aita-core 0.2.0

Shared core for AI Teaching Assistant (AITA) chatbots

aita-core

Shared core package for AI Teaching Assistant (AITA) chatbots. Provides the Streamlit UI, RAG pipeline, database logging, admin panel, and document ingestion utilities — parameterized by a CourseConfig so the same codebase serves multiple courses.

How It Works

AITA is an AI chatbot that helps students learn course material. It uses Retrieval-Augmented Generation (RAG) — your course documents (slides, handouts, homework) are indexed into a vector database, and when a student asks a question, the system retrieves relevant content and generates a pedagogically appropriate response using an LLM.

Key design principle: the chatbot never gives direct answers. It guides students through Socratic questioning, hints, and conceptual explanations.

Step-by-Step Setup Guide

Prerequisites

• Python 3.11+
• Docker (for deployment)
• An OpenAI API key
• (Optional) Google Cloud OAuth credentials for UMN login

Step 1: Create Your Course Repository

Create a new directory for your course:

mkdir AITA_XXXX
cd AITA_XXXX
git init

Step 2: Install aita-core

pip install aita-core

Or add to requirements.txt:

aita-core>=0.1.0

Step 3: Set Up Environment Variables

Create a .env file (never commit this):

OPENAI_API_KEY=sk-your-openai-api-key
ADMIN_PASSWORD=your-admin-password
GOOGLE_COOKIE_KEY=your-random-secret-string
GOOGLE_REDIRECT_URI=http://your-server:8501
AITA_DATA_DIR=/app/data

Step 4: Add Your Course Materials

Create a course_materials/ directory and organize your files:

course_materials/
├── Handouts/
│   └── Handouts/
│       ├── 1 Topic Name.pdf
│       ├── 2 Topic Name.pdf
│       └── ...
├── Homework handouts/
│   └── Homework handouts/
│       ├── HW1.pdf
│       ├── HW2.pdf
│       └── ...
├── Slides/
│   └── Slides/
│       ├── 1 Topic Name/
│       │   ├── content.tex    (or Notes.pdf)
│       │   └── Handout.pdf
│       └── ...
└── syllabus/
    └── Syllabus.pdf (or Syllabus.tex)

Important: Do NOT include homework solutions — the chatbot could leak them to students.

Step 5: Create config.py

This is where you define everything specific to your course. Copy the template below and fill in your course details:

import os
import sys
import glob
from dotenv import load_dotenv
from aita_core import CourseConfig

load_dotenv()

BASE_DIR = os.path.dirname(__file__)
_client_secret_matches = glob.glob(os.path.join(BASE_DIR, "client_secret*.json"))

# Google Auth requires all three: client_secret file + GOOGLE_COOKIE_KEY + GOOGLE_REDIRECT_URI
_google_cookie_key = os.getenv("GOOGLE_COOKIE_KEY")
_google_redirect_uri = os.getenv("GOOGLE_REDIRECT_URI")
if _client_secret_matches and _google_cookie_key and _google_redirect_uri:
    _google_client_secret = _client_secret_matches[0]
else:
    _google_client_secret = ""
    if _client_secret_matches:
        print("[WARN] Google OAuth: client_secret found but GOOGLE_COOKIE_KEY or "
              "GOOGLE_REDIRECT_URI not set. Falling back to student ID login.",
              file=sys.stderr)

SYSTEM_PROMPT = """\
You are an AI Teaching Assistant for COURSE_NAME \
at the University of Minnesota, taught by Prof. YOUR NAME.

YOUR CORE PRINCIPLE: You must NEVER give direct answers to homework or exam problems. \
Instead, you should:
- Ask Socratic questions to guide students toward understanding
- Provide hints and point students to relevant concepts or course materials
- Explain underlying principles without solving the specific problem
- Encourage students to attempt the problem first and share their reasoning
- When students share their work, help them identify errors conceptually

When responding:
- Cite source material when referencing course content
- Be encouraging, patient, and supportive
- Keep responses focused and concise
- If the question is not related to the course, politely redirect
- Use LaTeX for math: $inline$ and $$display$$
- IMPORTANT: Never use \\[ \\] or \\( \\) for LaTeX. Always use $...$ and $$...$$

You will be provided with relevant context from course materials to ground your responses.\
"""

CONFIG = CourseConfig(
    # --- Course identity ---
    course_id="XXXX",
    course_name="CEGE XXXX: AI Teaching Assistant",
    course_short_name="CEGE XXXX AITA",
    course_description=(
        "Welcome! This AI assistant helps you learn concepts for "
        "**CEGE XXXX: Your Course Title**."
    ),
    system_prompt=SYSTEM_PROMPT,

    # --- Week-to-topic mapping ---
    # What topics are covered each week? Used to prevent the chatbot
    # from discussing future topics before they're taught.
    week_topics={
        1:  ["Topic A"],
        2:  ["Topic B", "Topic C"],
        3:  ["Topic D"],
        # ... add all 15 weeks
        15: ["Final exam review"],
    },

    # --- Document-to-week mapping ---
    # Maps the number prefix in filenames (e.g., "3 Topic Name.pdf" -> topic 3)
    # to the week that topic is first covered.
    topic_num_to_week={
        1: 1, 2: 2, 3: 3,
        # ... one entry per handout/slide topic folder
    },

    # Maps homework number to the week it's assigned.
    hw_num_to_week={
        1: 2, 2: 3, 3: 4,
        # ... one entry per homework
    },

    # Maps lab number to week.
    lab_num_to_week={
        1: 1, 2: 2, 3: 3,
        # ... one entry per lab
    },

    # Maps study guide / quiz names to week. Leave empty if not applicable.
    study_guide_to_week={},

    # --- Example prompts ---
    # Shown as clickable buttons when chat is empty. 4 per week works well.
    example_prompts={
        1: [
            "What topics does this course cover?",
            "What are the prerequisites?",
            "How is grading structured?",
            "Help me with this week's homework",
        ],
        # ... add for each week
    },

    # --- Paths ---
    base_dir=BASE_DIR,
    course_materials_dir=os.path.join(BASE_DIR, "course_materials"),
    faiss_db_dir=os.path.join(BASE_DIR, "faiss_db"),
    docs_dir=os.path.join(BASE_DIR, "docs"),
    backup_dir=os.path.join(BASE_DIR, "backup"),
    data_dir=os.getenv("AITA_DATA_DIR", os.path.join(BASE_DIR, "data")),

    # --- Auth ---
    admin_password=os.getenv("ADMIN_PASSWORD", ""),
    cookie_name="aita_XXXX_auth",
    cookie_key=_google_cookie_key or "",
    redirect_uri=_google_redirect_uri or "http://localhost:8501",
    google_client_secret_file=_google_client_secret,
)

Step 6: Create main.py

from config import CONFIG
from aita_core import run

run(CONFIG)

Step 7: Create add_document.py

If your course materials follow the standard directory layout (see Step 4), this is all you need:

from config import CONFIG
from aita_core.ingest import run_ingestion

if __name__ == "__main__":
    run_ingestion(CONFIG)

The default pipeline collects handouts, homework (skipping solutions), slides, and syllabus, then builds the FAISS vector index.

If your directory layout differs, you can pass custom collectors:

from config import CONFIG
from aita_core.ingest import run_ingestion, load_pdf, collect_syllabus

def my_collect_handouts(config):
    # Custom logic to find and load handout PDFs
    docs = []
    # ... your code here ...
    return docs

if __name__ == "__main__":
    run_ingestion(CONFIG, collectors=[
        ("handouts", my_collect_handouts),
        ("syllabus", collect_syllabus),
    ])

Step 8: Build the Vector Store

python add_document.py

This reads your course materials, generates embeddings via OpenAI, and saves a FAISS index to faiss_db/.

Step 9: Test Locally

streamlit run main.py

Open http://localhost:8501 in your browser and test with sample questions.

Step 10: Deploy with Docker

Create a Dockerfile:

FROM python:3.11-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY config.py main.py ./
COPY client_secret*.json* ./
COPY faiss_db/ ./faiss_db/
COPY course_materials/ ./course_materials/

RUN mkdir -p /app/data
RUN mkdir -p /root/.streamlit
RUN echo '[server]\nheadless = true\nport = 8501\nenableCORS = false\nenableXsrfProtection = false\n\n[browser]\ngatherUsageStats = false' > /root/.streamlit/config.toml

EXPOSE 8501
ENTRYPOINT ["streamlit", "run", "main.py", "--server.port=8501", "--server.address=0.0.0.0"]

Create a docker-compose.yml:

services:
  aita:
    build: .
    ports:
      - "8501:8501"
    env_file:
      - .env
    volumes:
      - /path/to/persistent/data:/app/data
    restart: unless-stopped

Build and run:

docker compose build
docker compose up -d

Your chatbot is now live at http://your-server:8501.

Step 11: Set Up .gitignore

.env
.venv/
__pycache__/
*.py[cod]
*.egg-info/
faiss_db/
backup/
docs/
course_materials/
client_secret*.json
.DS_Store
.vscode/
.idea/
.streamlit/secrets.toml

Step 12: (Optional) Google OAuth

To restrict login to @umn.edu accounts:

1. Create a project in Google Cloud Console
2. Enable the Google+ API (or People API)
3. Create OAuth 2.0 credentials (Web application)
4. Add your redirect URI (e.g., http://your-server:8501)
5. Download the client secret JSON and place it in your project root as client_secret_*.json
6. Set GOOGLE_COOKIE_KEY and GOOGLE_REDIRECT_URI in your .env

If any of these are missing, the app automatically falls back to student ID login.

Features

• Pedagogical guardrails — Never gives direct answers; uses Socratic questioning
• Week-aware responses — Won't discuss topics not yet covered in class
• Source citations — References specific handouts, slides, and homework
• PDF downloads — Students can download referenced course materials
• Admin dashboard — View interaction logs, student feedback, and feature requests
• Google OAuth — Restrict access to @umn.edu accounts (optional)
• Mobile-friendly — Responsive UI works on phones and tablets

Cost

Using GPT-4o-mini (default), estimated cost is under $20/semester for a class of 80 students with heavy usage. See OpenAI pricing for current rates.

Course Repo Structure

AITA_XXXX/
├── config.py              # CourseConfig with all course-specific data
├── main.py                # 3 lines: import config, import aita_core, run
├── add_document.py        # 3 lines for standard layout, or custom collectors
├── course_materials/      # PDFs, LaTeX source (not committed)
├── faiss_db/              # Built vector index (not committed)
├── .env                   # API keys (not committed)
├── .gitignore
├── docker-compose.yml
├── Dockerfile
└── requirements.txt       # just: aita-core>=0.1.0