Summary¶
This completes the Vector Search hands-on. Great work! 🍺
What You Learned¶
Value of Building Blocks + IBM Bob¶
- Significant development time reduction: Completed in approximately 60 minutes what would take days to weeks
- High-quality implementation: Code generation based on best practices
- Natural language instructions: Feature addition possible without programming knowledge
Implemented Features¶
- Product image display: Added images to search results
- Price filter: Filter by price range
- Recommendation reason: Display why a product is recommended
Vector Search Overview¶
- Searches by understanding the "meaning" of words
- Unlike traditional character search, finds similar meanings even with different phrasing
Deployment to Production Environment¶
Current Configuration (For Learning)¶
- Hugging Face + Milvus: Completely free, offline support, optimal for learning
Migration to IBM Products¶
- watsonx.ai: Enterprise-grade, advanced models, commercial support
- watsonx.data: Large-scale data integration, governance features, petabyte support
Selection Guide¶
| Scale | Recommended Configuration |
|---|---|
| Learning, PoC, and small-scale production | Hugging Face + Milvus |
| Medium-scale production | watsonx.ai + Milvus |
| Large-scale production | watsonx.ai + watsonx.data |
Value in Customer Systems¶
When integrating Vector Search into a customer's existing system, it is not enough to build only a search API. You need to connect data integration, embedding generation, vector databases, search APIs, screen display, and operations design. By using Vector Search Builder + IBM Bob, teams can reuse the foundation for technology selection and implementation while focusing on customer-specific requirements.
When integrating into a customer system without Vector Search Builder:
When integrating into a customer system with Vector Search Builder + IBM Bob:
This difference makes it easier to deliver the following value in projects.
- Faster startup: Prepare the basic Vector Search configuration in a short time
- Focus on customer requirements: Spend time on differentiating parts such as business data, screens, search conditions, and explanation text
- Easier iteration: Quickly tune search results and UI by asking IBM Bob in natural language