DDE-GPT: An AI Assistant for Documentation Generation

Deep-time.org是一个一站式在线地球科学科研协作平台。秉承“链接”的核心理念，基于Deep-Time Engine深时探索引擎，链通全球科研要素，通过Resource Hub、Earth Explorer、Analysis、MyDDE、Global Layer五大核心模块，向用户提供强大的数据、知识、计算、场景能力。通过平台形式支撑云上数据驱动地学研究环境的构建，并促进全球科学家的链接与在线协作。目前平台已支撑了古地理重建、矿产资源预测等若干地学场景，未来平台将持续围绕大科学问题，以数据驱动研究为核心手段，支撑小尺度更好、大尺度更快、多学科交叉、多圈层复合的研究，实现深时四大演化的科研探索。

Introduction

We introduce DDE-GPT , a chatrobot focusing on documentation generation for Deep-time.org plantform.

Demo

Add a local doc into DDE-GPT and chat with it to generate report.

DDE白皮书（英文）.docx ~30MB

Chat in console without GUI.

1. Prepare the code and the environment

Clone our repository, create a Python environment, and activate it via the following command.

git clone https://github.com/Bili-Sakura/DDE-GPT
cd DDE-GPT
conda env create -f environment.yml
conda activate DDE-GPT

2. Get access to OpenAI by purchasing api_key

See OpenAI API Key Pricing

Here, we recommend gpt-3 series base model, typically gpt-3.5-turbo-0125, which is capable and cost-effective,supporting a 16K context window and is optimized for dialog. Here we list all the gpt-3 series base models, regarding to their model name, input cost and output cost.

Note: If you are going to use a multimodel, be it gpt-4, as your base model, you may overwrite the functions in chat_bubble.py to adapt the multimodel output.

Model	Input Cost per 1K tokens	Output Cost per 1K tokens
gpt-3.5-turbo-0125	$0.0005	$0.0015
gpt-3.5-turbo-instruct	$0.0015	$0.0020
gpt-3.5-turbo-1106	$0.0010	$0.0020
gpt-3.5-turbo-0613	$0.0015	$0.0020
gpt-3.5-turbo-16k-0613	$0.0030	$0.0040
gpt-3.5-turbo-0301	$0.0015	$0.0020
gpt-3.5-turbo	$0.0030	$0.0060

3. Set configurations of OpenAI

Create a .env file in the root directory of DDE-GPT project. Add following configurations to enable your own LLMs.

"""
Note: It is also availble to use other base model instead of OpenAI's gpt series. Following these steps:
1. Add configs for your LLMs base model in .env file.
2. Adapt the LLMs interface in src/llm.py.
3. If you are using an open-sourced LLMs base model such as LLaMA-2, it is required to download pre-trained model locally. Besides, running model locally requires better CPUs/GPUs. 
"""
# Replace the value with your own OpenAI API KEY.
OPENAI_API_KEY="sk-xxxx"
# Enable the base url config if your api key is transited.
# Otherwise, set as "".
OPENAI_BASE_URL="https://baseurl.example.com"

4. Run demo.ipynb