llmonpy 0.1.9

AI pipeline framework for Python.

LLMonPy

LLMonPy is a python library that aims to make it easy to build AI systems that generate "good enough" responses 99.9% of the time. LLMonPy makes it easy to use multiple language models (LLMs) to generate responses to prompts, to rank the responses, and to use the best responses as examples to improve the quality of future responses. It also generates question/best answer/worse answer (QBaWa) data that can be used for training models.

Getting Started

Setup Virtual Environment

I recommend setting up a virtual environment to isolate Python dependencies.

python3 -m venv .venv
source .venv/bin/activate

Install Package

Install the package from PyPi - this takes awhile because it also installs the python clients of multiple LLMs:

pip install llmonpy

Environment Variables

LLMonPy uses a lot of models, so you will probably need several API keys to use it. On startup, it looks for these keys and initializes the clients for associated models for the keys it finds. The following environment variables are used:

First Choice	Second Choice
LLMONPY_OPENAI_API_KEY	OPENAI_API_KEY
LLMONPY_ANTHROPIC_API_KEY	ANTHROPIC_API_KEY
LLMONPY_MISTRAL_API_KEY	MISTRAL_API_KEY
LLMONPY_GEMINI_API_KEY	GEMINI_API_KEY

Testing Setup

To determine what models are available, this command will list the models that are available:

llmonpy models

To test basic prompting, you can use the following command:

llmonpy prompt

To test the tourneys, you can use the following command (it will cost about $0.03 or less):

llmonpy tourney

To test the AdaptiveICLCycle, you can use the following command (it will cost about $0.9 or less):

llmonpy cycle

To test mixture of agents, what I call GenerateAggregateRank (GAR) you can use the following command (it will cost about $0.12 or less):

llmonpy gar

Creating Prompts

Prompts are classes that inherit from the LLMonPyPrompt class. The class defines the prompt_text and the data that is used to render the prompt (the prompt_text is used in a Jinja2 template). The class must also define a LLMonPyOutput nested class that defines the output of the prompt. If the prompt is used in a tourney, the class must also define a JudgePrompt nested class that inherits from the TournamentJudgePrompt class.

• prompt_text: Class field that is the text used for a Jinja2 template
• constructor: The constructor defines the data that is used to render the prompt
• to_dict: Method that returns a dictionary of the data that is used to render the prompt and to store the input data in the trace
• LLMonPyOutput: Nested class that defines the output of the prompt
• JudgePrompt: Inherits from TournamentJudgePrompt and is used to rank 2 outputs from the prompt. The output of a JudgePrompt is always TournamentJudgePrompt.LLMonPyOutput

You can see an example of a prompt in the steps_prompt.py file. If a prompt is used in an AdaptiveICLCycle, the prompt will probably include {% if example_list %}. Example_list is a list of examples of good responses. The data are instances of the LLMonPyOutput class.

Tourneys

Tourneys are LLMonPySteps that use the LLMonPyTournament class to have multiple LLMs generate responses to a prompt. It then ranks the responses by using LLM judges to compare each output against every other output. The winner of the tourney is the response with the most victories. There is an example of a tourney in steps_prompt.py file.

AdaptiveICLCycle

The AdaptiveICLCycle use a tourney to generate a list of examples of good responses. It then re-runs the tourney with the good responses used as examples to improve the quality of the responses. The cycle continues until it reaches a limit you set or the responses have stopped improving. There is an example of a cycle in steps_prompt.py file.

GenerateAggregateRank (GAR)

The GenerateAggregateRank (GAR) is a mixture of agents that uses a list of "generator" models to generate responses a first round of responses. The first rounds of responses are used as examples for the next round of responses. That process is repeated for the number of rounds you set. The last round of responses are ranked by a list of "judge" models.

Trace Viewer

LLMonPy has a trace viewer that can help you understand how your pipeline is working. To start it use this command:

llmonpy_viewer

The UI is at http://localhost:2304 and looks like this:



The trace viewer lets you see the "Victory Report" of tourneys and cycles. It also lets you see the input data, output data, the logs for each step and the sub-steps of each step. The Victory Report divides the total cost of each model's responses by the number of victories the model had in one on one battles. The trace data is stored in the "data" directory in your project directory. It is stored in a SQLite database.

Training Data (QBaWa)

LLMonPy stores the results of all the one on one battles used to rank the responses in a tourney. This data can be used to fine-tune models. It is indexed by the name of the prompt class. To get a list of prompts with training data, use the following command:

llmonpy qbawa_list

This command will return a list of prompt names. To get the actual training data as JSON, use the following command:

llmonpy qbawa -name=<prompt_name>