Welcome to Vector Search Hands-on¶
In this hands-on workshop, you will combine Building Blocks and IBM Bob to experience AI-driven development for building a "semantic search" feature (Vector Search).
Prerequisites
IBM Bob is already installed and available for use. This hands-on was verified with IBM Bob 2.2.1 (October 2026). Other versions may show different modes, approval prompts, or proposals.
What You'll Experience in This Hands-on¶
Value of Building Blocks + IBM Bob¶
This hands-on workshop demonstrates how combining Building Blocks (pre-built technical components) with IBM Bob (an AI development assistant) can complete development that would typically take days to weeks in approximately 60 minutes.
Without Building Blocks:
Without Building Blocks, the following work is required:
- Vector database selection and learning
- Embedding model selection and integration
- API design and implementation
- Error handling
- Performance tuning
With Building Blocks + IBM Bob (This hands-on, Time required: approximately 60 minutes):
Responsibilities for each process:
- Building Blocks:
- Technology selection (Milvus, embedding models)
- Environment setup support (Vector Search Builder mode, API samples)
- IBM Bob:
- Requirements definition
- Coding
- Testing
- Debugging
About IBM Bob's Coverage
IBM Bob can support the whole software development lifecycle, from requirements definition to debugging. In this hands-on, Building Blocks and the instructor's Milvus environment take care of technology selection and setup, so IBM Bob focuses on coding, testing, and debugging.
What are Building Blocks?¶
Building Blocks are pre-built technical components leveraging IBM's technology stack. Using Building Blocks accelerates solution development.
Features of Building Blocks¶
- Ready to use: Start using immediately without complex configuration or learning
- Best practices: Optimal implementation patterns designed by IBM's engineering team
- Domain-specific: Provides Vector Search-specific guidance and implementation patterns
- Customizable: Flexibly extend to meet business requirements using IBM Bob
Building Block Used in This Hands-on¶
Vector Search Builder (Milvus-based)
What it provides: Vector database (Milvus) construction and management capabilities
Included features:
- Milvus database setup
- Collection (data container) creation
- Local embedding model integration with Hugging Face Transformers
- Sample product data ingestion workflow
- Vector search optimization
Integration with IBM Bob: Using Vector Search Builder mode, IBM Bob provides specialized support for Vector Search
Value of Building Blocks
Without Building Blocks: Read Milvus documentation, learn Python SDK, select and integrate embedding models (days)
With Building Blocks: Install Vector Search Builder and instruct IBM Bob (minutes)
Unique Innovations in This Hands-on
- Shared Milvus: The instructor runs Milvus for everyone (setup/instructor/docker-compose.yml), so participants need only IBM Bob, the participant zip, and the connection information
- On-site or remote: The documentation is shared on the local network (
http://instructor IP:8001) or through GitHub Pages or ngrok - No API key: Hugging Face Transformers creates the embeddings locally
- Step by step: Part 1 tries Vector Search, Part 2 adds features with IBM Bob, and Part 3 checks them and cleans up
| Provider | What's Provided | Purpose |
|---|---|---|
| Building Blocks | Vector Search Builder mode FastAPI sample Milvus setup guide |
Technology foundation provision Development acceleration |
| This Hands-on | Instructor environment (Docker Compose) Participant scripts Educational documentation |
Educational design Learning experience optimization |
What is IBM Bob?¶
IBM Bob is a development tool where AI assists with coding.
What IBM Bob Can Do¶
- Natural language instructions: Communicate what you want to do in words
- Automatic code generation: Automatically writes high-quality code
- Code review: Points out code issues
- Integration with Building Blocks: Provides technology-specific support through custom modes
Synergy with Building Blocks¶
Building Blocks provides the foundation right away and IBM Bob customizes it from natural language instructions, so you reach production-level quality in the shortest time.
Comparison of Development Methods¶
| Development Method | Time Required | Required Skills | Code Quality |
|---|---|---|---|
| Without Building Blocks | Days to weeks | Programming, DB design, API design | Depends on developer skills |
| IBM Bob only | Hours to days | Basic technical understanding | High quality but time-consuming to build |
| Building Blocks + IBM Bob | Minutes to hours | Just need to instruct in natural language | Production-level high quality |
What is Vector Search?¶
Vector Search is a technology that searches by understanding the "meaning" of words.
Difference from Traditional Search¶
Traditional keyword search:
- "red sneakers" → Searches for products containing the characters "red" and "sneakers"
- "red running shoes" won't be found (different characters)
Vector Search (semantic search):
- "red sneakers" → Understands the meaning of "red" and "sneakers"
- "red running shoes" will be found (similar meaning)
- "beginner camera" → "entry-level digital camera" will be found
Real-world Use Cases¶
- E-commerce sites: "Find similar products" feature
- Internal search: "Find documents similar to this document"
- Customer support: "Find similar questions"
Hands-on Flow¶
Total: Approximately 60 minutes
| Part | Content | Time Required |
|---|---|---|
| Preparation | Vector Search Builder setup | 10 minutes |
| Part 1 | Experience Vector Search | 15 minutes |
| Part 2 | Add features with IBM Bob | 25 minutes |
| Part 3 | Verification and cleanup | 5 minutes |
| Summary | Review and Q&A | 5 minutes |
Requirements¶
- Computer (Mac, Windows) and internet connection
- IBM Bob (already installed)
- Web browser (Chrome, Firefox, Safari, Edge, etc.)
Distributed by instructor:
- Hands-on procedure URL
- Vector Search Builder participant package (
vector-search-builder-en.zip) - Connection information (Milvus connection information)