Create backend interface to LLM API

[cproctor]

Whichever features we choose to implement, we will need a backend means of calling out to a LLM. Instead of hard-coding OpenAI's APIs into the project, I would like a vendor-neutral API, which for now will wrap around OpenAI, but which could be extended to other LLM APIs in the future.

• Create a new django app llm within the Unfold Studio project.
• Create a module within unfold_studio.llm which provides an interface to LLM estimation. Probably this should be a class which can be initialized with settings.
• Within unfold_studio.llm.models, create a data model to log LLM queries. Should contain query, response, API name, timestamp, etc.
• When cache_queries is enabled, the API should check whether there is already a saved query response in the database.
• Using Django's database settings as a model, add settings to base_settings.py parameterizing the LLM API (e.g. OpenAI API keys; a bool cache_queries setting, etc. ).
• Also create a stub interface in unfold_studio.llm which only searches for cached responses, otherwise returns a constant string.
• Write tests using the stub interface.

Once this layer is in place, we'll write a route which the frontend can use to query the LLM. The front-end runtime will need to make dynamic calls to LLM for content during story playthrough.

[cproctor]

I have created an initial implementation, creating an app called text_generation instead of llm. (My thinking for the name is that we should focus on what it does rather than the technology on which it's implemented. However, this initial version is not adequate; I'll keep this issue open until it's improved.