https://github.com/team3256/wpilib-copilot

Your WPILib Pair Programmer
https://github.com/team3256/wpilib-copilot

Last synced: 11 days ago
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Your WPILib Pair Programmer

• Host: GitHub
• URL: https://github.com/team3256/wpilib-copilot
• Owner: Team3256
• License: gpl-3.0
• Created: 2023-04-17T17:36:12.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-04-17T23:58:59.000Z (over 1 year ago)
• Last Synced: 2024-11-05T16:14:39.979Z (about 2 months ago)
• Language: Python
• Homepage:
• Size: 270 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# WPILib-Copilot

This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [WPILib documentation](https://docs.wpilib.org/en/stable/).

Built with [LangChain](https://github.com/hwchase17/langchain/) and [FastAPI](https://fastapi.tiangolo.com/).

The app leverages LangChain's streaming support and async API to update the page in real time for multiple users.

## ✅ Running locally

1. Install dependencies: `pip install -r requirements.txt`

1. Run `ingest.sh` to ingest WPILib docs data into the vectorstore (only needs to be done once).

1. Run the app: `make start`

   1. To enable tracing, make sure `langchain-server` is running locally and pass `tracing=True` to `get_chain` in `main.py`. You can find more documentation [here](https://langchain.readthedocs.io/en/latest/tracing.html).

1. Open [localhost:9000](http://localhost:9000) in your browser.

## 📚 Technical description

There are two components: ingestion and question-answering.

Ingestion has the following steps:

1. Pull html from documentation site

2. Load html with LangChain's [ReadTheDocs Loader](https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/readthedocs_documentation.html)

3. Split documents with LangChain's [TextSplitter](https://langchain.readthedocs.io/en/latest/reference/modules/text_splitter.html)

4. Create a vectorstore of embeddings, using LangChain's [vectorstore wrapper](https://langchain.readthedocs.io/en/latest/reference/modules/vectorstore.html) (with OpenAI's embeddings and FAISS vectorstore).

Question-Answering has the following steps, all handled by [ConversationalRetrievalChain](https://python.langchain.com/en/latest/reference/modules/chains.html#langchain.chains.ConversationalRetrievalChain):

1. Given the chat history and new user input, determine what a standalone question would be (using ChatGPT).

2. Given that standalone question, look up relevant documents from the vectorstore.

3. Pass the standalone question and relevant documents to ChatGPT to generate a final answer.