Token Usage

As of v6.0.0, LibreChat accurately tracks token usage for the OpenAI/Plugins endpoints. This can be viewed in your database’s “Transactions” collection.

In the future, you will be able to toggle viewing how much a conversation has cost you.

Currently, you can limit user token usage by enabling user balances. To enables token credit limiting for the OpenAI/Plugins endpoints, set the following .env variable:

Key	Type	Description	Example
CHECK_BALANCE	boolean	Enable token credit balances for the OpenAI/Plugins endpoints.	CHECK_BALANCE=false

You manually add user balance, or you will need to build out a balance-accruing system for users. This may come as a feature to the app whenever an admin dashboard is introduced.

To manually add balances, run the following command (npm required):

npm run add-balance

You can also specify the email and token credit amount to add, e.g.:

npm run add-balance [email protected] 1000

This works well to track your own usage for personal use; 1000 credits = $0.001 (1 mill USD)

Listing of balances

To see the balances of your users, you can run:

npm run list-balances

Notes

With summarization enabled, you will be blocked from making an API request if the cost of the content that you need to summarize + your messages payload exceeds the current balance
Counting Prompt tokens is really accurate for OpenAI calls, but not 100% for plugins (due to function calling). It is really close and conservative, meaning its count may be higher by 2-5 tokens.
The system allows deficits incurred by the completion tokens. It only checks if you have enough for the prompt Tokens, and is pretty lenient with the completion. The graph below details the logic
The above said, plugins are checked at each generation step, since the process works with multiple API calls. Anything the LLM has generated since the initial user prompt is shared to the user in the error message as seen below.
There is a 150 token buffer for titling since this is a 2 step process, that averages around 200 total tokens. In the case of insufficient funds, the titling is cancelled before any spend happens and no error is thrown.

More details

source: LibreChat/discussions/1640

”rawAmount”: -000, // what’s this?

Raw amount of tokens as counted per the tokenizer algorithm.

”tokenValue”: -00000, // what’s this?

Token credits value. 1000 credits = $0.001 (1 mill USD)

“rate”: 00, // what’s this?

The rate at which tokens are charged as credits.

For example, gpt-3.5-turbo-1106 has a rate of 1 for user prompt (input) and 2 for completion (output)

Model	Input	Output
gpt-3.5-turbo-1106	$0.0010 / 1K tokens	$0.0020 / 1K tokens

Given the provided example:

    "rawAmount": -137
    "tokenValue": -205.5
    "rate": 1.5

\[\text{Token Value} = (\text{Raw Amount of Tokens}) \times (\text{Rate}) \] \[137 \times 1.5 = 205.5 \]

And to get the real amount of USD spend based on Token Value:

\[\frac{\text{Token Value}}{1,000,000} = \left(\frac{\text{Raw Amount of Tokens} \times \text{Rate}}{1,000,000}\right) \] \[\frac{205.5}{1,000,000} = \$0.0002055 \text{ USD} \]

The relevant file for editing rates is found in api/models/tx.js

There will be more customization for this soon from the librechat.yaml file.

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