199 lines
6.7 KiB
Markdown
199 lines
6.7 KiB
Markdown
# Dynavera: An Agentic Approach to Role-Specific Trainers
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[](https://vuejs.org/)
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[](https://vite.dev/)
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[](https://www.djangoproject.com/)
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[](https://www.django-rest-framework.org/)
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[](https://channels.readthedocs.io/)
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[](https://www.postgresql.org/)
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[](https://github.com/pgvector/pgvector)
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[](https://redis.io/)
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[](https://docs.celeryq.dev/en/stable/)
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[](https://fastapi.tiangolo.com/)
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[](https://www.docker.com/)
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Dynavera is a distributed agentic onboarding platform designed to solve the productivity gap in organizational training. The motivation was to create a system that delivers tailored, retrieval-grounded guidance for new roles, leveraging modern AI and orchestration techniques. This project was built to address the challenge of scalable, role-specific onboarding—where generic training fails to meet the needs of specialized positions. By combining curriculum generation, document-grounded retrieval, and live progress tracking, Dynavera enables organizations to onboard users efficiently and transparently. Through this project, I learned how to architect distributed systems, integrate retrieval-augmented generation, and design agent workflows for real-world impact.
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## Table of Contents
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- [Overview](#overview)
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- [Core Features](#core-features)
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- [Architecture](#architecture)
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- [Quick Start](#quick-start)
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- [Usage & Smoke Test](#usage--smoke-test)
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- [Project Structure](#project-structure)
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- [Demo Access](#demo-access)
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- [Operational Commands](#operational-commands)
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- [Screenshots](#screenshots)
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- [Documentation](#documentation)
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## Overview & Core Features
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Dynavera addresses the onboarding productivity gap by combining:
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- Role-aware curriculum generation
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- Retrieval-augmented responses grounded in uploaded organizational documents
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- Tool-aware orchestration over WebSockets
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- Local-first inference support for privacy-sensitive deployments
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The runtime is intentionally distributed: Django manages state and governance, while a dedicated inference service handles model-intensive workloads.
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Key features:
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- Distributed architecture separating application control plane and inference plane
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- Multi-agent style orchestration for curriculum, knowledge, assessment, and monitoring behaviors
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- RAG pipeline with semantic chunking, embeddings, and pgvector retrieval
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- Live onboarding session updates via Django Channels WebSockets
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- Persistent session/progress storage for auditability and recovery
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## Architecture
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High-level architecture diagram:
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Key backend runtime entry points:
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- `apps/onboarding/consumers/` for orchestration loop and WebSocket flow
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- `apps/onboarding/mcp.py` for tool routing and backend tool execution
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- `apps/knowledge/tasks.py` for ingestion/chunking/embedding workflow
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- `gpu_server.py` for inference and embedding endpoints
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## Quick Start
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Prerequisites:
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- Docker Engine or Docker Desktop
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- NVIDIA drivers and NVIDIA Container Toolkit (for GPU inference)
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1. Clone repository
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```bash
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git clone https://git.cs.bham.ac.uk/projects-2025-26/vxn217
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cd vxn217
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```
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2. Create environment file
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PowerShell:
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```powershell
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Copy-Item .env.template .env
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```
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CMD:
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```cmd
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copy .env.template .env
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```
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macOS/Linux:
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```bash
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cp .env.template .env
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```
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3. Start development stack
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```bash
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docker compose -f compose/dev/docker-compose.yml --env-file .env up -d --build
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```
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4. Open application
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- http://localhost:8000
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## Usage/Smoke Test
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Follow this end-to-end workflow to use the project and to run the smoke test:
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1. Create or select an organization and role
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2. Upload role-specific training files
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3. Wait for ingestion and embedding to complete (monitor the ingestion UI or logs)
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4. Invite a user to the configured role
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5. Log in as that user and start onboarding
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6. Complete at least one guided interaction and one assessment action
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Expected behaviour:
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- Workflow completes without manual page refresh
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- UI state transitions update live
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- No dropped WebSocket session during onboarding
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## Project Structure
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- `apps/accounts` user, organization, and role membership logic
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- `apps/knowledge` training file ingestion and vector document persistence
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- `apps/onboarding` sessions, orchestration runtime, and tool integration
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- `config` Django settings, routing, ASGI/WSGI wiring
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- `compose` development and production container configuration
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- `site` frontend application
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- `docs` architecture and deployment documentation
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## Demo Access
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Hosted URL:
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- https://fyp.viswamedha.com
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Evaluation credentials:
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| Role | Email | Password |
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| :--- | :--- | :--- |
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| Admin | admin@example.com | admin |
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| Manager | haleisaac@example.com | password |
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| User | j.thompson@example.com | password |
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Manager registration code: `MANAGER2026`
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## Operational Commands
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Stop services:
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```bash
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docker compose -f compose/dev/docker-compose.yml --env-file .env down
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```
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Tail logs:
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```bash
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docker compose -f compose/dev/docker-compose.yml --env-file .env logs -f
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```
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Run migrations:
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```bash
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docker exec -it fyp-django-dev python manage.py migrate
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```
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Reset seeded passwords:
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```bash
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docker exec -it fyp-django-dev python manage.py reset_passwords
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```
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## Screenshots
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Home:
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Organization:
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Onboarding generation state:
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Onboarding content flow:
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## Documentation
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- [Distributed Runtime Flow](docs/distributed-runtime-flow.md)
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- [Application Structure](docs/application-structure.md)
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- [Deployment Topologies](docs/deployment-topologies.md)
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