Dynamic Persona MoE RAG

using this guide to upgrade the first blog post I want you to write a series of prompts which will take the project to completion : https://danielkliewer.com/blog/2026-01-22-from-scaffolding-to-reality-building-the-dynamic-persona-moe-rag-system - https://danielkliewer.com/blog/2026-01-22-dynamic-persona-moe-rag

Here’s a **series of prompts** you can use to take your project described in your blog posts **“Building a Dynamic Persona-Based Mixture-of-Experts RAG System”** and **“From Scaffolding to Reality: Building the Dynamic Persona MOE RAG System”** from its current state to **full completion** (feature development, testing, productionization, documentation, and future enhancements). These prompts are structured so you can feed them to an LLM (e.g., Qwen, Claude, local Ollama instance) to generate code, docs, or planning artifacts. --- ## 🚀 PHASE 1 — Project Setup & Architecture Finalization **1. Project Initialization & Structure** > *Generate a detailed project setup script including file structure, package management (venv, Poetry), and starter boilerplate code for a Python-based Dynamic Persona MoE RAG system.* ``` Generate a complete initial project scaffold in Python for a Dynamic Persona MoE RAG system, including: - `src/core`, `src/graph`, `src/personas`, `src/agents`, `src/evaluation`, `src/storage` - `configs/` YAML templates - essential README sections - Makefile scripts for setup/test/run Include example base classes and minimal working files. Show directory tree and starter code. ``` --- **2. Architecture Blueprint Refinement** > *Produce a refined architecture diagram & narrative that includes graph knowledge, MoE routing, retrieval, and persona metrics.* ``` Describe the high-level architecture for a dynamic persona-driven Mixture-of-Experts (MoE) RAG system with: - Dynamic knowledge graph creation on query - Persona-driven traversal logic - Multi-provider model orchestration (Ollama + others) - RAG retrieval and context merge - Evaluation & metrics Include diagrams, data flow, and interface boundaries. ``` --- ## 🧠 PHASE 2 — Core System Implementation **3. Dynamic Knowledge Graph Module** > *Generate complete implementation code for dynamic knowledge graph with nodes/edges, query scoping, serialization, and traversal support.* ``` Write Python classes for a `DynamicKnowledgeGraph` with: - lazy node construction - edge creation and metadata - efficient traversal interface - export/serialization support Add docstrings and tests. ``` --- **4. Persona Traversal Strategies** > *Draft multiple persona traversal strategies with scoring functions (keyword, embedding, semantic) plus Python implementations.* ``` Provide Python implementations of: - Simple keyword-based persona traversal - Advanced semantic embedding traversal (e.g., using embeddings via Milvus) - Priority heuristic scoring Add tests and examples. ``` --- **5. Mixture-of-Experts (MoE) Orchestration** > *Build a fully functional MoE orchestrator that can route to multiple persona agents, handle token budgets, and merge results.* ``` Generate Python code for a `MoeOrchestrator` class with: - multi-model dispatch - persona prioritization - token-aware batching - fallback logic Include examples with Ollama backends and provider interface abstraction. ``` --- ## 📡 PHASE 3 — Retrieval & RAG Integration **6. RAG Module** > *Develop retrieval-augmented generation pipeline including index ingestion, retrieval, and merging retrieved context with graph context.* ``` Write a retrieval module that: - ingests documents into a vector store (e.g., Milvus) - embeds query and retrieves similar context - merges graph context and retrieval results - formats into prompt with persona context Add integration tests. ``` --- **7. Multi-Provider LLM Integration** > *Implement provider interface abstraction supporting different LLM backends (e.g., Ollama, Nemotron, others).* ``` Define a `ModelProviderInterface` with: - `generate_structured` - `max_context_tokens` - token accounting Provide concrete implementations for: - OllamaProvider - NemotronProvider - Dummy/mock provider for tests ``` --- ## 🧪 PHASE 4 — Testing, QA, & Evaluation **8. Comprehensive Test Suite** > *Generate tests for all core modules including graph, traversal, orchestrator, RAG, metrics, and API.* ``` Write pytest tests covering: - graph correctness - traversal behavior with mock personas - orchestrator routing and token management - retrieval + RAG pipeline - structured JSON outputs Include edge cases and performance checks. ``` --- **9. Metrics Tracking & Dashboards** > *Create code to track latencies, token usage, JSON validity rates, and visualize them via API endpoints.* ``` Generate Python modules/classes for: - collecting metrics (latency, tokens, provider usage) - persistent storage of metrics - FastAPI endpoints to serve metrics - example React components to visualize trends ``` --- ## ⚙️ PHASE 5 — Production API & Frontend **10. Production-Ready FastAPI** > *Produce a fully fleshed FastAPI deployment with routes for pipeline runs, persona management, and graph snapshots.* ``` Build a FastAPI service with endpoints: - `/run` to execute full pipeline - `/personas` for CRUD persona - `/graph/{run_id}` for visual snapshots - metrics endpoints Include security best practices, logging, caching. ``` --- **11. Real-Time Visualization Frontend** > *Generate Next.js + TypeScript UI for managing and visualizing personas, graphs, and runs.* ``` Produce a Next.js UI with: - Persona grid & filters - SVG graph viewer with highlighting - Real-time metrics dashboard - Run management UI Include API integration with FastAPI. ``` --- ## 📚 PHASE 6 — Docs & Deployment **12. Documentation & Tutorials** > *Write comprehensive docs, examples, and how-to guides for users & developers.* ``` Generate: - User guide (how to run/install) - Developer guide (architecture, modules) - API docs (OpenAPI + examples) - Tutorials (adding persona, building custom traversal) ``` --- **13. Deployment Scripts/CI** > *Create production deployment artifacts including Docker Compose, Kubernetes manifests, and CI workflows.* ``` Generate: - Dockerfiles for backend/frontend - Kubernetes manifests with autoscaling - GitHub Actions CI for tests + lint - Deployment to cloud provider (GCP/AWS) ``` --- ## 🧠 PHASE 7 — Advanced Intelligence & Extensions **14. ML-Enhanced Relevance & Persona Evolution** > *Draft prompts to build ML modules that learn relevance scoring and dynamic persona evolution.* ``` Design a system using machine learning to: - predict graph relevance - evolve persona thresholds dynamically - evaluate persona pathways Provide data pipelines and training scripts. ``` --- **15. Multimodal & Distributed Extensions** > *Plan and code extendable modules for multimodal inputs, distributed orchestration, and federated learning.* ``` Outline and implement support for: - images, audio inputs - distributed persona execution - federated learning for persona adaptability ``` --- If you want, I can **turn all of these prompts into a ready-to-run prompt script file** (with syntax tailored for Qwen/Nemotron/Ollama) or help you run them iteratively with checks and auto-test integration.