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Natural language to URI resolution and cross-platform local URI execution

Project description

nlp2uri

AI Cost Tracking

PyPI Version Python License AI Cost Human Time Model

  • 🤖 LLM usage: $1.3997 (7 commits)
  • 👤 Human dev: ~$266 (2.7h @ $100/h, 30min dedup)

Generated on 2026-06-06 using openrouter/qwen/qwen3-coder-next


Python library and CLI — kompilator NL → URI → akcje OS dla operacji desktopowych.

Wejście (naturalny język):

  • „otwórz VS Code w folderze ~/projekty/nlp2uri”
  • „zrób screenshot aktywnego okna przeglądarki”
  • „otwórz plik invoice-2025.pdf”

Wyjście:

  1. Abstrakcyjny URI (RFC 3986, niezależny od OS)
  2. Plan akcji OSAction[] — konkretne komendy per platforma
  3. (Opcjonalnie) wykonanie lub payload MCP (text/uri-list)

Architektura

flowchart LR
    NL[Natural language] --> PARSE[parse_nl]
    PARSE --> INTENT[UriIntent + slots]
    INTENT --> BUILD[schemes/build]
    BUILD --> SPEC[UriSpec abstract URI]
    SPEC --> COMPILE[compile_uri_to_actions]
    COMPILE --> ACTIONS[OSAction plan]
    ACTIONS --> EXEC[execute_uri / MCP]

Warstwy

Warstwa Moduł Opis
NLP → intencja parse_nl Heurystyki EN + PL → UriIntent
Intencja → URI schemes/* Abstrakcyjne schemy OS-neutral
URI → OS compile compile_uri_to_actions()
Wykonanie runtime subprocess / dry-run
MCP mcp text/uri-list, tool payloads

Abstrakcyjne schemy URI

Scheme Przykład Znaczenie
app:// app://vscode/open?path=/home/tom/... Otwórz aplikację / IDE
app://file/open app://file/open?path=/tmp/x.pdf Otwórz plik
desktop-screenshot:// desktop-screenshot://window?title=Chrome&mode=active Screenshot okna/ekranu
desktop-window:// desktop-window://focus?name=slack Fokus okna
natywne vscode://, cursor://, file://, ms-settings: Passthrough do handlera OS

Metadata native_uri zawiera deep-link IDE (vscode://file/...) gdy istnieje.

Konfiguracja (nlp2uri.yaml)

Domyślne ustawienia w pliku nlp2uri.yaml (auto-tworzony przy pierwszym uruchomieniu):

platform: auto          # auto | linux | darwin | windows
host_platform: linux  # ostatnio wykryty host (informacyjnie)
locale: null
dry_run: false
capture_dir: /tmp/nlp2uri-captures

Kolejność wyboru platformy: --platformNLP2URI_PLATFORMnlp2uri.yaml → auto-detect OS.

nlp2uri config show --json    # efektywna konfiguracja
nlp2uri config init           # zapisz domyślny nlp2uri.yaml

Ścieżki szukania configu: NLP2URI_CONFIG./nlp2uri.yaml~/.config/nlp2uri/nlp2uri.yaml.

Quick start

pip install -e ".[dev]"

# Platforma wykrywana automatycznie (bez --platform)
nlp2uri plan "otwórz vscode w folderze ~/github/semcod/nlp2uri" --json
nlp2uri resolve "zrób screenshot aktywnego okna przeglądarki" --json
nlp2uri execute "open firefox" --dry-run

Python API (service facade)

from nlp2uri import NLP2URIService
from nlp2uri.models import HostPlatform

svc = NLP2URIService.default()  # ładuje nlp2uri.yaml + auto-detect

plan = svc.from_prompt("otwórz vscode w folderze /tmp/foo")
print(plan.uri)                    # app://vscode/open?path=/tmp/foo
print(plan.actions[0].argv())      # ['xdg-open', 'vscode://file/tmp/foo']

payload = svc.handle_prompt("open firefox", dry_run=True)  # prompt → URI → run

Adaptery i integratory (reużywalne powierzchnie)

Warstwa Użycie Moduł
Service NLP2URIService.from_prompt() / handle_uri() nlp2uri.service
CLI nlp2uri plan|resolve|compile|execute nlp2uri.adapters.cli
Shell eval "$(nlp2uri shell export '…')" nlp2uri.adapters.shell
REST nlp2uri-serve --port 8766 nlp2uri.integrators.rest_server
MCP nlp2uri-mcp (stdio JSON-RPC) nlp2uri.integrators.mcp_server

REST (POST /v1/plan)

curl -s -X POST http://127.0.0.1:8766/v1/plan \
  -H 'Content-Type: application/json' \
  -d '{"prompt":"open firefox"}'

MCP (Cursor / Windsurf)

{ "mcpServers": { "nlp2uri": { "command": "nlp2uri-mcp" } } }

Tools: nlp2uri_plan, nlp2uri_resolve, nlp2uri_compile, nlp2uri_execute, nlp2uri_handle.

Shell

eval "$(nlp2uri shell export 'open firefox')"
echo "$NLP2URI_URI"    # app://firefox/open
$nlp2uri-run           # alias do skompilowanej komendy

Własny adapter

from nlp2uri.adapters.base import AdapterRequest
from nlp2uri.adapters.rest import RestAdapter

response = RestAdapter().dispatch("plan", {"prompt": "capture screen", "platform": "linux"})
print(response.data["uri"])

Standardy

Standard Rola
RFC 3986 Składnia URI (scheme, path, query)
RFC 8089 file://
RFC 9110 http(s)://
Freedesktop Desktop Entry .desktop, x-scheme-handler/<scheme>
XDG Desktop Portal Screenshot Wayland
Windows URI activation ms-settings:, rejestracja schemów
macOS URL Schemes CFBundleURLSchemes w Info.plist
MCP text/uri-list Lista URI dla hosta / MCP Apps

Przydatne biblioteki

Biblioteka Kiedy
urllib.parse, webbrowser, subprocess Już używane (stdlib)
psutil PID → okno
pyxdg Parsowanie .desktop
dbus-python + PyGObject XDG Portal screenshot
pywin32 Win32 SetForegroundWindow
pyobjc / Quartz macOS window ID
spaCy / transformers Zamiast heurystyk regex
nlp2dsl / nlp2cmd-intent IntentIR z LLM (jak nlpshim)

Testy i Docker

python -m pytest
docker compose build && docker compose run --rm nlp2uri-test
./examples/run-e2e.sh

Przykłady per kategoria: examples/README.md.

W Dockerze:

  • unit: NLP → URI → OSAction
  • integracja: rejestracja testapp:// przez .desktop + xdg-open (NLP2URI_INTEGRATION=1)

Orchestracja ekosystemu (MCP, usługi, artefakty, getv)

Pełny przewodnik z przykładami uruchomienia i kontroli wielu backendów:

pip install -e ".[envmap]"    # getv + env2llm
nlp2uri list-getv --json
nlp2uri resolve-getv "GROQ_API_KEY" --json
nlp2uri list-system-uris --example-dir ~/github/wronai/nlp2dsl/examples/01-invoice --json

Plan rozwoju

docs/roadmap.md · docs/mcp-tools.md · CI: .github/workflows/ci.yml

Relacja z ekosystemem Semcod

  • nlpshim — NLP → DSL / workflow
  • nlp2uri — NLP → URI → akcje (desktop, getv, SystemMap, MCP)
  • todomat — orchestrator MCP/HTTP (curllm, nlp2dsl, iterun, nlp2uri)
  • koru — bridge MCP koru_desktop_uri_* + planfile tickets
  • getv — zmienne env → getv:// URI

License

Licensed under Apache-2.0.

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