transcription/docs/requirements.md

Document Transcription System Requirements

This page captures a SysML v1.6-style requirements baseline for the production system described in index.md. The model is represented as concise tables and traceability lists that preserve SysML-style IDs and relationship semantics.

Scope

• System of interest: the single Python application service (NiceGUI + FastAPI) with PostgreSQL as the relational system of record and optional MongoDB for document-oriented persistence.
• Operational context: local-first execution with Docker Compose and an intentionally lightweight production trajectory.
• Primary concern: end-to-end transcription job lifecycle from upload through completion or failure.

Requirements Model (Concise Text Form)

Requirements

ID	Category	Requirement	Risk	Verify Method
REQ-0	System	Provide end-to-end document transcription with persistent, inspectable lifecycle state.	medium	demonstration
REQ-1	Functional	Allow users to upload one or more document images from the web UI.	low	test
REQ-2	Functional	Run each upload through asynchronous processing that returns a transcription or explicit failure.	high	test
REQ-3	Functional	Persist and expose job states: upload, queued, processing, transcribed, failed, completed.	high	inspection
REQ-4	Functional	Persist transcription output, processing history, and failure details.	medium	test
REQ-5	Interface	Expose API and UI views for status inspection and completed transcription reading.	medium	demonstration
REQ-6	Performance	Trigger background processing on upload to preserve UI responsiveness.	medium	analysis
REQ-7	Design Constraint	Keep lifespan-owned runtime resources: SQLAlchemy engine, async session factory, worker resources, provider clients.	medium	inspection
REQ-8	Design Constraint	Initialize configuration and logging once at startup through centralized mechanisms.	low	inspection
REQ-9	Design Constraint	Use Docker Compose baseline of app plus PostgreSQL; allow optional MongoDB container when enabled.	medium	demonstration
REQ-10	Design Constraint	Keep schema bootstrap explicit and opt-in; normal startup does not mutate production schema.	high	inspection
REQ-11	Design Constraint	Use service-backed persistence for core document and job data.	medium	inspection
REQ-12	Design Constraint	Store transcription prompts as individual Markdown artifacts for iterative refinement.	medium	inspection

Requirement Relationships

• Contains: REQ-0 contains REQ-1 through REQ-12.
• Derives: REQ-2 -> REQ-3, REQ-3 -> REQ-4.
• Traces: REQ-5 -> REQ-3.
• Refines: REQ-6 -> REQ-2.

Architecture Elements

Element	Type	Doc Reference
UI	NiceGUI pages	src/transcription/ui/pages
API	FastAPI routes	src/transcription/api/routes.py
GRAPH	Async processing workflow	src/transcription/services, src/transcription/ai
DBREL	PostgreSQL + SQLModel relational persistence	src/transcription/db
DBDOC	MongoDB document persistence	src/transcription/db, src/transcription/services
OPS	Docker Compose runtime	docker-compose.yml
PROMPTS	Transcription prompt artifact library (Markdown files)	.github/prompts, docs
TESTS	Pytest verification suite	tests

Satisfaction Mapping

• UI satisfies REQ-1, REQ-5.
• API satisfies REQ-5.
• GRAPH satisfies REQ-2, REQ-6.
• DBREL satisfies REQ-3, REQ-10.
• DBDOC satisfies REQ-4, REQ-11.
• OPS satisfies REQ-9.
• PROMPTS satisfies REQ-12.

Verification Mapping

• TESTS verifies REQ-1, REQ-2, REQ-3, REQ-4, REQ-5, REQ-10, REQ-11, REQ-12.

Requirement Notes

• Requirement IDs (REQ-*) are stable references for planning, implementation, and test traceability.
• The model uses compact tables and traceability lists for renderer compatibility while preserving SysML-style requirement IDs and relationship semantics.
• Requirement categories (functional, interface, performance, and design constraints) are preserved as explicit REQ entries and relationship labels to keep change impact visible.
• PostgreSQL containerization and optional MongoDB containerization are both treated as extremely lightweight and simple operational choices in this architecture.

Verification Intent

• Demonstration: validate end-to-end behavior via running system flows and operator-visible outcomes.
• Inspection: verify architecture and startup/runtime policies in code and configuration.
• Analysis: evaluate asynchronous execution behavior and design sufficiency.
• Test: automate behavioral checks through pytest suites and service-level tests.

Glossary

• Document-oriented persistence: A storage approach that uses flexible document structures for variable data shapes.
• Prompt artifact: A single Markdown file that defines one transcription prompt and is revised independently.
• SysML: Systems Modeling Language used to express structured requirements and traceability.
• System of record: The authoritative persistent store for canonical business data.