https://github.com/relevanceai/workflows
Relevance AI Workflows
https://github.com/relevanceai/workflows
Last synced: about 1 year ago
JSON representation
Relevance AI Workflows
- Host: GitHub
- URL: https://github.com/relevanceai/workflows
- Owner: RelevanceAI
- License: apache-2.0
- Created: 2022-03-01T01:34:06.000Z (about 4 years ago)
- Default Branch: development
- Last Pushed: 2022-11-24T23:00:30.000Z (over 3 years ago)
- Last Synced: 2023-03-09T20:16:42.386Z (about 3 years ago)
- Language: Jupyter Notebook
- Size: 6.9 MB
- Stars: 1
- Watchers: 3
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://relevanceai.readthedocs.io/en/latest/?badge=latest)
[](https://img.shields.io/pypi/l/relevanceai)
[Join our slack channel!](https://join.slack.com/t/relevance-ai/shared_invite/zt-11fo8oush-dHPd57wamhoQ7J5arNv1mg)
**Run Our Colab Notebook And Get Started In Less Than 10 Lines Of Code!**
[](https://hubs.ly/Q014Qbs10)
For guides and tutorials on how to use this package, visit https://docs.relevance.ai/docs.
## 🔥 Workflows by Relevance AI
This is a home for all of RelevanceAI's workflows as seen in the [dashboard](https://cloud.relevance.ai/). Sign up and getting started [here](https://cloud.relevance.ai/)!
Workflows provide users with a guide on how to run relevant code in Relevance AI.
It provides a guided interface through Relevance AI Features.
Some of these features include:
- Clustering
- Dimensionality Reduction
- Labelling/Tagging
- Launching Projectors
# How to add a workflow
How to add a workflow
1) Add a subfolder and move notebook there and push the notebook
2) Modify scripts/manual_add_to_db.py and add a new document in the `DOCS` variable and commit/push the script. Make sure to see what the other documents are doing and copy that structure else things may error!
3) Create PR
## 🔥 Features
- Fast vector search with free dashboard to preview and visualise results
- Vector clustering with support for libraries like scikit-learn and easy built-in customisation
- Store nested documents with support for multiple vectors and metadata in one object
- Multi-vector search with filtering, facets, weighting
- Hybrid search with support for weighting keyword matching and vector search
... and more!
## 🚧 Development
## 🛠️ Requirements
- [Python ^3.7.0](https://www.python.org/downloads/release/python-3713/)
- [AWS CLI v2](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) - if you need to upload new workflows to prod
### Getting Started
To get started with development, install the dev dependencies
```zsh
❯ make install
```
### Uploading workflows
Make sure your AWS SSO profile and creds configured in [`~/.aws/config`](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-sso.html).
Install [`yawsso`](https://github.com/victorskl/yawsso) or similar to sync API creds w/ SSO profile needed for CDK.
Set the `AWS_PROFILE` env var to bypass having to specify `--profile` on every AWS CLI call.
```zsh
❯ ENVIRONMENT=development AWS_PROFILE=Relevance-AI.WorkflowsAdminAccess make upload
```
### Testing
Then run testing using:
> Don't forget to set your test credentials!
```zsh
export TEST_ACTIVATION_TOKEN=
## For testing core workflows
export WORKFLOW_TOKEN_CLUSTER_YOUR_DATA_WITH_RELEVANCE_AI=
export WORKFLOW_TOKEN_VECTORIZE_YOUR_DATA_WITH_RELEVANCE_AI=
export WORKFLOW_TOKEN_REDUCE_THE_DIMENSIONS_OF_YOUR_DATA_WITH_RELEVANCE_AI=
export WORKFLOW_TOKEN_CORE_SUBCLUSTERING
```
Run test script
- tests all notebooks in `workflows`
- outputs error `notebook_error.log`
```zsh
❯ python scripts/test_notebooks.py
## Testing indiv notebook
❯ python scripts/test_notebooks.py --notebooks subclustering/core_subclustering.ipynb
```
## More Helpful Commands
```zsh
❯ make help
Available rules:
clean Delete all compiled Python files
install Install dependencies
lint Lint using flake8
test Test dependencies
update Update dependencies
upload Upload notebooks to S3 and update ds
```