Wrapper's Delight: An Enhanced OpenAI Wrapper

wrappers_delight is a Python wrapper built around OpenAI's ChatCompletion API. The main features of this wrapper are:

Automated logging to a CSV file for every interaction.
Analytics functions for visualizing model usage.
Parameter-based or AI-assisted querying of logs.

To get started, you'll need to clone this repository:

git clone https://github.com/yoheinakajima/wrappers_delight.git
cd wrappers_delight

Prerequisites

Ensure you have OpenAI's Python client installed:

pip install openai

Usage

Basic Chat Completion

To use the wrapper for a basic chat with the model, load the wrapper and every prompt input and output will be stored:
*Aside from line 2, this is a standard OpenAI ChatCompletion call.

import openai
from wrappers_delight.wrapper import _enhanced_chat_completion

# Set your OpenAI API key
openai.api_key = "YOUR_API_KEY"

# Engage in a conversation with the model
response = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a fun fact."},
    ]
)

print(response.choices[0].message.content)

With Custom Functions

To register and use a custom function:
*Aside from line 2, this is a standard OpenAI ChatCompletion call using functions.

import openai
from wrappers_delight.wrapper import _enhanced_chat_completion

# Set your OpenAI API key
openai.api_key = "YOUR_OPENAI_KEY"

completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo-0613",
    messages=[
        {
            "role": "user", 
            "content": "I visited Tokyo, then moved to San Francisco, and finally settled in Toronto."
        }
    ],
    functions=[
        {
            "name": "extract_locations",
            "description": "Extract all locations mentioned in the text",
            "parameters": {
                "type": "object",
                "properties": {
                    "locations": {
                        "type": "array",
                        "items": {
                            "type": "object",
                            "properties": {
                                "name": {
                                    "type": "string",
                                    "description": "The name of the location"
                                },
                                "country_iso_alpha2": {
                                    "type": "string",
                                    "description": "The ISO alpha-2 code of the country where the location is situated"
                                }
                            },
                            "required": ["name", "country_iso_alpha2"]
                        }
                    }
                },
                "required": ["locations"],
            },
        },
    ],
    function_call={"name": "extract_locations"}
)

response_data = completion.choices[0]['message']['function_call']['arguments']
print(response_data)

Logging

All interactions with the model are automatically logged to log.csv. Each row in the CSV consists of the request parameters and the corresponding model response.

Example Output

The following are 2 example outputs in log.csv. The first is a standard ChatCompletion call, the second is a function call:

{"timestamp": "2023-08-13 03:00:49", "params": {"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me a fun fact."}]}, "response": {"id": "chatcmpl-7mvdPu0H1QULvDZOUVJS6npdMslul", "object": "chat.completion", "created": 1691895647, "model": "gpt-3.5-turbo-0613", "choices": [{"index": 0, "message": {"role": "assistant", "content": "Sure! Here's a fun fact: Did you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still perfectly edible! Honey's low moisture content and acidic pH create an inhospitable environment for bacteria and other microorganisms, allowing it to last indefinitely."}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 23, "completion_tokens": 70, "total_tokens": 93}}, "total_tokens": 93, "model": "gpt-3.5-turbo"}
{"timestamp": "2023-08-13 03:01:16", "params": {"model": "gpt-3.5-turbo-0613", "messages": [{"role": "user", "content": "I visited Tokyo, then moved to San Francisco, and finally settled in Toronto."}], "functions": [{"name": "extract_locations", "description": "Extract all locations mentioned in the text", "parameters": {"type": "object", "properties": {"locations": {"type": "array", "items": {"type": "object", "properties": {"name": {"type": "string", "description": "The name of the location"}, "country_iso_alpha2": {"type": "string", "description": "The ISO alpha-2 code of the country where the location is situated"}}, "required": ["name", "country_iso_alpha2"]}}}, "required": ["locations"]}}], "function_call": {"name": "extract_locations"}}, "response": {"id": "chatcmpl-7mvdqfScl0uQ2tfye1HdfcPEV5XYI", "object": "chat.completion", "created": 1691895674, "model": "gpt-3.5-turbo-0613", "choices": [{"index": 0, "message": {"role": "assistant", "content": null, "function_call": {"name": "extract_locations", "arguments": "{\n  \"locations\": [\n    {\n      \"name\": \"Tokyo\",\n      \"country_iso_alpha2\": \"JP\"\n    },\n    {\n      \"name\": \"San Francisco\",\n      \"country_iso_alpha2\": \"US\"\n    },\n    {\n      \"name\": \"Toronto\",\n      \"country_iso_alpha2\": \"CA\"\n    }\n  ]\n}"}}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 83, "completion_tokens": 74, "total_tokens": 157}}, "total_tokens": 157, "model": "gpt-3.5-turbo-0613"}

Analytics

To run the analytics and visualize the model's usage, load the analytics:

from wrappers_delight.analytics import plot_token_usage, plot_model_distribution

# Generate and display the analytics plots
plot_token_usage()
plot_model_distribution()

Query Logs

To enable querying your logs, load the two query functions:

from wrappers_delight.analytics import query_log, query_log_with_ai

query_log() and query_log_with_ai() are functions that serve to retrieve specific entries from logs. While query_log directly queries the log based on various parameters, query_log_with_ai uses an AI model to further refine those results based on context or more complex requirements.

Prerequisits

Ensure you have the required libraries installed. This includes pandas, openai, and others depending on your needs.

using query_log()

Parameters:

columns: List of columns to be displayed in the result.
start_date: The starting date for the logs you want to retrieve.
end_date: The end date for the logs.
min_tokens: Minimum number of tokens in the response.
max_tokens: Maximum number of tokens in the response.
filter_by: Additional filter criteria.
sort_by: The column by which the results should be sorted (default is timestamp).
limit: Limit the number of rows in the result.
keyword: Search for a keyword in user message or response.
function_name: Filter by the function name.
model_version: Filter by model version.

Sample Code:

result = query_log(start_date="2023-08-01", end_date="2023-08-10", keyword="weather")
print(result)

using query_log_with_ai

This function interprets natural language user queries and translates them into parameters to fetch appropriate log entries using the query_log function.