wikijs_data/home.md

---
title: Jiki Documentation Strategy
description: home
published: true
date: 2025-04-06T03:36:30.278Z
tags: 
editor: markdown
dateCreated: 2025-04-04T07:01:55.964Z
---

<details>
<summary style="color: #888; font-style: italic;">🔍 Metadata (click to view)</summary>

```yaml
title: "Janusian Documentation Strategy"
description: "A strategy for building human-readable, RAG-optimized documentation for AI agents at NeuroMentum."
classification: "internal"
document_groups: "concept"
access_groups: "neuromentum"
project: "infra_docs"
category: "meta"
tags: ["documentation", "RAG", "ai_agent", "wiki", "postgres"]
hierarchy: ["meta"]
created: "2025-04-04"
last_reviewed: "2025-04-04"
agent_usage_hint: "Use this strategy to format all documentation for dual human-agent readability."
version: "v1"
```
</details>


# 🪶 **Janusian Documentation Strategy**  
### For AI Agent & PostgreSQL Infrastructure at NeuroMentum

---

## 🌟 **Preamble: The Janusian Vision**

Our documentation is more than technical scaffolding — it’s an interface between human insight and machine inference. Following **Janusian design principles**, this plan ensures:

- 🧠 **Human-readability** (wiki-style hierarchy, intuitive naming)
- 🤖 **AI-compatibility** (semantic metadata, chunked content, retriever-optimized)
- 🔄 **Dual-perspective thinking**: all docs are shaped for both **intelligent co-developers** and **intelligent consumers** (agents, LLMs, and future tools)
- 🔮 **Future-proofing** via metadata, version control, and traceable structure

---

## 📁 1. **Folder Hierarchy: Wiki-Readable, Logically Scoped**

Keep folder structure intuitive, max 3–4 levels deep:

```
docs/
├── postgres/
│   ├── schemas/
│   ├── monitoring/
│   └── admin_tasks/
├── personas/
│   ├── chatgpt/
│   ├── khebri/
│   ├── scarabone/
│   └── scarabtwo/
├── agent_architecture/
│   ├── kafka/
│   ├── llm_integration/
│   └── task_orchestration/
├── reference/
│   └── utf8_handling.md
└── manifest.md
```

📌 Each folder = a logical domain  
📌 Each file = a self-contained, RAG-chunkable unit

---

## 🏷️ 2. **Embedded Metadata per File**

Every file begins with **structured YAML frontmatter**:

```yaml
---
title: "Khebri Schema"
description: "PostgreSQL schema for thread tracking and status."
project: "chatBCI"
category: "schemas"
tags: ["postgres", "khebri", "thread", "active_status", "sql"]
hierarchy: ["postgres", "schemas"]
created: "2025-03-31"
last_reviewed: "2025-04-04"
visibility: ["human", "ai_agent", "scout_agent"]
agent_usage_hint: "Use when setting up or querying thread states"
version: "v1"
---
```

📎 Purpose:
- Helps RAG engines **filter** and **rank** results
- Enables **traceability** for future tooling
- Bridges intent between human and AI agents

---

## 📦 3. **Chunking + RAG Readiness Guidelines**

Each doc should:
- Be under **1000–1500 tokens per section**
- Use **semantic headings** (e.g., `## Create Thread Table`, `## Reset Identity Counter`)
- Include **examples + explanations** (`Why`, `When`, `How`)
- Add **"Janusian Intent"** callouts:

```md
> 🧠 Janusian Intent: This query resets identity counters to preserve clean state across simulation iterations and testing loops.
```

---

## 🧠 4. **Central Metadata Registry: `janus_index.json`**

Maintained at project root (`docs/`), this file tracks every document:

```json
{
  "docs/postgres/schemas/khebri.sql": {
    "title": "Khebri Schema",
    "tags": ["postgres", "schema", "threads"],
    "hierarchy": ["postgres", "schemas"],
    "version": "v1",
    "agent_usage_hint": "Used during thread setup, task tracking",
    "visibility": ["ai_agent", "human"]
  }
}
```

💡 **Bonus**: Later you can auto-generate this via a crawler script.

---

## 🧬 5. **Versioning + Change History**

Use simple folder or file-based versioning:
```
docs/
└── personas/
    └── khebri/
        ├── v1.md
        └── v2.md
```

Or append version into frontmatter and add `CHANGELOG.md` per folder:

```yaml
version: "v2"
changelog:
  - "2025-04-01: Added updated persona structure"
  - "2025-03-31: Initial draft"
```

---

## ⚙️ 6. **RAG Integration Features (Future-Ready)**

Later, these files can power:
- **Chunk-based vector search**
- **Metadata + embedding fusion retrieval**
- **Agent-specific prompt context filters** (via `tags`, `hierarchy`, `agent_usage_hint`)
- **Access gating** with `visibility` field

📦 Optional: Extend with `graph` or `relations` fields to define links:

```yaml
graph:
  links_to: ["state_transitions.md", "thread_multistates.md"]
  agent_role: "architect"
```

---

## 🚀 7. **Starter Scripts to Support This System**

We can build:
- `build_index.py`: Scans folders, extracts frontmatter, outputs `janus_index.json`
- `chunk_docs.py`: Breaks large docs into overlapping chunks with hierarchy/context
- `rag_query_preview.py`: Preview which doc chunks would be retrieved for a given question

Let me know if you'd like these generated! 💻

---

## 🧭 8. **Agent-Aware Style Guidelines**

| Style Area       | Guideline |
|------------------|-----------|
| 💬 Headings      | Use `##` or `###` consistently for chunk boundaries |
| 💡 Intent Blocks | Add "Janusian Intent" sections to explain *purpose* |
| 💻 Code Blocks   | Use real SQL / shell / markdown examples |
| 🔗 Cross-links   | Use relative paths: `[See thread states](../states.md)` |
| 🔒 Access Hints  | Use `visibility` to mark human vs. agent-readable |

---

## 📌 Final Summary

**Your RAG-ready Janusian doc system** will be:

- 🔍 **Searchable** by humans and AI
- 🧱 **Modular**, well-scoped, chunkable
- 🏷️ **Metadata-rich**, with project, version, intent, and role info
- 🧠 **Philosophically aligned** with human-AI co-development
- 💡 **Designed to scale**, as agents and tooling grow

---