# SanjeevaniAI — System Architecture & Design Specification

## 1. Architectural Overview

**SanjeevaniAI** is an industry-grade healthcare intelligence and clinical decision-support platform. The architecture is engineered around the principles of:
- **Local Machine Learning Sovereignty**: Pretrained neural NER model (`tner/roberta-large-bc5cdr`) runs completely on local hardware without sending sensitive clinical text to external third-party entity extraction APIs.
- **Provider-Agnostic Clinical Assistant**: Pluggable LLM interface supporting Google Gemini Pro and deterministic offline `MockLLMProvider` with heuristic red-flag emergency triage.
- **Data Integrity & Traceability**: Multi-format document ingestion (PDF, DOCX, TXT) with SHA-256 fingerprinting and immutable security audit trails.
- **Explicit Clinical Positioning**: Non-diagnostic language safeguards throughout all API contracts and UI surfaces.

```mermaid
graph TD
    Client["Client Layer (Next.js 14 + Tailwind CSS + Lucide)"]
    
    subgraph Gateway ["FastAPI Gateway & Security Layer"]
        CORS["CORS Middleware"]
        SecHeaders["Security Headers (CSP, XSS, HSTS)"]
        ReqID["Request ID Correlation"]
        Auth["JWT & RBAC Middleware"]
    end

    subgraph CoreServices ["Application & Domain Services"]
        NERService["NER Engine (BC5CDR Adapter)"]
        DocService["Document Ingestion & Chunking"]
        LLMService["Clinical AI Assistant (Gemini / Mock)"]
        AuditService["Audit & Timeline Service"]
    end

    subgraph LocalML ["Local Machine Learning Engine"]
        RoBERTa["tner/roberta-large-bc5cdr (355M Params)"]
        Tokenizer["Byte-Pair Encoding Tokenizer"]
        Torch["PyTorch (CUDA:0 / CPU fallback)"]
    end

    subgraph DataStorage ["Persistence Layer"]
        SQLite[("SQLAlchemy Async (aiosqlite / Postgres)")]
        DocStore["Encrypted Document Storage (/uploads)"]
    end

    Client --> Gateway
    Gateway --> CoreServices
    NERService --> LocalML
    DocService --> NERService
    CoreServices --> DataStorage
```

---

## 2. Component Hierarchy

### 2.1 Backend Architecture (`backend/app/`)
- **`core/`**:
  - `config.py`: Centralized Pydantic BaseSettings loaded from `.env`.
  - `security.py`: Direct `bcrypt` password hashing (protecting against the 72-byte passlib bug) and JWT token generation.
  - `database.py`: Asynchronous SQLAlchemy engine (`AsyncSessionLocal`) with automatic table initialization.
  - `logger.py`: Structured RFC-3339 logging with correlation IDs.
  - `exceptions.py`: Domain exception taxonomy mapping cleanly to HTTP 400/401/403/404/422/500 responses.
- **`models/`**:
  - `User`, `PatientProfile`: User accounts and clinical health profile (anthropometrics, allergies, chronic conditions, active medications).
  - `MedicalDocument`, `DocumentAnalysis`, `MedicalEntity`: Document storage metadata, SHA-256 hash, extracted summary, and labeled token entities (`CHEMICAL`, `DISEASE`).
  - `AIConversation`, `AIMessage`: Multi-turn consultation threads with structured JSON payloads.
  - `AuditLog`, `AnalysisHistory`, `SystemEvent`: Audit trail and chronological patient timeline.
- **`ml/`**:
  - `BC5CDRNERModel`: Thread-safe model wrapper loading weights from `models/bc5cdr-ner` using PyTorch. Performs sub-word token alignment, confidence thresholding (default $\tau = 0.85$), and entity offset calculation.
  - `ModelManager`: Singleton lifecycle manager preventing duplicate GPU VRAM allocations.
- **`services/`**:
  - `DocumentService`: Safe multi-format parser (`pypdf`, `pdfplumber`, `docx2txt`), chunker, and entity aggregator.
  - `LLMService`: Multi-provider abstraction (`GeminiProvider`, `MockLLMProvider`) implementing prompt defense guardrails and emergency red-flag heuristics.

---

## 3. Biomedical NER Inference Pipeline

```mermaid
sequenceDiagram
    participant UI as Next.js Visualizer
    participant API as /api/v1/ner/analyze
    participant ML as BC5CDRNERModel
    participant Torch as PyTorch / RoBERTa

    UI->>API: POST { text: "patient taking metformin for diabetes" }
    API->>ML: analyze(text, threshold=0.85)
    ML->>Torch: Tokenize & Forward Pass (roberta-large)
    Torch-->>ML: Logits [batch, seq_len, num_labels]
    ML->>ML: Argmax & Softmax Confidence Calibration
    ML->>ML: B- / I- Tag Aggregation & Character Span Mapping
    ML-->>API: List[NEREntity(text, label, start, end, confidence)]
    API-->>UI: 200 OK BaseResponse[NERResponse] (Latency: ~14ms)
    UI->>UI: Render emerald/rose token highlights & entity table
```

---

## 4. Emergency Triage & Decision-Support Flow

```mermaid
flowchart TD
    Query["Incoming Patient / Clinician Query"] --> CheckFlag{"Emergency Red-Flag Heuristics"}
    
    CheckFlag -- "Matched (Chest Pain, Stroke, SOB)" --> RedFlag["is_emergency = true"]
    RedFlag --> UrgentOutput["Generate Emergency Escalation Box + Call 911 / 112 / 108 Guidance"]
    
    CheckFlag -- "No Acute Red Flags" --> Consult["Prompt LLM with Non-Diagnostic Guardrails"]
    Consult --> StructOutput["Format Structured Response:\n- Clinical Summary\n- Considerations\n- Questions for Doctor\n- Mandatory Medical Disclaimer"]
    
    UrgentOutput --> Audit["Log to Immutable Audit Trail"]
    StructOutput --> Audit
    Audit --> Return["Return ChatCompletionResponse"]
```

---

## 5. Security & Healthcare Privacy Architecture

1. **Local-First Data Processing**: PHI in uploaded documents is parsed in memory and analyzed against the local neural network.
2. **Cryptographic Integrity**: Uploaded files are fingerprinted with SHA-256 before storage to detect tampering.
3. **Role-Based Access Control**:
   - `PATIENT`: Access own profile, medical documents, and consultations.
   - `DOCTOR`: Review patient clinical summaries and execute decision support.
   - `ADMIN`: View system telemetry, hardware metrics, and security audit logs.
4. **Standardized HTTP Security Headers**: HSTS, X-Content-Type-Options, X-Frame-Options (`DENY`), and Content-Security-Policy applied on every response.