Rememex

windows 10+ only for now. uses UWP OCR and mica backdrop.

why rememex?

what it does

• indexes 120+ file types (code, docs, images, configs, whatever)
• OCR on images via windows built-in engine
• reads EXIF → reverse geocodes GPS to city names. search "photos from istanbul" and it works
• EXIF dates → human words. "summer morning" finds a photo from july at 8am
• hybrid search: vector + full-text + JINA cross-encoder reranker
• smart chunking per language (rust at fn/struct, python at def/class, etc)
• semantic containers for isolation (work/personal/research)
• MCP server for AI agents. details → · agent instructions →
• annotations: attach searchable notes to any file, from the UI or via MCP. agents and humans share the same knowledge layer
• optional cloud embeddings -- plug in OpenAI, Gemini, Cohere, or any compatible API. default is still 100% local

architecture

indexing

graph LR
    W[file watcher] -->|change event| SI[index single file]
    WB[WalkBuilder] -->|bulk scan| B[collect files]
    B --> C{image?}
    C -->|yes| D[UWP OCR + EXIF]
    C -->|no| E[file_io reader]
    D --> F[git context]
    E --> F
    F --> G["semantic chunking (per-language)"]
    G --> H[embedding provider]
    H -->|local ONNX or remote API| I[(lancedb)]
    I --> J[ANN + FTS index build]

search

graph LR
    Q[query] --> QR[query router]
    QR -->|weights + hyde flag| HYDE{hyde?}
    HYDE -->|conceptual| LLM[LLM hypothetical doc]
    HYDE -->|other| EMB[embed query]
    LLM --> EMB
    Q --> EXP[expand query variants]
    EMB --> VS[vector search]
    EXP --> FTS[full-text search]
    VS --> HM["hybrid merge (RRF)"]
    FTS --> HM
    EMB --> AS[annotation search]
    AS --> AM[merge annotations]
    HM --> AM
    AM --> RR[JINA reranker]
    RR --> SC[score normalization]
    SC --> MMR[MMR diversity]
    MMR --> R[ranked results]

    UI[tauri UI] --> Q
    MCP[MCP server] -->|stdio| Q

run it

npm install
npm run tauri dev        # dev is slow
npm run tauri build      # release build, use this for real speed

Alt+Space to toggle. config & docs → CONFIG.md

RAM usage peaks during initial indexing — this is expected. once indexing completes, it drops and stays stable.

try it with real data

we ship a test dataset so you can see what semantic search actually feels like. 2,483 resume PDFs across 24 professions, accountants to teachers.

# unzip test-set/data.zip somewhere
# create a new container in rememex, point it at the unzipped folder
# wait for indexing (~30 min on local embeddings)

we indexed it and ran these queries. all results below used the most basic config — no cloud APIs, no fine-tuning:

real results, real scores:

the point: grep needs the exact keyword. rememex finds meaning — even when the words are different, even when the file is in the wrong folder.

agentic benchmark

same 5 tasks, same codebase. grep vs rememex MCP:

grep needs the exact keyword. rememex needs the idea.

agents using rememex are expected to use 5-10x fewer tokens and complete tasks significantly faster. fewer search attempts, fewer wrong files opened, fewer round-trips. the benchmark above shows 1 step vs 3-5 , that's both speed and cost.

project structure

rememex/
├── src/                          # react/ts frontend
│   ├── components/               # UI components
│   │   ├── Sidebar.tsx           # sidebar: containers, annotations, filters
│   │   ├── SearchBar.tsx         # search input
│   │   ├── ResultsList.tsx       # virtualized search results
│   │   ├── StatusBar.tsx         # indexing status bar
│   │   ├── TitleBar.tsx          # custom window title bar
│   │   ├── Settings.tsx          # settings panel
│   │   └── settings/            # modular settings sub-panels
│   ├── locales/                  # i18n translations (en, tr)
│   ├── Modal.tsx                 # modal dialog component
│   ├── i18n.tsx                  # internationalization setup
│   ├── types.ts                  # shared TypeScript types
│   └── App.tsx                   # main app shell
├── src-tauri/
│   └── src/
│       ├── indexer/              # core engine
│       │   ├── mod.rs            # indexer orchestration, batch embed, reranker
│       │   ├── chunking.rs       # per-language semantic splitting
│       │   ├── embedding.rs      # fastembed ONNX inference
│       │   ├── embedding_provider.rs  # local/remote provider trait
│       │   ├── search.rs         # hybrid vector + full-text + reranker
│       │   ├── pipeline.rs       # search pipeline scoring
│       │   ├── annotations.rs    # annotation CRUD operations
│       │   ├── ocr.rs            # UWP OCR bridge
│       │   ├── file_io.rs        # file reading (text, pdf, binary)
│       │   ├── git.rs            # git log integration
│       │   └── db.rs             # lancedb operations
│       ├── bin/mcp.rs            # MCP server binary (stdio)
│       ├── commands.rs           # tauri IPC commands
│       ├── config.rs             # config loading / migration
│       ├── state.rs              # shared app state types
│       ├── watcher.rs            # notify-based file watcher
│       └── lib.rs                # app setup, tray, shortcuts
├── config.schema.json            # JSON schema for config validation
├── AGENT.md                      # agent instructions for MCP
├── MCP.md                        # MCP server documentation
├── CONFIG.md                     # configuration reference
└── ROADMAP.md                    # what's done, what's next

stack

rust (tauri 2), react/ts, lancedb, Multilingual-E5-Base, JINA Reranker v2, rayon

roadmap

ROADMAP.md

docs

star history

contributors

license

MIT