cmbagent 0.0.0b1

Multi-agent system for data analysis, made by cosmologists, powered by autogen.

cmbagent

Multi-agent system for data analysis, made by cosmologists, powered by autogen.

Note: This software is under MIT license. We bear no responsibility for any misuse of this software or its outputs.

See our examples here to have a preview of our work.

Contributed by:

• Boris Bolliet (Cambridge)
• Andrew Laverick (Independent)
• Inigo Zubeldia (Cambridge)
• Kristen Surrao (Columbia)
• Miles Cranmer (Cambridge)
• Antony Lewis (Sussex)
• Blake Sherwin (Cambridge)
• Julien Lesgourgues (Aachen)

Installation

To install cmbagent, follow these steps:

Clone and install our package from the cmbagent repository:

pip install cmbagent

Before pip installing cmbagent, creating a virual environment is envouraged:

python -m venv /path/to/your/envs/cmbagent_env
source /path/to/your/envs/cmbagent_env/bin/activate

You can then pip install cmbagent in this fresh environment.

Structure

RAG agents are defined in a generic way. The core of the code is located in cmbagent.py.

To generate a RAG agent, create a .py and .yaml file and place them in the assistants directory. Additionally, create a directory named after the agent and include associated files in the data directory of cmbagent.

Apart from the RAG agents, we have assistant agents (engineer and planner) and a code agent (executor).

Agents

All agents inherit from the BaseAgent class. You can find the definition of BaseAgent in the base_agent.py file.

Usage

Before you can use cmbagent, you need to set your OpenAI API key as an environment variable:

For Unix-based systems (Linux, macOS):

export OPENAI_API_KEY="sk-..."

(paste in your bashrc or zshrc file, if possible.)

For Windows:

setx OPENAI_API_KEY "sk-..."

You can also pass your API key to cmbagent as an argument when you instantiate it:

cmbagent = CMBAgent(llm_api_key="sk-...")

Instantiate the CMBAgent with:

from cmbagent import CMBAgent
cmbagent = CMBAgent(verbose=True)

Define a task as:

task = """
       Get cosmological parameter values from Planck 2018 analysis of TT,TE,EE+lowE+lensing with the Plik likelihood in LCDM. 
       Use Cobaya with Classy_SZ to evaluate the ACT DR6 lensing likelihood for sigma8=0.8 and Omega_m=0.31. Other parameters set to Planck 2018.  
       To set Omega_m, adjust the value of omch2. 
       Give me the value of log-likelihood.
       """

Solve the task with:

cmbagent.solve(task)

If you request any output, it will be saved in the output directory.

Show the plot with:

cmbagent.show_plot("cmb_tt_power_spectrum.png")

Restore session with:

cmbagent.restore()

Push vector stores of RAG agents into the OpenAI platform:

cmbagent = CMBAgent(make_vector_stores=True)

Push selected vector stores of RAG agents into the OpenAI platform:

cmbagent = CMBAgent(make_vector_stores=['act', 'camb'])

Start session with only a subset of RAG agents:

cmbagent = CMBAgent(agent_list=['classy', 'planck'])

Show allowed transitions:

cmbagent.show_allowed_transitions()

cmbagent uses cache to speed up the process and reduce costs when asking the same questions. When developing, it can be useful to clear the cache. Do this with:

cmbagent.clear_cache()