Next-Generation Professional Discovery Platform
Combining vector embeddings, semantic search, and structured data for intelligent skill matching and portfolio discovery
The Hybrid Unified Portfolio System is an advanced framework that bridges traditional structured portfolio data with cutting-edge AI/ML technologies. It enables:
┌─────────────────────────────────────────────┐
│ APPLICATION LAYER │
│ ├─ GitHub Profile (Frontend) │
│ ├─ Portfolio Website │
│ └─ Discovery API │
└──────────────────┬──────────────────────────┘
│
┌──────────┴──────────┐
▼ ▼
┌──────────────────┐ ┌──────────────────┐
│ VECTOR LAYER │ │ DATA LAYER │
│ │ │ │
│ - Skill vectors │ │ - Skill matrix │
│ - Project vecs │ │ - Projects DB │
│ - Experience │ │ - Achievements │
│ - Code samples │ │ - Experience │
└────────┬─────────┘ └────────┬─────────┘
│ │
└──────────┬──────────┘
▼
┌──────────────────────┐
│ UNIFIED INDEX │
│ (Hybrid Search) │
│ │
│ - Semantic matching │
│ - Cross-domain link │
│ - Recommendation │
└──────────┬───────────┘
│
┌──────────┴──────────┐
▼ ▼
┌──────────────────┐ ┌──────────────────┐
│ DISCOVERY API │ │ ANALYTICS │
│ │ │ │
│ - REST/GraphQL │ │ - Embeddings │
│ - WebSocket │ │ - Metrics │
│ - Real-time │ │ - Insights │
└──────────────────┘ └──────────────────┘
Each professional is represented as a multi-dimensional vector across these axes:
AI/ML Engineering:
- Machine Learning Models: 8.5/10
- NLP & LLMs: 8.0/10
- Computer Vision: 7.5/10
- MLOps & Deployment: 8.2/10
Web3 & Blockchain:
- Smart Contract Auditing: 8.8/10
- DApp Development: 8.0/10
- Security Analysis: 8.5/10
- Protocol Design: 7.8/10
DevOps & Infrastructure:
- Kubernetes Orchestration: 8.3/10
- CI/CD Pipeline Development: 8.5/10
- Infrastructure as Code: 8.2/10
- Cloud Architecture: 8.0/10
Years in Field: 5+ years
Project Complexity: 8.5/10
Team Leadership: 7.5/10
Innovation Index: 8.8/10
Open Source Contribution: 861 commits/year
Input: GitHub username
↓
Fetch: Profile data, README, repositories
↓
Parse: Skills, projects, contributions
↓
Generate: Embeddings & vectors
↓
Output: Unified portfolio representation
query = "Python AI/ML engineer with Web3 expertise"
# Search across multiple dimensions:
1. Semantic similarity (embeddings)
2. Structured skill matching (taxonomy)
3. Project relevance (code analysis)
4. Experience alignment (timeline)
5. Community impact (contributions)
result = hybrid_search(query, weights=[0.3, 0.2, 0.2, 0.15, 0.15])
hybrid-unified-portfolio/
├── README.md # This file
├── docs/
│ ├── architecture.md # Detailed architecture
│ ├── embedding-strategy.md # Vector embedding approach
│ ├── api-reference.md # API documentation
│ └── examples.md # Usage examples
├── src/
│ ├── embeddings/
│ │ ├── skill_embedder.py # Skill → vector conversion
│ │ ├── project_embedder.py # Project → vector conversion
│ │ └── hybrid_index.py # Unified index management
│ ├── data/
│ │ ├── github_client.py # GitHub API integration
│ │ ├── skill_matrix.py # Structured skill data
│ │ └── portfolio_db.py # Portfolio storage
│ ├── search/
│ │ ├── semantic_search.py # Vector-based search
│ │ ├── structured_search.py # SQL/filter-based search
│ │ └── hybrid_search.py # Combined search
│ └── api/
│ ├── routes.py # API endpoints
│ └── models.py # Data models
├── config/
│ ├── embeddings.yaml # Embedding configs
│ ├── database.yaml # Database configs
│ └── github.yaml # GitHub API configs
├── tests/
│ ├── test_embeddings.py
│ ├── test_search.py
│ └── test_integration.py
├── requirements.txt
├── Dockerfile
└── docker-compose.yml
# Clone repository
git clone https://github.com/romanchaa997/hybrid-unified-portfolio.git
cd hybrid-unified-portfolio
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your configurations
# Initialize database
python scripts/init_db.py
from src.embeddings import PortfolioEmbedder
from src.search import HybridSearch
# Initialize embedder
embedder = PortfolioEmbedder(
model="sentence-transformers/all-MiniLM-L6-v2",
vector_db="pinecone"
)
# Add GitHub profile
embedder.add_github_profile("romanchaa997")
# Perform hybrid search
search = HybridSearch(embedder)
results = search.find_matching_opportunities(
query="AI/ML engineer with Python and Web3",
filters={"location": "Remote", "salary_min": 150000}
)
for result in results:
print(f"{result.title}: {result.match_score}%")
✨ Vector Embeddings
🔍 Semantic Search
📊 Structured Data
🔗 Hybrid Matching
🌐 GitHub Integration
Detailed documentation available in /docs:
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
MIT License - See LICENSE for details
Built with ❤️ for the future of intelligent professional discovery
Last updated: December 2025