The system prompt for the medical assistant LLM has been updated to
improve clarity and explicitly state the desired formatting for
corrections and uncertainties. This includes:
- Consolidating similar correction examples.
- Specifying that only "==text==" annotations are allowed for
corrections and uncertainties.
- Explicitly disallowing other annotation formats like "(unsicher)" or
"[TODO]".
The gazetteer has been refactored to act as a post-AI normalization
filter. Previously, it was used to annotate LLM input with potential
corrections from a vocabulary. This approach was ineffective because
LLMs often override such hints.
The new approach applies the gazetteer *after* the LLM has generated its
output. This allows for deterministic correction of known terminology,
including fixing LLM output drift (e.g., anglicized drug names).
Key changes:
- `annotate` function renamed to `replace`.
- The output format changes from `Canonical [?original]` to simply the
`Canonical` form.
- The gazetteer now operates on the final LLM output before persistence,
ensuring consistency.
- Tests have been updated to reflect this new behavior, focusing on the
final output rather than the LLM request payload.
The gazetteer now annotates text with `Canonical [?original]`. This
prioritizes the corrected term for the LLM while keeping the original
transcription as a fallback.
This change aligns the gazetteer's annotation strategy with the LLM's
prompt, which expects corrections to be marked for review. Previously,
the format was `original [?Canonical]`, which could lead to the LLM
using the potentially incorrect original term.
This commit introduces functionality to annotate the user prompt with
potential proper name corrections from a gazetteer. The LLM will then
use these annotations to improve the accuracy of transcriptions,
especially for medical terms and names.
The `SYSTEM_PROMPT` has also been updated to inform the LLM about these
new annotations and how to handle them.
Pass a `Gazetteer` to the analyze worker to enable proper-name
correction. The gazetteer is loaded from disk at application startup. If
the directory is missing or unreadable, the worker will start without
proper-name correction capabilities.
This commit introduces a new module `analyze::render` to handle the
rendering of Markdown content produced by the LLM into safe HTML.
The LLM output now includes a custom `==text==` highlighting convention
to indicate sections that require doctor review due to potential
transcription errors or incomplete information. This highlighting is
converted into `<mark>` tags in the final HTML.
To ensure security, all LLM-generated Markdown is first HTML-escaped.
This prevents any malicious HTML or script injection from being executed
in the browser. Only the custom `<mark>` tags are preserved as
functional HTML elements.
The process is as follows:
1. The raw Markdown from the LLM is processed.
2. All HTML special characters (`<`, `>`, `&`, `"`, `'`) are escaped.
3. The `==text==` highlights are replaced with `<mark>text</mark>`.
4. The resulting string is parsed as Markdown by `pulldown-cmark`.
5. The parsed Markdown is converted to HTML, which is then safe to
inject into the Askama template using the `|safe` filter.
The `Cargo.toml` and `Cargo.lock` files have been updated to include the
`pulldown-cmark` dependency. The `document.html` template has been
modified to use a `div` with the class `doc-content` instead of a `pre`
tag, allowing the rendered HTML to be displayed correctly. The
`handle_document_view` function now calls the new `md_to_html` rendering
function.
The system prompt for the transcription Ollama LLM has been updated to
improve robustness and clarity, especially for handling empty or
non-medical transcripts.
The prompt has been refactored to:
- Use English for instructions to improve robustness with smaller LLMs.
- Clearly define the behavior for empty or non-medical transcripts,
requiring an empty string response.
- Reduce the maximum character limit from 120 to 60 to better fit
smartwatch displays.
- Ensure the output is German, adhering to the original language
requirement for the output.
- Add an explicit instruction to correct transcription errors in the
input dictation for the consolidation LLM.
The LLM client now conditionally adds the `Authorization` header only
when an API key is provided. The `llm_configured` check is updated to
reflect that an API key is not strictly required for Ollama-style
endpoints. A new integration test verifies the functionality against an
Ollama-compatible endpoint without an API key.
The versioning of analysis input and document files
(`analysis_input_v{N}.json`, `document_v{N}.md`) has been removed. All
analysis inputs will now use `analysis_input.json` and generated
documents will use `document.md`.
This simplifies file management, as there's no longer a need to track
and manage multiple versions of these files within a case directory. The
analysis worker will now overwrite the existing `document.md` if it
exists, ensuring that the latest analysis result is always present. The
`version` field has also been removed from `AnalyzeJob` and
`AnalysisInput`.
The case listing on the "My Cases" page is now grouped by local date,
with labels for "Heute", "Gestern", or the ISO date. This improves
readability and organization.
Additionally, the handling of silent recordings has been refined.
Previously, the absence of usable recordings would result in an error.
Now, the analysis worker gracefully handles this by writing a stub
document and skipping the LLM call. This prevents unnecessary errors and
provides a clearer status for silent cases.
The `dictate.sh` script has been updated to simplify the case directory
lookup logic. Instead of iterating through `open` and `done`
subdirectories, it now directly checks for the case ID and ensures it's
not marked as deleted. This simplifies the script and improves
efficiency.
The `server/Cargo.toml` and `server/Cargo.lock` have been updated to
include the `num_threads` dependency and enable additional features for
the `time` crate, which are necessary for proper local time zone
handling.
The `WorkerBusy` type was previously a single `Arc<AtomicBool>` shared
between the analyze and transcribe workers. This commit refactors this
to:
- Introduce `AnalyzeBusy` and `TranscribeBusy` newtype wrappers around
`WorkerBusy` to distinguish between the two flags. This allows
`axum::extract::State` to target them individually.
- Move the `BusyGuard` RAII guard into `src/lib.rs` and make it generic
to work with any `WorkerBusy` instance.
- Update the `main.rs`, `worker.rs`, and `routes/user_web.rs` files to
use the new types and guards, ensuring correct state management for
both pipelines.
- Enhance the transcription recovery scan
(`transcribe::recovery::scan_and_enqueue`) to iterate over user
directories and call `enqueue_pending_for_user` for each, making it
more robust and aligned with the per-user self-healing mechanism.
- Add a check for `transcribe_busy` in the `case_detail.html` template
to correctly display "Transkription läuft…" only when the transcribe
worker is actually active.
The analyze recovery scan has been refactored to iterate through user
directories and enqueue pending analysis jobs more efficiently. The
`WorkerBusy` status has been introduced as an `Arc<AtomicBool>` to track
whether the analyze worker is currently processing a job.
This `WorkerBusy` flag is used in the `analyze::worker::run` function
and managed by a `BusyGuard` RAII struct, ensuring the flag is correctly
set and unset even in case of panics.
The `handle_my_cases` and `handle_case_detail` handlers in `user_web.rs`
now utilize the `WorkerBusy` flag to accurately display the "analyzing"
status and to trigger a self-heal of orphaned analysis inputs when the
worker is idle.
Additionally, the `WorkerBusy` type is now exported from
`server/src/lib.rs` to be accessible by other modules. The test cases
have been updated to include the `WorkerBusy` parameter when spawning
the analyze worker.
Introduces a new `/web/cases/bulk` endpoint to handle multiple case
actions simultaneously.
This change adds the following:
- A new `bulk` module for handling bulk operations.
- The `handle_bulk_action` function in `bulk.rs` to dispatch to specific
actions.
- `bulk_analyze` and `bulk_delete` functions to process the actions.
- Updates `Cargo.toml` and `Cargo.lock` to include necessary
dependencies: `form_urlencoded`, `serde_core`, `serde_html_form`, and
`serde_path_to_error`.
- Modified `axum-extra` features to include `form`.
- Adds checks for deleted cases in `collect_pending` for both analysis
and transcription recovery.
- Renames and modifies `handle_close_case` to `handle_analyze_case` in
`case_actions.rs` to better reflect its functionality.
- Adds a new `handle_delete_case` and `handle_undo_delete` to
`case_actions.rs`.
- Updates `my_cases.html` and `case_detail.html` to support bulk
actions, including checkboxes, a bulk action bar, and an "Undo last
delete" feature.
- Modifies `handle_audio` and `scan_cases` in `web.rs` to respect delete
markers.
- Updates `analyze_test.rs` with new request builders for analyze and
delete actions.
The distinction between `open/` and `done/` directories for cases has
been removed.
All cases for a user now reside directly under the user's directory
(e.g., `<data_path>/<slug>/<case_id>/`).
This simplifies path management and eliminates redundant directory
traversals.
Key changes include:
- Removed `open/` and `done/` subdirectories in path resolution.
- Introduced a `paths::case_dir` function as a single source of truth
for case directory layout.
- Updated various modules (`recovery`, `auth`, `routes`, `transcribe`,
`tests`) to use the new path structure.
- Adjusted templates to reflect the simplified case status
representation.
Allow overriding the LLM system prompt via the `LLM_SYSTEM_PROMPT`
environment variable. This provides flexibility for customizing LLM
behavior without code changes. A default prompt is used if the
environment variable is unset or empty.
This commit introduces the `analyze` module, which orchestrates
communication with Large Language Models (LLMs) for text summarization
and analysis.
Key components include:
- `llm.rs`: Contains the `LlmError` enum and the `chat_once` function
for interacting with OpenAI-compatible LLM APIs.
- `prompt.rs`: Defines the system prompt and logic for rendering user
content from analysis inputs.
- `recovery.rs`: Implements logic to scan for and re-enqueue pending
analysis jobs upon server startup.
- `worker.rs`: The core worker that consumes analysis jobs, processes
them with the LLM, and writes the output.
- `mod.rs`: Defines `AnalyzeJob` and associated channel types for
inter-component communication.
This module enables the server to process dictated recordings, summarize
them using an LLM, and store the results.
Brummel