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title: Buster | |
emoji: π€ | |
colorFrom: red | |
colorTo: blue | |
sdk: gradio | |
app_file: buster/apps/gradio_app.py | |
python_version: 3.10.8 | |
pinned: false | |
# Buster, the QA documentation chatbot! | |
Buster is a question-answering chatbot that can be tuned to any source of documentations. | |
# Demo | |
You can try out our [live demo here](https://huggingface.co/spaces/jerpint/buster), where it will answer questions about a bunch of libraries we've already scraped, including [π€ Transformers](https://huggingface.co/docs/transformers/index). | |
# Quickstart | |
Here is a quick guide to help you deploy buster on your own dataset! | |
First step, install buster locally. Note that buster requires python>=3.10. | |
``` | |
git clone https://github.com/jerpint/buster.git | |
pip install -e . | |
``` | |
Then, go to the examples folder. We've attached a sample `stackoverflow.csv` file to help you get started. You will convert the .csv to a `documents.db` file. | |
``` | |
buster_csv_parser stackoverflow.csv --output-filepath documents.db | |
``` | |
This will generate the embeddings and save them locally. Finally, run | |
``` | |
gradio gradio_app.py | |
``` | |
This will launch the gradio app locally, which you should be able to view on [localhost]( http://127.0.0.1:7860) | |
## How does Buster work? | |
First, we parsed the documentation into snippets. For each snippet, we obtain an embedding by using the [OpenAI API](https://beta.openai.com/docs/guides/embeddings/what-are-embeddings). | |
Then, when a user asks a question, we compute its embedding, and find the snippets from the doc with the highest cosine similarity to the question. | |
Finally, we craft the prompt: | |
- The most relevant snippets from the doc. | |
- The engineering prompt. | |
- The user's question. | |
We send the prompt to the [OpenAI API](https://beta.openai.com/docs/api-reference/completions), and display the answer to the user! | |
### Currently available models | |
- For embeddings: "text-embedding-ada-002" | |
- For completion: We support both "text-davinci-003" and "gpt-3.5-turbo" | |
### Livestream | |
For more information, you can watch the livestream where explain how buster works in detail! | |
- [Livestream recording](https://youtu.be/LB5g-AhfPG8) | |
- [Livestream notebook](https://colab.research.google.com/drive/1CosxSNod48KrkyBn5_vkeleb7u0CrBa6) | |