| Name | tokenlog JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | None |
| Summary | Simplest token log system for your LLM, embedding model calls. |
| upload_time | 2024-03-31 07:02:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Jeffrey (Dongkyu) Kim Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
llm
logging
log
embedding
openai
huggingface
token
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# tokenlog
Simplest token log system for your LLM, embedding model calls.
## Installation
Get on pypi.
```bash
pip install tokenlog
```
## How to use
Start with initializing the logger.
Each logger with the same name is singleton.
```python
import tokenlog
t_logger = tokenlog.getLogger('session_1', 'gpt-3.5-turbo') # write logger name and model name that you are using
q1 = t_logger.query('This is the query that you used in LLM') # log the query
t_logger.answer('This is an answer from LLM', q1) # log the answer
t_logger.get_token_usage() # get total token usage from all queries
t_logger.get_history() # get history of token usage
t_logger.clear() # clear all histories
```
### Batch logging
You can log multiple queries and answers at once.
```python
import tokenlog
t_logger = tokenlog.getLogger('session_2', 'gpt-3.5-turbo') # write logger name and model name that you are using
query_ids = t_logger.query_batch(['This is the query that you used in LLM', 'This is the second query'])
t_logger.query(['This is the first answer', 'This is the second answer'], query_ids)
```
## Support Models
We support all **OpenAI** models with tiktoken and **Huggingface** models that support `AutoTokenizer`.
## Use Case
This library used in [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG) project.
## To-do
- [ ] Add Handlers for exporting logs
- [ ] Support more models
- [ ] Batch logging
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