Gentopia-Mason

IMPORTANT NOTICE: This code repository was adapted from Gentopia.AI.

Authors: Ziyu Yao ([email protected]) and Ashutosh Pathak ([email protected])
Copyright and license should go to Gentopia.AI.

I created a Movie-Madness Agent that can query imdb and get movie/series data and present them to user upon query. If a movie is not present, it shows relevant message.

Installation 💻

First, clone this repository:

git clone [email protected]:pathak-ashutosh/gentopia-mason.git
cd gentopia-mason

Next, we will create a virtual environment and install the library:

conda create --name gentenv python=3.10
conda activate gentenv
pip install -r requirements.txt

Most of the agent construction and execution activities will happen within GentPool. For the gentopia library to be used within GentPool, set up the global environment:

export PYTHONPATH="$PWD/Gentopia:$PYTHONPATH"

In addition, since we will be using OpenAI's API, we also need to create a .env file under GentPool and put the API Key inside. The key will be registered as environmental variables at run time.

cd GentPool
touch .env
echo "OPENAI_API_KEY=<your_openai_api_key>" >> .env

Now you are all set! Let's create your first Gentopia Agent.

Quick Start: Clone a Vanilla LLM Agent

GentPool has provided multiple template LLM agents. To get started, we will clone the "vanilla agent" from GentPool/gentpool/pool/vanilla_template with the following command:

./clone_agent vanilla_template <your_agent_name> 

This command will initiate an agent template under ./GentPool/gentpool/pool/<your_agent_name>. The agent configuration can be found in ./GentPool/gentpool/pool/<your_agent_name>/agent.yaml (note the agent type vanilla). The vanilla prompt it uses can be found in the source code of Gentopia; see ./Gentopia/gentopia/prompt/vanilla.py.

You can now run your agent via:

python assemble.py <your_agent_name>

This vanilla agent simply sends the received user query to the backend LLM and returns its output. Therefore, for many complicated tasks, such as those requiring accessing the latest materials, it will fail.

Implement a Scholar LLM Agent with Tool Augmentation

In the second trial, we will create a Scholar agent which is augmented with multiple functions to access Google Scholar in real time.

This is based on the scholar agent we have created in the pool. As before, in this demo we simply clone it:

./clone_agent scholar <your_agent_name> 

Like before, this command created an agent under ./GentPool/gentpool/pool/<your_agent_name>. Note from its configuration that scholar is an openai-type agent. As stated in its Gentopia's implementation, this type of agent allows for function calling:

OpenAIFunctionChatAgent class inherited from BaseAgent. Implementing OpenAI function call api as agent.

The available functions to the scholar agent have been listed in its configuration file ./GentPool/gentpool/pool/<your_agent_name>/agent.yaml, and the implementation of these tools can be found in Gentopia's source code (mostly coming from the google_scholar.py file, in this example).

Now, you are all set to query this scholar agent for the latest papers by certain authors, the summary of a certain paper, paper citations, etc.

Remove an Agent

Sometimes an agent can upset you. To wipe it out completely,

./delete_agent <your_agent_name>