[FEATURE] Implement AI/ML Embedding & Multi-Depth Routing for .txt files

[dkstlzk]

💡 What's your idea?

• Build the basic AI/ML pipeline for this project
• Start with .txt file embedding generation and multi-depth routing via clustering
• Integrate FAISS-based similarity search at Depth 1, and hierarchical routing till Depth 4
• Implement peertable-based routing and dynamic Tagging Vector (TV) creation

🤔 Why would this be useful?

• Forms the core semantic intelligence of FileNest
• Enables multi-depth decentralized storage and retrieval of file embeddings
• Provides foundation for multi-modal (image, video, pdf) support in future
• Decentralized, scalable, and AI-powered — as per the project proposal

🎯 Where should this feature go?

• 🧠 AI/ML Models (AI components and machine learning)
• � Backend (Core server logic and APIs)
• 🌐 Frontend (CLI & React.js user interface)
• 🌐 Network Layer (P2P networking and communication)
• 🔗 Shared Utilities (Common code and libraries)
• 📚 Documentation
• 🛠️ Development tools

🖼️ How should it look/work?

• Peers generate text embeddings
• FAISS at Depth 1 finds nearest D1TV → routes embedding to selected peer
• At each next depth:
  Peer performs similarity check (cosine similarity) against its TVs
  If no match (similarity < threshold), assign new TV to a peer
  Continue till Depth 4, where metadata is stored
• Each peer maintains a peertable.json:
  Tracks its child TVs at one lower depth
  Contains available peers list for assigning new TVs
• When creating a new TV:
  Pick peer from available list/DHT (if available)
  Send CreateTV message with centroid, TV ID, parent ID, thresholds, timestamp

🌟 How important is this feature?

• 🔥 Critical - I really need this to use the app
• 🎯 High - This would make the app much better
• 😊 Medium - This would be nice to have
• 🤷 Low - Just a small improvement

📚 Examples

🔧 Implementation Ideas (Optional)

• Implement embedding generation module for .txt files
• Build FAISS clustering and search for Depth 1
• Define and integrate peertable JSON schema and CRUD logic
• Implement routing logic for Depth 1 to Depth 4
• Build CreateTV message structure and handler
• Simulate multi-depth routing locally using multiple peer processes
• Integrate libp2p mDNS peer discovery (optionally DHT later)

📱💻 Additional Context

• Core part of AI/ML deliverables per FileNest RAID proposal
• Focused on text files initially; will extend to other content types later
• Cleanly modular, decentralized, and scalable architecture

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🚀 Want to build this feature yourself?

Great! Comment "I'd like to work on this" and we'll help you get started. Check out our Contributing Guide if you're new to contributing!