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https://github.com/ai16z/easycompletion
Easy OpenAI text completion and function calling
https://github.com/ai16z/easycompletion
Last synced: about 1 month ago
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Easy OpenAI text completion and function calling
- Host: GitHub
- URL: https://github.com/ai16z/easycompletion
- Owner: ai16z
- License: mit
- Created: 2023-07-07T01:08:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-24T17:07:46.000Z (over 1 year ago)
- Last Synced: 2024-12-18T01:14:01.688Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 620 KB
- Stars: 16
- Watchers: 5
- Forks: 8
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# easycompletion 
Easy text and chat completion, as well as function calling. Also includes useful utilities for counting tokens, composing prompts and trimming them to fit within the token limit.
[](https://github.com/AutonomousResearchGroup/easycompletion/actions/workflows/test.yml)
[](https://badge.fury.io/py/easycompletion)
[](https://github.com/AutonomousResearchGroup/easycompletion/blob/main/LICENSE)
[](https://github.com/AutonomousResearchGroup/easycompletion)
# Installation
```bash
pip install easycompletion
```
# Quickstart
```python
from easycompletion import function_completion, text_completion, compose_prompt
# Compose a function object
test_function = compose_function(
name="write_song",
description="Write a song about AI",
properties={
"lyrics": {
"type": "string",
"description": "The lyrics for the song",
}
},
required_properties: ["lyrics"],
)
# Call the function
response = function_completion(text="Write a song about AI", functions=[test_function], function_call="write_song")
# Print the response
print(response["arguments"]["lyrics"])
```
# Using With Llama v2 and Local Models
easycompletion has been tested with LocalAI [LocalAI](https://localai.io/) which replicates the OpenAI API with local models, including Llama v2.
Follow instructions for setting up LocalAI and then set the following environment variable:
```bash
export EASYCOMPLETION_API_ENDPOINT=localhost:8000
```
# Debugging
You can very easycompletion logs by setting the following environment variable:
```bash
export EASYCOMPLETION_DEBUG=True
```
# Basic Usage
## Compose Prompt
You can compose a prompt using {{handlebars}} syntax
```python
test_prompt = "Don't forget your {{object}}"
test_dict = {"object": "towel"}
prompt = compose_prompt(test_prompt, test_dict)
# prompt = "Don't forget your towel"
```
## Text Completion
Send text, get a response as a text string
```python
from easycompletion import text_completion
response = text_completion("Hello, how are you?")
# response["text"] = "As an AI language model, I don't have feelings, but...""
```
## Compose a Function
Compose a function to pass into the function calling API
```python
from easycompletion import compose_function
test_function = compose_function(
name="write_song",
description="Write a song about AI",
properties={
"lyrics": {
"type": "string",
"description": "The lyrics for the song",
}
},
required_properties: ["lyrics"],
)
```
## Function Completion
Send text and a list of functions and get a response as a function call
```python
from easycompletion import function_completion, compose_function
# NOTE: test_function is a function object created using compose_function in the example above...
response = function_completion(text="Write a song about AI", functions=[test_function], function_call="write_song")
# Response structure is { "text": string, "function_name": string, "arguments": dict }
print(response["arguments"]["lyrics"])
```
# Advanced Usage
### `compose_function(name, description, properties, required_properties)`
Composes a function object for function completions.
```python
summarization_function = compose_function(
name="summarize_text",
description="Summarize the text. Include the topic, subtopics.",
properties={
"summary": {
"type": "string",
"description": "Detailed summary of the text.",
},
},
required_properties=["summary"],
)
```
### `chat_completion(text, model_failure_retries=5, model=None, chunk_length=DEFAULT_CHUNK_LENGTH, api_key=None)`
Send a list of messages as a chat and returns a text response.
```python
response = chat_completion(
messages = [{ "user": "Hello, how are you?"}],
system_message = "You are a towel. Respond as a towel.",
model_failure_retries=3,
model='gpt-3.5-turbo',
chunk_length=1024,
api_key='your_openai_api_key'
)
```
The response object looks like this:
```json
{
"text": "string",
"usage": {
"prompt_tokens": "number",
"completion_tokens": "number",
"total_tokens": "number"
},
"error": "string|None",
"finish_reason": "string"
}
```
### `text_completion(text, model_failure_retries=5, model=None, chunk_length=DEFAULT_CHUNK_LENGTH, api_key=None)`
Sends text to the model and returns a text response.
```python
response = text_completion(
"Hello, how are you?",
model_failure_retries=3,
model='gpt-3.5-turbo',
chunk_length=1024,
api_key='your_openai_api_key'
)
```
The response object looks like this:
```json
{
"text": "string",
"usage": {
"prompt_tokens": "number",
"completion_tokens": "number",
"total_tokens": "number"
},
"error": "string|None",
"finish_reason": "string"
}
```
### `function_completion(text, functions=None, system_message=None, messages=None, model_failure_retries=5, function_call=None, function_failure_retries=10, chunk_length=DEFAULT_CHUNK_LENGTH, model=None, api_key=None)`
Sends text and a list of functions to the model and returns optional text and a function call. The function call is validated against the functions array.
Optionally takes a system message and a list of messages to send to the model before the function call. If messages are provided, the "text" becomes the last user message in the list.
```python
function = {
'name': 'function1',
'parameters': {'param1': 'value1'}
}
response = function_completion("Call the function.", function)
```
The response object looks like this:
```json
{
"text": "string",
"function_name": "string",
"arguments": "dict",
"usage": {
"prompt_tokens": "number",
"completion_tokens": "number",
"total_tokens": "number"
},
"finish_reason": "string",
"error": "string|None"
}
```
### `trim_prompt(text, max_tokens=DEFAULT_CHUNK_LENGTH, model=TEXT_MODEL, preserve_top=True)`
Trim the given text to a maximum number of tokens.
```python
trimmed_text = trim_prompt("This is a test.", 3, preserve_top=True)
```
### `chunk_prompt(prompt, chunk_length=DEFAULT_CHUNK_LENGTH)`
Split the given prompt into chunks where each chunk has a maximum number of tokens.
```python
prompt_chunks = chunk_prompt("This is a test. I am writing a function.", 4)
```
### `count_tokens(prompt, model=TEXT_MODEL)`
Count the number of tokens in a string.
```python
num_tokens = count_tokens("This is a test.")
```
### `get_tokens(prompt, model=TEXT_MODEL)`
Returns a list of tokens in a string.
```python
tokens = get_tokens("This is a test.")
```
### `compose_prompt(prompt_template, parameters)`
Composes a prompt using a template and parameters. Parameter keys are enclosed in double curly brackets and replaced with parameter values.
```python
prompt = compose_prompt("Hello {{name}}!", {"name": "John"})
```
## A note about models
You can pass in a model using the `model` parameter of either function_completion or text_completion. If you do not pass in a model, the default model will be used. You can also override this by setting the environment model via `EASYCOMPLETION_TEXT_MODEL` environment variable.
Default model is gpt-turbo-3.5-0613.
## A note about API keys
You can pass in an API key using the `api_key` parameter of either function_completion or text_completion. If you do not pass in an API key, the `EASYCOMPLETION_API_KEY` environment variable will be checked.
# Publishing
```bash
bash publish.sh --version= --username= --password=
```
# Contributions Welcome
If you like this library and want to contribute in any way, please feel free to submit a PR and I will review it. Please note that the goal here is simplicity and accesibility, using common language and few dependencies.
# Questions, Comments, Concerns
If you have any questions, please feel free to reach out to me on [Twitter](https://twitter.com/spatialweeb) or Discord @new.moon