S5 Slidefactory - Documentation Status Quick Reference¶

Overall Health¶

Module Documentation Status¶

Tier 1: Well-Documented (90%+) ⭐¶

• workflowengine/ - 98% functions, 97% classes, 100% files
• api/ - 100% functions, 62% classes, 100% files
• n8nmanager/ - 100% functions, 100% classes, 57% files
• filemanager/ - 98% functions, 100% classes, 56% files
• results/ - 100% functions, 100% classes, 100% files

Tier 2: Moderate Documentation (50-80%) ⚠️¶

• ai/ - 75% functions, 47% classes, 67% files
• util/ - 77% functions, 56% classes, 26% files (large module)
• resources/ - 100% functions, 0% classes, 100% files
• tags/ - 100% functions, 0% classes, 100% files
• taskmanager/ - 100% functions, 0% classes, 100% files

Tier 3: Needs Documentation (<50%) ❌¶

• auth/ - 59% functions, 15% classes, 46% files
• context/ - 29% functions, 0% classes, 9% files
• office365/ - 36% functions, 0% classes, 0% files
• chat/ - 56% functions, 0% classes, 0% files
• main.py - 31% functions, 0% classes, 0% files
• celery_app.py - 0% functions, 0% classes, 0% files

What's Living in the Code (Ready to Use)¶

Excellent Examples of Documentation¶

1. API Schemas (api/schemas.py)¶

• 30 Pydantic models with comprehensive documentation
• Field descriptions on every model field
• JSON schema examples in json_schema_extra blocks
• Ready for OpenAPI/Swagger generation
• Best practice: Reference this when documenting other schemas

class PresentationGenerateRequest(BaseModel):
    """Request schema for presentation generation."""
    template_id: Optional[int] = Field(None, description="Template file ID from database")
    data: Optional[Dict[str, Any]] = Field(None, description="Data to populate the presentation")

    class Config:
        json_schema_extra = {
            "example": {
                "template_id": 123,
                "data": {"title": "Q4 Report", ...}
            }
        }

2. Workflow Engine Base Classes (workflowengine/base.py)¶

• Comprehensive abstract method documentation
• Parameter descriptions with types
• Return type documentation
• Error handling clearly specified
• Best practice: Reference when adding new workflow engines

@abstractmethod
async def list_workflows(self, user_session: SessionData) -> List[WorkflowInfo]:
    """
    List all workflows available to the user from this engine.

    Args:
        user_session: User session data for permission checking

    Returns:
        List of WorkflowInfo objects
    """
    pass

3. AI Provider System (ai/base.py)¶

• Clear provider interface with method signatures
• Validation patterns documented
• Factory pattern for provider registration
• Best practice: Reference when adding new AI providers

Available Auto-Documentation¶

FastAPI Documentation (Built-in)¶

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• OpenAPI JSON: http://localhost:8000/openapi.json

Auto-generates from: - Router endpoints with summary and description - Pydantic models with Field() descriptions - Response schemas with error models

Critical Documentation Gaps (Priority Order)¶

🔴 CRITICAL: Context Module (9.1% file-level docs)¶

This is the RAG (Retrieval-Augmented Generation) system. Needs documentation for:

1. context/models.py - SQLAlchemy models
2. Document: Document class structure
3. Document: Chunk class structure

4. Document: Vector storage strategy

5. context/util/ingest.py - Document ingestion

6. Document: Upload pipeline
7. Document: File type handling

8. Document: Storage strategy

9. context/util/chunking.py - Text chunking

10. Document: Chunking strategy
11. Document: Jina API integration

12. Document: Chunk size tuning

13. context/util/embedding.py - Vector embeddings

14. Document: Embedding model used
15. Document: Dimension configuration

16. Document: API integration

17. context/util/db.py - Vector search

18. Document: Similarity search implementation
19. Document: pgvector operations
20. Document: Query patterns

🟠 HIGH: Authentication System (15-59% coverage)¶

Essential for security and user management:

1. auth/auth.py - Core authentication
2. SessionData structure
3. Session lifecycle

4. Cookie management

5. auth/azure_auth.py - Azure AD integration

6. OAuth flow
7. Token handling

8. Scopes configuration

9. auth/permissions.py - Permission checking

10. Permission model
11. Role-based access control

12. Resource access patterns

13. auth/provisioning.py - User provisioning

14. User creation flow
15. Role assignment
16. Profile setup

🟠 HIGH: Utility Functions (26.1% file-level docs)¶

Large module with many helpers (23 files):

1. util/ppt/ - PowerPoint processing
2. ImageProcessor.py
3. ImageTransformer.py

4. extract.py, prepare.py, replace.py

5. util/database.py - Database utilities

6. Session management

7. Connection pooling

8. util/cacher.py - Caching system

9. Cache strategies

10. Invalidation patterns

11. util/template_filters.py - Jinja filters

12. Available filters
13. Usage patterns

🟡 MEDIUM: Core Application (30% coverage)¶

1. main.py (775 lines)
2. Startup/shutdown flow
3. Middleware setup
4. Router registration
5. Error handling strategy

6. Health check configuration

7. celery_app.py

8. Task configuration
9. Worker setup
10. Queue management
11. Result backend

🟡 MEDIUM: Peripheral Modules¶

1. chat/router.py - Chat integration
2. Widget setup
3. Message flow

4. Context management

5. office365/router.py - Office integration

6. OAuth flow
7. File handling
8. Integration points

How to Add Documentation¶

For Pydantic Schemas¶

Current example (GOOD):

class MySchema(BaseModel):
    """Brief description of schema."""

    field_one: str = Field(..., description="What this field is for")
    field_two: Optional[int] = Field(None, description="Optional field description")

    class Config:
        json_schema_extra = {
            "example": {
                "field_one": "example value",
                "field_two": 42
            }
        }

For Functions¶

Current example (GOOD):

async def list_workflows(self, user_session: SessionData) -> List[WorkflowInfo]:
    """
    List all workflows available to the user.

    Args:
        user_session: User session data for permission checking

    Returns:
        List of WorkflowInfo objects

    Raises:
        WorkflowEngineError: If unable to retrieve workflows
    """
    pass

For Classes¶

Current example (GOOD):

class WorkflowEngine(ABC):
    """Abstract base class for all workflow engines."""

    def __init__(self, engine_type: WorkflowEngineType, config: Optional[Dict[str, Any]] = None):
        """
        Initialize workflow engine.

        Args:
            engine_type: Type of workflow engine (N8N, Prefect, etc)
            config: Engine-specific configuration dictionary
        """
        pass

For Modules (File-level)¶

Add at the top of each Python file:

"""
Module description here.

This module handles [main responsibility].

Classes:
    ClassName: Brief description

Functions:
    function_name: Brief description
"""

Quick Wins (Can Complete in <1 day)¶

1. Add Field descriptions to auth/users/schemas.py
2. 8 classes, minimal effort

3. Complete the API documentation

4. Add Field descriptions to context/schemas.py

5. 8 classes, minimal effort

6. Enable OpenAPI docs for context

7. Add file-level docstrings to util/ module

8. 23 files, ~2-3 minutes per file

9. Major improvement to overall coverage

10. Document PPT processing pipeline (util/ppt/)

11. 6 files with processing logic
12. Critical for understanding presentation generation

Recommended Reading Order¶

For developers new to the project:

1. Read CLAUDE.md (project guidelines)
2. Read DOCUMENTATION/README.md (overview)
3. Read N8N_INTEGRATION.md (workflow system)
4. Read USER_MANAGEMENT.md (auth system)
5. Explore /docs Swagger UI (API reference)
6. Read workflowengine/base.py (architecture)
7. Read api/schemas.py (data models)

Auto-Documentation Access¶

FastAPI Swagger UI¶

• URL: http://localhost:8000/docs
• Shows: All 192 API endpoints with:
• Request/response schemas
• Query parameters
• Error responses
• Try-it-out functionality

OpenAPI JSON Schema¶

• URL: http://localhost:8000/openapi.json
• Use: Generate API clients, documentation sites, etc.
• Tool: Works with OpenAPI generators

ReDoc (Alternative UI)¶

• URL: http://localhost:8000/redoc
• Shows: Same info as Swagger, different layout

Tools Already Available¶

• FastAPI: Automatic OpenAPI generation
• Pydantic: Schema validation and documentation
• Type hints: 90% coverage for IDE support
• SQLAlchemy: ORM with model documentation
• .claude/DOCUMENTATION/: 9 existing guides
• .claude/../reports/technical/: 17 technical assessments

Next Steps¶

1. Review: This document and the full audit report
2. Prioritize: Context module first (highest impact)
3. Document: Auth system second (critical for security)
4. Enhance: Utility modules third (breadth of codebase)
5. Create: Architecture diagrams and data flow docs
6. Generate: API client from OpenAPI spec (optional)

Contact Points¶

For questions about specific modules, refer to: - Workflows: See workflowengine/base.py and registry.py - AI: See ai/lib.py and providers/ - API: See /docs or api/schemas.py - Storage: See filemanager/storage/base.py - Database: See CLAUDE.md configuration section - Auth: See auth/auth.py and Azure integration guide