AI Model Orchestration System

Enterprise-grade orchestration platform for managing multiple local AI models with intelligent routing, RAG capabilities, and dynamic data ingestion.

Overview

This system addresses enterprise AI infrastructure challenges by providing intelligent model orchestration, retrieval-augmented generation (RAG), and dynamic knowledge base management. Built for production scalability with local-first architecture.

Key Capabilities:

Routes queries to optimal AI models based on content analysis
Manages concurrent requests with load balancing
Maintains dynamic knowledge base through web crawling
Provides REST APIs and web interfaces
Monitors system health and performance metrics

System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Web Crawlers  │    │  RAG Pipeline   │    │ Model Orchestra │
│                 │    │                 │    │                 │
│ • DuckDuckGo    │    │ • ChromaDB      │    │ • Query Router  │
│ • StackOverflow │────│ • Embeddings    │────│ • Load Balancer │
│ • GitHub API    │    │ • Retrieval     │    │ • Health Monitor│
│ • RSS Feeds     │    │ • Generation    │    │ • Model Pool    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 │
         ┌─────────────────────────────────────────────────┐
         │              Interface Layer                    │
         │                                                 │
         │  ┌─────────────┐  ┌─────────────┐  ┌──────────┐ │
         │  │ FastAPI     │  │ Streamlit   │  │ Direct   │ │
         │  │ REST API    │  │ Dashboard   │  │ Python   │ │
         │  └─────────────┘  └─────────────┘  └──────────┘ │
         └─────────────────────────────────────────────────┘

Features

Model Orchestration

Intelligent Routing: Automatically selects optimal models based on query analysis
Load Balancing: Handles concurrent requests with configurable limits (default: 3)
Health Monitoring: Tracks model availability, response times, and error rates
Auto-scaling: Models load/unload based on memory constraints and demand

RAG System

Vector Database: ChromaDB with persistent storage and semantic search
Document Processing: Automated chunking and metadata extraction
Context Enhancement: Retrieves relevant documents to augment model responses
Multi-format Support: Text files, markdown, web content, API data

Web Crawling

Real-time Data: Pulls latest information from multiple sources
API Integration: StackOverflow, GitHub, NewsAPI, Alpha Vantage
Search Capabilities: DuckDuckGo integration for current web content
RSS Processing: Finance and tech news feeds
Scheduled Updates: Configurable crawling intervals

Interfaces

REST API: Production-ready endpoints with auto-documentation
Web Dashboard: Real-time monitoring and query interface
Side-by-side Comparison: RAG vs non-RAG response analysis
Database Explorer: Browse and search stored knowledge

Models & Technologies

AI Models (Ollama)

Model	Size	Use Case	Category
neural-chat:7b-v3.3-q4_0	4.1GB	Fast responses, greetings	General
llama3.1:8b	4.9GB	General purpose queries	General
codellama:13b	7.4GB	Programming tasks	Coding
mixtral:8x7b-instruct	26GB	Complex analysis	Analysis
llama3.1:70b	42GB	Deep reasoning	Reasoning

Technical Stack

Python 3.13 - Core language
FastAPI - REST API framework
Streamlit - Web dashboard
ChromaDB - Vector database
Sentence Transformers - Text embeddings
BeautifulSoup - Web scraping
Ollama - Local model management

Hardware Requirements

GPU: RTX 5090 (24GB VRAM) or equivalent
RAM: 141GB DDR5 (recommended)
Storage: 100GB+ for models and data
OS: Ubuntu 22.04+ (tested)

Installation

Prerequisites

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Download required models
ollama pull neural-chat:7b-v3.3-q4_0
ollama pull llama3.1:8b
ollama pull codellama:13b
ollama pull mixtral:8x7b-instruct-v0.1-q4_0
ollama pull llama3.1:70b

Setup

# Clone repository
git clone https://github.com/yourusername/ai-model-orchestration-system.git
cd ai-model-orchestration-system

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Environment Variables (Optional)

# For enhanced crawling capabilities
export NEWS_API_KEY="your_newsapi_key"
export ALPHA_VANTAGE_KEY="your_alphavantage_key"

Usage

Start Core Services

# Start model orchestration API
python api/orchestration_api.py

# Start RAG-enhanced API (separate terminal)
python api/rag_api.py

# Launch web dashboard (separate terminal)
streamlit run dashboard/rag_dashboard.py

Basic Usage Examples

Query Routing

from orchestration.core.orchestrator import ModelOrchestrator

orchestrator = ModelOrchestrator()
result = orchestrator.process_request_sync("Write Python code for sorting")
# Routes to codellama:13b automatically

RAG Queries

from rag.retrieval.rag_orchestrator import RAGOrchestrator

rag = RAGOrchestrator()
result = rag.search_and_generate("What is machine learning?")
# Retrieves relevant docs + generates enhanced response

Web Crawling

from rag.crawler.api_crawler import APICrawler

crawler = APICrawler()
result = crawler.comprehensive_crawl([
    "artificial intelligence trends",
    "python machine learning"
])

API Reference

Model Orchestration Endpoints

POST /orchestrate - Submit query for intelligent routing
GET /system/status - Get system health and metrics
GET /recommendations/{query} - Get routing recommendations

RAG Endpoints

POST /rag/query - RAG-enhanced query processing
GET /rag/stats - Knowledge base statistics

Example API Call

curl -X POST "http://localhost:8001/orchestrate" \
     -H "Content-Type: application/json" \
     -d '{"query": "Explain quantum computing", "priority": "accuracy"}'

Project Structure

ai-model-orchestration-system/
├── orchestration/                 # Model orchestration core
│   ├── core/
│   │   ├── pool/                 # Model pool management
│   │   ├── router/               # Query routing logic
│   │   ├── balancer/             # Load balancing
│   │   └── orchestrator.py       # Main orchestration
│   └── config/                   # Configuration
├── rag/                          # RAG system components
│   ├── vector_store/             # ChromaDB management
│   ├── ingestion/                # Document processing
│   ├── retrieval/                # RAG orchestration
│   ├── crawler/                  # Web crawling
│   └── viewer/                   # Data visualization
├── api/                          # REST API endpoints
├── dashboard/                    # Web interface
├── requirements.txt              # Dependencies
└── README.md                     # Documentation

Performance

Benchmarks (RTX 5090, 141GB RAM)

Model	Avg Response Time	Concurrent Capacity	VRAM Usage
neural-chat:7b	0.95s	8 requests	4.1GB
llama3.1:8b	8.63s	3 requests	4.9GB
codellama:13b	12.02s	2 requests	7.4GB

System Metrics

Routing Decision: ~0.003s average
Document Retrieval: ~0.1s for 5 results
Concurrent Load: Up to 3 simultaneous requests
Knowledge Base: 100+ documents, growing via crawlers

Next Steps

Phase 1: Agentic AI (Current Priority)

Tool-calling capabilities
Multi-step reasoning workflows
Agent coordination framework
Decision-making algorithms

Phase 2: Enterprise Features

Advanced monitoring and alerting
Multi-user authentication and authorization
Rate limiting and quotas
Audit logging

Phase 3: Scaling & Integration

Model Context Protocol (MCP) support
Kubernetes deployment configurations
External API integrations (OpenAI, Anthropic)
Performance optimization

Phase 4: Advanced Capabilities

Multi-modal support (images, audio)
Fine-tuning pipeline integration
A/B testing framework
Advanced analytics dashboard

Contributing

Development Setup

source venv/bin/activate

# Verify Python version
python --version

# Install core dependencies if not already done
pip install fastapi uvicorn streamlit requests pydantic beautifulsoup4 lxml

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

# Code formatting
black . && flake8 .

Test Orchestration Component

Test model pool first

python orchestration/core/pool/model_pool.py

# Test model router
python -c "
import sys
sys.path.append('.')
from orchestration.core.router.model_router import ModelRouter

router = ModelRouter()
decision = router.route_request('Write Python code for sorting')
print(f'Query routed to: {decision[\"selected_model\"]}')
print(f'Category: {decision[\"category\"]}')
"

Contribution Guidelines

Follow existing code structure and patterns
Include tests for new features
Update documentation for API changes
Respect rate limits for external APIs

Achievements Summary

This system demonstrates enterprise-level AI infrastructure management:

Production Architecture: Handles concurrent users with intelligent load balancing
Dynamic Knowledge: Automatically updates from 6+ data sources
Cost Efficiency: $0 operational costs vs $1000s/month for cloud alternatives
Performance: Sub-second responses for simple queries, <15s for complex analysis
Scalability: Modular design supports horizontal scaling
Integration Ready: REST APIs enable integration with existing systems

The architecture addresses key enterprise AI challenges: model selection, resource management, knowledge currency, and system reliability.

License

MIT License - See LICENSE file for details.

Hardware Tested On: RTX 5090 (24GB VRAM), 141GB DDR5 RAM, Ubuntu 22.04
Last Updated: September 2025
Version: 1.0.0

Run Locally

Step 1: Ensure Fresh Environment setup

cd ~/ai-model-orchestration-system
source venv/bin/activate

# Verify Python version
python --version

# Install core dependencies if not already done
pip install fastapi uvicorn streamlit requests pydantic beautifulsoup4 lxml

Step 2: Test core orchestration components

# Test model pool first
python orchestration/core/pool/model_pool.py

# Test model router
python -c "
import sys
sys.path.append('.')
from orchestration.core.router.model_router import ModelRouter

router = ModelRouter()
decision = router.route_request('Write Python code for sorting')
print(f'Query routed to: {decision[\"selected_model\"]}')
print(f'Category: {decision[\"category\"]}')
"

Step 3: Start Orchestration API

# Terminal 1 - Start orchestration API
python api/orchestration_api.py

You should see

Load balancer started
INFO:     Uvicorn running on http://0.0.0.0:8001

Step 4: Test the API (in another Terminal)

# Terminal 2 - Test the API
source venv/bin/activate

# Test system status
curl http://localhost:8001/system/status

# Test orchestration
curl -X POST "http://localhost:8001/orchestrate" \
     -H "Content-Type: application/json" \
     -d '{"query": "Write Python code for bubble sort"}'

Step 5: Start the dashboard

# Terminal 3 - Start Streamlit dashboard (if API is working)
source venv/bin/activate
streamlit run dashboard/orchestration_dashboard.py

Step 6: Test RAG System

# Install RAG dependencies
pip install chromadb sentence-transformers

# Test ChromaDB
python rag/vector_store/chroma_manager.py

# Test RAG orchestration
python rag/retrieval/rag_orchestrator.py

Step 7 : Access your system

API Documentation: http://localhost:8001/docs
System Status: http://localhost:8001/system/status
Dashboard: http://localhost:8501 (if running Streamlit)

Quick Verification commands:

# Check if Ollama models are available
ollama list

# Test a simple orchestration request
python -c "
import sys
sys.path.append('.')
from orchestration.core.orchestrator import ModelOrchestrator

orchestrator = ModelOrchestrator()
result = orchestrator.process_request_sync('Hello, test the system')
print(f'Response from {result[\"model\"]}: {result[\"response\"][:100]}...')
"

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
.github/workflows		.github/workflows
api		api
dashboard		dashboard
orchestration		orchestration
rag		rag
tests		tests
.flake8		.flake8
.gitignore		.gitignore
README.md		README.md
activate_env.sh		activate_env.sh
dev_tools.py		dev_tools.py
fix_all_issues.sh		fix_all_issues.sh
fix_imports.py		fix_imports.py
fix_remaining_issues.py		fix_remaining_issues.py
pyproject.toml		pyproject.toml
quick_fix.py		quick_fix.py
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt

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AI Model Orchestration System

Table of Contents

Overview

System Architecture

Features

Model Orchestration

RAG System

Web Crawling

Interfaces

Models & Technologies

AI Models (Ollama)

Technical Stack

Hardware Requirements

Installation

Prerequisites

Setup

Environment Variables (Optional)

Usage

Start Core Services

Basic Usage Examples

Query Routing

RAG Queries

Web Crawling

API Reference

Model Orchestration Endpoints

RAG Endpoints

Example API Call

Project Structure

Performance

Benchmarks (RTX 5090, 141GB RAM)

System Metrics

Next Steps

Phase 1: Agentic AI (Current Priority)

Phase 2: Enterprise Features

Phase 3: Scaling & Integration

Phase 4: Advanced Capabilities

Contributing

Development Setup

Test Orchestration Component

Test model pool first

Contribution Guidelines

Achievements Summary

License

Run Locally

Step 1: Ensure Fresh Environment setup

Step 2: Test core orchestration components

Step 3: Start Orchestration API

Step 4: Test the API (in another Terminal)

Step 5: Start the dashboard

Step 6: Test RAG System

Step 7 : Access your system

Quick Verification commands:

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