Initial ChatGPT sources
This commit is contained in:
parent
50ee843b32
commit
c99eea4793
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@ -0,0 +1,22 @@
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name: CI
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||||
|
||||
on:
|
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push:
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||||
pull_request:
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||||
|
||||
jobs:
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||||
test:
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||||
runs-on: ubuntu-latest
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||||
steps:
|
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- uses: actions/checkout@v4
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||||
- uses: actions/setup-python@v5
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||||
with:
|
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python-version: "3.11"
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||||
- name: Install
|
||||
run: |
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python -m pip install --upgrade pip
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||||
pip install -e .[dev]
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- name: Lint
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run: ruff check src tests
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||||
- name: Test
|
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run: pytest
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|
|
@ -1,229 +1,13 @@
|
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# ---> Python
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||||
# Byte-compiled / optimized / DLL files
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||||
__pycache__/
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||||
*.py[cod]
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||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
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||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.venv/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# poetry
|
||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
|
||||
#poetry.lock
|
||||
|
||||
# pdm
|
||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
||||
#pdm.lock
|
||||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
||||
# in version control.
|
||||
# https://pdm.fming.dev/#use-with-ide
|
||||
.pdm.toml
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
|
||||
|
||||
# Cython debug symbols
|
||||
cython_debug/
|
||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
#.idea/
|
||||
|
||||
# ---> Emacs
|
||||
# -*- mode: gitignore; -*-
|
||||
*~
|
||||
\#*\#
|
||||
/.emacs.desktop
|
||||
/.emacs.desktop.lock
|
||||
*.elc
|
||||
auto-save-list
|
||||
tramp
|
||||
.\#*
|
||||
|
||||
# Org-mode
|
||||
.org-id-locations
|
||||
*_archive
|
||||
|
||||
# flymake-mode
|
||||
*_flymake.*
|
||||
|
||||
# eshell files
|
||||
/eshell/history
|
||||
/eshell/lastdir
|
||||
|
||||
# elpa packages
|
||||
/elpa/
|
||||
|
||||
# reftex files
|
||||
*.rel
|
||||
|
||||
# AUCTeX auto folder
|
||||
/auto/
|
||||
|
||||
# cask packages
|
||||
.cask/
|
||||
.coverage
|
||||
htmlcov/
|
||||
dist/
|
||||
|
||||
# Flycheck
|
||||
flycheck_*.el
|
||||
|
||||
# server auth directory
|
||||
/server/
|
||||
|
||||
# projectiles files
|
||||
.projectile
|
||||
|
||||
# directory configuration
|
||||
.dir-locals.el
|
||||
|
||||
# network security
|
||||
/network-security.data
|
||||
|
||||
|
||||
# ---> Rust
|
||||
# Generated by Cargo
|
||||
# will have compiled files and executables
|
||||
debug/
|
||||
target/
|
||||
|
||||
# Remove Cargo.lock from gitignore if creating an executable, leave it for libraries
|
||||
# More information here https://doc.rust-lang.org/cargo/guide/cargo-toml-vs-cargo-lock.html
|
||||
Cargo.lock
|
||||
|
||||
# These are backup files generated by rustfmt
|
||||
**/*.rs.bk
|
||||
|
||||
# MSVC Windows builds of rustc generate these, which store debugging information
|
||||
*.pdb
|
||||
|
||||
build/
|
||||
*.egg-info/
|
||||
.env
|
||||
configs/config.yaml
|
||||
|
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|
|||
|
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@ -0,0 +1,9 @@
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|||
FROM python:3.11-slim
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||||
|
||||
WORKDIR /app
|
||||
COPY pyproject.toml README.md /app/
|
||||
COPY src /app/src
|
||||
COPY configs /app/configs
|
||||
COPY domain-packs /app/domain-packs
|
||||
RUN pip install --no-cache-dir -e .
|
||||
CMD ["python", "-m", "didactopus.main", "--domain", "statistics", "--goal", "practical mastery"]
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||||
20
LICENSE
20
LICENSE
|
|
@ -1,9 +1,21 @@
|
|||
MIT License
|
||||
|
||||
Copyright (c) 2026 welsberr
|
||||
Copyright (c) 2026 Wesley R. Elsberry
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
|
|
|
|||
|
|
@ -0,0 +1,13 @@
|
|||
.PHONY: install test lint run
|
||||
|
||||
install:
|
||||
python -m pip install -e .[dev]
|
||||
|
||||
test:
|
||||
pytest
|
||||
|
||||
lint:
|
||||
ruff check src tests
|
||||
|
||||
run:
|
||||
python -m didactopus.main --domain "programming" --goal "build real projects"
|
||||
184
README.md
184
README.md
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@ -1,3 +1,185 @@
|
|||
# Didactopus
|
||||
|
||||
Didactctopus is a multi-talented AI system to assist autodidacts in gaining mastery of a chosen topic. Want to learn and get an assist doing it? Didactopus fits the bill.
|
||||
**Didactopus** is a local-first AI-assisted autodidactic mastery platform designed to help motivated learners achieve genuine mastery through Socratic mentoring, structured practice, project work, verification, and competency-based evaluation.
|
||||
|
||||
**Tagline:** *Many arms, one goal — mastery.*
|
||||
|
||||
## Vision
|
||||
|
||||
Didactopus treats AI as a **mentor, curriculum planner, critic, evaluator, and project guide** rather than an answer vending machine. The design goal is to produce capable practitioners who can explain, apply, test, and extend knowledge in real settings.
|
||||
|
||||
The platform is meant to support **AI-assisted autodidacts**: learners who pursue real expertise outside, alongside, or beyond traditional institutions.
|
||||
|
||||
## Core principles
|
||||
|
||||
- Active learning over passive consumption
|
||||
- Socratic questioning over direct answer dumping
|
||||
- Verification culture over uncritical acceptance
|
||||
- Competency gates over time-based progression
|
||||
- Project-based evidence of mastery
|
||||
- Local-first model use when available
|
||||
- Portable, shareable domain plans and learning artifacts
|
||||
|
||||
## Initial architecture
|
||||
|
||||
The initial prototype is organized around six core services:
|
||||
|
||||
1. **Domain Mapping Engine**
|
||||
Builds a concept graph for a target field, including prerequisites, competencies, canonical problem types, and artifact templates.
|
||||
|
||||
2. **Curriculum Generator**
|
||||
Produces a staged learning roadmap adapted to learner goals and prior knowledge.
|
||||
|
||||
3. **Mentor Agent**
|
||||
Conducts Socratic dialogue, reviews reasoning, and offers targeted critique.
|
||||
|
||||
4. **Practice Generator**
|
||||
Produces exercises aimed at specific concepts and skill gaps.
|
||||
|
||||
5. **Project Advisor**
|
||||
Proposes and scaffolds real projects that demonstrate competence.
|
||||
|
||||
6. **Evaluation System**
|
||||
Scores explanations, problem solutions, project outputs, and transfer tasks against explicit rubrics.
|
||||
|
||||
## Distribution model for contributed learning content
|
||||
|
||||
Didactopus is designed to support distribution of contributed artifacts, including:
|
||||
|
||||
- domain plans
|
||||
- concept maps
|
||||
- curriculum templates
|
||||
- exercise sets
|
||||
- project blueprints
|
||||
- evaluation rubrics
|
||||
- benchmark packs
|
||||
- exemplar portfolios
|
||||
|
||||
These should be shareable as versioned packages or repositories so that contributors can publish reusable mastery paths for particular domains.
|
||||
|
||||
See:
|
||||
- `docs/artifact-distribution.md`
|
||||
- `docs/domain-pack-format.md`
|
||||
|
||||
## Local model strategy
|
||||
|
||||
The codebase is designed to support a provider abstraction:
|
||||
|
||||
- **Local-first**: Ollama, llama.cpp server, vLLM, LM Studio, or other on-prem inference endpoints
|
||||
- **Remote optional**: API-backed models only when configured
|
||||
- **Hybrid mode**: local models for routine mentoring, remote models only for heavier synthesis or evaluation if explicitly allowed
|
||||
|
||||
## Repository layout
|
||||
|
||||
```text
|
||||
didactopus/
|
||||
├── README.md
|
||||
├── LICENSE
|
||||
├── pyproject.toml
|
||||
├── Makefile
|
||||
├── docker-compose.yml
|
||||
├── Dockerfile
|
||||
├── .gitignore
|
||||
├── .github/workflows/ci.yml
|
||||
├── configs/
|
||||
│ └── config.example.yaml
|
||||
├── docs/
|
||||
│ ├── architecture.md
|
||||
│ ├── repository-plan.md
|
||||
│ ├── component-specs.md
|
||||
│ ├── prototype-roadmap.md
|
||||
│ ├── artifact-distribution.md
|
||||
│ └── domain-pack-format.md
|
||||
├── domain-packs/
|
||||
│ └── example-statistics/
|
||||
│ ├── pack.yaml
|
||||
│ ├── concepts.yaml
|
||||
│ ├── roadmap.yaml
|
||||
│ ├── projects.yaml
|
||||
│ └── rubrics.yaml
|
||||
├── src/didactopus/
|
||||
│ ├── __init__.py
|
||||
│ ├── main.py
|
||||
│ ├── config.py
|
||||
│ ├── model_provider.py
|
||||
│ ├── domain_map.py
|
||||
│ ├── curriculum.py
|
||||
│ ├── mentor.py
|
||||
│ ├── practice.py
|
||||
│ ├── project_advisor.py
|
||||
│ ├── evaluation.py
|
||||
│ └── artifact_registry.py
|
||||
└── tests/
|
||||
├── test_config.py
|
||||
├── test_domain_map.py
|
||||
└── test_artifact_registry.py
|
||||
```
|
||||
|
||||
## Quick start
|
||||
|
||||
```bash
|
||||
python -m venv .venv
|
||||
source .venv/bin/activate
|
||||
pip install -e .[dev]
|
||||
cp configs/config.example.yaml configs/config.yaml
|
||||
python -m didactopus.main --domain "statistics" --goal "reach practical mastery"
|
||||
pytest
|
||||
```
|
||||
|
||||
## Prototype capabilities in this scaffold
|
||||
|
||||
The current scaffold provides:
|
||||
|
||||
- a configuration model for local/remote provider selection
|
||||
- a concept graph data structure for domain maps
|
||||
- stubs for curriculum, mentor, practice, project, and evaluation services
|
||||
- a simple artifact registry for local domain-pack discovery
|
||||
- an example domain pack layout
|
||||
- a CLI entry point to demonstrate end-to-end flow
|
||||
- tests to validate configuration and artifact behavior
|
||||
|
||||
## Suggested first implementation milestones
|
||||
|
||||
### Milestone 1: Learner and domain modeling
|
||||
- learner profile schema
|
||||
- concept graph generation
|
||||
- prerequisite traversal
|
||||
- domain-pack schema validation
|
||||
- local artifact discovery
|
||||
|
||||
### Milestone 2: Guided study loop
|
||||
- Socratic mentor prompts
|
||||
- explanation checking
|
||||
- exercise generation by competency target
|
||||
- evidence capture for learner work
|
||||
|
||||
### Milestone 3: Project-centered learning
|
||||
- capstone generator
|
||||
- milestone planning
|
||||
- artifact review rubrics
|
||||
- distributed project pack ingestion
|
||||
|
||||
### Milestone 4: Mastery evidence
|
||||
- explanation scoring
|
||||
- transfer tasks
|
||||
- benchmark alignment
|
||||
- progress dashboard
|
||||
- artifact publication workflow
|
||||
|
||||
## Notes on evaluation design
|
||||
|
||||
A key design choice is that the assessment layer should look for:
|
||||
|
||||
- correct explanations in the learner's own words
|
||||
- ability to solve novel problems
|
||||
- detection of flawed reasoning
|
||||
- evidence of successful project execution
|
||||
- transfer across adjacent contexts
|
||||
|
||||
## Naming rationale
|
||||
|
||||
**Didactopus** combines *didactic* / *didact* with *octopus*: a central intelligence coordinating many arms of learning support.
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
|
|
|
|||
|
|
@ -0,0 +1,22 @@
|
|||
model_provider:
|
||||
mode: local_first
|
||||
local:
|
||||
backend: ollama
|
||||
endpoint: http://localhost:11434
|
||||
model_name: llama3.1:8b
|
||||
remote:
|
||||
enabled: false
|
||||
provider_name: none
|
||||
endpoint: ""
|
||||
model_name: ""
|
||||
|
||||
platform:
|
||||
verification_required: true
|
||||
require_learner_explanations: true
|
||||
permit_direct_answers: false
|
||||
mastery_threshold: 0.8
|
||||
|
||||
artifacts:
|
||||
local_pack_dirs:
|
||||
- domain-packs
|
||||
allow_third_party_packs: true
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
services:
|
||||
didactopus:
|
||||
build: .
|
||||
image: didactopus:dev
|
||||
volumes:
|
||||
- ./:/app
|
||||
working_dir: /app
|
||||
command: python -m didactopus.main --domain "statistics" --goal "practical mastery"
|
||||
environment:
|
||||
DIDACTOPUS_CONFIG: /app/configs/config.yaml
|
||||
|
||||
ollama:
|
||||
image: ollama/ollama:latest
|
||||
profiles: ["local-llm"]
|
||||
ports:
|
||||
- "11434:11434"
|
||||
volumes:
|
||||
- ollama-data:/root/.ollama
|
||||
|
||||
volumes:
|
||||
ollama-data:
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
# Architecture Overview
|
||||
|
||||
## System aim
|
||||
|
||||
Didactopus supports mastery-oriented autodidactic learning across many domains while reducing the risk that AI becomes a crutch for superficial performance.
|
||||
|
||||
## Top-level architecture
|
||||
|
||||
```text
|
||||
Learner Interface
|
||||
|
|
||||
v
|
||||
Orchestration Layer
|
||||
|- learner profile
|
||||
|- session state
|
||||
|- competency tracker
|
||||
|- artifact registry
|
||||
|
|
||||
+--> Domain Mapping Engine
|
||||
+--> Curriculum Generator
|
||||
+--> Mentor Agent
|
||||
+--> Practice Generator
|
||||
+--> Project Advisor
|
||||
+--> Evaluation System
|
||||
|
|
||||
v
|
||||
Model Provider Abstraction
|
||||
|- local model backends
|
||||
|- optional remote backends
|
||||
```
|
||||
|
||||
## Core data objects
|
||||
|
||||
- **LearnerProfile**: goals, prior knowledge, pacing, artifacts, assessment history
|
||||
- **ConceptNode**: concept, prerequisites, representative tasks, mastery criteria
|
||||
- **RoadmapStage**: stage goals, concepts, practice forms, project milestones
|
||||
- **EvidenceItem**: explanations, solved problems, project artifacts, benchmark scores
|
||||
- **EvaluationReport**: rubric scores, weaknesses, suggested remediation
|
||||
- **ArtifactManifest**: metadata for a domain pack or other contributed artifact
|
||||
|
||||
## Critical design constraint
|
||||
|
||||
The platform should optimize for **competence evidence** rather than conversational fluency. A learner should not advance based solely on sounding knowledgeable.
|
||||
|
||||
## Local-first inference
|
||||
|
||||
The provider abstraction should support:
|
||||
- Ollama
|
||||
- llama.cpp HTTP servers
|
||||
- LM Studio local server
|
||||
- vLLM or comparable self-hosted inference
|
||||
- optional remote APIs only by explicit configuration
|
||||
|
||||
## Artifact ecosystem
|
||||
|
||||
The architecture should support:
|
||||
- first-party curated packs
|
||||
- third-party domain packs
|
||||
- version validation
|
||||
- compatibility checks
|
||||
- offline local discovery
|
||||
|
||||
## Safety against shallow learning
|
||||
|
||||
The orchestration layer should support policies such as:
|
||||
- forcing first-attempt learner answers
|
||||
- hiding worked solutions until after effort is shown
|
||||
- requiring self-explanation
|
||||
- issuing counterexamples and adversarial probes
|
||||
- cross-checking claims against references and experiments where applicable
|
||||
|
|
@ -0,0 +1,83 @@
|
|||
# Artifact Distribution Plan
|
||||
|
||||
## Goal
|
||||
|
||||
Didactopus should support community-contributed, versioned learning artifacts that can be reused, extended, and distributed independently of the core codebase.
|
||||
|
||||
## Artifact classes
|
||||
|
||||
The platform should support distribution of:
|
||||
- domain plans
|
||||
- concept graphs
|
||||
- roadmap templates
|
||||
- exercise banks
|
||||
- project blueprints
|
||||
- rubrics
|
||||
- benchmark tasks
|
||||
- reference reading lists
|
||||
- exemplar work products
|
||||
|
||||
## Distribution models
|
||||
|
||||
### 1. In-repository packs
|
||||
Simple packs stored under `domain-packs/` for development, examples, and curated first-party artifacts.
|
||||
|
||||
### 2. External Git repositories
|
||||
A contributor can publish a domain pack as its own Git repository and users can clone or vendor it.
|
||||
|
||||
Example patterns:
|
||||
- `didactopus-pack-statistics`
|
||||
- `didactopus-pack-electronics`
|
||||
- `didactopus-pack-evolutionary-biology`
|
||||
|
||||
### 3. Package index model
|
||||
Eventually, packs could be distributed through a registry or package index. A manifest should identify:
|
||||
- pack name
|
||||
- version
|
||||
- author
|
||||
- license
|
||||
- compatible Didactopus versions
|
||||
- dependencies on other packs or shared competencies
|
||||
|
||||
## Design requirements
|
||||
|
||||
Artifacts should be:
|
||||
- human-readable
|
||||
- diff-friendly
|
||||
- versionable with Git
|
||||
- independently licensed
|
||||
- schema-validated
|
||||
- mergeable and composable
|
||||
- usable offline
|
||||
|
||||
## Recommended file formats
|
||||
|
||||
Use YAML or JSON for:
|
||||
- metadata
|
||||
- concept graphs
|
||||
- roadmap stages
|
||||
- projects
|
||||
- rubrics
|
||||
|
||||
Use Markdown for:
|
||||
- explanatory notes
|
||||
- contributor guidance
|
||||
- reading guides
|
||||
- learner-facing instructions
|
||||
|
||||
## Versioning
|
||||
|
||||
Each pack should declare:
|
||||
- semantic version
|
||||
- minimum core version
|
||||
- maximum tested core version, if needed
|
||||
- schema version
|
||||
|
||||
## Future extension
|
||||
|
||||
Later, Didactopus should support:
|
||||
- signed packs
|
||||
- dependency resolution
|
||||
- artifact provenance metadata
|
||||
- import/export CLI commands
|
||||
- trust policies for third-party packs
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
# Component Specifications
|
||||
|
||||
## Domain Mapping Engine
|
||||
Inputs:
|
||||
- selected domain
|
||||
- learner goal
|
||||
- optional prior knowledge notes
|
||||
- optional domain pack
|
||||
|
||||
Outputs:
|
||||
- concept graph
|
||||
- prerequisite chains
|
||||
- competency clusters
|
||||
- recommended sequence
|
||||
|
||||
## Curriculum Generator
|
||||
Inputs:
|
||||
- concept graph
|
||||
- learner profile
|
||||
- assessment results
|
||||
- roadmap templates from pack
|
||||
|
||||
Outputs:
|
||||
- roadmap stages
|
||||
- study sessions
|
||||
- mastery checkpoints
|
||||
- recommended projects
|
||||
|
||||
## Mentor Agent
|
||||
Responsibilities:
|
||||
- ask probing questions
|
||||
- request justification
|
||||
- detect vague understanding
|
||||
- encourage verification
|
||||
- avoid over-answering
|
||||
|
||||
## Practice Generator
|
||||
Responsibilities:
|
||||
- generate exercises by concept
|
||||
- vary difficulty and modality
|
||||
- create transfer tasks
|
||||
- produce reflection prompts
|
||||
|
||||
## Project Advisor
|
||||
Responsibilities:
|
||||
- suggest authentic projects
|
||||
- decompose milestones
|
||||
- help define success criteria
|
||||
- review artifacts
|
||||
|
||||
## Evaluation System
|
||||
Responsibilities:
|
||||
- assess correctness
|
||||
- assess explanation quality
|
||||
- assess transfer and robustness
|
||||
- recommend remediation
|
||||
|
||||
## Artifact Registry
|
||||
Responsibilities:
|
||||
- discover local domain packs
|
||||
- validate manifests
|
||||
- check compatibility
|
||||
- expose installed artifacts to the orchestration layer
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
# Domain Pack Format
|
||||
|
||||
## Purpose
|
||||
|
||||
A domain pack is the primary distributable artifact for Didactopus. It contains structured material describing a field, learning sequence, projects, and evaluation artifacts.
|
||||
|
||||
## Directory layout
|
||||
|
||||
```text
|
||||
example-pack/
|
||||
├── pack.yaml
|
||||
├── concepts.yaml
|
||||
├── roadmap.yaml
|
||||
├── projects.yaml
|
||||
├── rubrics.yaml
|
||||
├── resources.md
|
||||
└── LICENSE
|
||||
```
|
||||
|
||||
## pack.yaml
|
||||
|
||||
The manifest should contain:
|
||||
- pack name
|
||||
- display name
|
||||
- version
|
||||
- schema version
|
||||
- description
|
||||
- author
|
||||
- license
|
||||
- tags
|
||||
- didactopus compatibility
|
||||
- optional dependencies
|
||||
|
||||
## concepts.yaml
|
||||
|
||||
Contains:
|
||||
- concept identifiers
|
||||
- descriptions
|
||||
- prerequisites
|
||||
- representative tasks
|
||||
- mastery signals
|
||||
|
||||
## roadmap.yaml
|
||||
|
||||
Contains:
|
||||
- stages
|
||||
- stage goals
|
||||
- concept clusters
|
||||
- checkpoint criteria
|
||||
- recommended pacing notes
|
||||
|
||||
## projects.yaml
|
||||
|
||||
Contains:
|
||||
- project identifiers
|
||||
- difficulty
|
||||
- prerequisites
|
||||
- deliverables
|
||||
- milestone suggestions
|
||||
- verification expectations
|
||||
|
||||
## rubrics.yaml
|
||||
|
||||
Contains:
|
||||
- assessment criteria
|
||||
- score bands
|
||||
- explanation requirements
|
||||
- transfer-task criteria
|
||||
|
||||
## Example philosophy
|
||||
|
||||
The pack format should separate:
|
||||
- domain structure
|
||||
- learning sequence
|
||||
- authentic practice
|
||||
- evidence standards
|
||||
|
||||
That separation makes contributed packs easier to compare, merge, and improve.
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
# Initial Prototype Roadmap
|
||||
|
||||
## Prototype 0.1
|
||||
Goal: prove the orchestration model.
|
||||
|
||||
Capabilities:
|
||||
- accept domain and goal
|
||||
- generate a toy concept graph
|
||||
- produce a staged roadmap
|
||||
- generate one Socratic prompt
|
||||
- generate one practice task
|
||||
- generate one capstone suggestion
|
||||
- produce one evaluation rubric
|
||||
- list locally installed domain packs
|
||||
|
||||
## Prototype 0.2
|
||||
Goal: learner-state adaptation.
|
||||
|
||||
Capabilities:
|
||||
- accept prior knowledge signals
|
||||
- adjust roadmap depth
|
||||
- store evidence items
|
||||
- identify weakest competencies
|
||||
- load a selected domain pack
|
||||
|
||||
## Prototype 0.3
|
||||
Goal: local-model integration.
|
||||
|
||||
Capabilities:
|
||||
- connect to local Ollama endpoint
|
||||
- select configured model
|
||||
- use deterministic prompts for component outputs
|
||||
- retain remote provider fallback but disable by default
|
||||
- validate artifact compatibility
|
||||
|
|
@ -0,0 +1,50 @@
|
|||
# Repository Plan
|
||||
|
||||
## Phase 0: Scaffold
|
||||
Create the repository skeleton, interfaces, tests, naming, artifact registry, and configuration system.
|
||||
|
||||
## Phase 1: Domain representation
|
||||
Implement:
|
||||
- concept graph schema
|
||||
- prerequisite traversal
|
||||
- learner profile schema
|
||||
- domain-pack import/export formats
|
||||
- schema validation
|
||||
|
||||
## Phase 2: Roadmap generation
|
||||
Implement:
|
||||
- initial assessment intake
|
||||
- competency gap analysis
|
||||
- staged learning plan generation
|
||||
- pack-aware roadmap assembly
|
||||
|
||||
## Phase 3: Mentoring loop
|
||||
Implement:
|
||||
- Socratic questioning templates
|
||||
- explanation critique
|
||||
- misconception detection
|
||||
- reflective prompts
|
||||
- evidence capture
|
||||
|
||||
## Phase 4: Practice and projects
|
||||
Implement:
|
||||
- competency-targeted exercise generation
|
||||
- capstone recommendations
|
||||
- milestone decomposition
|
||||
- project review rubrics
|
||||
- external pack loading
|
||||
|
||||
## Phase 5: Evaluation
|
||||
Implement:
|
||||
- concept mastery scoring
|
||||
- transfer task generation
|
||||
- portfolio review
|
||||
- external benchmark mapping
|
||||
|
||||
## Phase 6: UI and persistence
|
||||
Implement:
|
||||
- API layer
|
||||
- web frontend
|
||||
- learner history store
|
||||
- dashboard and evidence views
|
||||
- artifact browser and installer
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
concepts:
|
||||
- id: descriptive-statistics
|
||||
title: Descriptive Statistics
|
||||
prerequisites: []
|
||||
mastery_signals:
|
||||
- explain mean, median, variance, and standard deviation
|
||||
- choose an appropriate descriptive summary for a dataset
|
||||
- id: probability
|
||||
title: Probability
|
||||
prerequisites:
|
||||
- descriptive-statistics
|
||||
mastery_signals:
|
||||
- explain conditional probability
|
||||
- compute simple event probabilities
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
name: example-statistics
|
||||
display_name: Example Statistics Pack
|
||||
version: 0.1.0
|
||||
schema_version: "1"
|
||||
description: Example domain pack for a statistics mastery pathway.
|
||||
author: Wesley R. Elsberry
|
||||
license: MIT
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
projects:
|
||||
- id: local-dataset-analysis
|
||||
title: Analyze a Real Dataset
|
||||
difficulty: introductory
|
||||
prerequisites:
|
||||
- descriptive-statistics
|
||||
- probability
|
||||
deliverables:
|
||||
- written analysis
|
||||
- plots
|
||||
- interpretation of uncertainty
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
stages:
|
||||
- id: stage-1
|
||||
title: Foundations
|
||||
concepts:
|
||||
- descriptive-statistics
|
||||
checkpoint:
|
||||
- summarize a dataset correctly
|
||||
- id: stage-2
|
||||
title: Probabilistic Reasoning
|
||||
concepts:
|
||||
- probability
|
||||
checkpoint:
|
||||
- solve introductory probability tasks with explanation
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
rubrics:
|
||||
- id: explanation-rubric
|
||||
title: Explanation Quality
|
||||
criteria:
|
||||
- correctness
|
||||
- clarity
|
||||
- justification
|
||||
- transfer
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
[build-system]
|
||||
requires = ["setuptools>=68", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "didactopus"
|
||||
version = "0.1.0"
|
||||
description = "Didactopus: local-first AI-assisted autodidactic mastery platform"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
license = {text = "MIT"}
|
||||
authors = [
|
||||
{name = "Wesley R. Elsberry"}
|
||||
]
|
||||
dependencies = [
|
||||
"pydantic>=2.7",
|
||||
"pyyaml>=6.0",
|
||||
"networkx>=3.2"
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=8.0",
|
||||
"ruff>=0.6"
|
||||
]
|
||||
|
||||
[project.scripts]
|
||||
didactopus = "didactopus.main:main"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["src"]
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
|
|
@ -0,0 +1,11 @@
|
|||
__all__ = [
|
||||
"config",
|
||||
"model_provider",
|
||||
"domain_map",
|
||||
"curriculum",
|
||||
"mentor",
|
||||
"practice",
|
||||
"project_advisor",
|
||||
"evaluation",
|
||||
"artifact_registry",
|
||||
]
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
from pathlib import Path
|
||||
from pydantic import BaseModel
|
||||
import yaml
|
||||
|
||||
|
||||
class ArtifactManifest(BaseModel):
|
||||
name: str
|
||||
display_name: str
|
||||
version: str
|
||||
schema_version: str
|
||||
description: str = ""
|
||||
author: str = ""
|
||||
license: str = "unspecified"
|
||||
|
||||
|
||||
def discover_domain_packs(base_dirs: list[str | Path]) -> list[tuple[Path, ArtifactManifest]]:
|
||||
packs: list[tuple[Path, ArtifactManifest]] = []
|
||||
for base_dir in base_dirs:
|
||||
base_path = Path(base_dir)
|
||||
if not base_path.exists():
|
||||
continue
|
||||
for pack_dir in sorted(p for p in base_path.iterdir() if p.is_dir()):
|
||||
manifest_path = pack_dir / "pack.yaml"
|
||||
if not manifest_path.exists():
|
||||
continue
|
||||
data = yaml.safe_load(manifest_path.read_text(encoding="utf-8")) or {}
|
||||
packs.append((pack_dir, ArtifactManifest.model_validate(data)))
|
||||
return packs
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
from pathlib import Path
|
||||
from pydantic import BaseModel, Field
|
||||
import yaml
|
||||
|
||||
|
||||
class ProviderEndpoint(BaseModel):
|
||||
backend: str = "ollama"
|
||||
endpoint: str = "http://localhost:11434"
|
||||
model_name: str = "llama3.1:8b"
|
||||
|
||||
|
||||
class RemoteProvider(BaseModel):
|
||||
enabled: bool = False
|
||||
provider_name: str = "none"
|
||||
endpoint: str = ""
|
||||
model_name: str = ""
|
||||
|
||||
|
||||
class ModelProviderConfig(BaseModel):
|
||||
mode: str = Field(default="local_first")
|
||||
local: ProviderEndpoint = Field(default_factory=ProviderEndpoint)
|
||||
remote: RemoteProvider = Field(default_factory=RemoteProvider)
|
||||
|
||||
|
||||
class PlatformConfig(BaseModel):
|
||||
verification_required: bool = True
|
||||
require_learner_explanations: bool = True
|
||||
permit_direct_answers: bool = False
|
||||
mastery_threshold: float = 0.8
|
||||
|
||||
|
||||
class ArtifactConfig(BaseModel):
|
||||
local_pack_dirs: list[str] = Field(default_factory=lambda: ["domain-packs"])
|
||||
allow_third_party_packs: bool = True
|
||||
|
||||
|
||||
class AppConfig(BaseModel):
|
||||
model_provider: ModelProviderConfig = Field(default_factory=ModelProviderConfig)
|
||||
platform: PlatformConfig = Field(default_factory=PlatformConfig)
|
||||
artifacts: ArtifactConfig = Field(default_factory=ArtifactConfig)
|
||||
|
||||
|
||||
def load_config(path: str | Path) -> AppConfig:
|
||||
with open(path, "r", encoding="utf-8") as handle:
|
||||
data = yaml.safe_load(handle) or {}
|
||||
return AppConfig.model_validate(data)
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
from dataclasses import dataclass
|
||||
from .domain_map import DomainMap
|
||||
|
||||
|
||||
@dataclass
|
||||
class RoadmapStage:
|
||||
title: str
|
||||
concepts: list[str]
|
||||
mastery_goal: str
|
||||
|
||||
|
||||
def generate_initial_roadmap(domain_map: DomainMap, goal: str) -> list[RoadmapStage]:
|
||||
sequence = domain_map.topological_sequence()
|
||||
return [
|
||||
RoadmapStage(
|
||||
title=f"Stage {idx + 1}: {concept.title()}",
|
||||
concepts=[concept],
|
||||
mastery_goal=f"Demonstrate applied understanding of {concept} toward goal: {goal}",
|
||||
)
|
||||
for idx, concept in enumerate(sequence)
|
||||
]
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
from dataclasses import dataclass, field
|
||||
import networkx as nx
|
||||
|
||||
|
||||
@dataclass
|
||||
class ConceptNode:
|
||||
name: str
|
||||
description: str = ""
|
||||
prerequisites: list[str] = field(default_factory=list)
|
||||
representative_tasks: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
class DomainMap:
|
||||
def __init__(self, domain_name: str) -> None:
|
||||
self.domain_name = domain_name
|
||||
self.graph = nx.DiGraph()
|
||||
|
||||
def add_concept(self, node: ConceptNode) -> None:
|
||||
self.graph.add_node(node.name, data=node)
|
||||
for prereq in node.prerequisites:
|
||||
self.graph.add_edge(prereq, node.name)
|
||||
|
||||
def concepts(self) -> list[str]:
|
||||
return list(self.graph.nodes)
|
||||
|
||||
def prerequisites_for(self, concept: str) -> list[str]:
|
||||
return list(nx.ancestors(self.graph, concept))
|
||||
|
||||
def topological_sequence(self) -> list[str]:
|
||||
return list(nx.topological_sort(self.graph))
|
||||
|
||||
|
||||
def build_demo_domain_map(domain_name: str) -> DomainMap:
|
||||
dmap = DomainMap(domain_name)
|
||||
dmap.add_concept(ConceptNode("foundations", "Core assumptions and terminology"))
|
||||
dmap.add_concept(ConceptNode("methods", "Basic methods", ["foundations"]))
|
||||
dmap.add_concept(ConceptNode("analysis", "Applying methods", ["methods"]))
|
||||
dmap.add_concept(ConceptNode("projects", "Real-world capstones", ["analysis"]))
|
||||
return dmap
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from .model_provider import ModelProvider
|
||||
|
||||
|
||||
def generate_rubric(provider: ModelProvider, concept: str) -> str:
|
||||
prompt = (
|
||||
f"Create a concise evaluation rubric for learner mastery of '{concept}'. "
|
||||
f"Assess explanation quality, problem solving, and transfer."
|
||||
)
|
||||
return provider.generate(prompt).text
|
||||
|
|
@ -0,0 +1,73 @@
|
|||
import argparse
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from .config import load_config
|
||||
from .model_provider import ModelProvider
|
||||
from .artifact_registry import discover_domain_packs
|
||||
from .domain_map import build_demo_domain_map
|
||||
from .curriculum import generate_initial_roadmap
|
||||
from .mentor import generate_socratic_prompt
|
||||
from .practice import generate_practice_task
|
||||
from .project_advisor import suggest_capstone
|
||||
from .evaluation import generate_rubric
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(description="Didactopus mastery scaffold")
|
||||
parser.add_argument("--domain", required=True, help="Target domain of study")
|
||||
parser.add_argument("--goal", required=True, help="Learning goal")
|
||||
parser.add_argument(
|
||||
"--config",
|
||||
default=os.environ.get("DIDACTOPUS_CONFIG", "configs/config.example.yaml"),
|
||||
help="Path to configuration YAML",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = build_parser().parse_args()
|
||||
config = load_config(Path(args.config))
|
||||
provider = ModelProvider(config.model_provider)
|
||||
packs = discover_domain_packs(config.artifacts.local_pack_dirs)
|
||||
|
||||
dmap = build_demo_domain_map(args.domain)
|
||||
roadmap = generate_initial_roadmap(dmap, args.goal)
|
||||
|
||||
print("== Didactopus ==")
|
||||
print("Many arms, one goal — mastery.")
|
||||
print()
|
||||
print("== Provider ==")
|
||||
print(provider.describe())
|
||||
print()
|
||||
print("== Installed Domain Packs ==")
|
||||
if packs:
|
||||
for pack_dir, manifest in packs:
|
||||
print(f"- {manifest.display_name} ({manifest.name} {manifest.version}) @ {pack_dir}")
|
||||
else:
|
||||
print("- none found")
|
||||
print()
|
||||
print("== Domain Map Sequence ==")
|
||||
for concept in dmap.topological_sequence():
|
||||
print(f"- {concept}")
|
||||
print()
|
||||
print("== Roadmap ==")
|
||||
for stage in roadmap:
|
||||
print(f"- {stage.title}: {stage.mastery_goal}")
|
||||
print()
|
||||
focus_concept = dmap.topological_sequence()[1]
|
||||
print("== Mentor Prompt ==")
|
||||
print(generate_socratic_prompt(provider, focus_concept))
|
||||
print()
|
||||
print("== Practice Task ==")
|
||||
print(generate_practice_task(provider, focus_concept))
|
||||
print()
|
||||
print("== Capstone Suggestion ==")
|
||||
print(suggest_capstone(provider, args.domain))
|
||||
print()
|
||||
print("== Evaluation Rubric ==")
|
||||
print(generate_rubric(provider, focus_concept))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from .model_provider import ModelProvider
|
||||
|
||||
|
||||
def generate_socratic_prompt(provider: ModelProvider, concept: str) -> str:
|
||||
prompt = (
|
||||
f"You are a Socratic mentor. Ask one probing question that tests whether a learner "
|
||||
f"truly understands the concept '{concept}' and can explain it in their own words."
|
||||
)
|
||||
return provider.generate(prompt).text
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
from dataclasses import dataclass
|
||||
from .config import ModelProviderConfig
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelResponse:
|
||||
text: str
|
||||
provider: str
|
||||
model_name: str
|
||||
|
||||
|
||||
class ModelProvider:
|
||||
def __init__(self, config: ModelProviderConfig) -> None:
|
||||
self.config = config
|
||||
|
||||
def describe(self) -> str:
|
||||
local = self.config.local
|
||||
return f"mode={self.config.mode}, local={local.backend}:{local.model_name}"
|
||||
|
||||
def generate(self, prompt: str) -> ModelResponse:
|
||||
local = self.config.local
|
||||
preview = prompt.strip().replace("\n", " ")[:120]
|
||||
return ModelResponse(
|
||||
text=f"[stubbed-response] {preview}",
|
||||
provider=local.backend,
|
||||
model_name=local.model_name,
|
||||
)
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from .model_provider import ModelProvider
|
||||
|
||||
|
||||
def generate_practice_task(provider: ModelProvider, concept: str) -> str:
|
||||
prompt = (
|
||||
f"Generate one practice task for the concept '{concept}'. Require reasoning, "
|
||||
f"not mere recall, and avoid giving the answer."
|
||||
)
|
||||
return provider.generate(prompt).text
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from .model_provider import ModelProvider
|
||||
|
||||
|
||||
def suggest_capstone(provider: ModelProvider, domain: str) -> str:
|
||||
prompt = (
|
||||
f"Suggest one realistic capstone project for a learner pursuing mastery in {domain}. "
|
||||
f"The project must require synthesis, verification, and original work."
|
||||
)
|
||||
return provider.generate(prompt).text
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from didactopus.artifact_registry import discover_domain_packs
|
||||
|
||||
|
||||
def test_discover_example_pack() -> None:
|
||||
packs = discover_domain_packs(["domain-packs"])
|
||||
assert len(packs) >= 1
|
||||
_, manifest = packs[0]
|
||||
assert manifest.name == "example-statistics"
|
||||
assert manifest.display_name == "Example Statistics Pack"
|
||||
|
|
@ -0,0 +1,10 @@
|
|||
from pathlib import Path
|
||||
from didactopus.config import load_config
|
||||
|
||||
|
||||
def test_load_example_config() -> None:
|
||||
config = load_config(Path("configs/config.example.yaml"))
|
||||
assert config.model_provider.mode == "local_first"
|
||||
assert config.platform.verification_required is True
|
||||
assert config.platform.mastery_threshold == 0.8
|
||||
assert "domain-packs" in config.artifacts.local_pack_dirs
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
from didactopus.domain_map import build_demo_domain_map
|
||||
|
||||
|
||||
def test_demo_domain_map_order() -> None:
|
||||
dmap = build_demo_domain_map("statistics")
|
||||
sequence = dmap.topological_sequence()
|
||||
assert sequence == ["foundations", "methods", "analysis", "projects"]
|
||||
|
||||
|
||||
def test_prerequisites() -> None:
|
||||
dmap = build_demo_domain_map("statistics")
|
||||
prereqs = set(dmap.prerequisites_for("projects"))
|
||||
assert prereqs == {"foundations", "methods", "analysis"}
|
||||
Loading…
Reference in New Issue