[PR #7881] chore: local ai chat with pages #16594

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opened 2026-03-24 21:57:01 +00:00 by mirror · 0 comments

mirror commented

2026-03-24 21:57:01 +00:00

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Original Pull Request: https://github.com/AppFlowy-IO/AppFlowy/pull/7881

State: closed
Merged: Yes

Feature Preview

PR Checklist

My code adheres to AppFlowy's Conventions
I've listed at least one issue that this PR fixes in the description above.
I've added a test(s) to validate changes in this PR, or this PR only contains semantic changes.
All existing tests are passing.

Summary by Sourcery

Enhance local AI chat functionality with improved document indexing, embedding, and retrieval capabilities across multiple components

New Features:

Add support for retrieving and searching embedded documents
Implement more robust RAG (Retrieval Augmented Generation) document handling
Enhance local AI chat with dynamic RAG ID management

Enhancements:

Improve vector store search and embedding mechanisms
Refactor indexing and embedding data providers
Add more detailed tracing and logging for AI and embedding processes

Chores:

Restructure code to support more flexible document indexing
Update interfaces for better separation of concerns

**Original Pull Request:** https://github.com/AppFlowy-IO/AppFlowy/pull/7881 **State:** closed **Merged:** Yes ---   ### Feature Preview  ---  #### PR Checklist - [ ] My code adheres to [AppFlowy's Conventions](https://docs.appflowy.io/docs/documentation/software-contributions/conventions) - [ ] I've listed at least one issue that this PR fixes in the description above. - [ ] I've added a test(s) to validate changes in this PR, or this PR only contains semantic changes. - [ ] All existing tests are passing. ## Summary by Sourcery Enhance local AI chat functionality with improved document indexing, embedding, and retrieval capabilities across multiple components New Features: - Add support for retrieving and searching embedded documents - Implement more robust RAG (Retrieval Augmented Generation) document handling - Enhance local AI chat with dynamic RAG ID management Enhancements: - Improve vector store search and embedding mechanisms - Refactor indexing and embedding data providers - Add more detailed tracing and logging for AI and embedding processes Chores: - Restructure code to support more flexible document indexing - Update interfaces for better separation of concerns