. Adaptive Triggering

Intelligent decision-making for when to use ensemble:

• Confidence threshold triggers (ensemble on low-confidence queries)
• Query complexity detection (ensemble for complex questions)
• Cost-aware routing (balance cost vs accuracy)
• Fallback chains (start cheap, escalate as needed)

Expected Benefits

Accuracy & Reliability

• Accuracy improvement on complex reasoning tasks through multi-model consensus
• Reduced hallucination rate via voting and cross-validation
• Higher confidence outputs from aggregated responses
• Graceful degradation with fallback mechanisms

Cost Optimization

• Lower cost per query by using multiple small models instead of one large model
• Adaptive cost management by triggering ensemble only when needed
• Flexible trade-offs between accuracy and inference cost
• Better ROI on model investments by combining existing deployments

Operational Excellence

• Improved reliability thro...