AI providers and API keys
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An AI provider is a company or a service that gives you the ability to send requests to and receive responses from an artificial intelligence. At date of writing, the most capable AI systems are based on Large Language Model (LLM) architectures.
Here is the list of AI providers currently supported by JabRef:
OpenAI
Mistral AI
Hugging Face.
GPT4All
Ollama
You can find more information about providers in the . This is the framework that we use in JabRef. This page lists available integrations. It should be noted that JabRef is compatible with any provider that itself is compatible with the OpenAI API.
We cannot give a clear recommendation. Providers change their service and their prices regularly and our documentation page is too static to keep up with daily changes. It is recommended to look up LLM benchmarks on the internet or to use the trial and error method. To date, remote AI providers like OpenAI, Google, Mistral and others offer state of the art quality.
If you want to , choose GPT4All or Ollama. In comparison to remote AI providers, open weight local models that are compatible with average consumer devices offer less capabilities. There are state of the art local models available, but they are very large (in terms of number of parameters) and the higher the number of parameters, the more memory is needed. To run the largest models, very expensive and capable hardware (preferably VRAM in GPU's or ASICs) is required. That said, even small models can be sufficient for the workflow. Hugging Face is a special case. One the one hand, it serves as a free hosting platform from where you can download numerous models to host them yourself locally. On the other hand Huggingface also is a remote AI provider that offers running numerous large and small open weight models for you.
In order to use any of the (proprietary) remote services and to receive a response, you always need an API key to authenticate and manage billing. The following sections down below teach you how to receive a key and where to enter it in the preferences.
An API key or API token is like a password that lets an app or program access information or services from another app or website, such as an LLM service. It ensures that only authorized users or applications can use the service. For example, when an app uses an LLM service to generate text or answer questions, it includes its unique API key in the request. The LLM service checks this key to make sure the request is legitimate before providing the response. This process keeps the data secure and helps track how the service is being used.
To get an OpenAI API key, follow these steps:
Go to the "API" section
Go to the "Dashboard" (upper-right corner)
Go to the "API keys" (left menu)
Click "Create new secret key"
Click "Create secret key"
OpenAI will display the key
There you will find a button "Create new key". Click on it
You can optionally set up a name for the API key and its expiration date
After the creation, you will see "Your key is:" with a string of random characters after that
Hugging Face refers to an "API key" as an "Access Token". It does not make much difference, you can interchangeably use either "API key", or "API token", or "access token".
Set "Token Type" to "Read"
Name a token
After you click "Create token", a popup will be shown with the API key
Do not share the key with anyone, it is a secret that was created only for your account. Do not enter this key into unknown or unverified services.
Now you need to copy and paste it into JabRef preferences. To do this:
Launch JabRef
Go "File" -> "Preferences" -> "AI"
Check "Enable AI functionality"
Paste the key into the "API key" field
Click "Save"
If you have some money on your credit balance, you can chat with your library!
To increase your credit balance on OpenAI, follow these steps:
Log in or create an account on the
Login or create an account on the
Go to the
or on Hugging Face
Go to
Add payment method .
Add credit balance on page.
Make the subscription on .
You do not have to pay anything for Hugging Face in order to send requests to LLMs. Though, the speed is very slow by default. It may take a long time to allocate free compute resources to your instance, resulting in longer response times. You can switch to faster inference by or by .