fix interface handling with generic model

move api endpoint init to AbstractModel

This should finally solve the chicken-egg issue. This also renames the
ollama url parameter from baseurl to apiurl and sets a sensible default

refactor HTTP Client initialization

allow models to have a zero token limit

This allows for configuring completely unknown models. For these models
no token limit is known and we will simply do not apply any. Instead we
trust that the

allow models to have a zero token limit

This allows for configuring completely unknown models. For these models
no token limit is known and we will simply do not apply any. Instead we
trust that the model will be either large enough to handle our input or
at least throw useful error messages.

show more ...

fix model full name

introduce a "Generic" model

This is basically an OpenAI implementation, which then can be reused by
most other providers since they usually implement a somewhat compatible
API. The API URL can be se

introduce a "Generic" model

This is basically an OpenAI implementation, which then can be reused by
most other providers since they usually implement a somewhat compatible
API. The API URL can be set manually so it should be easy to add new
unsupported providers.

show more ...

add remote component to ask questions to the bot

The endpoint allows to override model and language settings on demand.

allow to set arbitrary models

We now initialize a model configuration even if we have no info in
model.json using some default values for the token limits.

Models can implement the loadUnkonwModelI

allow to set arbitrary models

We now initialize a model configuration even if we have no info in
model.json using some default values for the token limits.

Models can implement the loadUnkonwModelInfo() method to fetch the info
from an API if such a thing exist. Implemented for gemini and ollama
currently.

show more ...

fix info output on used models

automatic stylefixes

various refactoring and introduction of a simulate command

The new command makes it easier to run the same chat questions against
multiple models and compare the results in a spreadsheet

separate the rephrasing model from the chat model

Rephrasing can be done with faster, simpler models as there is not much
reasoning needed.

added Mistral to the list of models

move model configuration into json files

This removes the use of individual classes for each model in favor of
more general client classes that only get a model name passed. The meta
info about pric

move model configuration into json files

This removes the use of individual classes for each model in favor of
more general client classes that only get a model name passed. The meta
info about price and token limits is configured in a json file

show more ...

more refactoring on chat and embed model support

* differentiate between input and output tokens
* make use of much larger input contexts

first tries with Anthropic

stricter interface inheritance

This ensures we have the apropriate methods when a class does not
inherit from AbstractModel.

Use interfaces for Chat and Embedding classes

This way it's easier to have a base OpenAI class. This also moves much
of the statistics and http handling into the base class making model
implementati

Use interfaces for Chat and Embedding classes

This way it's easier to have a base OpenAI class. This also moves much
of the statistics and http handling into the base class making model
implementations even leaner

show more ...

First start on refactoring the class hierarchy

This splits embedding models from chat completion models.

�� Automatic code style fixes

refactoring to make models selectable

This makes it much easier to add new models. Models can now be selected
via the configuration