Initial thoughts

Overview This guide outlines the technical architecture and implementation strategy for integrating a JSON-based model of mental state dynamics into a Retrieval-Augmented Generation (RAG) system, powered by an LLM such as OpenAI or a local model via Ollama. The goal is to support dynamic, meaning-based conversations about mind transitions, especially those described in Buddhist sources such as the Bardo Thödol.

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1. Project Structure

rag-project/
├── data/                # Stores uploaded JSON mental-state descriptors
├── chroma_db/           # Vectorstore backed by ChromaDB
├── load_and_embeded.py  # Script for embedding and adding JSONs to vectorstore
├── qm.py               # OpenAI-based query tool
├── qr.py               # Local model (e.g., Ollama) query tool
├── rag_web_service.py  # FastAPI-based backend service
├── templates/          # HTML templates for web client
├── static/             # CSS and frontend JS assets

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2. JSON Descriptor Model Each file represents a mental state or phase change, e.g.:

{
  "id": "earth_dissolution",
  "stage": "dissolution",
  "element": "earth",
  "withdrawal": "solidity and form dissolve",
  "bodily_signs": "loss of strength, sinking feeling",
  "mental_signs": "scattered thoughts, detachment",
  "symbolic_image": "flickering yellow light",
  "constraints_released": ["form", "body-attachment"],
  "field_effect": "loss of grounding",
  "next": "water_dissolution"
}

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3. Data Loading and Embedding

• Run load_and_embeded.py to embed all files in data/ into ChromaDB using HuggingFace embeddings
• Update imports to use langchain_community.* as needed
• Automatically persists Chroma state

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4. Backend Web Service (FastAPI)

• Endpoints:
  • POST /upload-json: Upload a JSON file with form data
  • GET /list-jsons: List summaries of uploaded JSONs
  • GET /json/{filename}: View full JSON content
  • DELETE /json/{filename}: Delete a JSON descriptor
• Runs via Uvicorn with CORS enabled for cross-site interaction
• Summarizes each descriptor dynamically

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5. Web Client Page

• HTML client can be embedded in WordPress or standalone
• Allows upload of JSON mental descriptors
• Lists and previews descriptors via list-jsons
• Simple RAG query form sends user input to backend

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6. Querying

• qm.py: Uses OpenAI LLM to run a RAG query with embedded context
• qr.py: Uses Ollama LLM locally for the same purpose
• Both read from ChromaDB

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7. Notes on Hosting & Deployment

• Can be served locally or remotely
• Port 8000 may require firewall opening
• RAG system accessible from other servers/sites via FastAPI
• JSON uploads dynamically alter the RAG corpus

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Next Steps

• Improve JSON editing pipeline (web-based preview and edit)
• Incorporate long-form source parsing (e.g. full Bardo Thödol)
• Develop “state tracker” that follows evolving awareness states across time
• Design backend support for generating or proposing JSON descriptors from source text

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This document provides the technical baseline needed for operationalizing the deeper vision captured in the companion document: Building a Dynamic Model of Mind from Buddhist Descriptions of Mental Transitions.