The `document.md` file has been replaced by `document.json`. This new
format acts as a JSON envelope, containing the original markdown content
along with metadata such as `covered_mtime`, `written_at`, and
`backend_id`.
This change addresses a bug where new recordings arriving during an LLM
analysis call could be ignored. The `covered_mtime` field in the new
JSON envelope ensures that the document's metadata accurately reflects
the state of recordings at the time of analysis, preventing incorrect
"analysis current" states.
Additionally, the `analysis_input.json` file is now removed upon
successful analysis, and a failure marker is used to indicate analysis
failures. This ensures that a case is re-analyzed if necessary.
The `analysis_idle_threshold_secs` configuration option has been
introduced to control the delay before a case in the `Required` state is
automatically promoted to `Idle` and enqueued for LLM analysis.
Failure markers now carry finish_reason and usage tokens (prompt /
completion / reasoning) pulled from the chat-completions response, so
the case-page "Technische Details" block can show why an analysis
truncated — e.g. gpt-oss-120b hitting max_completion_tokens with the
chain-of-thought before producing any visible answer.
The worker also emits a new CaseEventKind::AnalysisFailed after writing
the marker, so subscribed browsers reload immediately instead of leaving
the page on the "Wird analysiert …" placeholder.
Configure Llama 3.1 405B with specific temperature, top_p, and a format
instruction. This addresses issues with infinite repetition and empty
document generation observed with this model. The format instruction is
added as a separate system message to avoid modifying the core medical
prompt.
Replace the single [llm] block in settings.toml with a curated catalog
of LLM "backends" (provider + model + sampling params + system prompt)
in server/src/analyze/backend.rs. Two backends ship initially:
gpt_oss_120b (default, reasoning_effort=medium) and llama_3_1_405b
(uses response_format: json_schema to sidestep the <|eot_id|> leak).
The case page now renders one submit button per available backend; the
clicked button's name=backend value rides through AnalysisInput.backend_id
to the worker, which looks it up via find_backend() with default fallback.
A submitted unknown backend id is rejected as 400. .analysis_failed.json
records the failed backend and the failure banner surfaces its label.
The API key now comes from IONOS_API_KEY (env), not settings.toml; the
[llm] section is read into a discarded field so old configs still load.
Backends without a satisfied requires_api_key are filtered from the UI
(any_backend_available()). Three end-to-end tests that mocked the LLM
endpoint via settings.llm.url are #[ignore]'d with a clear note — they
need per-test backend injection (Arc<Vec<LlmBackend>> through the worker)
before they can be re-enabled.
Config sheds its 14 Bucket-B fields (retention, whisper, ollama, llm) and
gains a sibling Arc<Settings> in AppState, loaded from settings.toml at
startup via Settings::load_or_default. Workers (transcribe, analyze,
recovery, auto_trigger) and routes (health, bulk, case_actions,
user_web) take settings as a separate parameter or State<> extractor;
Config::llm_configured moves to Settings::llm_configured.
TestConfig::build_pair() returns (Arc<Config>, Arc<Settings>); the new
create_router_with_settings / create_router_and_session_store_with_settings
helpers wire both into integration tests that exercise LLM/Whisper/Ollama.
The legacy single-Arc create_router falls back to Settings::default()
so the 17 non-LLM tests stay untouched.
Verified: cargo build clean, clippy --all-targets clean, 318 tests pass.
Introduces a new module `auto_trigger` responsible for opportunistically
initiating LLM analysis jobs. This mechanism scans case directories for
new or updated recordings and transcripts, automatically creating and
queuing analysis jobs when appropriate.
Key components:
- `evaluate_case`: Pure function to determine if a case is ready for
analysis based on recording status, transcriptions, document
modification times, and existing job/failure markers.
- `try_enqueue`: Orchestrates the analysis job creation and queuing
process if `evaluate_case` returns `AutoDecision::Enqueue`.
- `try_enqueue_all_for_user`: Iterates through all user cases to trigger
`try_enqueue` for eligible ones.
- `write_failure_marker`/`remove_failure_marker`: Handles persistent
recording of analysis failures and their cleanup.
This feature aims to reduce manual intervention by automatically
analyzing new or changed case data.
Introduces a system for broadcasting real-time events to the web UI.
This enables features like automatic page reloads when case data changes
in the background, improving the user experience by keeping the UI
synchronized with the backend.
Key components:
- `events` module: Contains the `CaseEventKind` enum, `CaseEvent`
struct, and `EventSender` type for managing the broadcast channel.
- SSE endpoint (`/web/events`): Streams events to connected browsers.
- Client-side JavaScript: Listens for events and triggers debounced page
reloads.
- Integration points: Workers and route handlers now emit events when
relevant state changes occur.
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.
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.
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.
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