xb

xb Project Vision & Development Direction

Post v1.0.0: Embracing Rapid Technological Iteration

🎯 Core Vision

Empower Go developers to:

Seamlessly switch between relational databases (PostgreSQL/MySQL) and vector databases (Qdrant/pgvector)
Unified API for traditional queries and AI scenarios (RAG/vector search/hybrid queries)
Stay simple without drowning in technical complexity

🌊 Adapting to the Era of Rapid Tech Iteration

Why No Fixed Roadmap?

In the AI/Vector DB era:

New technologies emerge monthly (new vector databases, new ANN algorithms)
User needs evolve rapidly (from RAG to Agent, from search to recommendation)
Best practices continuously evolve (Embedding models, Chunking strategies)

A fixed Roadmap would make us miss truly important opportunities!

🧭 Development Principles (Unchanging)

1. Pragmatism First

✅ Solve real problems, not chase tech hype
✅ Prioritize proven technologies and patterns
✅ Keep API simple and consistent

2. Community-Driven

✅ Listen to user feedback and needs
✅ Welcome contributors to propose new directions
✅ Decide priorities through Issues/Discussions

3. Backward Compatibility

✅ v1.x series maintains API stability
✅ New features through extension, not disruption
✅ Deprecation requires clear migration paths

🔮 Potential Directions (No Commitment)

Current Focus Areas

1. Vector DB Ecosystem

Emerging vector database support (Milvus / Weaviate / Pinecone)
ANN algorithm optimization (IVF / HNSW parameter tuning)
Hybrid Search performance optimization

2. AI Agent Scenarios

JSON Schema generation optimization
Tool Calling pattern support
Multi-step Query orchestration

3. RAG Best Practices

Chunking strategy integration
Reranking support
Enhanced metadata filtering

4. Performance & Observability

Connection pool best practices
Query performance analysis tools
Slow query logging interceptor

🤝 How to Influence xb’s Future?

1. Propose Needs (Issue)

Title: [Feature Request] Support Milvus Vector Database
Description:
- Business scenario: Large-scale vector search (1B+ vectors)
- Expected API: Unified interface similar to QdrantX
- References: Milvus official documentation link

Title: [Best Practice] Building Multi-tenant RAG System with xb
Content:
- Architecture design
- Performance optimization experience
- Pitfalls and solutions

3. Contribute Code (Pull Request)

- New database support
- Performance optimization
- Documentation improvements
- Example applications

📊 Decision Process

How to Decide Whether to Adopt a New Feature?

Question 1: Is there a real user need?
           ↓ Yes
Question 2: Does it align with xb's core vision?
           ↓ Yes
Question 3: Will it break existing API?
           ↓ No
Question 4: Is there a community contributor willing to maintain it?
           ↓ Yes
           
✅ Adopt!

If any step is "No" → Discuss alternatives in Issue

🎓 Learning from AI Assistants

Technology Research Process

When considering support for new technology:

Ask AI Assistants:

"What are the most popular vector databases in 2025? What are their advantages?"
"What are the latest best practices for RAG systems?"
"What new algorithms and optimization techniques exist for Hybrid Search?"

Verify Information:
- Check GitHub Star count and activity
- Read official docs and benchmarks
- Find real-world use cases in the community
Quick Prototyping:
- Create example apps under examples/
- Validate API design reasonableness
- Collect community feedback
Iterate & Optimize:
- Adjust API based on feedback
- Improve docs and tests
- Release new version

📅 Release Cadence

Version Number Rules

v1.x.0 (Minor): New features, maintaining backward compatibility
- New database support
- New API extensions
- Major performance optimizations
v1.x.y (Patch): Bug fixes and minor improvements
- Bug fixes
- Documentation updates
- Performance fine-tuning
v2.0.0 (Major): Major architectural changes (use caution!)
- Incompatible API changes
- Major architectural refactoring
- Requires extensive community discussion

Release Timing

No fixed schedule, but based on:

✅ Sufficient accumulated improvements
✅ Strong community demand
✅ Tests and docs are complete

🌟 Success Metrics

How to Measure xb’s Success?

Not by GitHub Stars, but by:

Real Users: How many production projects use it?
Community Activity: Quality and quantity of Issues/PRs/Discussions
Ecosystem Richness: How many third-party tools and integrations?
Documentation Quality: Can new users get started quickly?
Technical Influence: Does it advance best practices?

🚀 Next Steps (Open-Ended)

Possible Directions (Depending on Community Feedback)

Milvus Support (if user demand exists)
Performance Benchmarks (if contributors propose)
GraphQL Integration (if AI Agent scenarios need it)
Cloud-Native Tools (if Kubernetes usage grows)
More AI Framework Integrations (if new popular frameworks emerge)

Priorities are determined by the community, not a preset Roadmap!

💬 Stay Connected

GitHub Issues: Bug reports and feature requests
GitHub Discussions: Best practices and technical discussions
AI Assistant Consultation: Quickly understand new tech trends

Core Philosophy:

In the era of rapid technological iteration, flexibility matters more than perfect planning.
Let’s embrace change, listen to the community, and keep learning!

v1.0.0 is the starting point. The future is shaped by the community! 🌟

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