-
Notifications
You must be signed in to change notification settings - Fork 3.7k
Python: Update Redis Memory Connector to new Text Search Design #6837
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Labels
Ignite
Features planned for next Ignite conference
python
Pull requests for the Python Semantic Kernel
sk team issue
A tag to denote issues that where created by the Semantic Kernel team (i.e., not the community)
Comments
This issue is stale because it has been open for 90 days with no activity. |
4 tasks
github-merge-queue bot
pushed a commit
that referenced
this issue
Nov 14, 2024
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Adds the vector search pieces to the two Redis collection types Features vectorized search and vector text search. Also features a slight change to the storage format for Hashsets since the existing was wrong because the index was not picking up those fields, fixed now, but if you have a Redis Hash Collection running, this will break things. Closes #6837 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone 😄
github-merge-queue bot
pushed a commit
that referenced
this issue
Nov 15, 2024
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> Adds the vector search pieces to the two Redis collection types Features vectorized search and vector text search. Also features a slight change to the storage format for Hashsets since the existing was wrong because the index was not picking up those fields, fixed now, but if you have a Redis Hash Collection running, this will break things. Closes #6837 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [x] The code builds clean without any errors or warnings - [x] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [x] All unit tests pass, and I have added new tests where possible - [ ] I didn't break anyone 😄
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Labels
Ignite
Features planned for next Ignite conference
python
Pull requests for the Python Semantic Kernel
sk team issue
A tag to denote issues that where created by the Semantic Kernel team (i.e., not the community)
Text Search ADR is here: #5799
Implement new abstractions
Unit tests
Integration tests
Document any limitations in the implementation
Implement a basic RAG samples
Work with Sofia on a new Blog post
Obsolete the old implementation
Create task to graduate the Text Search Service
The text was updated successfully, but these errors were encountered: