Reconcile Meeting Assistant architecture with Meeting Lab

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## Purpose
The Meeting Knowledge Assistant transforms recorded meetings into structured organizational knowledge through a modular processing pipeline.
Meeting Assistant is the user-facing application for preparing meeting
context, running the Meeting Lab pipeline and reviewing its results. Meeting
Lab is the reusable processing backend and experimental engine.
---
# Design Principles
- Architecture first
- Offline first where practical
- Immutable source data
- Replaceable AI components
- Small, focused modules
- Explicit interfaces
- Reproducible AI outputs
- External recording and internal processing are separate responsibilities.
- The processing pipeline must not depend on OBS-specific metadata or behavior.
- Imported recordings must be treated like recordings from any other supported source.
---
## High-Level Pipeline
External Recorder
↓
Audio Import
↓
Recording Validation
↓
Meeting Storage
↓
Transcription
↓
Speaker Diarization
↓
Working Transcript
↓
LLM Analysis
↓
Knowledge Extraction
↓
Export
---
## Core Pipeline
External Recording
↓
Audio Import and Validation
↓
Transcription
↓
Speaker Diarization
↓
Working Transcript
↓
LLM Analysis
↓
Knowledge Extraction
↓
Storage and Export
# Domain Model
Meeting
│
├── Recording
├── Transcript
│ ├── Canonical
│ └── Working
├── Speakers
├── Participants
├── AI Artifacts
├── Knowledge Objects
├── Attachments
└── Exports
---
# Module Responsibilities
## Input and Ingest
Responsible for importing existing recordings into the application.
Initial reference source:
- OBS Studio
Initial supported formats:
- WAV
- FLAC
Responsibilities:
- validate the input file
- collect technical metadata
- calculate checksums
- copy or move the recording into the meeting directory
- create the initial Recording domain object
The ingest component must not perform transcription or modify the audio content.
## Recorder
The recorder module is reserved for a future integrated recording implementation.
It is not required for the MVP.
The initial application workflow uses externally created recordings, with OBS
Studio as the recommended reference recorder.
---
## Transcription
Responsible only for speech-to-text conversion.
Input:
Recording
Output:
Canonical Transcript
---
## Diarization
Responsible only for speaker identification.
Input:
Recording + Canonical Transcript
Output:
Working Transcript
---
## LLM
Responsible for semantic analysis.
Input:
Transcript
Output:
AI Artifacts
---
## Knowledge Extraction
Responsible for creating structured knowledge.
Input:
AI Artifacts
Output:
Knowledge Objects
---
## Export
Responsible for creating user-facing documents.
Input:
Knowledge Objects
Output:
Markdown
PDF
DOCX
---
## Artifact Lifecycle
## System Boundary
```text
Imported Recording
↓
Validated Source Recording
↓
Canonical Transcript
↓
Working Transcript
↓
AI Artifacts
↓
Knowledge Objects
↓
Exports
Meeting Assistant Meeting Lab
----------------- -----------
Audio selection ------> Audio preparation
Meeting Context editor ------> Transcription
Participant management ------> Optional diarization
Explicit speaker mapping ------> Protocol generation
Progress presentation <------ Progress events
Protocol editor and export <------ Run result and artifacts
```
---
Meeting Assistant calls the Meeting Lab Python API directly. The Meeting Lab
CLI is a thin adapter over that API and must not be launched as an application
subprocess.
## Initial Recording Strategy
## MVP Processing Flow
The MVP does not implement platform-specific audio capture.
```text
Source audio
-> FFmpeg normalization/preparation
-> mono, 16 kHz PCM WAV
-> whisper.cpp transcription with large-v3-turbo
-> optional pyannote.audio Community-1 diarization
-> direct full-transcript protocol generation
-> human review and editing
-> export
```
OBS Studio is the recommended reference recorder for online meetings.
The MVP does not require fixed five-minute audio chunks, semantic chunking, a
separate evidence-extraction pipeline or multiple LLMs. Any internal
segmentation remains a Meeting Lab implementation detail.
The application initially processes existing WAV or FLAC recordings. Integrated
## Backend Integration
recording remains a future extension and must not be required by transcription,
The reusable Meeting Lab interface consists of:
diarization or analysis modules.
- `MvpMeetingConfig`, the run configuration
- `run_mvp_meeting(...)`, the orchestration entry point
- `MvpRunResult`, the completed run result
- stage-based progress events
---
The known stages are:
# Storage Strategy
```text
preparing
transcription
diarization
protocol_generation
completed
failed
```
The domain model is independent of the storage backend.
The GUI shows the current stage. It shows a percentage only when the event
contains real measurable progress; stage changes must not be presented as
invented percentages.
Possible implementations:
## Meeting Context
- File System
- SQLite
- PostgreSQL
- Cloud Storage
`MeetingContext` is structured domain input rather than an informal prompt or a
file users must author manually. It contains meeting metadata and participants
and can include optional mappings:
---
```text
SPEAKER_XX -> participant_id
```
# Future Extensions
Mappings are authoritative only after explicit user confirmation. Diarization
labels otherwise remain anonymous, and the application must not infer speaker
names automatically. The Meeting Assistant GUI owns creation and editing of
this context and its mappings.
- Live transcription
- Video processing
- OCR
- Semantic search
- Knowledge graph
- Company glossary
- Multi-language meetings
- Local LLM support
## Processing Components
### Audio Preparation
Meeting Lab uses FFmpeg to prepare a consistent local-processing input. The
current practical target is mono, 16 kHz PCM WAV. The imported source remains a
separate source artifact.
### Transcription
`whisper.cpp` with `large-v3-turbo` is the preferred local backend. Vulkan is a
validated acceleration path on North's AMD RX 9070, while CPU execution remains
a compatibility fallback. Engine and model details should be retained with the
result for traceability.
### Diarization
`pyannote.audio` Community-1 is preferred when diarization is enabled. It can
run on CPU and may use GPU acceleration such as PyTorch/ROCm where available.
Diarization is optional and its anonymous labels do not modify the canonical
transcript or assert participant identity.
### Protocol Generation
The direct full-transcript path is the practical MVP. Current model experiments
favor `qwen3.8:27B` for readability and contextual synthesis, while
`qwen3.6:35B-A3B` has shown more conservative behavior in some areas. Neither a
specific dual-model arrangement nor an evidence pipeline is an application
requirement. Generated protocols require human review.
## Application Responsibilities
The first GUI milestone provides:
- audio-file selection
- structured Meeting Context and participant editing
- optional explicit speaker mapping
- pipeline start and configuration
- progress and failure presentation
- detailed protocol display and editing
- export of the reviewed result
The product direction also includes a shorter participant/distribution
protocol. It may be delivered after the first GUI milestone.
## Artifact and Storage Principles
The existing Meeting domain remains the application-level container for source
recordings, canonical and working transcripts, context, generated artifacts and
exports. Storage remains independent of processing segmentation. In particular,
chunks are not primary Meeting Assistant domain objects or the required unit of
MVP persistence.
Source audio and the canonical transcription output are preserved. Corrections
and reviewed protocols are derived versions. Generated artifacts should retain
their input version, backend/model configuration, prompt version and timestamp
where practical.
## Future Extensions
- shorter distribution protocols
- integrated recording
- searchable meeting history and optional database indexes
- live transcription
- OCR and video processing
- semantic search and organizational knowledge features