# main-overview

## Development Guidelines

- Only modify code directly relevant to the specific request. Avoid changing unrelated functionality.
- Never replace code with placeholders like `# ... rest of the processing ...`. Always include complete code.
- Break problems into smaller steps. Think through each step separately before implementing.
- Always provide a complete PLAN with REASONING based on evidence from code and logs before making changes.
- Explain your OBSERVATIONS clearly, then provide REASONING to identify the exact issue. Add console logs when needed to gather more information.


The LIMS Microservice System implements a laboratory information management platform with three core components:

## Core Services Architecture
- Rust-based microservices handle laboratory data management and API endpoints
- Python services manage AI analysis and data processing
- React/TypeScript frontend provides lab technician interface
- PostgreSQL stores experiment and sample data

## Primary Business Components

### Laboratory Data Management
- Sample and batch tracking system
- Experiment workflow orchestration
- Result validation and flagging
- Integration with existing lab systems

### AI Analysis Pipeline
File Path: `/lims-ai/src/ai_features.py`
- Abnormal result detection
- Automated data analysis
- Pattern recognition in lab results
- Predictive analytics for sample outcomes

### Domain-Specific Implementations
File Path: `/lims-core/src/validation/`
- Custom validation rules for laboratory data
- Sample metadata verification
- Result range checking
- Batch processing rules

### Experiment Management
File Path: `/lims-core/src/api/experiments.rs`
- Experiment lifecycle tracking
- Sample status monitoring
- Result aggregation
- Quality control workflows

### Web Dashboard
File Path: `/lims-ui/src/components/ExperimentsDashboard.tsx`
- Real-time experiment monitoring
- Result visualization
- Sample tracking interface
- Analysis report generation

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