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docs: Update markdown files to reflect current project state

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google-labs-jules[bot] 2025-09-07 12:01:11 +00:00
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12
TODO.md
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@ -8,9 +8,9 @@
- [x] Ensure resolvers call application services only and add dataloaders per aggregate.
- [ ] Adopt a migrations tool and move all SQL to migration files.
- [ ] Implement full observability with centralized logging, metrics, and tracing.
- [ ] **Full Test Coverage (High, 5d):** Increase test coverage across the application to ensure stability and prevent regressions.
- [ ] Write unit tests for all models, repositories, and services.
- [ ] Refactor existing tests to use mocks instead of a real database.
- [x] **Full Test Coverage (High, 5d):** Increase test coverage across the application to ensure stability and prevent regressions.
- [x] Write unit tests for all models, repositories, and services.
- [x] Refactor existing tests to use mocks instead of a real database.
- [ ] **Implement Analytics Features (High, 3d):** Add analytics to provide insights into user engagement and content popularity.
- [ ] Implement view, like, comment, and bookmark counting.
- [ ] Track translation analytics to identify popular translations.
@ -38,9 +38,9 @@
- [ ] Observability: centralize logging; add Prometheus metrics and OpenTelemetry tracing; request IDs (High, 3d)
- [ ] CI: add `make lint test test-integration` and integration tests with Docker compose (High, 2d)
### [ ] Testing
- [ ] Add unit tests for all models, repositories, and services (High, 3d)
- [ ] Remove DB logic from `BaseSuite` for mock-based integration tests (High, 2d)
### [x] Testing
- [x] Add unit tests for all models, repositories, and services (High, 3d)
- [x] Remove DB logic from `BaseSuite` for mock-based integration tests (High, 2d)
### [ ] Features
- [ ] Implement analytics data collection (High, 3d)

66
report.md
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@ -29,7 +29,7 @@ The application uses the repository pattern for data access:
- `WorkRepository`: CRUD operations for Work model
- Various other repositories for specific entity types
The repositories provide a clean abstraction over the database operations, but there's inconsistency in implementation with some repositories using the generic repository pattern and others implementing the pattern directly.
The repositories provide a clean abstraction over the database operations.
#### 3. Synchronization Jobs
The application includes a synchronization mechanism between PostgreSQL and Weaviate:
@ -66,51 +66,41 @@ The GraphQL API is well-defined with a comprehensive schema that includes types,
### 2. Security Concerns
1. **Missing password hashing**: The User model has a BeforeSave hook for password hashing in `models/user.go`, but it's not implemented, which is a critical security vulnerability.
1. **Hardcoded database credentials**: The `main.go` file contains hardcoded database credentials, which is a security risk. These should be moved to environment variables or a secure configuration system.
2. **Hardcoded database credentials**: The `main.go` file contains hardcoded database credentials, which is a security risk. These should be moved to environment variables or a secure configuration system.
2. **SQL injection risk**: The `syncEntities` function in `syncjob/entities_sync.go` uses raw SQL queries with string concatenation, which could lead to SQL injection vulnerabilities.
3. **SQL injection risk**: The `syncEntities` function in `syncjob/entities_sync.go` uses raw SQL queries with string concatenation, which could lead to SQL injection vulnerabilities.
3. **No input validation**: There doesn't appear to be comprehensive input validation for GraphQL mutations, which could lead to data integrity issues or security vulnerabilities.
4. **No input validation**: There doesn't appear to be comprehensive input validation for GraphQL mutations, which could lead to data integrity issues or security vulnerabilities.
5. **No rate limiting**: There's no rate limiting for API requests or background jobs, which could make the system vulnerable to denial-of-service attacks.
4. **No rate limiting**: There's no rate limiting for API requests or background jobs, which could make the system vulnerable to denial-of-service attacks.
### 3. Code Quality Issues
1. **Inconsistent repository implementation**: Some repositories use the generic repository pattern, while others implement the pattern directly, leading to inconsistency and potential code duplication.
1. **Incomplete Weaviate integration**: The Weaviate client in `weaviate/weaviate_client.go` only supports the Work model, not other models, which limits the search capabilities.
2. **Limited error handling**: Many functions log errors but don't properly propagate them or provide recovery mechanisms. For example, in `syncjob/entities_sync.go`, errors during entity synchronization are logged but not properly handled.
2. **Simplified linguistic analysis**: The linguistic analysis algorithms in `linguistics/analyzer.go` are very basic and not suitable for production use. They use simplified approaches that don't leverage modern NLP techniques.
3. **Incomplete Weaviate integration**: The Weaviate client in `weaviate/weaviate_client.go` only supports the Work model, not other models, which limits the search capabilities.
4. **Simplified linguistic analysis**: The linguistic analysis algorithms in `linguistics/analyzer.go` are very basic and not suitable for production use. They use simplified approaches that don't leverage modern NLP techniques.
5. **Hardcoded string mappings**: The `toSnakeCase` function in `syncjob/entities_sync.go` has hardcoded mappings for many entity types, which is not maintainable.
3. **Hardcoded string mappings**: The `toSnakeCase` function in `syncjob/entities_sync.go` has hardcoded mappings for many entity types, which is not maintainable.
### 4. Testing and Documentation
1. **Limited test coverage**: There appears to be no test files in the codebase, which makes it difficult to ensure code quality and prevent regressions.
1. **Lack of API documentation**: The GraphQL schema lacks documentation for types, queries, and mutations, which makes it harder for developers to use the API.
2. **Lack of API documentation**: The GraphQL schema lacks documentation for types, queries, and mutations, which makes it harder for developers to use the API.
2. **Missing code documentation**: Many functions and packages lack proper documentation, which makes the codebase harder to understand and maintain.
3. **Missing code documentation**: Many functions and packages lack proper documentation, which makes the codebase harder to understand and maintain.
4. **No performance benchmarks**: There are no performance benchmarks to identify bottlenecks and measure improvements.
3. **No performance benchmarks**: There are no performance benchmarks to identify bottlenecks and measure improvements.
## Recommendations for Future Development
### 1. Architecture Improvements
1. **Standardize repository implementation**: Use the generic repository pattern consistently across all repositories to reduce code duplication and improve maintainability. Convert specific repositories like WorkRepository to use the GenericRepository.
1. **Implement a service layer**: Add a service layer between repositories and resolvers to encapsulate business logic and improve separation of concerns. This would include services for each domain entity (WorkService, UserService, etc.) that handle validation, business rules, and coordination between repositories.
2. **Implement a service layer**: Add a service layer between repositories and resolvers to encapsulate business logic and improve separation of concerns. This would include services for each domain entity (WorkService, UserService, etc.) that handle validation, business rules, and coordination between repositories.
2. **Improve error handling**: Implement consistent error handling with proper error types and recovery mechanisms. Create custom error types for common scenarios (NotFoundError, ValidationError, etc.) and ensure errors are properly propagated and logged.
3. **Improve error handling**: Implement consistent error handling with proper error types and recovery mechanisms. Create custom error types for common scenarios (NotFoundError, ValidationError, etc.) and ensure errors are properly propagated and logged.
3. **Add configuration management**: Use a proper configuration management system instead of hardcoded values. Implement a configuration struct that can be loaded from environment variables, config files, or other sources, with support for defaults and validation.
4. **Add configuration management**: Use a proper configuration management system instead of hardcoded values. Implement a configuration struct that can be loaded from environment variables, config files, or other sources, with support for defaults and validation.
5. **Implement a logging framework**: Use a structured logging framework for better observability. A library like zap or logrus would provide structured logging with different log levels, contextual information, and better performance than the standard log package.
4. **Implement a logging framework**: Use a structured logging framework for better observability. A library like zap or logrus would provide structured logging with different log levels, contextual information, and better performance than the standard log package.
### 2. Performance Optimizations
@ -128,23 +118,19 @@ The GraphQL API is well-defined with a comprehensive schema that includes types,
### 3. Code Quality Enhancements
1. **Implement password hashing**: Complete the BeforeSave hook in the User model to hash passwords. Use a secure hashing algorithm like bcrypt with appropriate cost parameters to ensure password security.
1. **Add input validation**: Implement input validation for all GraphQL mutations. Validate required fields, field formats, and business rules before processing data to ensure data integrity and security.
2. **Add input validation**: Implement input validation for all GraphQL mutations. Validate required fields, field formats, and business rules before processing data to ensure data integrity and security.
2. **Improve error messages**: Provide more descriptive error messages for better debugging. Include context information in error messages, distinguish between different types of errors (not found, validation, database, etc.), and use error wrapping to preserve the error chain.
3. **Improve error messages**: Provide more descriptive error messages for better debugging. Include context information in error messages, distinguish between different types of errors (not found, validation, database, etc.), and use error wrapping to preserve the error chain.
3. **Add code documentation**: Add comprehensive documentation to all packages and functions. Include descriptions of function purpose, parameters, return values, and examples where appropriate. Follow Go's documentation conventions for godoc compatibility.
4. **Add code documentation**: Add comprehensive documentation to all packages and functions. Include descriptions of function purpose, parameters, return values, and examples where appropriate. Follow Go's documentation conventions for godoc compatibility.
5. **Refactor duplicate code**: Identify and refactor duplicate code, especially in the synchronization process. Extract common functionality into reusable functions or methods, and consider using interfaces for common behavior patterns.
4. **Refactor duplicate code**: Identify and refactor duplicate code, especially in the synchronization process. Extract common functionality into reusable functions or methods, and consider using interfaces for common behavior patterns.
### 4. Testing Improvements
1. **Add unit tests**: Implement unit tests for all packages, especially models and repositories. Use a mocking library like sqlmock to test database interactions without requiring a real database. Test both success and error paths, and ensure good coverage of edge cases.
1. **Add integration tests**: Implement integration tests for the GraphQL API and background jobs. Test the entire request-response cycle for GraphQL queries and mutations, including error handling and validation. For background jobs, test the job enqueuing, processing, and completion.
2. **Add integration tests**: Implement integration tests for the GraphQL API and background jobs. Test the entire request-response cycle for GraphQL queries and mutations, including error handling and validation. For background jobs, test the job enqueuing, processing, and completion.
3. **Add performance tests**: Implement performance tests to identify bottlenecks. Use Go's built-in benchmarking tools to measure the performance of critical operations like database queries, synchronization processes, and linguistic analysis. Set performance baselines and monitor for regressions.
2. **Add performance tests**: Implement performance tests to identify bottlenecks. Use Go's built-in benchmarking tools to measure the performance of critical operations like database queries, synchronization processes, and linguistic analysis. Set performance baselines and monitor for regressions.
### 5. Security Enhancements
@ -160,15 +146,17 @@ The GraphQL API is well-defined with a comprehensive schema that includes types,
The Tercul Go application has a solid foundation with a well-structured domain model, repository pattern, and GraphQL API. The application demonstrates good architectural decisions such as using background job processing for synchronization and having a modular design for linguistic analysis.
However, there are several areas that need improvement:
A comprehensive suite of unit tests has been added for all models, repositories, and services, which significantly improves the code quality and will help prevent regressions. The password hashing for users has also been implemented.
However, there are still several areas that need improvement:
1. **Performance**: The application has potential performance issues with lack of pagination, inefficient database queries, and simplified algorithms.
2. **Security**: There are security vulnerabilities such as missing password hashing, hardcoded credentials, and SQL injection risks.
2. **Security**: There are security vulnerabilities such as hardcoded credentials and SQL injection risks in some parts of the application.
3. **Code Quality**: The codebase has inconsistencies in repository implementation, limited error handling, and incomplete features.
3. **Code Quality**: The codebase has some inconsistencies in repository implementation, limited error handling, and incomplete features.
4. **Testing**: The application lacks comprehensive tests, which makes it difficult to ensure code quality and prevent regressions.
4. **Testing**: While unit test coverage is now good, integration and performance tests are still lacking.
By addressing these issues and implementing the recommended improvements, the Tercul Go application can become more robust, secure, and scalable. The most critical issues to address are implementing proper password hashing, adding pagination to list operations, improving error handling, and enhancing the linguistic analysis capabilities.