IMPETUS Educational Chatbot Project

The IMPETUS project focuses on developing specialized chatbots for educational purposes, analyzing student-bot interactions to optimize learning experiences. The initiative leverages LangChain for chatbot prototyping and Flowise for workflow management, with emphasis on secure deployment, database integration, and access control.

Project Overview

Primary Goal: Investigate optimization strategies for educational chatbots and identify effective prompting techniques to maximize student engagement.
Focus Areas:

• Interaction analytics via custom database logging
• Secure deployment architecture for chatbot interfaces
• Migration from SQLite to PostgreSQL for long-term scalability
• Access control separation between admin/student interfaces

Core Technologies:

• LangChain: Python-based framework for LLM pipeline construction (Documentation)
• Flowise: Drag-and-drop UI for chatbot workflows (GitHub Integration Example)
• Docker: Containerization for service isolation and deployment reproducibility

Project Repositories:
https://transfer.hft-stuttgart.de/gitlab/22baar1mst/flowise-ollama

https://github.com/ArtemBaranovsky/flowise-ollama (mirror)

Key Objectives

1. Global Service Installation:

   • Dockerize Flowise/LangChain for system-wide availability
   • Separate student-facing chat interface (port 3000) from admin panel

2. Database Migration:

   • Replace SQLite with PostgreSQL for production-grade reliability
   • Implement volume persistence for critical data

3. Security Hardening:

   • Isolate administrative endpoints via Nginx routing
   • Implement Basic Auth with fallback to JWT/OAuth2

4. LLM Infrastructure:

   • Integrate Ollama for local model management
   • Configure RAG (Retrieval-Augmented Generation) workflows

Implementation Highlights

1. Dockerized Architecture

# docker-compose.yml (simplified)  
services:  
  flowise:  
    image: flowiseai/flowise:latest  
    ports: ["3000:3000"]  
    depends_on: [postgres, ollama]  

  postgres:  
    image: postgres:16-alpine  
    volumes: ["./pgdata:/var/lib/postgresql/data"]  

  ollama:  
    build: ./ollama-models  
    ports: ["11435:11434"]  

  nginx:  
    image: nginx:alpine  
    ports: ["8080:80"]  
    configs: ["./nginx/:/etc/nginx/"]  

2. Access Control Implementation

# nginx.conf (security routing)  
location /admin/ {  
  auth_basic "Restricted Area";  
  auth_basic_user_file /etc/nginx/.htpasswd;  
  proxy_pass http://flowise:3000/api/v1/;  
}  

location /chat/ {  
  proxy_pass http://flowise:3000/;  
}  

3. Ollama Model Integration

# Custom Ollama Dockerfile  
FROM ollama/ollama  
RUN ollama pull ${MODEL_NAME}  
CMD ["ollama", "serve"]  

Challenges & Solutions

Stable implemented version of this simple solution is at 5c4d4470

Unresolved Issues:

• Basic Auth bypass vulnerabilities in shared port configuration
• LangChain migration incomplete due to time constraints

Deployment Workflow

1. Infrastructure Setup:

ssh impetus@193.196.53.83 -i <key.pem>  
docker-compose up -d --build  

2. Environment Configuration:

DATABASE_URL=postgresql://user:pass@postgres:5432/db  
OLLAMA_MODEL=llama2-uncensored  
NGINX_PORT=8080  

3. Security Hardening:

# Generate admin credentials  
htpasswd -c ./nginx/.htpasswd admin  

Future Development Plan

1. LangChain Migration:

   • Port Flowise workflows to LangChain agents
   • Implement dual interface system:
     • /chat – student-facing endpoint
     • /admin – JWT-protected management UI

2. Enhanced Security:

   • Implement OAuth2 via Keycloak/Linkding
   • Add audit logging for all admin actions

3. CI/CD Pipeline:

   • Automated Docker builds on GitLab
   • Canary deployment strategy

4. Analytics Integration:

   • ClickHouse for interaction telemetry
   • Grafana dashboard for usage metrics

Conclusion

The IMPETUS project demonstrates a viable architecture for educational chatbots using Dockerized LLM components. While Flowise provided rapid prototyping capabilities, future work will focus on transitioning to LangChain for greater flexibility. The current implementation achieves:

• Isolated PostgreSQL database with persistent storage
• Basic access control through Nginx
• Local LLM management via Ollama

Next Steps: Address security vulnerabilities in access control and complete LangChain migration using Flowise-LangChain integration templates.

(Due to technical issues, the search service is temporarily unavailable.)

Deployment Guide: Flowise-Ollama Chatbot System

Prerequisites

1. Docker and Docker Compose installed
2. Hugging Face Account (for accessing LLM models)
3. Minimum 8GB RAM and 20GB Disk Space

Step-by-Step Deployment

1. Clone Repository

git clone https://transfer.hft-stuttgart.de/gitlab/22baar1mst/flowise-ollama.git  
cd flowise-ollama  

2. Configure Environment

Create .env file or copy from .env.Example:

# Database  
DATABASE_NAME=flowise  
DATABASE_USER=admin  
DATABASE_PASSWORD=SecurePass123!  
DATABASE_TYPE=postgres  
DATABASE_PORT=5432  

# Flowise  
PORT=3000  
FLOWISE_USERNAME=admin  
FLOWISE_PASSWORD=AdminSecure456!  

# Ollama  
HF_TOKEN=your_huggingface_token  
GIN_MODE=release  

Don't forget to register and use credentials for HF_TOKEN= LANGCHAIN_API_KEY=

3. Start Services

docker-compose up -d --build  

This will:

• Launch PostgreSQL database with persistent storage
• Start Flowise with Ollama integration
• Automatically load specified LLM models

4. Verify Services

docker ps -a  

Expected output:

CONTAINER ID   IMAGE                      STATUS                   PORTS  
a1b2c3d4e5f6   flowiseai/flowise:latest   Up 5 minutes (healthy)   0.0.0.0:3000->3000/tcp  
g7h8i9j0k1l2   postgres:16-alpine         Up 5 minutes (healthy)   5432/tcp  
m3n4o5p6q7r8   ollama                     Up 5 minutes            0.0.0.0:11434->11434/tcp  

5. Access Flowise UI

1. Open http://localhost:3000 in browser
2. Log in using credentials from .env:
   • Username: admin
   • Password: AdminSecure456!

6. Import Chatflow

1. Navigate to Chatflows → Add New
2. Import Ollama_RAG_Chatflow.json (drag-and-drop or use file picker)
3. Configure RAG parameters:

   {  
     "model": "llama2-uncensored",  
     "temperature": 0.7,  
     "max_tokens": 500  
   }  

7. Test Ollama Integration

curl http://localhost:11434/api/tags  

Expected response:

{"models":[{"name":"llama2-uncensored","modified_at":"2024-03-15T12:34:56.789Z"}]}  

Key Endpoints

Troubleshooting

1. Healthcheck Failures:

   docker logs flowiseai --tail 50  
   docker exec -it flowise-db psql -U admin -d flowise  

2. Model Loading Issues:

   docker exec ollama ollama list  
   docker exec ollama ollama pull llama2-uncensored  

3. Port Conflicts:
   Update PORT in .env and restart:

   docker-compose down && docker-compose up -d  

Security Recommendations

1. Add Nginx reverse proxy with SSL
2. Implement IP whitelisting for /api/v1 endpoints
3. Rotate database credentials monthly
4. Monitor Ollama GPU utilization:

   docker stats ollama  

This deployment provides a fully functional educational chatbot system with RAG capabilities. For advanced configuration, refer to Flowise-Ollama Integration Guide.