Intelligent Chatbot for Biomedical Knowledge Retrieval and Execution, Using Retrieval-Augmented Generation with LLMs

We implemented a pipeline in a prototype chatbot that leverages LLMs for natural language understanding and retrieval-augmented generation, using biomedical knowledge objects as contexts to answer biomedical questions with expert knowledge. To execute knowledge representations (code) attached to each knowledge object, we integrated an OpenAI Assistant API with a code_interpreter tool into our pipeline. This implementation is more reliable compared to other approaches that do not use a code_interpreter tool, examples of which are available in the archive folder of this repository. Here is an example output for this prototype chatbot:

Environment setup

Make sure poetry is installed Clone the repository using

git clone https://github.com/kgrid-lab/LLM_with_RAG_chatbot.git

setup a virtual environment using poetry

cd LLM_with_RAG_chatbot
poetry env use 3.11
poetry shell
poetry install

Create a .env file in the root of the project and store the following values in it

KNOWLEDGE_BASE="KO/"
MODEL="gpt-4o"
MODEL_SEED=1762259501
OPENAI_API_KEY="YOUR_OPENAI_API_KEY"

• The KNOWLEDGE_BASE points to the location of knowledge object context files.
• MODEL defines the version of the GPT to be used. We recomment use of gpt-4o or newer versions.
• Get your own API key at OpenAI's API keys section and set OPENAI_API_KEY with its value.

Run the app

Once the environment is set up you can run this app to be useed from command line using

python llm_with_rag_chatbot/openai_chatbot_with_assistant_api.py 

To run this app locally to access web interface use

python llm_with_rag_chatbot/chatbot.py