AI Tutoring Platform Customization
Budget: $2 – $8 USD
Project Overview:
We are building a modular AI tutoring platform for Undergrad students. I have a fork of OpenWebUI that needs customization to support:
Module-Based Data Loading – The AI should only load embeddings relevant to the requested module (Math, Science, etc.) instead of all modules at once.
Multi-Model Support – Integrate three LLM tiers: Lite, Regular, Heavy. The system should select the appropriate model based on mode or prompt.
Dynamic Retrieval System – Implement on-the-fly retrieval from vector stores for each module, including caching to reduce repeated queries.
Session Management – Maintain short-term context per session without storing any sensitive student data (COPPA compliance).
Prompt / Mode Switching – Suggested prompts should act as mode switches (Tutor, Exam, Socratic, etc.).
Requirements:
Experience with Python, FastAPI/Django, or similar backend frameworks.
Experience integrating LLMs and vector databases (e.g., FAISS, Milvus, Weaviate).
Understanding of multi-model orchestration and prompt engineering.
Ability to write clean, documented, modular code.
Optional: experience with OpenWebUI or similar UI for LLM frontends.
Deliverables:
Customized OpenWebUI fork with module-based loading fully implemented.
Multi-model selection logic integrated.
Retrieval + caching system per module.
Documentation for setup and module integration.
Optional: guidance on connecting front-end queries to the backend.
We are building a modular AI tutoring platform for Undergrad students. I have a fork of OpenWebUI that needs customization to support:
Module-Based Data Loading – The AI should only load embeddings relevant to the requested module (Math, Science, etc.) instead of all modules at once.
Multi-Model Support – Integrate three LLM tiers: Lite, Regular, Heavy. The system should select the appropriate model based on mode or prompt.
Dynamic Retrieval System – Implement on-the-fly retrieval from vector stores for each module, including caching to reduce repeated queries.
Session Management – Maintain short-term context per session without storing any sensitive student data (COPPA compliance).
Prompt / Mode Switching – Suggested prompts should act as mode switches (Tutor, Exam, Socratic, etc.).
Requirements:
Experience with Python, FastAPI/Django, or similar backend frameworks.
Experience integrating LLMs and vector databases (e.g., FAISS, Milvus, Weaviate).
Understanding of multi-model orchestration and prompt engineering.
Ability to write clean, documented, modular code.
Optional: experience with OpenWebUI or similar UI for LLM frontends.
Deliverables:
Customized OpenWebUI fork with module-based loading fully implemented.
Multi-model selection logic integrated.
Retrieval + caching system per module.
Documentation for setup and module integration.
Optional: guidance on connecting front-end queries to the backend.
Related categories:
PHP
Python
Graphic Design
Django
Machine Learning (ML)
FastAPI
Prompt Engineering
Large Language Model