Metadata-Version: 2.1
Name: gpt-trim
Version: 0.1
Summary: Trims the messages array for ChatGPT API
Author-email: AWeirdDev <aweirdscratcher@gmail.com>
License: MIT License
        
        Copyright (c) 2023 JC
        
        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.
        
Project-URL: Homepage, https://github.com/AWeirdScratcher/gpt-trim
Project-URL: Bug Tracker, https://github.com/AWeirdScratcher/gpt-trim/issues
Keywords: gpt,trim,gpt-trim,chatgpt,token,tiktoken
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
License-File: LICENSE

# gpt-trim

This is a (slightly) faster version of [KillianLucas/tokentrim](https://pypi.org/project/tokentrim) for longer message arrays.

In average, gpt-trim is \~80% faster than tokentrim, and that tokentrim is around 5x\~7x slower.

Although gpt-trim is fast, I still need to finish my LeetCode problems that I left years ago, just so that I can make it 20x faster than 95% of people.

## Usage

The usage is quite similiar to `tokentrim`.

```python
import gpt_trim

trimmed = gpt_trim.trim(
    messages, 
    model="gpt-3.5-turbo"
)
print(trimmed)
```

Alternatively, you can assign the token limit manually:

```python
gpt_trim.trim(
    messages,
    max_tokens=100
)
```

You can also add system messages with ease:

```python
import gpt_trim

messages = [
    ..., # long, long content
    {
        "role": "user",
        "content": "It's about drive, it's about power"
    }
]
trimmed = gpt_trim.advanced_trim(
    messages,
    system_messages=[
        {
            "role": "system",
            "content": "You'll act like the celebrity: The Rock."
        }
    ],
    model="gpt-3.5-turbo",
)
print(trimmed)
```

The catch? It's slower. With great power comes great... patience.

## Comparison

You can compare this project to [KillianLucas/tokentrim](https://pypi.org/project/tokentrim) like so:

```python
import time

import gpt_trim
import tiktoken
import tokentrim

pattern = "d!3h.l7$fj" # 10 tokens
messages = [
    {
        "role": "user",
        "content": pattern * 5000 # 50000 tokens
    }
]

# cache first
enc = tiktoken.get_encoding("cl100k_base")
gpt_trim.num_tokens_from_messages(
    messages,
    enc
)

def test(provider):
    print("Testing", provider.__name__)

    s = time.time()
    result = provider.trim(
        messages,
        model="gpt-3.5-turbo",
    )

    print(f"took {(time.time() - s):.4f}s\n")

# Swap the following for every test and see tokentrim 
# struggles when dealing with longer context.
test(gpt_trim)
test(tokentrim)
```

***

Right. I was bored.
