locallens 0.0.1

Search your files by talking to them — 100% offline semantic search with voice

Search your files by talking to them - 100% offline

What It Does

• Semantic + keyword search - find files by meaning or exact match with hybrid retrieval (RRF)
• RAG Q&A - ask natural-language questions, get grounded answers from a local LLM
• Voice input + playback - speak your question, hear the answer (Moonshine STT + Piper TTS)
• CLI + web app - fast Typer CLI for power users, React UI for visual browsing
• Watch mode - auto-indexes new and modified files as you work
• Namespaces - isolate collections per project, switch between them in the UI
• Plugin extractors - extend with custom file formats via Python entry points
• Offline-first - everything runs on your machine, no cloud APIs, no telemetry

Quickstart

Prerequisites

• Python 3.11+
• Ollama installed and running
• Docker + Docker Compose (for the web app)
• Node.js 18+ (for the frontend)

Web app (3 steps)

git clone https://github.com/mahimai/ask-local-files.git && cd ask-local-files
make setup    # starts Qdrant container, pulls Ollama model, installs deps
make dev      # Qdrant :6333, uvicorn :8000, Vite :5173

Open http://localhost:5173. Pages: Dashboard (stats), Index (folder picker + progress + watch toggle), Search (hybrid/semantic/keyword + filters), Ask (chat with voice), Stack (architecture docs).

CLI only (no Docker needed)

pip install -e .
locallens doctor            # verify setup
locallens index ~/Documents
locallens search "meeting notes from last week"
locallens ask "What did the Q3 report say about revenue?"

CLI + web sharing one index

export QDRANT_SYNC_URL=http://localhost:6333
locallens index ~/Documents    # dual-writes to local shard AND Docker Qdrant

The web app sees everything the CLI indexes -- instantly, no extra step. See Sync commands below for manual push/pull.

Architecture

graph TB
    USER([User])

    subgraph CLI ["CLI (locallens/)"]
        CLI_CMD[Typer commands]
        CLI_CMD --> EXT_CLI[Extractors]
        EXT_CLI --> CHK_CLI[Chunker]
        CHK_CLI --> EMB_CLI["Embedder<br/>(MiniLM-L6)"]
        EMB_CLI --> EDGE["Qdrant Edge<br/>(~/.locallens/qdrant_data)"]
    end

    subgraph WEB ["Web App"]
        UI["React Frontend<br/>(:5173)"] --> API["FastAPI Backend<br/>(:8000)"]
        API --> EXT_WEB[Extractors]
        EXT_WEB --> CHK_WEB[Chunker]
        CHK_WEB --> EMB_WEB["Embedder<br/>(MiniLM-L6)"]
        EMB_WEB --> QS["Qdrant Server<br/>(Docker :6333)"]
    end

    EDGE -. "push/pull sync<br/>(QDRANT_SYNC_URL)" .-> QS
    EDGE --> OLLAMA["Ollama qwen2.5:3b<br/>(optional, for ask/RAG)"]
    QS --> OLLAMA

    USER --> CLI_CMD
    USER --> UI

Two stores, one schema. The CLI uses Qdrant Edge (qdrant-edge-py) for embedded, on-device vector search -- no server needed. The web app talks to a Dockerized Qdrant server via qdrant-client. Both use the same named vector ("text", 384-dim, cosine) and keyword payload indexes, so points sync cleanly between them. See the /stack page in the running app for a feature-by-feature breakdown.

Directory layout

Directory	Description
locallens/	Typer CLI tool (pip-installable, uses Qdrant Edge)
backend/	FastAPI web API (uses Qdrant Server via Docker)
frontend/	React 19 + Vite + Tailwind web app
tests/	Pytest test suite (pipeline, API, integration)
assets/	Logo and static assets

Stack

Component	Choice
CLI vector store	Qdrant Edge via qdrant-edge-py (embedded, on-device)
Web backend store	Qdrant server (qdrant/qdrant:v1.14.0, Docker) via qdrant-client
CLI <-> server sync	Push on index + snapshot pull (locallens/sync.py)
Embeddings	all-MiniLM-L6-v2 via sentence-transformers (384-dim, cosine)
Keyword search	rank-bm25 (in-memory, persisted to JSON)
LLM	Ollama with qwen2.5:3b (Q4_K_M quantized, ~2.2 GB RAM)
STT	Moonshine tiny-en via moonshine-voice (bundled assets)
TTS	Piper en_US-lessac-medium via piper-tts (ONNX, auto-downloaded)
Backend	FastAPI -- WebSockets for index progress, SSE for answer streaming
Frontend	React 19 + Vite 8 + Tailwind 4 + shadcn/base-ui
CLI	Typer + Rich

How It Works

1. Indexing

Files are recursively discovered, text extracted via a pluggable extractor system (PyMuPDF for PDF with OCR fallback, python-docx for DOCX, openpyxl for spreadsheets, LiteParse when installed for higher quality). Text is split using an adaptive chunker that respects document structure: headings in markdown, function/class boundaries in code, paragraphs in documents, sheets in spreadsheets. Chunks are embedded into 384-dim vectors and stored in both the Qdrant vector index and an in-memory BM25 keyword index. Each chunk gets a deterministic UUID5 id, and dedup is O(1) via a keyword payload index on the file's SHA-256 hash.

2. Search

Hybrid by default: your query runs through both semantic (cosine similarity in Qdrant) and keyword (BM25) retrieval, combined via Reciprocal Rank Fusion (RRF, k=60). You can switch to pure semantic or pure keyword mode. Optional filters -- file type, path prefix, date range -- run server-side against keyword payload indexes. The web UI exposes dropdowns and date pickers for all filters.

3. Retrieval Augmented Generation

Top-k relevant chunks are retrieved and assembled into a context prompt. Ollama's qwen2.5:3b generates a grounded answer constrained to only use the retrieved context. Responses stream token-by-token via SSE. Source files are attached to each answer.

4. Voice

On the Ask page, click the mic button to record. The browser captures webm/opus via MediaRecorder, the backend decodes it through ffmpeg, and Moonshine tiny-en transcribes it. The transcript is auto-sent through the same RAG pipeline. Each assistant message has an inline "Listen" button that synthesizes audio via Piper TTS with visual playback state (loading, playing with animated bars, stop).

5. Watch Mode

The backend runs a filesystem watcher (watchdog) as a background thread. When files are created or modified, they're re-indexed automatically within seconds. Deleted files have their points removed from Qdrant. The CLI offers locallens watch <folder> for the same behavior with Rich terminal output. The web UI has a watch toggle per indexed folder.

6. Sync

Command	What it does
locallens index ~/docs (with QDRANT_SYNC_URL set)	Dual-write: local Edge shard + remote Qdrant server
locallens sync push	Push all local points to the server (catch-up after offline indexing)
locallens sync pull	Download a full server snapshot into the local shard
locallens sync pull --incremental	Transfer only changed segments (keeps warm shard warm)

7. Namespaces

Collections are isolated by namespace. The default namespace maps to the existing locallens collection for backward compatibility. Create new namespaces via the sidebar dropdown or --namespace CLI flag to keep projects separate.

locallens index ~/work --namespace work
locallens index ~/personal --namespace personal
locallens search "budget" --namespace work

8. Authentication (optional)

Set LOCALLENS_API_KEY in .env to require Authorization: Bearer <key> on all API endpoints. When unset, everything is open (default). Per-namespace access control is available via ~/.locallens/access.json mapping keys to allowed namespaces. The frontend Settings page stores the key in localStorage.

Supported File Types

Type	Extensions	Extractor
Text	.txt, .md	TextExtractor (UTF-8/latin-1 fallback)
Obsidian vaults	.md (in .obsidian/ dirs)	ObsidianExtractor (strips frontmatter, resolves wikilinks)
Documents	.pdf, .docx, .pptx, .html	PdfExtractor (pymupdf + OCR fallback), DocxExtractor, or LiteParse
Spreadsheets	.xlsx, .xls, .csv, .tsv	SpreadsheetExtractor (openpyxl / stdlib csv)
Code	.py, .js, .ts, .go, .rs, .java, .c, .cpp, .rb	CodeExtractor
Email	.eml, .msg	EmailExtractor (stdlib email / python-oletools)
Ebooks	.epub	EpubExtractor (ebooklib)

Plugin Extractors

Third-party packages can register custom extractors via the locallens.extractors entry-point group:

# in your package's pyproject.toml
[project.entry-points."locallens.extractors"]
my_format = "my_package.extractor:MyExtractor"

Your class must inherit from locallens.extractors.base.LocalLensExtractor and implement supported_extensions(), extract(), and name().

Optional Dependencies

pip install -e ".[voice]"      # Moonshine STT + Piper TTS + sounddevice
pip install -e ".[parsing]"    # LiteParse + openpyxl (better doc parsing)
pip install -e ".[ocr]"        # pytesseract + Pillow (OCR for scanned PDFs)
pip install -e ".[watch]"      # watchdog (filesystem watcher for CLI)
pip install -e ".[email]"      # python-oletools (for .msg files)
pip install -e ".[ebooks]"     # ebooklib (for .epub files)
pip install -e ".[dev]"        # pytest + pytest-asyncio + ruff

Qdrant Edge Features

LocalLens leverages these Qdrant Edge capabilities (documented live at /stack):

Feature	How we use it
Named vectors	Both stores declare vector "text" -- schema-compatible sync
Keyword payload indexes	O(1) dedup via file_hash index, scoped search via file_type
Filtered search	--file-type .pdf on CLI, dropdown on web -- single indexed query
Facets	Stats page and locallens stats get file-type breakdowns server-side
Push sync (dual-write)	locallens index writes locally and uploads to the Docker Qdrant
Snapshot pull	locallens sync pull restores a shard from a server snapshot
Partial snapshot	--incremental flag transfers only changed segments
Optimizer tuning	Eager vacuum for personal-corpus re-indexing patterns

Health Check

Run locallens doctor to verify all dependencies are working:

$ locallens doctor

              LocalLens Doctor
+-----------------+--------+--------------------------------------------------+
| Check           | Status | Detail                                           |
+-----------------+--------+--------------------------------------------------+
| Qdrant Edge     |   OK   | Shard OK at ~/.locallens/qdrant_data             |
| Qdrant Server   |   OK   | HTTP reachable at localhost:6333                 |
| Ollama          |   OK   | Running at http://localhost:11434                |
| Embedding Model |   OK   | all-MiniLM-L6-v2 (384-dim)                      |
| Voice STT       |   --   | Not installed (optional)                         |
| Voice TTS       |   --   | Not installed (optional)                         |
| Disk Space      |   OK   | 42.3 GB free                                    |
+-----------------+--------+--------------------------------------------------+

The web app also exposes GET /api/health returning structured status:

{
  "qdrant": "ok",
  "ollama": "unreachable",
  "search_available": true,
  "ask_available": false
}

Memory Usage

Component	RAM
Embeddings (all-MiniLM-L6-v2)	~150 MB
Qdrant Edge shard	~50 MB
LLM (Ollama qwen2.5:3b)	~2.2 GB
STT (Moonshine tiny-en)	~200 MB
TTS (Piper lessac-medium)	~80 MB
App overhead	~500 MB
Total (with voice)	~3.2 GB
Total (without voice)	~2.9 GB

Testing

make test          # starts Qdrant, installs test deps, runs full suite
make test-quick    # skip slow tests (Ollama-dependent)
make lint          # ruff check (if installed)
make clean         # remove caches and build artifacts

Tests use a separate locallens_test_{uuid} collection per session that is automatically created and destroyed, so production data is never touched.

Upgrading from pre-Edge versions

Earlier versions used qdrant-client's legacy embedded mode (incompatible storage format):

rm -rf ~/.locallens/qdrant_data
docker compose down -v && docker compose up -d qdrant
locallens index ~/Documents

Contributing

See CONTRIBUTING.md for setup instructions and contribution guidelines.

License

MIT