deepset-ai / deepset-ai/deepset-cloud-sdk
local .env file in project root supersedes global ~/.deepset-cloud/.env
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- Dominant language
- Python
- Stars
- 16
- Forks
- 5
- PR merge metrics
- No merged PRs in 30d
Description
Describe the bug
The CLI does not look for the global .env-file created using deepset-cloud login when a local .env-file in the project root is present. This means that CLI-commands cannot be executed as long as the project-.env file is present in the project-root.
The related error messages are not intuitively hinting at this conflict.
To Reproduce
Steps to reproduce the behavior:
- create new project with .env-file in root folder and some example texts to upload to dP workspace
deepset-cloud logindeepset-cloud upload ./data/examples- the CLI raises
WorkspaceNotDefinedError("Workspace name is not defined. Got ''. Enter the name of the workspace in workspace_name.") deepset-cloud upload ./data/examples --workspace-name "default"- the CLI raises
FailedToUploadFileException('Failed to upload file with status code 404. response was: {"detail":"Not Found"}') - moving the local .env-file out of the project root solves the issue
Expected behavior
Have CLI always default to using ~/.deepset-cloud/.env or choose during deepset-cloud login whether to use global or local .env-file
Screenshots
Contributor guide
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 with the deepset-cloud login and deepset-cloud upload entry points and reproduce the conflict between the project .env file and ~/.deepset-cloud/.env. Trace which environment source is selected and how the workspace and upload errors are produced. Done means the global credentials remain usable with a project-root .env file, with an unambiguous behavior for choosing the environment source.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- cli
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100