Capstone Project: Build a Production-Grade Scalable Vector Search Platform
The final challenge. Synthesize everything you've learned to build a secure, scaled, and multimodal vector search system.
Capstone Project: Build a Production-Grade Scalable Vector Search Platform
Congratulations! You have reached the final stage of the Vector Databases: From Fundamentals to Production AI Systems course. This capstone project is designed to test your mastery across all 19 modules. You won't just build a "Search script"; you will architect a Resilient Search Platform.
1. The Challenge: "The Global Asset Portal"
You have been hired by a major media company (GlobalMedia Corp). They have 1 million assets across text, images, and short videos. They need a single search bar that "Just works."
The Requirements:
- Multimodal Core: Users can search via Text ("A sunny day at the park") or Image (Upload a photo of a park) to find matching assets.
- Hybrid Logic: The system must handle exact metadata filtering (e.g., "Find only videos from 2023").
- Security: The system must enforce Tenant Isolation—Users from 'Department A' cannot see assets from 'Department B'.
- Resilience: The system must be able to recover from a database corruption event using a snapshot (DR Strategy).
- Performance: Query latency (embedding + search) must be under 300ms.
2. Recommended Stack
- Database: Pinecone (for scaling) or Weaviate/OpenSearch (for hybrid).
- Embedding Models: CLIP (Images) + text-embedding-3-small (Text).
- Backend: FastAPI (Python).
- Storage: AWS S3 (for the raw images and videos).
3. Implementation Steps
Phase 1: Data Architecting
- Design your Metadata Schema. What fields are searchable? What fields are for filtering?
- Set up your Tenant Segregation logic (Namespaces or Metadata Filters).
Phase 2: The Ingestion Engine
- Build a Python script that reads from a local directory (simulating S3).
- Implement Batch Ingestion with error handling and backoff.
Phase 3: The Unified Search API
- Create a single endpoint
/searchthat detects if the input is a string or an image. - Perform the retrieval and return a clean JSON response with URLs and similarity scores.
Phase 4: Operational Hardening
- Implement Audit Logging for every search.
- Create a manual Backup/Restore script.
4. Final Submission Checklist
- Does it handle Multimodal inputs?
- Is every query Isolated by a
tenant_id? - Is there an Audit Log being generated?
- Is the code Environment-Aware (Dev vs. Prod)?
- Is there a README explaining how to run the ingestion and search?
Conclusion: The Future of Vector Databases
By completing this capstone, you have moved from "Using a tool" to "Designing a system." You are now among a small group of engineers who understand the deep mechanics of AI memory and retrieval.
Good luck, and we can't wait to see what you build!