* feat: add getTokensCount method to compute token usage
Implemented a new async `getTokensCount` function in the LLaMA.cpp extension.
The method validates the model session, checks process health, applies the request template, and tokenizes the resulting prompt to return the token count. Includes detailed error handling for crashed models and API failures, enabling callers to assess token usage before sending completions.
* Fix: typos
* chore: update ui token usage
* chore: remove unused code
* feat: add image token handling for multimodal LlamaCPP models
Implemented support for counting image tokens when using vision-enabled models:
- Extended `SessionInfo` with optional `mmprojPath` to store the multimodal project file.
- Propagated `mmproj_path` from the Tauri plugin into the session info.
- Added import of `chatCompletionRequestMessage` and enhanced token calculation logic in the LlamaCPP extension:
- Detects image content in messages.
- Reads GGUF metadata from `mmprojPath` to compute accurate image token counts.
- Provides a fallback estimation if metadata reading fails.
- Returns the sum of text and image tokens.
- Introduced helper methods `calculateImageTokens` and `estimateImageTokensFallback`.
- Minor clean‑ups such as comment capitalization and debug logging.
* chore: update FE send params message include content type image_url
* fix mmproj path from session info and num tokens calculation
* fix: Correct image token estimation calculation in llamacpp extension
This commit addresses an inaccurate token count for images in the llama.cpp extension.
The previous logic incorrectly calculated the token count based on image patch size and dimensions. This has been replaced with a more precise method that uses the clip.vision.projection_dim value from the model metadata.
Additionally, unnecessary debug logging was removed, and a new log was added to show the mmproj metadata for improved visibility.
* fix per image calc
* fix: crash due to force unwrap
---------
Co-authored-by: Faisal Amir <urmauur@gmail.com>
Co-authored-by: Louis <louis@jan.ai>
* feat: Prompt progress when streaming
- BE changes:
- Add a `return_progress` flag to `chatCompletionRequest` and a corresponding `prompt_progress` payload in `chatCompletionChunk`. Introduce `chatCompletionPromptProgress` interface to capture cache, processed, time, and total token counts.
- Update the Llamacpp extension to always request progress data when streaming, enabling UI components to display real‑time generation progress and leverage llama.cpp’s built‑in progress reporting.
* Make return_progress optional
* chore: update ui prompt progress before streaming content
* chore: remove log
* chore: remove progress when percentage >= 100
* chore: set timeout prompt progress
* chore: move prompt progress outside streaming content
* fix: tests
---------
Co-authored-by: Faisal Amir <urmauur@gmail.com>
Co-authored-by: Louis <louis@jan.ai>
Feature:
- Adjust homecreen and chatscreen for mobile device
- Fix tests for both FE and BE
Self-test:
- Confirm runnable on both Android and iOS
- Confirm runnable on desktop app
- All test suites passed
- Working with ChatGPT API
Feat:
- Using Tauri v2 by default
- Add new configuration to initiate mobile app
- Add dependencies needed for mobile build
Test:
- Confirm to be built successfully
- Confirm to keep settings for desktop and build successfully
- Reuse most of components from desktop version
Expose the new `mmproj_offload` option in the model settings UI and include it in the `ModelPlan` type. The component now collects the offload flag (`result.offloadMmproj`) and queues it with other setting updates to ensure a single atomic change, preventing race conditions when toggling this feature. This enables users to control MMProj offloading directly from the app.
