This project generates very long, textbook quality pretraining data. Here's a 70M token example. It can run generations in parallel, against OpenAI, or your own API. It can generate the topics from scratch, or use a set of seeds you provide.

The generator uses retrieval to improve quality. By default, it will use Serply to do the retrieval, but you can also use SerpAPI, or disable retrieval.

The core is extensible, so you can add your own adaptors to connect to new APIs and retrieval backends.

• Python 3.9+ (ideally 3.11)
• You will need postgres installed. You can install it with brew install postgres on a Mac.

• psql postgres -c "create database textbook;"
• git clone https://github.com/VikParuchuri/textbook_quality.git
• cd textbook_quality
• poetry install
• invoke migrate-dev

First, create a local.env file in the root directory of the repo to store your secret keys. Alternatively, you can set any key below as an env var.

You can see all the available configuration values in app/settings.py.

• Add your OpenAI key, like OPENAI_KEY=sk-xxxxxx
• Add your serply key (SERPLY_KEY="...") or serpapi key (SERPAPI_KEY="...").
• Add SEARCH_BACKEND=serply or SEARCH_BACKEND=serpapi to use the appropriate backend.

• Set OPENAI_KEY to the value of your API key, or a dummy value.
• Set OPENAI_BASE_URL to the url of your API (like https://vllm-api.com/v1)
• Set the LLM_TYPE, LLM_INSTRUCT_TYPE, and LLM_EXTENDED_TYPE settings to your model name (like llama)
• Set the model name and max tokens in the LLM_TYPES setting.
• Follow the instructions above for the retrieval setup.

The generator ideally needs a context length of up to 16k, but you can get away with 12k if you need to.

• Set SEARCH_BACKEND=none

There are three main scripts in the repo. You can run each script on the output of the previous one. All outputs will appear by default in app/data, which is the specified DATA_DIR in settings.

You enter a subject, a file you want to save the topics to, and the number of iterations. The topics will be deduplicated.

Usage example:

python topic_generator.py "computer science with python" python_cs_titles.json --iterations 50

Take a file with existing seeds (in a flat json list), and augment them. You can pass in the output file from the topic generator as the seed file, or use your own seeds. Domain is an optional flag to constrain the topics within a domain.

This will also deduplicate the topics semantically.

Usage example:

python topic_augmentor.py python_titles.json python_topics.json --domain python

This will take a file with a flat json list of topics, and generate one textbook per topic. The workers flag controls the number of parallel generations. Lower it if you hit rate limits.

Usage example:

python book_generator.py topics.json books.jsonl --workers 5

You can also override settings with environment variables (instead of using local.env). This example will use a vllm api instead of openai:

LLM_TYPE=llama LLM_INSTRUCT_TYPE=llama LLM_EXTENDED_TYPE=llama OPENAI_KEY="llama" OPENAI_BASE_URL="https://vllm-api.com/v1" python book_generator.py topics.json books.jsonl --workers 10

Note that courses are cached by default, so regenerating a course with the same name twice will not hit the API again. The cache is specific to each model and each topic.

You can extend this to add in new LLM adaptors, retrieval methods, or tasks. PRs are very welcome.

• LLM adapters are in app/llm/adaptors
• Retrieval methods are in app/services/adaptors. You may also need to adjust settings in services/generators/pdf.py
• Tasks are in app/llm/generators

By default, a lot of exceptions will be hidden to avoid console noise. Use DEBUG=true to display them, like this:

DEBUG=true python book_generator.py python_topics.json books.jsonl --max 5 --workers 5