[Community Contribution] Advanced Documentation: RAG Architecture, Vision Workflows & System Integration

[ORIORIS-FR]

Hi @Lapis0x0,

First, thank you for the "You Orchestrate, LLM Operates" paradigm. It is a significant shift from standard chat interfaces, and I have built my entire daily workflow around it.

I am a French Power User and System Architect, and I have been pushing the plugin to its limits within a broader Cognitive System (integrating n8n, MCP, and Local LLMs).

🎁 Contribution: Technical Documentation
To give back to the project, I have written three technical reports based on my testing. I am sharing them here in case they can be useful for your Wiki, documentation, or simply to see how your tool is used in complex production environments.

1. 🏗️ Architecture RAG Obsidian YOLO & Pipelines YOLOv11

• Scope: A technical deep dive into PGlite implementation vs. standard vector stores, and data pipelines.

2. 🏗️ Architecture des Systèmes Cognitifs — MCP, Orchestration & Obsidian

• Scope: How I integrated Obsidian YOLO as the "Hippocampus" of a larger agentic system (connected to n8n and MCP servers).

3. 📘 COMPLÉMENT — Module Vision Locale (YOLO + Ollama)

• Scope: A practical tutorial for setting up local vision models (Llava/Moondream) within the plugin context.

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💡 Request for Technical Exchange
As I am refining this architecture, I have hit a few specific bottlenecks. If you are open to it (and without taking too much of your time), I would love to exchange a few thoughts regarding:

1. Model Switching Logic: Best practices for segregating "Chat" models vs "Tool" models to optimize costs/performance (e.g., using flash models for file ops vs reasoning models for synthesis).
2. In-Note Interaction: How to best manage the context window when the agent interacts with multiple large notes simultaneously.

Let me know if you would be open to discussing these points here or in a separate thread.

Best regards,

Patrice
🏗️ Architecture des Systèmes Cognitifs — MCP, Orchestration & Obsidian.md
📘 COMPLÉMENT — Module Vision Locale (YOLO + Ollama).md
🏗️ Architecture RAG Obsidian YOLO & Pipelines YOLOv11.md

[Lapis0x0]

Hi @ORIORIS-FR,

Thank you so much for this incredible contribution! It's truly inspiring to see how you've integrated Obsidian YOLO into such a sophisticated cognitive system architecture.

I'll go through your technical reports in detail over the next few days and will share my feedback here once I've had a chance to review them properly.

Regarding your technical questions on model switching logic and context window management — these are great topics and I'd be happy to discuss them. Let me first digest your documentation, and then we can dive into those specifics.

Thanks again for pushing the plugin to its limits and sharing your findings with the community! 🙏

[ORIORIS-FR]

Thanks for your feedback, @Lapis0x0!

It's a pleasure to discuss this with you. Take all the time you need to read the reports.
I'm preparing a list of more focused technical questions regarding the orchestration of tools (MCP + n8n), the management of agentic hallucinations, the 'Ghost Tool' issue, and a few other points.

We'll discuss it again as soon as you're ready.
Thanks again for your work on YOLO!

[Lapis0x0]

Thank you so much for taking the time to write and share these reports, and for pushing Obsidian YOLO inside such a complex cognitive system setup. It is very helpful for me to see how you integrate the plugin with MCP, n8n and local vision models, and your notes about PGlite and performance bottlenecks are especially valuable.

On my side I will keep working on improving the overall performance and stability of YOLO, with a special focus on indexing and the PGlite based RAG store.

In the next version I am also planning to introduce built-in Agent capabilities in YOLO, so that it can orchestrate tools and workflows more easily. I hope you will enjoy it once it is available.

[ORIORIS-FR]

Hi @Lapis0x0,
Thank you for your latest update and for taking the time to review my reports. I'm especially keen on the upcoming built-in Agent capabilities you mentioned for YOLO.
This has sparked further thought, particularly as I explore how YOLO integrates with more comprehensive agent orchestration frameworks. I've been documenting my findings on platforms like OpenClaw – which offers robust capabilities for code generation, video production, multi-format content, and web scanning, often via its ClawHub with thousands of skills.
This leads to a few key questions I'd be eager to discuss when you're ready:

1. YOLO's Role in a Multi-Agent System: How do you envision YOLO interacting with these more specialized external agent frameworks? Is the goal for YOLO to primarily be an agent for Obsidian-specific tasks, or to also act as an orchestrator for other external agents (e.g., triggering an OpenClaw skill from within Obsidian via YOLO's agent capabilities)?
2. Native File System Operations: Does "built-in Agent capabilities" imply YOLO will handle direct file system operations (like creating new notes/folders, moving files) natively, reducing the need for external MCP tools for these core functions? This would significantly streamline vault interaction for automated tasks like RAG output-based note creation or inbox processing.
3. Managing Agentic Hallucinations: Given that even advanced external agents like OpenClaw still exhibit 5-10% hallucinations, how does YOLO plan to mitigate this, especially when performing critical actions within the vault?
   I believe exploring these points, alongside the previous questions on model switching and context management, will be highly valuable as we both push the boundaries of agentic PKM.
   Thanks again for your remarkable work on YOLO!
   Best regards,
   Patrice

[Lapis0x0]

Thank you for your thoughtful questions! Let me address them one by one:

1. YOLO's Role and Vision

To be honest, I have no plans to turn YOLO into a fundraising project or build a company around it. At least for the foreseeable future, I'd prefer it to remain focused as a tool for handling Obsidian-specific tasks.

The agent capability will be one of YOLO's foundational features and will play an important role in future iterations. However, YOLO won't stop at being just an AI Agent. I plan to build more interesting modules on top of the AI capabilities, such as a better whiteboard experience, learning modes, and more.

2. Native File System Operations

Yes, the built-in agent capability means YOLO will natively handle file system operations. There's really no need to introduce external MCP for these core functionalities.

In upcoming versions, I'll continue to add features like automatic triggers, scheduled triggers, and background multi-agent tasks.

3. Managing Hallucinations

In the next major update, I'll introduce a brand-new search tool that enables the AI agent to search more accurately and effectively. At the same time, acknowledging that AI hallucinations can be difficult to completely eliminate in certain situations, I'll implement stricter and more fine-grained permission controls to mitigate the risks associated with hallucinations.

Thanks again for your interest and support for YOLO!