Establish initial project architecture

docs/adr/0001-project-vision.md

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+# ADR 0001: Project Vision
+
+## Status
+
+Accepted
+
+## Context
+
+The project aims to create a local-first meeting assistant that transforms recorded meetings into high-quality transcripts and structured organizational knowledge.
+
+Existing commercial meeting assistants often depend on bots, cloud services, vendor-specific workflows or opaque AI pipelines. This project is intended to provide more control over audio capture, transcription, knowledge extraction and long-term data ownership.
+
+## Decision
+
+The project will be developed as a modular Python application with a clear pipeline:
+
+Recording
+→ Transcription
+→ Speaker Diarization
+→ AI Analysis
+→ Knowledge Extraction
+→ Export
+
+The transcript is treated as the canonical source. AI-generated summaries and knowledge objects are derived artifacts.
+
+## Consequences
+
+- Audio and transcript quality have priority over UI features.
+- The system must preserve original recordings and original transcripts.
+- AI outputs must be reproducible where practical.
+- Modules should remain replaceable.
+- The project should avoid unnecessary vendor lock-in.

docs/adr/0002-use-faster-whisper.md

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+# ADR 0002: Use faster-whisper for Transcription
+
+## Status
+
+Accepted
+
+## Context
+
+The project requires high-quality speech-to-text transcription for German and English meetings.
+
+The transcription component should support local execution where practical and should remain replaceable in the future. The project should not depend on a meeting bot or on a proprietary meeting platform.
+
+## Decision
+
+The project will use `faster-whisper` as the initial transcription engine.
+
+The preferred first model is `large-v3-turbo`, with `large-v3` as quality-oriented fallback if required.
+
+## Consequences
+
+- Transcription can run locally on suitable hardware.
+- NVIDIA CUDA acceleration can be used later on an office AI PC.
+- The transcription module must hide the concrete engine behind an internal interface.
+- Model name, language setting, timestamp and engine version should be stored with every transcript.
+- The decision can be revisited if another engine provides clearly better quality, speed or deployment characteristics.

docs/adr/0003-use-pyannote.md

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+# ADR 0003: Use pyannote for Speaker Diarization
+
+## Status
+
+Accepted
+
+## Context
+
+The project requires speaker diarization to assign transcript segments to speakers.
+
+The first implementation should provide reliable separation of speakers in meetings, while keeping the diarization component replaceable.
+
+## Decision
+
+The project will use `pyannote.audio` as the initial speaker diarization engine.
+
+Speaker diarization will be treated as a separate pipeline step after transcription.
+
+## Consequences
+
+- Diarization can be improved or replaced independently from transcription.
+- Speaker labels are derived metadata and must not modify the canonical transcript.
+- Human correction of speaker names should be supported later.
+- The diarization module must hide the concrete engine behind an internal interface.
+- Model name, engine version and timestamp should be stored with every diarization result.

docs/adr/0004-store-original-transcript.md

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+# ADR 0004: Store Original Transcript as Canonical Source
+
+## Status
+
+Accepted
+
+## Context
+
+Speech-to-text systems continuously improve over time. Likewise, AI-generated summaries, action items and other derived artifacts may evolve as better models become available.
+
+To ensure reproducibility, traceability and future reprocessing, the project requires a single immutable source of truth.
+
+## Decision
+
+The original transcript generated by the transcription engine is stored as the canonical transcript.
+
+The canonical transcript is immutable and must never be modified.
+
+A separate working transcript may be created for manual corrections such as:
+
+- spelling corrections
+- terminology normalization
+- speaker name assignment
+- punctuation improvements
+
+The working transcript shall maintain a reference to the canonical transcript from which it was created.
+
+All AI-generated artifacts (summaries, decisions, action items, knowledge objects, etc.) are derived from either the canonical transcript or a specific working transcript. The transcript version used must always be recorded.
+
+## Consequences
+
+- The original transcript remains permanently available.
+- Future AI models can regenerate improved summaries without requiring a new recording.
+- Manual corrections do not overwrite the canonical transcript.
+- Traceability and reproducibility are preserved throughout the system.
+- A working transcript enables manual improvements without compromising data integrity.
+- Every derived artifact can reference the transcript version from which it was generated.
+- Multiple working transcript revisions may coexist if required.

docs/adr/0005-knowledge-model.md

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+# ADR 0005: Meeting Domain Model
+
+## Status
+
+Accepted
+
+## Context
+
+The application manages meetings as structured collections of related artifacts rather than as individual files.
+
+A consistent domain model is required to support future extensions such as semantic search, knowledge extraction, versioning and database storage.
+
+## Decision
+
+The central entity of the application is the Meeting.
+
+A Meeting owns or references all artifacts created during its lifecycle.
+
+The initial domain model consists of:
+
+- Meeting
+- Recording
+- Transcript
+- Working Transcript
+- Speaker
+- Participant
+- AI Artifact
+- Action Item
+- Decision
+- Knowledge Object
+- Attachment
+
+Each entity has a unique identifier.
+
+Relationships between entities shall be maintained explicitly.
+
+## Consequences
+
+- New artifact types can be added without changing the overall architecture.
+- Storage implementation (files, SQLite, PostgreSQL, etc.) remains independent from the domain model.
+- AI outputs become first-class entities rather than generated text files.
+- Future integrations can operate on domain objects instead of file names.