learningcircuit--local-deep-research/docs/architecture.md

# Architecture Overview

This document provides detailed technical diagrams of Local Deep Research's architecture.

## System Architecture

```mermaid
flowchart TB
    subgraph USER["👤 User Interface"]
        WEB[Web Browser<br/>localhost:5000]
    end

    subgraph FLASK["🌐 Flask Backend"]
        API[REST API + WebSocket]
        AUTH[Authentication<br/>CSRF Protection]
        ROUTES[Research Routes]
    end

    subgraph RESEARCH["🔬 Research Engine"]
        STRAT[Strategy Selector]
        QGEN[Question Generator]
        EXEC[Search Executor]
        SYNTH[Report Synthesizer]
    end

    subgraph LLM["🤖 LLM Providers"]
        direction TB
        LOCAL_LLM[/"🏠 Local LLMs"\]
        OLLAMA[Ollama]
        LMSTUDIO[LM Studio]
        CLOUD_LLM[/"☁️ Cloud LLMs"\]
        OPENAI[OpenAI]
        ANTHROPIC[Anthropic]
        GEMINI[Google Gemini]
        OPENROUTER[OpenRouter<br/>100+ models]
    end

    subgraph SEARCH["🔍 Search Engines"]
        direction TB
        LOCAL_SEARCH[/"🏠 Local Search"\]
        SEARXNG[SearXNG]
        ELASTIC[Elasticsearch]
        LIBRARY[Document Library]
        WEB_SEARCH[/"🌐 Web Search"\]
        TAVILY[Tavily]
        BRAVE[Brave]
        DDG[DuckDuckGo]
        ACADEMIC[/"📚 Academic"\]
        ARXIV[ArXiv]
        PUBMED[PubMed]
        SEMANTIC[Semantic Scholar]
    end

    subgraph STORAGE["💾 Storage Layer"]
        SQLCIPHER[(SQLCipher DB<br/>AES-256 Encrypted)]
        VECTORS[(Vector Store<br/>Embeddings)]
        FILES[(File Storage<br/>PDFs & Docs)]
    end

    subgraph OUTPUT["📄 Output"]
        MD[Markdown]
        PDF[PDF Export]
        LATEX[LaTeX]
        QUARTO[Quarto]
        RIS[RIS/BibTeX]
    end

    WEB <--> API
    API --> AUTH
    AUTH --> ROUTES
    ROUTES --> STRAT

    STRAT --> QGEN
    QGEN --> EXEC
    EXEC --> SYNTH

    EXEC <--> OLLAMA & LMSTUDIO
    EXEC <--> OPENAI & ANTHROPIC & GEMINI & OPENROUTER

    EXEC <--> SEARXNG & ELASTIC & LIBRARY
    EXEC <--> TAVILY & BRAVE & DDG
    EXEC <--> ARXIV & PUBMED & SEMANTIC

    SYNTH --> SQLCIPHER
    LIBRARY <--> VECTORS
    LIBRARY <--> FILES

    SYNTH --> MD & PDF & LATEX & QUARTO & RIS

    LOCAL_LLM ~~~ OLLAMA
    CLOUD_LLM ~~~ OPENAI
    LOCAL_SEARCH ~~~ SEARXNG
    WEB_SEARCH ~~~ TAVILY
    ACADEMIC ~~~ ARXIV

    style USER fill:#e1f5fe
    style FLASK fill:#fff3e0
    style RESEARCH fill:#f3e5f5
    style LLM fill:#e8f5e9
    style SEARCH fill:#fce4ec
    style STORAGE fill:#fff8e1
    style OUTPUT fill:#e0f2f1
```

## Research Flow

```mermaid
flowchart LR
    subgraph INPUT["1️⃣ Input"]
        Q[Research Query]
    end

    subgraph ITERATE["2️⃣ Iterative Research"]
        direction TB
        GEN[Generate<br/>Questions]
        SEARCH[Parallel<br/>Search]
        ANALYZE[Analyze<br/>Results]
        GEN --> SEARCH --> ANALYZE
        ANALYZE -.->|"Need more info?"| GEN
    end

    subgraph SYNTHESIZE["3️⃣ Synthesis"]
        REPORT[Generate<br/>Report]
        CITE[Add<br/>Citations]
        REPORT --> CITE
    end

    subgraph OUTPUT["4️⃣ Output"]
        RESULT[Final Report<br/>with Sources]
    end

    Q --> GEN
    ANALYZE --> REPORT
    CITE --> RESULT

    style INPUT fill:#e3f2fd
    style ITERATE fill:#f3e5f5
    style SYNTHESIZE fill:#e8f5e9
    style OUTPUT fill:#fff3e0
```

## Deployment Options

```mermaid
flowchart TB
    subgraph FULL_LOCAL["🏠 Fully Local (Maximum Privacy)"]
        direction LR
        L_LDR[LDR] <--> L_OLLAMA[Ollama]
        L_LDR <--> L_SEARX[SearXNG]
        L_LDR <--> L_DB[(Encrypted DB)]
    end

    subgraph HYBRID["⚡ Hybrid (Balanced)"]
        direction LR
        H_LDR[LDR] <--> H_OLLAMA[Ollama]
        H_LDR <-->|"Web Search"| H_CLOUD[Cloud APIs]
        H_LDR <--> H_DB[(Encrypted DB)]
    end

    subgraph CLOUD["☁️ Cloud-Powered (Maximum Speed)"]
        direction LR
        C_LDR[LDR] <--> C_OPENAI[OpenAI/Claude]
        C_LDR <--> C_TAVILY[Tavily/Brave]
        C_LDR <--> C_DB[(Encrypted DB)]
    end

    style FULL_LOCAL fill:#e8f5e9
    style HYBRID fill:#fff3e0
    style CLOUD fill:#e3f2fd
```

## Feature Map

```mermaid
mindmap
  root((Local Deep<br/>Research))
    Research
      Quick Summary
      Detailed Reports
      Follow-up Questions
      Research Strategies
    Search Sources
      Web Search
        SearXNG
        Tavily
        Brave
        DuckDuckGo
      Academic
        ArXiv
        PubMed
        Semantic Scholar
      Local
        Document Library
        Collections
        Elasticsearch
    LLM Support
      Local
        Ollama
        LM Studio
      Cloud
        OpenAI
        Anthropic
        Google
        OpenRouter
    Output
      Markdown
      PDF
      LaTeX
      Quarto
      RIS/BibTeX
    Features
      News Subscriptions
      Cost Analytics
      Benchmarking
      RAG Search
      Per-User Encryption
```

## Component Details

### LLM Providers

| Provider | Type | Description |
|----------|------|-------------|
| Ollama | Local | Self-hosted open-source models |
| LM Studio | Local | Desktop app for local models |
| OpenAI | Cloud | GPT-4, GPT-3.5 |
| Anthropic | Cloud | Claude 3 family |
| Google | Cloud | Gemini models |
| OpenRouter | Cloud | 100+ models via single API |

### Search Engines

| Engine | Type | Best For |
|--------|------|----------|
| SearXNG | Local/Self-hosted | Privacy, aggregated results |
| Tavily | Cloud | AI-optimized search |
| ArXiv | Academic | Physics, CS, Math papers |
| PubMed | Academic | Biomedical research |
| Semantic Scholar | Academic | Cross-discipline papers |
| Wikipedia | Knowledge | General knowledge |
| Your Documents | Local | Private document search |

### Output Formats

| Format | Use Case |
|--------|----------|
| Markdown | Default, web display |
| PDF | Sharing, printing |
| LaTeX | Academic papers |
| Quarto | Reproducible documents |
| RIS/BibTeX | Reference managers |

## Knowledge Loop: Research → Library → Future Research

One of LDR's powerful features is the ability to build a personal knowledge base that improves future research.

```mermaid
flowchart TB
    subgraph RESEARCH["1️⃣ Research"]
        Q[Your Question] --> Engine[Research Engine]
        Engine --> Results[Results + Sources]
    end

    subgraph DOWNLOAD["2️⃣ Download"]
        Results --> Track[Track Sources]
        Track --> Queue[Download Queue]
        Queue --> Extract[Download & Extract Text]
    end

    subgraph LIBRARY["3️⃣ Library"]
        Extract --> Store[(Document Storage)]
        Store --> Organize[Organize into Collections]
    end

    subgraph INDEX["4️⃣ Index"]
        Organize --> Chunk[Chunk Documents]
        Chunk --> Embed[Generate Embeddings]
        Embed --> FAISS[(Vector Index)]
    end

    subgraph REUSE["5️⃣ Reuse"]
        FAISS --> SearchEngine[Collection as Search Engine]
        SearchEngine --> Q
    end

    style RESEARCH fill:#e3f2fd
    style DOWNLOAD fill:#fff3e0
    style LIBRARY fill:#e8f5e9
    style INDEX fill:#f3e5f5
    style REUSE fill:#fce4ec
```

### How It Works

1. **Research Completes** → Sources are tracked in `ResearchResource` table
2. **Download Sources** → Click "Get All Research PDFs" to queue downloads
   - Smart downloaders for ArXiv, PubMed, Semantic Scholar, etc.
   - Automatic text extraction from PDFs
3. **Build Library** → Documents stored in encrypted database
   - Deduplication via content hash
   - Multiple storage modes: database (encrypted), filesystem, text-only
4. **Create Collections** → Organize documents by topic/project
   - Each collection can have different embedding settings
   - Documents can belong to multiple collections
5. **Index for Search** → Generate vector embeddings
   - Configurable chunk size and overlap
   - FAISS index for fast similarity search
6. **Use in Future Research** → Select collection as search engine
   - RAG search finds relevant passages
   - Results cite back to your documents

### Key Components

| Component | Purpose |
|-----------|---------|
| `DownloadService` | Manages PDF downloads with source-specific strategies |
| `LibraryService` | Queries and manages document library |
| `LibraryRAGService` | Creates vector indices for semantic search |
| `CollectionSearchEngine` | Searches collections using RAG |
| `Document` | Stores text content, metadata, file references |
| `DocumentChunk` | Stores indexed text chunks with embeddings |
| `Collection` | Groups documents with shared embedding settings |

### Storage Options

| Mode | Security | Use Case |
|------|----------|----------|
| Database | AES-256 encrypted | Default, maximum security |
| Filesystem | Unencrypted | Need external tool access |
| Text Only | Encrypted text, no PDFs | Minimal storage |

---

## Technical Analysis & Project Health

*Last updated: December 2024*

This section provides a comprehensive technical analysis of the codebase, including quality metrics, architecture patterns, and project health indicators.

### Project Statistics

| Metric | Count |
|--------|-------|
| Test Classes | 809+ |
| Search Engine Implementations | 25 |
| LLM Provider Implementations | 9 |
| Search Strategies | 5 |
| Abstract Base Classes | 26 |
| CI/CD Workflows | 57 |
| Security Scanners in CI | 22+ |
| Core Dependencies | 63 |

### Architecture Patterns

#### Extensibility Design

The codebase follows a consistent pattern for extensibility:

```
┌─────────────────────────────────────────────────────────────┐
│                    Abstract Base Classes                     │
├─────────────────────────────────────────────────────────────┤
│ BaseSearchEngine      │ Common interface for 25+ engines    │
│ BaseSearchStrategy    │ Strategy pattern for research       │
│ BaseCitationHandler   │ Citation processing abstraction     │
│ BaseQuestionGenerator │ Question generation interface       │
│ BaseExporter          │ Export format abstraction           │
└─────────────────────────────────────────────────────────────┘
```

#### Search Engine Plugin System

New search engines can be added by:
1. Creating a class inheriting from `BaseSearchEngine`
2. Placing it in `web_search_engines/engines/`
3. Auto-discovery handles registration

```python
# Example: Adding a new search engine
class SearchEngineCustom(BaseSearchEngine):
    def run(self, query: str) -> List[Dict]:
        # Implementation
        pass
```

#### LLM Provider Integration

Supports 9 LLM providers with auto-discovery:
- Ollama (local)
- LM Studio (local)
- OpenAI
- Anthropic
- Google Gemini
- OpenRouter (100+ models)
- DeepSeek
- Mistral
- Groq

### Quality Ratings

```mermaid
pie title Project Health Ratings
    "Code Quality (86)" : 86
    "Security (93)" : 93
    "Extensibility (93)" : 93
    "Performance (85)" : 85
    "UX/DX (83)" : 83
    "Maintenance (89)" : 89
```

#### Detailed Ratings

| Category | Score | Highlights |
|----------|-------|------------|
| **Code Quality** | 86/100 | 809+ test classes, ruff/mypy enforcement, comprehensive pre-commit hooks |
| **Security** | 93/100 | SQLCipher AES-256 encryption, full SHA pinning in CI, 22+ security scanners |
| **Extensibility** | 93/100 | 26 abstract base classes, plugin architecture, strategy pattern throughout |
| **Performance** | 85/100 | Adaptive rate limiting, cache stampede protection, parallel search execution |
| **UX/Developer Experience** | 83/100 | Real-time WebSocket updates, in-tool documentation, comprehensive error handling |
| **Maintenance** | 89/100 | 57 CI/CD workflows, automated security scanning, structured changelog |
| **Overall** | 88/100 | Production-ready with excellent security and extensibility |

### Security Architecture

```mermaid
flowchart TB
    subgraph SECURITY["🔐 Security Layers"]
        direction TB
        AUTH[Flask Session Auth<br/>CSRF Protection]
        ENCRYPT[SQLCipher<br/>AES-256 Encryption]
        SCAN[22+ Security Scanners<br/>CodeQL, Semgrep, Bandit...]
        PIN[Full SHA Pinning<br/>All 57 CI Workflows]
    end

    AUTH --> ENCRYPT
    ENCRYPT --> SCAN
    SCAN --> PIN

    style SECURITY fill:#e8f5e9
```

**Security Features:**
- Per-user encrypted databases (SQLCipher with AES-256)
- Full GitHub Action SHA pinning (not tag-based)
- Comprehensive CI security scanning:
  - CodeQL (Python, JavaScript)
  - Semgrep (custom rulesets)
  - Bandit (Python security)
  - Trivy (container scanning)
  - Dependency review
  - Secret scanning

### Performance Optimizations

| Feature | Implementation |
|---------|----------------|
| **Parallel Search** | `concurrent.futures.ThreadPoolExecutor` for multi-question search |
| **Rate Limiting** | Adaptive system with `learning_rate=0.3` |
| **Cache Protection** | `fetch_events` + `fetch_locks` for stampede prevention |
| **Progress Streaming** | SocketIO for real-time UI updates |
| **Cross-Engine Filtering** | LLM-powered relevance scoring and deduplication |

#### Thread & Resource Lifecycle

Unlike typical web apps that share a single database connection pool, this application maintains **separate database engines per user** because each user has their own encrypted SQLite file with a unique SQLCipher key ([SQLCipher](https://www.zetetic.net/sqlcipher/) is an encrypted extension of SQLite). This creates a threading challenge: every background thread needs its own engine for the user it serves.

A single **QueuePool** engine is kept per user (`pool_size=20`,
`max_overflow=40`, `pool_timeout=10`) in
`DatabaseManager.connections[username]`. It is created with
`check_same_thread=False`, so it is shared safely across Flask
request-handler threads AND background threads (research workers,
scheduler jobs, metric writers). All of these acquire sessions from the
same pool, which keeps FD usage bounded by `pool_size + max_overflow`
per user.

> Earlier revisions of the code maintained a second, parallel system of
> per-`(username, thread_id)` **NullPool** engines for background metric
> writes. That system was removed because orphaned entries leaked
> SQLCipher + WAL file handles (3 FDs per active connection) under load,
> eventually exhausting the 1024 FD soft limit. Routing all work through
> the single per-user QueuePool made FD usage bounded and let us delete
> ~200 lines of thread-engine bookkeeping.

##### Resource Cleanup Layers

Cleanup is defense-in-depth with multiple layers:

| Layer | Trigger | What it cleans |
|-------|---------|----------------|
| `@thread_cleanup` decorator | Thread function exit (normal or exception) | DB session (returned to per-user pool), settings context, search context |
| `finally` blocks | Per-research in `run_research_process()` | Search engine HTTP sessions, strategy thread pool executors |
| `teardown_appcontext` | After each HTTP request | QueuePool session, thread-local session, triggers dead-thread credential sweep |
| Periodic pool dispose | Every 30 min | Calls `engine.dispose()` on per-user QueuePool engines to release SQLCipher+WAL file handles that accumulate from out-of-order connection closes |
| Logout cascade | User logout | Scheduler unregister (removes password) → DB close (`engine.dispose()`) → session destroy |
| Stale session cleanup | `before_request` (~1% of requests, sampled) | Clears Flask sessions for users whose DB connection is gone |

##### Research Thread Lifecycle

```mermaid
flowchart TD
    A["POST /api/start_research"] --> B{"Slots available?"}
    B -- "Yes (direct)" --> C["start_research_process()"]
    B -- "No (queued)" --> D["Database queue"]
    D --> E["_process_queue_loop<br/>(daemon thread, 10s poll)"]
    E --> C

    C --> F["Research thread<br/>(daemon, semaphore-gated)"]
    F --> G["@thread_cleanup wraps<br/>run_research_process()"]

    G --> H["Strategy.find_relevant_information()"]
    H --> I["ThreadPoolExecutor sub-tasks<br/>(within strategies only)"]
    I --> J["Worker @thread_cleanup:<br/>close worker DB session"]

    J --> K["finally: search_engine.close()<br/>system.close() → strategy.close()"]
    K --> L["Main @thread_cleanup:<br/>close DB session, return connection to pool"]

    subgraph sweep ["Defense-in-depth (independent timers)"]
        M["Dead-thread credential sweep<br/>(~60s via processor_v2)"] -.-> N["Remove stale credentials<br/>for dead thread IDs"]
        O["Pool dispose<br/>(30 min via connection_cleanup)"] -.-> P["engine.dispose() on all<br/>QueuePool engines"]
    end

    style sweep fill:#f0f0f0,stroke:#999
```

##### FD Budget

Each SQLCipher connection in WAL (Write-Ahead Logging) mode uses **2 file descriptors** (main db + WAL file). All connections to the same database within a process share **1 SHM** (shared-memory) file descriptor. The formula per user database is: `connections × 2 + 1`.

| Component | FD Formula | With defaults |
|-----------|-----------|---------------|
| QueuePool (steady state) | `logged_in_users × (pool_size × 2 + 1)` | `users × 41` FDs |
| QueuePool (peak) | `logged_in_users × ((pool_size + max_overflow) × 2 + 1)` | `users × 121` FDs |

Default Linux ulimit is 1024 soft (bare metal), which is tight for multi-user deployments. Docker's daemon default (typically 1M+) is adequate. QueuePool engines are created at login and disposed at logout, so only active users consume FDs.

For more on diagnosing FD exhaustion, see [Troubleshooting - Resource Exhaustion](./troubleshooting.md#resource-exhaustion).

##### Key Files

| File | Role |
|------|------|
| `src/local_deep_research/database/encrypted_db.py` | `DatabaseManager`, engine lifecycle, pool management |
| `src/local_deep_research/database/thread_local_session.py` | `@thread_cleanup` decorator, thread-local sessions, credential cleanup |
| `src/local_deep_research/web/app_factory.py` | `teardown_appcontext` handler, cleanup orchestration |
| `src/local_deep_research/web/services/research_service.py` | Research thread creation, `run_research_process()` |
| `src/local_deep_research/web/queue/processor_v2.py` | Queue processing, credential cleanup trigger |

### Areas for Improvement

While the project scores highly overall, these areas have room for growth:

1. **Integration Testing** - More end-to-end tests for full research workflows
2. **API Documentation** - OpenAPI/Swagger spec for REST endpoints
3. **Metrics Dashboard** - Prometheus/Grafana integration for monitoring
4. **Resource Observability** - Expose FD count, thread count, and connection pool stats in /api/v1/health; add periodic sweep logging
5. **Async Architecture** - Migration to async/await for I/O-bound operations

### Key Source Files

| Component | Location | Purpose |
|-----------|----------|---------|
| Research Engine | `src/local_deep_research/search_system.py` | Main `AdvancedSearchSystem` class |
| Strategies | `src/local_deep_research/advanced_search_system/strategies/` | Research strategy implementations |
| Search Engines | `src/local_deep_research/web_search_engines/engines/` | 25 search engine implementations |
| Report Generation | `src/local_deep_research/report_generator.py` | `IntegratedReportGenerator` |
| Web API | `src/local_deep_research/web/routes/` | Flask routes and WebSocket handlers |
| Database | `src/local_deep_research/web/database/` | SQLCipher models and migrations |
| Encrypted DB | `src/local_deep_research/database/encrypted_db.py` | Per-user SQLCipher engine lifecycle |
| Thread Sessions | `src/local_deep_research/database/thread_local_session.py` | Thread-safe session management and cleanup |
| Settings | `src/local_deep_research/config/` | Configuration and LLM setup |

### Contributing to Architecture

When extending the system:

1. **Adding Search Engines**: Inherit from `BaseSearchEngine`, implement `run()` method
2. **Adding Strategies**: Inherit from `BaseSearchStrategy`, implement `analyze_topic()` method
3. **Adding LLM Providers**: Add to `config/llm_config.py` with proper initialization
4. **Adding Export Formats**: Inherit from base exporter pattern in `utilities/`

See [CONTRIBUTING.md](../CONTRIBUTING.md) for detailed guidelines.