# Project Journey

*A log of how Context Nexus came together.*

---

## Where We Are

**Current Phase**: Documentation & Design Complete → Ready for Implementation

---

## The Story So Far

### Day 1: Defining the Vision

We started by asking: *What's actually broken with AI systems in production?*

The answer was clear:
- RAG demos work great, but scaling them is a nightmare
- Context windows overflow constantly
- No one knows why the AI said what it said
- Everything falls apart under load

So we designed Context Nexus to solve these problems from the ground up.

### The Key Decisions

**1. Python + Rust Architecture**

We decided early that pure Python wouldn't cut it for performance-critical paths. But Rust everywhere would kill developer experience. So we split it:
- Python for the API, agent logic, and integrations
- Rust for token counting, vector scoring, and graph traversal

This gives us the best of both worlds.

**2. Hybrid Retrieval by Default**

Most RAG systems only do vector search. We're combining:
- Semantic search (meaning)
- Graph search (relationships)
- Keyword fallback (exact matches)

And fusing the scores intelligently.

**3. Token Budgets, Not Token Limits**

Instead of hoping context doesn't overflow, we enforce budgets. The system automatically compresses and prioritizes to stay within bounds.

**4. Observability First**

Every query produces a trace. You can see exactly what happened, why, and how long it took.

---

## What's Been Built

### Documentation
- [x] Product document explaining what we're building and why
- [x] Architecture document with technical deep-dives
- [x] 8 architecture diagrams visualizing the system
- [x] Use case guide with 4 complete workflow examples
- [x] Quickstart tutorial for beginners
- [x] This blog for the journey

### Open Source Foundation
- [x] Professional README with badges
- [x] MIT License
- [x] Contributing guide
- [x] Code of conduct
- [x] .gitignore

---

## What's Next

### Implementation Phase
- [ ] Python package structure
- [ ] Rust core with PyO3 bindings
- [ ] Basic ingestion pipeline
- [ ] Vector retrieval
- [ ] First working demo

---

## Lessons Learned


---

*Last updated: January 2026*
