wikijs_data/home.md

title	description	published	date	tags	editor	dateCreated
home	home	true	2025-04-04T07:08:00.826Z		markdown	2025-04-04T07:01:55.964Z

🔍 Metadata (click to view)

title: "Janusian Documentation Strategy"
description: "A strategy for building human-readable, RAG-optimized documentation for AI agents at NeuroMentum."
project: "infra_docs"
category: "meta"
tags: ["documentation", "RAG", "ai_agent", "wiki", "postgres"]
hierarchy: ["meta"]
created: "2025-04-04"
last_reviewed: "2025-04-04"
visibility: ["human", "ai_agent"]
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:

> 🧠 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:

{
  "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:

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:

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

---