posit-dev / posit-dev/shinychat
Conversation history: support R and Python workers without a Shiny session
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- Dominant language
- TypeScript
- Stars
- 139
- Forks
- 28
- Avg merge
- 23h 44m
- Merged PRs (30d)
- 50
Description
I’m looking at the history work in #379 and wondering whether both the Python implementation and planned R port could support workers without an active Shiny session.
In Deputy, I’d like to let someone select a conversation branch in a Shiny app, continue it in a background R worker, and see the saved continuation when they return. I’d pass the user, chat and conversation identifiers to the worker from the app.
My immediate use case is R with Deputy and ellmer, but I’d like the same workflow to be available in Python with chatlas.
The separation of display history and provider turns looks useful here. I may summarize older turns for the model while keeping the original conversation available. I’d also like to save a Deputy run ID through the planned application-state support.
Would this fit the history redesign? I’d like to reuse the history operations and validation you’re building.
This also relates to #307’s conversation identity work and #61’s distinction between UI content and model turns. I’m tracking the Deputy integration in JamesHWade/deputy#66.
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the history redesign in #379, then compare the conversation identity work in #307 and the UI-content/model-turn distinction in #61. Review the Deputy integration tracking in JamesHWade/deputy#66 and determine whether reusable history operations, validation, and application-state support can cover workers without an active Shiny session; done requires an agreed scope for both R and Python.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, r
- Domain
- api, backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100