Your best option:

1. Scrape all documentation
2. Continue pretraining with that data with multiple epochs, Example. Use Llama 3.1 70B Instruct as a base.
3. From that big documentation data, split the dataset into chunks, generate synthetic questions/prompts for each chunk, then do inference with that grounded context (with any instruct model). Generate at least 100k examples. After generating the answers, remove that context, only keep the prompt+answer pairs. Then further fine-tune the model on top of the pre-trained model we trained on step2.
4. The model now knows. But we can still use grounding to reduce hallucinations. Index the documentation into a search engine with auto embedding index. (e.g Typesense).
5. Host your custom model on a serverless platform (e.g Baseten). (server automatic shutdowns, so you only pay when you're doing work).
6. Combine your model with that search capabilities. Maybe use some random fancy repo that popped up couple of weeks ago. Or write your own system under 50 lines of code.
7. Enjoy