E2e workflow (#21 )

Reviewed-on: #21 Co-authored-by: Mathias Scheider <mathias2784@web.de>
E2e workflow (#20 )
2026-10-18 12:02:18 +00:00 · 2026-10-18 07:24:49 +00:00 · 2026-10-18 06:29:37 +00:00 · 2026-10-10 18:37:09 +00:00 · 2026-10-10 12:22:15 +00:00 · 2026-10-03 08:24:17 +00:00
33 changed files with 3982 additions and 29 deletions
@@ -0,0 +1,10 @@
+LLM_API_BASE_URL = "http://localhost:1234/v1"
+LLM_API_KEY = "not-relevant"
+LLM_DEFAULT_TEMPERATURE = 1.0
+
+METADATA_PREFIX = "photools|"
+LOG_LEVEL=INFO
+LLM_REQUEST_TIMEOUT_SECONDS=10
+LLM_MAX_COMPLETION_TOKENS=1024
+LLM_MAX_RETRIES=5
+LLM_MAX_KEYWORDS=15
@@ -0,0 +1,37 @@
+---
+name: Tests
+
+on: [push]
+
+jobs:
+  test_and_lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.14"
+      - name: Install uv
+        run: pip install uv
+      - name: Install dependencies
+        run: uv sync --frozen
+      - name: copy env
+        run: cp .env.template .env
+      - name: Install exiftool (system dependency)
+        run: |
+          if command -v exiftool >/dev/null 2>&1; then
+            echo "exiftool already installed"
+          elif command -v sudo >/dev/null 2>&1; then
+            sudo apt-get update && sudo apt-get install -y --no-install-recommends libimage-exiftool-perl
+          else
+            apt-get update && apt-get install -y --no-install-recommends libimage-exiftool-perl
+          fi
+      - name: Run pytest
+        run: uv run pytest .
+      - name: Lint (ruff)
+        run: uv run ruff check .
+      - name: Typing (mypy)
+        run: uv run mypy .
+      - name: Check formatting (black)
+        run: uv run black --check . --extend-exclude .venv
@@ -10,3 +10,4 @@ wheels/
 .venv

 .env
+logs/
@@ -0,0 +1,42 @@
+# Agent Instructions: PhotoTools3
+
+## Environment Setup
+
+Requires a `.env` file with the following variables:
+
+- `llm_api_key`: API key for the LLM provider.
+- `llm_api_base_url`: Base URL for the LLM API.
+- `llm_default_temperature`: Float value for temperature.
+
+## System Dependencies
+
+- `exiftool` binary: required by `pyexiftool` at runtime, not installable via pip/uv.
+  - Arch: `perl-image-exiftool`
+  - Debian/Ubuntu: `libimage-exiftool-perl`
+
+## Development Toolchain
+
+- **Dependency Management**: Use `uv`.
+- **Python Version**: Requires Python `>=3.14`.
+- **Testing**: Run `pytest` to execute the test suite in `test/`.
+- **Linting & Formatting**: Use `ruff` and `black`.
+
+## CLI Usage
+
+The primary entry point is the `photools3` command:
+
+- `photools3 keywords <image_path>`: Extracts keywords and captions from a single image.
+
+## Architecture & Logic
+
+- **`src/photools3/photo.py`**: Core image processing logic, including resizing, RGB conversion, and generating base64 JPEG encodings specifically for LLM vision compatibility.
+- **`src/photools3/llm_session.py`**: Manages the workflow of sending processed images to the LLM and parsing results.
+- **Configuration**: The list of keywords to extract is maintained in `config/keywords.txt`.
+
+## Code style
+
+- Avoid overly verbose docstrings or comments. Be precise!
+
+## Comunication
+
+- When done with code modifications, give a one-liner for the currently unstaged changes that can be used as a commit message.
@@ -0,0 +1,101 @@
+# PhotoTools3
+
+PhotoTools3 uses an OpenAI-compatible vision API to generate photo captions and
+keywords, then writes the results to photo metadata. It can also assess photo
+quality.
+
+## Setup
+
+Install the Python dependencies with `uv` and make sure ExifTool is installed and
+available on `PATH` (it is a system dependency, not a Python package):
+
+```bash
+uv sync
+```
+
+Create a `.env` file in the project root with the LLM connection details and the
+prefix used to identify metadata written by PhotoTools3:
+
+```dotenv
+llm_api_base_url=http://localhost:1234/v1
+llm_api_key=your-api-key
+llm_default_temperature=0.2
+metadata_prefix=photools3:
+LOG_LEVEL=DEBUG
+LLM_REQUEST_TIMEOUT_SECONDS=10
+LLM_MAX_COMPLETION_TOKENS=1024
+LLM_MAX_RETRIES=5
+LLM_MAX_KEYWORDS=15
+```
+
+Keyword suggestions are restricted to the vocabulary in `config/keywords.txt`.
+`LOG_LEVEL=DEBUG` enables detailed per-request and per-photo timings.
+`LLM_MAX_RETRIES` controls retries for malformed or structurally invalid
+responses; `5` means up to 6 requests for a photo. A valid response with
+unsupported keywords gets at most one targeted correction retry, then remaining
+invalid keywords are discarded.
+Each request is limited by `LLM_REQUEST_TIMEOUT_SECONDS`; a timeout reloads the
+LM Studio model before retrying. `LLM_MAX_COMPLETION_TOKENS` caps the generated
+response length. When all retries are exhausted, the model is reloaded before
+the failed photo is reported. `LLM_MAX_KEYWORDS` sets the maximum number of
+distinct keywords requested and stored per photo; excess keywords are discarded.
+
+## Generate metadata
+
+The `metadata-magic` command accepts one or more image paths and/or directories.
+The default mode is `both`, which generates and writes a caption and keywords:
+
+```bash
+uv run photools3 metadata-magic photo.dng
+```
+
+Choose a mode with `--mode`:
+
+```bash
+# Generate only keywords
+uv run photools3 metadata-magic photo.dng --mode keywords
+
+# Generate only a caption
+uv run photools3 metadata-magic photo.dng --mode captions
+
+# Explicitly request both (the default)
+uv run photools3 metadata-magic photo.dng --mode both
+```
+
+Pass multiple files to process them sequentially as a batch:
+
+```bash
+uv run photools3 metadata-magic photo1.dng photo2.jpg --mode both
+```
+
+Directories are scanned at the top level by default. Add `--recursive` to
+include supported images in subdirectories. You can combine directories and
+individual file paths:
+
+```bash
+uv run photools3 metadata-magic ./photos --recursive --mode keywords
+uv run photools3 metadata-magic ./photos photo-to-include.dng --mode captions
+```
+
+The command reports each processed file and exits with a non-zero status if any
+file fails. In a batch, it continues processing the remaining files after an
+individual failure. Generated metadata is written using ExifTool and XMP
+sidecars.
+
+## Assess photo quality
+
+The `quality` command reports an assessment without changing photo metadata:
+
+```bash
+uv run photools3 quality photo.dng
+```
+
+## Help
+
+Show the available commands and options with:
+
+```bash
+uv run photools3 --help
+uv run photools3 metadata-magic --help
+uv run photools3 quality --help
+```
@@ -0,0 +1,371 @@
+architektur
+architektur|gebäude
+architektur|bauwerke
+architektur|architecture
+architektur|brücke
+architektur|brücke|bridge
+architektur|burg
+architektur|burg|castle
+architektur|dach
+architektur|dach|roof
+architektur|dächer
+architektur|garten
+architektur|garten|garden
+architektur|gleis
+architektur|gleis|rail
+architektur|industrie
+architektur|kirche
+architektur|kirche|church
+architektur|leuchtturm
+architektur|leuchtturm|lighhouse
+architektur|levada
+architektur|modell
+architektur|park
+architektur|ruinen
+architektur|ruinen|ruins
+architektur|siedlung
+architektur|siedlung|dorf ortschaft
+architektur|siedlung|village
+architektur|skyline
+architektur|stadt
+architektur|stadt|city
+architektur|strasse
+architektur|strasse|street
+architektur|fenster
+architektur|fenster|window
+architektur|tür
+architektur|tür|door
+architektur|treppe
+architektur|treppe|stairs
+architektur|turm
+architektur|turm|tower
+architektur|hafen
+architektur|hafen|harbor
+architektur|hafen|port
+architektur|bahnhof
+architektur|bahnhof|train station
+architektur|museum
+essen&trinken
+essen&trinken|Lebensmittel
+essen&trinken|food
+essen&trinken|braten
+essen&trinken|buffett
+essen&trinken|burger
+essen&trinken|kaffee
+essen&trinken|kaffee|coffee
+essen&trinken|shrimps
+essen&trinken|tapas
+essen&trinken|bier
+essen&trinken|bier|beer
+essen&trinken|wein
+essen&trinken|wein|wine
+essen&trinken|obst
+essen&trinken|obst|fruit
+essen&trinken|gemüse
+essen&trinken|gemüse|vegetables
+essen&trinken|kuchen
+essen&trinken|kuchen|cake
+essen&trinken|dessert
+himmel/wetter
+himmel/wetter|weather
+himmel/wetter|sky
+himmel/wetter|abendrot
+himmel/wetter|abendrot|dusk
+himmel/wetter|blauer himmel
+himmel/wetter|blauer himmel|blue sky
+himmel/wetter|diesig
+himmel/wetter|diesig|hazy
+himmel/wetter|diesig|misty
+himmel/wetter|eis
+himmel/wetter|eis|ice
+himmel/wetter|grau
+himmel/wetter|grau|grey
+himmel/wetter|mond
+himmel/wetter|mond|moon
+himmel/wetter|morgengrauen
+himmel/wetter|morgengrauen|dawn
+himmel/wetter|morgenrot
+himmel/wetter|morgenrot|dawn
+himmel/wetter|nebel
+himmel/wetter|nebel|fog
+himmel/wetter|regen
+himmel/wetter|regen|rain
+himmel/wetter|regenbogen
+himmel/wetter|regenbogen|rainbow
+himmel/wetter|schnee
+himmel/wetter|schnee|snow
+himmel/wetter|sonne
+himmel/wetter|sonne|sun
+himmel/wetter|sonne|sonnenaufgang
+himmel/wetter|sonne|sonnenaufgang|sunrise
+himmel/wetter|sonne|sonnenstrahlen
+himmel/wetter|sonne|sonnenstrahlen|sun beams
+himmel/wetter|sonne|sonnenstrahlen|light rays
+himmel/wetter|sonne|sonnenuntergang
+himmel/wetter|sonne|sonnenuntergang|sunset
+himmel/wetter|sterne
+himmel/wetter|sterne|stars
+himmel/wetter|windig
+himmel/wetter|windig|windy
+himmel/wetter|wolken
+himmel/wetter|wolken|clouds
+himmel/wetter|gewitter
+himmel/wetter|gewitter|thunderstorm
+himmel/wetter|blitz
+himmel/wetter|blitz|lightning
+himmel/wetter|frost
+jahreszeit
+jahreszeit|season
+jahreszeit|frühling
+jahreszeit|frühling|spring
+jahreszeit|sommer
+jahreszeit|sommer|summer
+jahreszeit|herbst
+jahreszeit|herbst|autumn
+jahreszeit|herbst|fall
+jahreszeit|winter
+landschaft
+landschaft|landscape
+landschaft|berge
+landschaft|berge|mountains
+landschaft|dünen
+landschaft|dünen|dunes
+landschaft|dünen|sand
+landschaft|feld
+landschaft|feld|field
+landschaft|feld|meadow
+landschaft|felsen
+landschaft|felsen|rock
+landschaft|höhle
+landschaft|höhle|cave
+landschaft|insel
+landschaft|insel|island
+landschaft|klippen
+landschaft|klippen|cliffs
+landschaft|küste
+landschaft|küste|coast
+landschaft|steinbruch
+landschaft|steinbruch|quarry
+landschaft|strand
+landschaft|strand|beach
+landschaft|vulkan
+landschaft|vulkan|vulcano
+landschaft|wald
+landschaft|wald|forest
+landschaft|wald|wood
+landschaft|wasser
+landschaft|wasser|water
+landschaft|wasser|bach
+landschaft|wasser|bach|creek
+landschaft|wasser|brandung
+landschaft|wasser|brandung|breakers
+landschaft|wasser|brandung|surf
+landschaft|wasser|fluss
+landschaft|wasser|fluss|river
+landschaft|wasser|hochwasser
+landschaft|wasser|hochwasser|flood
+landschaft|wasser|hochwasser|high water
+landschaft|wasser|meer
+landschaft|wasser|meer|sea
+landschaft|wasser|meer|ocean
+landschaft|wasser|see
+landschaft|wasser|see|lake
+landschaft|wasser|wasserfall
+landschaft|wasser|wasserfall|waterfall
+landschaft|weinberge
+landschaft|weinberge|vineyard
+landschaft|wüste
+landschaft|wüste|desert
+landschaft|hügel
+landschaft|hügel|hills
+landschaft|tal
+landschaft|tal|valley
+landschaft|schlucht
+landschaft|schlucht|canyon
+landschaft|schlucht|gorge
+landschaft|gipfel
+landschaft|gipfel|summit
+landschaft|gipfel|peak
+nacht
+nacht|night
+nacht|langzeitbelichtung
+nacht|langzeitbelichtung|long exposure
+nacht|lichtspuren
+nacht|lichtspuren|light trails
+nacht|nachtaufnahme
+nacht|nachtaufnahme|night shot
+nacht|stadtlichter
+nacht|stadtlichter|city lights
+personen
+personen|auge
+personen|auge|eye
+personen|geste
+personen|geste|gesture
+personen|gruppenbild
+personen|gruppenbild|group
+personen|menschenmenge
+personen|menschenmenge|crowd
+personen|portrait
+personen|pose
+personen|quatsch
+personen|quatsch|silly
+personen|verkleidung
+personen|kind
+personen|kind|kinder
+personen|kind|child
+personen|kind|children
+personen|paar
+personen|paar|couple
+personen|silhouette
+personen|schatten
+personen|schatten|shadow
+pflanze
+pflanze|plant
+pflanze|bäume
+pflanze|bäume|trees
+pflanze|blumen
+pflanze|blumen|flower
+pflanze|blüten
+pflanze|blüten|flower
+pflanze|blüten|bloomy
+pflanze|grass
+pflanze|grass|gras
+pflanze|kaktus
+pflanze|kaktus|cactus
+pflanze|laub
+pflanze|laub|foliage
+pflanze|moos
+pflanze|moos|moss
+pflanze|palmen
+pflanze|palmen|palms
+pflanze|pilze
+pflanze|pilze|mushrooms
+pflanze|schilf
+pflanze|schilf|reed
+pflanze|sonnenblumen
+pflanze|sonnenblumen|sunflower
+pflanze|sonnentau
+pflanze|sonnentau|sundew
+pflanze|venusfliegenfalle
+pflanze|venusfliegenfalle|venus trap
+pflanze|farn
+pflanze|farn|fern
+pflanze|strauch
+pflanze|strauch|bush
+pflanze|strauch|shrub
+pflanze|efeu
+pflanze|efeu|ivy
+pflanze|hecke
+pflanze|hecke|hedge
+sport
+sport|fussball
+sport|fussball|football
+sport|fussball|soccer
+sport|schwimmen
+sport|schwimmen|swimming
+sport|klettern
+sport|klettern|climbing
+sport|wandern
+sport|wandern|hiking
+sport|radfahren
+sport|radfahren|cycling
+sport|ski
+sport|ski|skiing
+stil
+stil|style
+stil|schwarzweiss
+stil|schwarzweiss|black and white
+stil|schwarzweiss|bw
+stil|vintage
+stil|hdr
+stil|panorama
+technik
+technik|technology
+technik|auto
+technik|auto|car
+technik|eisenbahn
+technik|eisenbahn|train
+technik|fahrrad
+technik|fahrrad|bike
+technik|fahrrad|bicycle
+technik|flugzeug
+technik|flugzeug|plane
+technik|handy
+technik|handy|smartphone
+technik|handy|cell phone
+technik|kamera
+technik|kamera|camera
+technik|musikinstrument
+technik|musikinstrument|musical instrument
+technik|musikinstrument|Gitarre
+technik|musikinstrument|Gitarre|guitar
+technik|musikinstrument|Gitarren Effektgerät
+technik|musikinstrument|Gitarrenverstärker
+technik|musikinstrument|Gitarrenverstärker|guitar amp
+technik|objektiv
+technik|objektiv|lens
+technik|schiff
+technik|schiff|ship
+technik|seilbahn
+technik|seilbahn|teleferico
+technik|seilbahn|cable car
+technik|motorrad
+technik|motorrad|motorcycle
+technik|lastwagen
+technik|lastwagen|truck
+technik|bus
+technik|helikopter
+technik|helikopter|helicopter
+technik|drohne
+technik|drohne|drone
+technik|windrad
+technik|windrad|wind turbine
+tier
+tier|animal
+tier|elefant
+tier|elefant|elephant
+tier|fisch
+tier|fisch|fish
+tier|insekten
+tier|insekten|insects
+tier|insekten|biene
+tier|insekten|biene|bee
+tier|katze
+tier|katze|cat
+tier|nager
+tier|nager|gnawer
+tier|reptil
+tier|reptil|reptile
+tier|rind
+tier|rind|cattle
+tier|Rot$DamWild
+tier|Rot$DamWild|deer
+tier|spinne
+tier|spinne|spider
+tier|tiger
+tier|vogel
+tier|vogel|bird
+tier|waschbär
+tier|waschbär|raccoon
+tier|Wildschwein
+tier|Wildschwein|boar
+tier|ZiegeBockSchaf
+tier|ZiegeBockSchaf|goat
+tier|hund
+tier|hund|dog
+tier|pferd
+tier|pferd|horse
+tier|eichhörnchen
+tier|eichhörnchen|squirrel
+tier|fuchs
+tier|fuchs|fox
+tier|seevogel
+tier|seevogel|seabird
+tier|eidechse
+tier|eidechse|lizard
+tier|frosch
+tier|frosch|frog
+tier|schmetterling
+tier|schmetterling|butterfly
+tier|libelle
+tier|libelle|dragonfly
@@ -8,10 +8,17 @@ authors = [
 ]
 requires-python = ">=3.14"
 dependencies = [
+    "black>=26.5.1",
+    "mypy>=2.4.0",
    "openai>=3.22.0",
+    "pillow>=12.3.0",
    "pydantic>=2.13.5",
    "pydantic-settings>=2.15.0",
+    "pyexiftool>=0.5.6",
    "pytest>=9.1.1",
+    "ruff>=0.16.9",
+    "typer>=0.12.0",
+    "utils>=0.1.3",
 ]

 [project.scripts]
@@ -20,3 +27,18 @@ photools3 = "photools3:main"
 [build-system]
 requires = ["uv_build>=0.12.19,<0.13.0"]
 build-backend = "uv_build"
+
+[tool.uv.sources]
+utils = { index = "gitea" }
+
+[tool.black]
+target-version = [ "py314" ]
+
+[[tool.mypy.overrides]]
+module = ["exiftool", "exiftool.*", "utils", "utils.*"]
+ignore_missing_imports = true
+
+[[tool.uv.index]]
+name = "gitea"
+url = "https://gitea.schneiderserver.site/api/packages/mathias2784/pypi/simple"
+explicit = true
@@ -1,2 +1,5 @@
-def main() -> None:
-    print("Hello from photools3!")
+from .cli import app
+
+
+def main():
+    app()
@@ -0,0 +1,4 @@
+from .cli import app
+
+if __name__ == "__main__":
+    app()
@@ -0,0 +1,163 @@
+import logging
+from pathlib import Path
+from typing import Annotated
+
+import typer
+from PIL import Image
+from utils.logging_config import LoggingSettings
+
+from photools3.llm_session import LlmSessionQuality
+from photools3.photo import Photo
+from photools3.processing import (
+    ProcessingMode,
+    process_batch,
+    process_photo,
+)
+from photools3.settings import settings
+
+app = typer.Typer(help="PhotoTools3: A CLI for image processing.")
+LoggingSettings(LOG_LEVEL=settings.log_level)
+for name in (
+    "uvicorn",
+    "uvicorn.error",
+    "uvicorn.access",
+    "PIL",
+    "openai",
+    "httpx2",
+    "httpcore2",
+):
+    logging.getLogger(name).setLevel(logging.WARNING)
+logger = logging.getLogger(__name__)
+
+
+@app.callback(invoke_without_command=True)
+def main(
+    ctx: typer.Context,
+    version: bool | None = typer.Option(None, "--version", is_eager=True),
+):
+    """Main entry point for PhotoTools3 CLI."""
+    if version:
+        typer.echo("photools3 version 0.1.0")
+        raise typer.Exit()
+
+    if ctx.invoked_subcommand is None:
+        typer.echo(ctx.get_help())
+        raise typer.Exit()
+
+
+@app.command("metadata-magic")
+def metadata_magic(
+    paths: Annotated[
+        list[Path], typer.Argument(..., help="Photo file(s) and/or directory paths")
+    ],
+    mode: Annotated[
+        ProcessingMode,
+        typer.Option(
+            "--mode",
+            case_sensitive=False,
+            help="Metadata to generate and write",
+        ),
+    ] = ProcessingMode.BOTH,
+    recursive: Annotated[
+        bool,
+        typer.Option(
+            "--recursive/--no-recursive",
+            help="Recursively discover photos inside input directories",
+        ),
+    ] = False,
+):
+    """Generate and write keywords, captions, or both for photos."""
+    _run_processing(paths, mode, recursive)
+
+
+@app.command("quality")
+def quality(
+    file_path: Annotated[Path, typer.Argument(..., help="Path to the image file")],
+):
+    """Assess a photo's quality without changing its metadata."""
+    try:
+        with LlmSessionQuality() as session, Photo(file_path) as photo:
+            result = session.call([photo])
+        logger.info("Quality assessment for %s: %s", file_path, result)
+    except FileNotFoundError:
+        logger.error("Photo not found: %s", file_path)
+        raise typer.Exit(code=1) from None
+    except Exception as exc:
+        logger.error("Error assessing photo %s: %s", file_path, exc)
+        raise typer.Exit(code=1) from exc
+
+
+def _run_processing(inputs: list[Path], mode: ProcessingMode, recursive: bool) -> None:
+    photo_paths = _expand_photo_paths(inputs, recursive)
+    if not photo_paths:
+        logger.error("No supported photo files found in the given input paths.")
+        raise typer.Exit(code=1)
+
+    logger.info("Processing %d photo(s) in %s mode", len(photo_paths), mode.value)
+    if len(photo_paths) == 1:
+        path = photo_paths[0]
+        try:
+            process_photo(path, mode)
+        except FileNotFoundError:
+            logger.error("Photo not found: %s", path)
+            raise typer.Exit(code=1) from None
+        except Exception as exc:
+            logger.error("Failed to process %s: %s", path, exc)
+            raise typer.Exit(code=1) from exc
+        logger.info("Processed: %s", path)
+        return
+
+    try:
+        outcomes = process_batch(photo_paths, mode)
+    except Exception as exc:
+        logger.error("Batch processing failed: %s", exc)
+        raise typer.Exit(code=1) from exc
+
+    failures = 0
+    for outcome in outcomes:
+        if outcome.succeeded:
+            logger.info("Processed: %s", outcome.path)
+        else:
+            failures += 1
+            logger.error("Failed to process %s: %s", outcome.path, outcome.error)
+
+    logger.info(
+        "Batch complete: %d succeeded, %d failed", len(outcomes) - failures, failures
+    )
+    if failures:
+        raise typer.Exit(code=1)
+
+
+def _expand_photo_paths(inputs: list[Path], recursive: bool) -> list[Path]:
+    """Expand directories to supported image files, preserving input order."""
+    supported_extensions = {
+        ".dng",
+        *(extension.lower() for extension in Image.registered_extensions()),
+    }
+    discovered: list[Path] = []
+
+    for path in inputs:
+        if not path.is_dir():
+            discovered.append(path)
+            continue
+
+        try:
+            candidates = path.rglob("*") if recursive else path.iterdir()
+            discovered.extend(
+                sorted(
+                    candidate
+                    for candidate in candidates
+                    if candidate.is_file()
+                    and candidate.suffix.lower() in supported_extensions
+                )
+            )
+        except OSError as exc:
+            logger.error("Could not read directory %s: %s", path, exc)
+            raise typer.Exit(code=1) from exc
+
+    # Avoid processing the same exact path more than once while retaining order.
+    return list(dict.fromkeys(discovered))
+
+
+if __name__ == "__main__":
+    app()
@@ -1,25 +1,108 @@
+import json
+import logging
+import re
+import time
+
+import openai
 from openai import OpenAI
-from openai.types.chat import ChatCompletion
+from pydantic import BaseModel, ValidationError

-from photools3.models import LlmRequest
-from photools3.settings import Settings
+from photools3.lm_studio import LMStudioModelManager
+from photools3.models import (
+    LlmMessageContent,
+    LlmMessageRole,
+    LlmMessageType,
+    LlmRequest,
+    LlmRequestMessage,
+    LlmResponseCaption,
+    LlmResponseCaptionsAndKeywords,
+    LlmResponseGeneral,
+    LlmResponseKeywords,
+    LlmResponseQuality,
+    load_valid_keywords,
+)
+from photools3.photo import Photo
+from photools3.settings import settings

-settings = Settings()
+logger = logging.getLogger(__name__)
+
+
+def _clean_llm_response(txt: str):
+    # remove ```json ... ``` wrap
+    txt = re.sub(r"^```json(.*)```$", r"\1", txt, flags=re.DOTALL).strip()
+    return txt
+
+
+def _correction_prompt(instruction: str) -> LlmRequestMessage:
+    return LlmRequestMessage(
+        role=LlmMessageRole.user,
+        content=instruction,
+    )
+
+
+def _request_with_correction(base_request: LlmRequest, instruction: str) -> LlmRequest:
+    return base_request.model_copy(
+        update={"messages": [*base_request.messages, _correction_prompt(instruction)]}
+    )
+
+
+def _validation_summary(exception: ValidationError) -> str:
+    return "; ".join(
+        f"{'.'.join(map(str, error['loc']))}: {error['msg']}"
+        for error in exception.errors(include_input=False)
+    )


 class LlmSession:
    base_url: str = str(settings.llm_api_base_url)
    api_key: str = settings.llm_api_key
+    response_model: type[BaseModel] = LlmResponseGeneral
+    prompt: str = ""

-    def __init__(self, system_prompt: str):
+    def __init__(
+        self, system_prompt: str, temperature: float = settings.llm_default_temperature
+    ):
        self._models: list = []
+        self._model_id: str | None = None

        self.system_prompt = system_prompt
+        self.temperature = temperature

        self.client = OpenAI(
            base_url=self.base_url,
            api_key=self.api_key,
+            max_retries=0,
        )
+        self._model_manager = LMStudioModelManager(self.base_url, self.api_key)
+
+    def close(self):
+        self.client.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def flush_model(self):
+        """Unload and reload this session's model through LM Studio."""
+        if self._model_id is None:
+            self._model_id = self.models[0].id
+        self._model_manager.reload_model(self._model_id)
+
+    def client_chat_completion_create(self, request: LlmRequest, choice_no: int = 0):
+        response = self.client.chat.completions.create(
+            **request.model_dump(),
+            timeout=settings.llm_request_timeout_seconds,
+        )
+        return response.choices[choice_no].message.content
+
+    def is_api_reachable(self) -> bool:
+        try:
+            _ = self.client.models.list().data
+            return True
+        except openai.APIConnectionError:
+            return False

    @property
    def models(self):
@@ -27,5 +110,247 @@ class LlmSession:
            self._models = self.client.models.list().data
        return self._models

-    def call_raw(self, request: LlmRequest) -> ChatCompletion:
-        return self.client.chat.completions.create(**request.model_dump())
+    def call(
+        self,
+        photos: list[Photo],
+        model_id=None,
+        max_retries: int | None = None,
+        prompt: str | None = None,
+    ):
+        retry_limit = settings.llm_max_retries if max_retries is None else max_retries
+        if retry_limit < 0:
+            raise ValueError("max_retries must be greater than or equal to zero")
+
+        if model_id is None:
+            if self._model_id is None:
+                self._model_id = self.models[0].id
+            model_id = self._model_id
+        else:
+            self._model_id = model_id
+
+        base_request = self._build_request(prompt or self.prompt, photos, model_id)
+        request = base_request
+        tag_correction_used = False
+        valid_keywords = load_valid_keywords()
+        max_keywords = settings.llm_max_keywords
+
+        for attempt in range(retry_limit + 1):
+            request_started = time.perf_counter()
+            try:
+                result_raw = self.client_chat_completion_create(request)
+            except openai.APITimeoutError as exc:
+                logger.warning(
+                    "LLM request timed out after %.2f seconds; reloading model",
+                    time.perf_counter() - request_started,
+                )
+                self.flush_model()
+                if attempt < retry_limit:
+                    continue
+                raise ValueError(
+                    f"LLM request timed out after {retry_limit + 1} attempts"
+                ) from exc
+            except Exception:
+                logger.debug(
+                    "LLM request attempt %d/%d failed after %.2f seconds",
+                    attempt + 1,
+                    retry_limit + 1,
+                    time.perf_counter() - request_started,
+                )
+                raise
+
+            logger.debug(
+                "LLM request attempt %d/%d completed in %.2f seconds",
+                attempt + 1,
+                retry_limit + 1,
+                time.perf_counter() - request_started,
+            )
+            try:
+                response_data = json.loads(_clean_llm_response(result_raw))
+            except (json.JSONDecodeError, TypeError) as exc:
+                if attempt < retry_limit:
+                    logger.info("Chatbot response was not valid JSON; retrying")
+                    request = _request_with_correction(
+                        base_request,
+                        "Return one complete JSON object matching the schema. "
+                        f"The previous response was invalid: {exc}",
+                    )
+                    continue
+
+                logger.warning(
+                    "LLM retry limit reached; reloading model before failing"
+                )
+                self.flush_model()
+                raise ValueError(
+                    f"Failed to parse LLM response after {retry_limit} retries: {exc}"
+                ) from exc
+
+            if (
+                isinstance(response_data, dict)
+                and "tags" in self.response_model.model_fields
+                and isinstance(response_data.get("tags"), list)
+            ):
+                tags = response_data["tags"]
+                considered_tags = tags[:max_keywords]
+                invalid_tags = [
+                    tag
+                    for tag in considered_tags
+                    if not isinstance(tag, str) or tag not in valid_keywords
+                ]
+
+                if invalid_tags and not tag_correction_used and attempt < retry_limit:
+                    tag_correction_used = True
+                    logger.info(
+                        "LLM returned unsupported keywords; requesting one correction"
+                    )
+                    request = _request_with_correction(
+                        base_request,
+                        f"These keywords are not allowed: {invalid_tags!r}. "
+                        "Replace them with exact allowed keywords when sensible; "
+                        f"otherwise omit them. Return at most {max_keywords} "
+                        "distinct keywords and do not list the vocabulary.",
+                    )
+                    continue
+
+                if len(tags) > max_keywords:
+                    logger.info(
+                        "Discarding keywords beyond configured limit of %d",
+                        max_keywords,
+                    )
+                if invalid_tags:
+                    logger.warning("Discarding unsupported keywords: %s", invalid_tags)
+
+                response_data = dict(response_data)
+                valid_tags = [
+                    tag
+                    for tag in tags
+                    if isinstance(tag, str) and tag in valid_keywords
+                ]
+                response_data["tags"] = list(dict.fromkeys(valid_tags))[:max_keywords]
+
+            try:
+                return self.response_model.model_validate(response_data)
+            except ValidationError as exc:
+                if attempt < retry_limit:
+                    logger.info("LLM response did not match the schema; retrying")
+                    request = _request_with_correction(
+                        base_request,
+                        "Return one complete JSON object matching the schema. "
+                        f"Validation issues: {_validation_summary(exc)}",
+                    )
+                    continue
+
+                logger.warning(
+                    "LLM retry limit reached; reloading model before failing"
+                )
+                self.flush_model()
+                raise ValueError(
+                    f"Failed to validate LLM response after {retry_limit} retries: "
+                    f"{_validation_summary(exc)}"
+                ) from exc
+
+    def _build_request(
+        self,
+        prompt: str,
+        photos: list[Photo],
+        model_id: str,
+    ) -> LlmRequest:
+        """Build a valid request that complies with openai API"""
+        return LlmRequest(
+            model=model_id,
+            temperature=self.temperature,
+            max_tokens=settings.llm_max_completion_tokens,
+            messages=[
+                # System prompt
+                LlmRequestMessage(
+                    role=LlmMessageRole.system,
+                    content=self.system_prompt,
+                ),
+                # User prompt
+                LlmRequestMessage(
+                    role=LlmMessageRole.user,
+                    content=[
+                        # Text prompt
+                        LlmMessageContent(
+                            type=LlmMessageType.text,
+                            text=prompt,
+                        ),
+                        # Image contents
+                        *[
+                            LlmMessageContent(
+                                type=LlmMessageType.image_url,
+                                image_url=photo.to_llm_image_url(),
+                            )
+                            for photo in photos
+                        ],
+                    ],
+                ),
+            ],
+        )
+
+
+class LlmSessionCaptionsAndKeywords(LlmSession):
+    response_model = LlmResponseCaptionsAndKeywords
+    prompt = "Give me a caption and keywords for the following photo"
+
+    def __init__(self, temperature: float = settings.llm_default_temperature):
+        super().__init__(
+            system_prompt=(
+                "You are a helpful photographer's assistant that helps to write **captions** and **keywords** to photos:\n"
+                "Answer in German.\n"
+                f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
+                "Do not repeat or enumerate the allowed-keyword list.\n"
+                "Output must be a JSON of the following schema:\n"
+                f"{self.response_model.model_json_schema()}"
+            ),
+            temperature=temperature,
+        )
+
+
+class LlmSessionCaption(LlmSession):
+    response_model = LlmResponseCaption
+    prompt = "Write a caption for the following photo"
+
+    def __init__(self, temperature: float = settings.llm_default_temperature):
+        super().__init__(
+            system_prompt=(
+                "You are a helpful photographer's assistant that writes captions "
+                "for photos. Answer in German.\n"
+                "Output must be a JSON of the following schema:\n"
+                f"{self.response_model.model_json_schema()}"
+            ),
+            temperature=temperature,
+        )
+
+
+class LlmSessionKeywords(LlmSession):
+    response_model = LlmResponseKeywords
+    prompt = "Choose appropriate keywords for the following photo"
+
+    def __init__(self, temperature: float = settings.llm_default_temperature):
+        super().__init__(
+            system_prompt=(
+                "You are a helpful photographer's assistant that chooses keywords "
+                "for photos. Answer in German.\n"
+                f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
+                "Do not repeat or enumerate the allowed-keyword list.\n"
+                "Output must be a JSON of the following schema:\n"
+                f"{self.response_model.model_json_schema()}"
+            ),
+            temperature=temperature,
+        )
+
+
+class LlmSessionQuality(LlmSession):
+    response_model = LlmResponseQuality
+    prompt = "Assess the quality of the following photos to help me decide which ones to sort out"
+
+    def __init__(self, temperature: float = settings.llm_default_temperature):
+        super().__init__(
+            system_prompt=(
+                "You are a helpful photographer's assistant that helps to identify "
+                "photos that can be sorted out due to qulity issues:\n"
+                "Output must be a JSON of the following schema:\n"
+                f"{self.response_model.model_json_schema()}"
+            ),
+            temperature=temperature,
+        )
@@ -0,0 +1,135 @@
+import json
+import logging
+import time
+from urllib.error import HTTPError, URLError
+from urllib.parse import urlsplit, urlunsplit
+from urllib.request import Request, urlopen
+
+logger = logging.getLogger(__name__)
+
+_LOAD_CONFIG_KEYS = (
+    "context_length",
+    "eval_batch_size",
+    "flash_attention",
+    "num_experts",
+    "offload_kv_cache_to_gpu",
+)
+
+
+class LMStudioError(RuntimeError):
+    """Raised when LM Studio model management fails."""
+
+
+class LMStudioModelManager:
+    """Unload and reload a model through LM Studio's native REST API."""
+
+    def __init__(self, openai_base_url: str, api_key: str, timeout: float = 600):
+        parsed_url = urlsplit(openai_base_url)
+        path = parsed_url.path.rstrip("/")
+        path = path.removesuffix("/v1")
+        root_url = urlunsplit(
+            (parsed_url.scheme, parsed_url.netloc, path, "", "")
+        ).rstrip("/")
+        self.api_base_url = f"{root_url}/api/v1"
+        self.api_key = api_key
+        self.timeout = timeout
+
+    def reload_model(self, model_id: str) -> None:
+        """Unload the matching loaded instance and reload it with its config."""
+        started = time.perf_counter()
+        models_response = self._request("GET", "/models")
+        model, loaded_instance = self._find_model(models_response, model_id)
+        model_key = model["key"]
+
+        if loaded_instance is not None:
+            self._request(
+                "POST",
+                "/models/unload",
+                {"instance_id": loaded_instance["id"]},
+            )
+            load_config = loaded_instance.get("config", {})
+        else:
+            load_config = {}
+
+        load_body = {"model": model_key}
+        load_body.update(
+            {
+                key: load_config[key]
+                for key in _LOAD_CONFIG_KEYS
+                if key in load_config and load_config[key] is not None
+            }
+        )
+        self._request("POST", "/models/load", load_body)
+        logger.info(
+            "Reloaded LM Studio model %s in %.1f seconds",
+            model_key,
+            time.perf_counter() - started,
+        )
+
+    @staticmethod
+    def _find_model(models_response: dict, model_id: str) -> tuple[dict, dict | None]:
+        models = models_response.get("models")
+        if not isinstance(models, list):
+            raise LMStudioError("LM Studio model list response has no models array")
+
+        for model in models:
+            loaded_instances = model.get("loaded_instances", [])
+            for instance in loaded_instances:
+                if instance.get("id") == model_id:
+                    return model, instance
+
+        matching_models = [model for model in models if model.get("key") == model_id]
+        if len(matching_models) != 1:
+            raise LMStudioError(
+                f"Could not uniquely identify LM Studio model {model_id!r}"
+            )
+
+        model = matching_models[0]
+        loaded_instances = model.get("loaded_instances", [])
+        if len(loaded_instances) > 1:
+            raise LMStudioError(
+                f"Multiple loaded instances found for LM Studio model {model_id!r}"
+            )
+        loaded_instance = loaded_instances[0] if loaded_instances else None
+        return model, loaded_instance
+
+    def _request(self, method: str, path: str, body: dict | None = None) -> dict:
+        data = None if body is None else json.dumps(body).encode("utf-8")
+        headers = {"Accept": "application/json"}
+        if data is not None:
+            headers["Content-Type"] = "application/json"
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
+        request = Request(
+            f"{self.api_base_url}{path}",
+            data=data,
+            headers=headers,
+            method=method,
+        )
+        try:
+            with urlopen(request, timeout=self.timeout) as response:
+                response_body = response.read()
+        except HTTPError as exc:
+            detail = exc.read().decode("utf-8", errors="replace")
+            raise LMStudioError(
+                f"LM Studio {method} {path} returned HTTP {exc.code}: {detail}"
+            ) from exc
+        except (URLError, TimeoutError) as exc:
+            raise LMStudioError(
+                f"Could not reach LM Studio at {self.api_base_url}: {exc}"
+            ) from exc
+
+        if not response_body:
+            return {}
+        try:
+            result = json.loads(response_body)
+        except json.JSONDecodeError as exc:
+            raise LMStudioError(
+                f"LM Studio {method} {path} returned invalid JSON"
+            ) from exc
+        if not isinstance(result, dict):
+            raise LMStudioError(
+                f"LM Studio {method} {path} returned an unexpected response"
+            )
+        return result
@@ -0,0 +1,180 @@
+import logging
+import shutil
+from pathlib import Path
+from typing import Annotated, Any
+
+import exiftool
+from pydantic import StringConstraints
+
+from photools3.settings import settings
+
+log = logging.getLogger(__name__)
+
+_EXIFTOOL_MISSING_MSG = (
+    "exiftool binary not found in PATH. "
+    "Install the system package: 'perl-image-exiftool' (Arch) or 'libimage-exiftool-perl' (Debian/Ubuntu)."
+)
+if shutil.which("exiftool") is None:
+    raise FileNotFoundError(_EXIFTOOL_MISSING_MSG)
+
+XmpKey = Annotated[str, StringConstraints(pattern=r"^\w+\:\w+")]
+
+type Caption = str
+type Keywords = list[str]
+type Color = int
+type XmpMetadata = dict[str, Any]
+
+XMP_KEYS_CAPTION: list[XmpKey] = [
+    "XMP:Description",
+    "EXIF:ImageDescription",
+    "IPTC:Caption-Abstract",
+]
+
+XMP_KEYS_KEYWORDS: list[XmpKey] = [
+    "XMP:HierarchicalSubject",
+    "XMP:Subject",
+    "IPTC:Keywords",
+]
+
+
+def _prefixed(txt: str) -> str:
+    return f"{settings.metadata_prefix}{txt}"
+
+
+def _is_prefixed(txt: str) -> bool:
+    return txt.startswith(settings.metadata_prefix)
+
+
+def _separate_prefixed_caption(txt: str) -> tuple[str, str | None]:
+    separated = [x.strip() for x in txt.split(settings.metadata_prefix)]
+    return (separated[0], separated[1] if len(separated) > 1 else None)
+
+
+class MetadataWriter:
+    """
+    Write metadata (captions and keywords) to image files.
+    For DNG files, creates/updates XMP sidecar files.
+    """
+
+    def __init__(
+        self,
+        path: Path,
+        force_sidecar: bool = False,
+        append: bool = True,
+    ):
+        self.path: Path = path
+        self.force_sidecar: bool = force_sidecar
+        self.append: bool = append
+
+        self._metadata: XmpMetadata | None = None
+
+    @property
+    def sidecar_path(self) -> Path:
+        return Path(str(self.path) + ".xmp")
+
+    @property
+    def metadata(self) -> Any:
+        path = self.sidecar_path if self.sidecar_path.exists() else self.path
+        if not path.exists():
+            log.warning(f"File not found: {path}")
+            return None
+        try:
+            with exiftool.ExifToolHelper() as exif:
+                metadata = exif.get_metadata([path])[0]
+        except Exception as exc:  # noqa: BLE001
+            log.error(f"Could not read metadata from {path}: {exc}")
+            return None
+        return metadata
+
+    def get_caption(self) -> str:
+        result = (self.metadata or {}).get(XMP_KEYS_CAPTION[0], "")
+        assert isinstance(result, str)
+        return result
+
+    def get_keywords(self) -> list[str]:
+        result = (self.metadata or {}).get(XMP_KEYS_KEYWORDS[0], []) or []
+        if isinstance(result, str):
+            result = [result]
+        assert isinstance(result, list)
+        return result
+
+    def set_metadata(self, metadata_tags: dict[str, Any]):
+        try:
+            with exiftool.ExifToolHelper() as exif:
+                if not self.sidecar_path.exists():
+                    exif.execute("-o", str(self.sidecar_path), str(self.path))
+                exif.set_tags(
+                    [str(self.sidecar_path)],
+                    tags=metadata_tags,
+                    params=["-overwrite_original"],
+                )
+        except Exception as exc:
+            log.error(f"Could not write metadata to file {self.sidecar_path}: {exc}")
+            raise RuntimeError(
+                f"Could not write metadata to file {self.sidecar_path}"
+            ) from exc
+
+    def write_caption(
+        self, caption: Caption, include_prefix: bool = True, override: bool = True
+    ):
+        existing_no_prefix, existing_prefix = _separate_prefixed_caption(
+            self.get_caption()
+        )
+        if existing_no_prefix:
+            existing_no_prefix = (
+                f"{existing_no_prefix}."
+                if not existing_no_prefix.endswith(".")
+                else existing_no_prefix
+            )
+            existing_no_prefix = f"{existing_no_prefix} "
+
+        if include_prefix and existing_prefix and not override:
+            return
+        if not include_prefix and existing_no_prefix and not override:
+            return
+        if include_prefix:
+            caption = f"{existing_no_prefix}{_prefixed(caption)}"
+        metadata_tags = {key: caption for key in XMP_KEYS_CAPTION}
+        self.set_metadata(metadata_tags)
+
+    def write_keywords(self, keywords: Keywords, include_prefix: bool = True):
+        if include_prefix:
+            keywords = list(map(_prefixed, keywords))
+        existing = self.get_keywords()
+        keywords = list(set(existing + keywords))
+        metadata_tags = {tag: keywords for tag in XMP_KEYS_KEYWORDS}
+        self.set_metadata(metadata_tags)
+
+    def write_color(self, color: Color):
+        self.set_metadata({"XMP-darktable:Colorlabels": color})
+
+    def write(
+        self,
+        caption: Caption | None = None,
+        keywords: Keywords | None = None,
+        color: Color | None = None,
+    ):
+        metadata_tags: XmpMetadata = {}
+        if caption is not None:
+            existing_no_prefix, _ = _separate_prefixed_caption(self.get_caption())
+            if existing_no_prefix:
+                existing_no_prefix = (
+                    f"{existing_no_prefix}."
+                    if not existing_no_prefix.endswith(".")
+                    else existing_no_prefix
+                )
+                existing_no_prefix = f"{existing_no_prefix} "
+            caption = f"{existing_no_prefix}{_prefixed(caption)}"
+            metadata_tags.update({key: caption for key in XMP_KEYS_CAPTION})
+
+        if keywords is not None:
+            keywords = list(map(_prefixed, keywords))
+            existing = self.get_keywords()
+            keywords = list(set(existing + keywords))
+            metadata_tags.update({tag: keywords for tag in XMP_KEYS_KEYWORDS})
+
+        if color is not None:
+            metadata_tags["XMP-darktable:Colorlabels"] = color
+
+        if metadata_tags:
+            self.set_metadata(metadata_tags)
@@ -1,23 +1,79 @@
-from typing import Literal
+import json
+from enum import StrEnum
+from functools import lru_cache
+from typing import Annotated, Any, Literal

-from pydantic import BaseModel, model_validator
+from pydantic import (
+    BaseModel,
+    BeforeValidator,
+    Field,
+    field_validator,
+    model_validator,
+)
+
+from .settings import KEYWORDS_LIST_PATH, settings
+
+
+@lru_cache
+def load_valid_keywords() -> set[str]:
+    """Loads all valid keywords from the configuration file."""
+    if not KEYWORDS_LIST_PATH.exists():
+        return set()
+
+    keywords = set()
+    with open(KEYWORDS_LIST_PATH, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            # Each line can contain hierarchical tags separated by '|'
+            # e.g., 'architektur|gebäude' means both 'architektur' and 'gebäude' are valid.
+            parts = line.split("|")
+            current_path = ""
+            for part in parts:
+                if current_path:
+                    current_path += "|" + part
+                else:
+                    current_path = part
+                keywords.add(current_path)
+    return keywords


 class LlmRequest(BaseModel):
-    model_id: int
+    model: str = "gpt-4o"
    messages: list[LlmRequestMessage]
    temperature: float | None = None
+    max_tokens: int
+
+
+class LlmMessageRole(StrEnum):
+    system = "system"
+    user = "user"


 class LlmRequestMessage(BaseModel):
-    role: Literal["system", "user"]
-    content: list[LlmMessageContent]
+    role: LlmMessageRole
+    content: list[LlmMessageContent] | str
+
+
+def transform_to_image_dict(v: Any) -> Any:
+    if isinstance(v, str):
+        return {"url": f"data:image/png;base64,{v}"}
+    return v
+
+
+Base64ImageUrl = Annotated[dict[str, str], BeforeValidator(transform_to_image_dict)]
+
+
+class LlmMessageType(StrEnum):
+    text = "text"
+    image_url = "image_url"


 class LlmMessageContent(BaseModel):
-    type: Literal["text", "image_url"]
+    type: LlmMessageType
    text: str | None = None
-    image_url: str | None = None
+    image_url: Base64ImageUrl | None = None

    @model_validator(mode="after")
    def _type_matches_content(self):
@@ -26,3 +82,100 @@ class LlmMessageContent(BaseModel):
        if self.type == "image_url" and self.image_url is None:
            raise ValueError("image_url must be set when type is 'image_url'")
        return self
+
+
+class LlmResponseGeneral(BaseModel):
+    text: str
+
+
+class LlmResponseCaption(BaseModel):
+    caption: Annotated[
+        str,
+        Field(
+            description=(
+                "A brief but precise description of what the picture shows. "
+                "No interpretation; just what is visible"
+            )
+        ),
+    ]
+
+
+def _keyword_tags_field():
+    allowed_keywords = json.dumps(
+        sorted(load_valid_keywords()), ensure_ascii=False, separators=(",", ":")
+    )
+    return Field(
+        max_length=settings.llm_max_keywords,
+        description=(
+            f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
+            "Do not enumerate the vocabulary. Allowed keywords: "
+            f"{allowed_keywords}"
+        ),
+    )
+
+
+class _LlmResponseWithTags(BaseModel):
+    tags: list[str] = _keyword_tags_field()
+
+    @field_validator("tags")
+    @classmethod
+    def validate_tags(cls, v: list[str]) -> list[str]:
+        valid_keywords = load_valid_keywords()
+        for tag in v:
+            if tag not in valid_keywords:
+                raise ValueError(
+                    f"'{tag}' is not a valid keyword. Must be one from the configuration."
+                )
+        return v
+
+
+class LlmResponseKeywords(_LlmResponseWithTags):
+    pass
+
+
+class LlmResponseCaptionsAndKeywords(_LlmResponseWithTags):
+    caption: Annotated[
+        str,
+        Field(
+            description=(
+                "A brief but precise description of what the picture shows. "
+                "No interpretation; just what is visible"
+            )
+        ),
+    ]
+
+
+class LlmResponseQuality(BaseModel):
+    rating: Annotated[
+        Literal["ok", "review", "not_ok"],
+        Field(
+            description=(
+                "General quality assessment of the photo. "
+                'Helping to decide whether to keep ("ok"), '
+                'to consider sorting out ("review"), '
+                'or to almost certainly sort out ("not_ok")'
+            )
+        ),
+    ]
+    reason: (
+        None
+        | Annotated[
+            Literal["blur", "exposure", "style"],
+            Field(
+                description=(
+                    'Possible reasons for a lower-than-"ok" assessment. '
+                    '"blur": Obviously mis-focussed or motion-blurred. '
+                    '"exposure": severly under- or over-exposed. '
+                    '"style": photo has severe style issues (beyond personal taste).'
+                )
+            ),
+        ]
+    )
+
+    @model_validator(mode="after")
+    def _not_ok_needs_reason(self):
+        if self.rating != "ok" and self.reason is None:
+            raise ValueError("lower-than-ok rating needs a reason")
+        elif self.rating == "ok" and self.reason is not None:
+            raise ValueError("ok rating needs no reason")
+        return self
@@ -1,18 +1,49 @@
-
+import base64
+from io import BytesIO
 from pathlib import Path
+from typing import Self
+
+from PIL import Image
+
+from photools3.models import transform_to_image_dict
+
+
+def _exist_guard(path: Path):
+    pass


 class Photo:

-    def __init__(self):
-        ...
+    def __init__(self, path: Path):
+        self.path = path
+        self.image = Image.open(path)

-    @classmethod
-    def from_file(cls, path: Path) -> Photo:
-        # TODO
-        return cls()
+    def close(self):
+        self.image.close()

-    def resize(self) -> Photo:
-        # TODO
+    def __enter__(self) -> Self:
        return self

+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def resize(self, image_max_size=1024) -> Photo:
+        """Limiting max dimensions while maintaining aspect ratio"""
+        self.image.thumbnail((image_max_size, image_max_size), Image.Resampling.LANCZOS)
+        return self
+
+    def to_rgb(self):
+        if self.image.mode != "RGB":
+            original = self.image
+            self.image = original.convert("RGB")
+            original.close()
+        return self
+
+    def get_encoded(self):
+        buffer = BytesIO()
+        self.image.save(buffer, format="JPEG", quality=85)
+        image_data = base64.b64encode(buffer.getvalue()).decode("utf-8")
+        return image_data
+
+    def to_llm_image_url(self):
+        return transform_to_image_dict(self.resize().to_rgb().get_encoded())
@@ -0,0 +1,138 @@
+import logging
+import time
+from collections.abc import Iterable
+from dataclasses import dataclass
+from enum import StrEnum
+from pathlib import Path
+
+from photools3.llm_session import (
+    LlmSession,
+    LlmSessionCaption,
+    LlmSessionCaptionsAndKeywords,
+    LlmSessionKeywords,
+)
+from photools3.metadata_writer import MetadataWriter
+from photools3.models import (
+    LlmResponseCaption,
+    LlmResponseCaptionsAndKeywords,
+    LlmResponseKeywords,
+)
+from photools3.photo import Photo
+
+logger = logging.getLogger(__name__)
+
+type ProcessingResponse = (
+    LlmResponseCaption | LlmResponseKeywords | LlmResponseCaptionsAndKeywords
+)
+
+
+class ProcessingMode(StrEnum):
+    KEYWORDS = "keywords"
+    CAPTIONS = "captions"
+    BOTH = "both"
+
+
+@dataclass(frozen=True)
+class ProcessingOutcome:
+    path: Path
+    result: ProcessingResponse | None = None
+    error: str | None = None
+
+    @property
+    def succeeded(self) -> bool:
+        return self.error is None and self.result is not None
+
+
+def _create_session(mode: ProcessingMode) -> LlmSession:
+    if mode == ProcessingMode.KEYWORDS:
+        return LlmSessionKeywords()
+    if mode == ProcessingMode.CAPTIONS:
+        return LlmSessionCaption()
+    return LlmSessionCaptionsAndKeywords()
+
+
+def process_photo(
+    path: Path | str,
+    mode: ProcessingMode | str = ProcessingMode.BOTH,
+    *,
+    session: LlmSession | None = None,
+) -> ProcessingResponse:
+    """Process one photo and write the requested LLM result to its metadata.
+
+    If a session is supplied, its lifecycle remains the caller's responsibility.
+    """
+    path = Path(path)
+    mode = ProcessingMode(mode)
+    owns_session = session is None
+    active_session = session if session is not None else _create_session(mode)
+
+    logger.info(f"Processing photo: {path!s}")
+
+    try:
+        with Photo(path) as photo:
+            result = active_session.call([photo])
+
+        caption: str | None = None
+        keywords: list[str] | None = None
+        if mode == ProcessingMode.CAPTIONS:
+            if not isinstance(result, LlmResponseCaption):
+                raise TypeError("Caption mode returned an unexpected response type")
+            caption = result.caption
+        elif mode == ProcessingMode.KEYWORDS:
+            if not isinstance(result, LlmResponseKeywords):
+                raise TypeError("Keywords mode returned an unexpected response type")
+            keywords = result.tags
+        else:
+            if not isinstance(result, LlmResponseCaptionsAndKeywords):
+                raise TypeError("Both mode returned an unexpected response type")
+            caption = result.caption
+            keywords = result.tags
+
+        MetadataWriter(path).write(caption=caption, keywords=keywords)
+        return result
+    finally:
+        if owns_session:
+            active_session.close()
+
+
+def process_batch(
+    paths: Iterable[Path | str],
+    mode: ProcessingMode | str = ProcessingMode.BOTH,
+) -> list[ProcessingOutcome]:
+    """Process photos sequentially, reusing one LLM client for the whole batch.
+
+    A failed photo is recorded in its outcome and does not stop later photos.
+    Only one decoded image is held at a time.
+    """
+    mode = ProcessingMode(mode)
+    session = _create_session(mode)
+    outcomes: list[ProcessingOutcome] = []
+
+    try:
+        for item in paths:
+            path = Path(item)
+            photo_started = time.perf_counter()
+            try:
+                result = process_photo(path, mode, session=session)
+            except Exception as exc:
+                logger.exception("Failed to process %s", path)
+                # Keep only a string, not the exception/traceback, which can
+                # retain references to the just-processed image and stack.
+                outcomes.append(
+                    ProcessingOutcome(
+                        path=path,
+                        error=f"{type(exc).__name__}: {exc}",
+                    )
+                )
+            else:
+                outcomes.append(ProcessingOutcome(path=path, result=result))
+            finally:
+                logger.debug(
+                    "Photo processing for %s took %.2f seconds",
+                    path,
+                    time.perf_counter() - photo_started,
+                )
+    finally:
+        session.close()
+
+    return outcomes
@@ -1,9 +1,31 @@
+from pathlib import Path
+
+from pydantic import Field
 from pydantic_core import Url
 from pydantic_settings import BaseSettings, SettingsConfigDict

+PHOTOOLS3_DIR = Path(__file__).parent.resolve()
+SRC_DIR = PHOTOOLS3_DIR.parent
+ROOT_DIR = SRC_DIR.parent
+EXAMPLES_DIR = ROOT_DIR / "examples"
+DNG_EXAMPLES_DIR = EXAMPLES_DIR / "dng"
+CONFIG_DIR = ROOT_DIR / "config"
+KEYWORDS_LIST_PATH = CONFIG_DIR / "keywords.txt"
+

 class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=".env")
+    model_config = SettingsConfigDict(env_file=".env", extra="ignore")

    llm_api_base_url: Url
    llm_api_key: str
+    llm_default_temperature: float
+    llm_request_timeout_seconds: float = Field(default=10, gt=0)
+    llm_max_completion_tokens: int = Field(default=1024, gt=0)
+    llm_max_retries: int = Field(default=5, ge=0)
+    llm_max_keywords: int = Field(default=15, ge=1)
+    log_level: str = "INFO"
+
+    metadata_prefix: str
+
+
+settings = Settings()  # type: ignore[call-arg]
@@ -0,0 +1,22 @@
+import pytest
+
+from photools3.llm_session import LlmSession
+from photools3.photo import Photo
+from test.test_photo import PATHS_DNG_EXAMPLES
+
+
+@pytest.fixture
+def test_photo():
+    return Photo(PATHS_DNG_EXAMPLES[0])
+
+
+@pytest.fixture
+def llm_session():
+    return LlmSession(system_prompt="You are a helpful assistant.")
+
+
+@pytest.fixture
+def llm_session_captions_and_keywords():
+    from photools3.llm_session import LlmSessionCaptionsAndKeywords
+
+    return LlmSessionCaptionsAndKeywords()
@@ -0,0 +1,198 @@
+from pathlib import Path
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from typer.testing import CliRunner
+
+from photools3.cli import app
+from photools3.processing import ProcessingMode
+
+runner = CliRunner()
+
+
+def _success_outcome(path: Path):
+    return SimpleNamespace(path=path, succeeded=True, error=None)
+
+
+def test_metadata_magic_command_defaults_to_both_for_single_photo(tmp_path):
+    image_path = tmp_path / "photo.jpg"
+    image_path.write_bytes(b"test image")
+
+    with (
+        patch("photools3.cli.process_photo") as process_photo,
+        patch("photools3.cli.process_batch") as process_batch,
+    ):
+        result = runner.invoke(app, ["metadata-magic", str(image_path)])
+
+    assert result.exit_code == 0
+    process_photo.assert_called_once_with(image_path, ProcessingMode.BOTH)
+    process_batch.assert_not_called()
+
+
+def test_metadata_magic_command_selects_caption_mode(tmp_path):
+    image_path = tmp_path / "photo.jpg"
+    image_path.write_bytes(b"test image")
+
+    with patch("photools3.cli.process_photo") as process_photo:
+        result = runner.invoke(
+            app, ["metadata-magic", str(image_path), "--mode", "captions"]
+        )
+
+    assert result.exit_code == 0
+    process_photo.assert_called_once_with(image_path, ProcessingMode.CAPTIONS)
+
+
+def test_metadata_magic_command_batches_multiple_files(tmp_path):
+    paths = [tmp_path / "one.jpg", tmp_path / "two.jpg"]
+    for path in paths:
+        path.write_bytes(b"test image")
+
+    with (
+        patch("photools3.cli.process_photo") as process_photo,
+        patch("photools3.cli.process_batch") as process_batch,
+    ):
+        process_batch.return_value = [_success_outcome(path) for path in paths]
+        result = runner.invoke(
+            app,
+            ["metadata-magic", *(str(path) for path in paths), "--mode", "keywords"],
+        )
+
+    assert result.exit_code == 0
+    process_photo.assert_not_called()
+    process_batch.assert_called_once_with(paths, ProcessingMode.KEYWORDS)
+
+
+def test_metadata_magic_command_expands_directories_recursively(tmp_path):
+    root = tmp_path / "photos"
+    nested = root / "nested"
+    nested.mkdir(parents=True)
+    top_level_photo = root / "one.jpg"
+    nested_photo = nested / "two.png"
+    sidecar = root / "one.jpg.xmp"
+    for path in (top_level_photo, nested_photo, sidecar):
+        path.write_bytes(b"placeholder")
+
+    with (
+        patch("photools3.cli.process_photo") as process_photo,
+        patch("photools3.cli.process_batch") as process_batch,
+    ):
+        process_batch.return_value = [
+            _success_outcome(nested_photo),
+            _success_outcome(top_level_photo),
+        ]
+        result = runner.invoke(
+            app, ["metadata-magic", str(root), "--recursive", "--mode", "both"]
+        )
+
+    assert result.exit_code == 0
+    process_photo.assert_not_called()
+    process_batch.assert_called_once_with(
+        [nested_photo, top_level_photo], ProcessingMode.BOTH
+    )
+
+
+def test_metadata_magic_command_does_not_recurse_by_default(tmp_path):
+    root = tmp_path / "photos"
+    nested = root / "nested"
+    nested.mkdir(parents=True)
+    top_level_photo = root / "one.jpg"
+    nested_photo = nested / "two.jpg"
+    top_level_photo.write_bytes(b"placeholder")
+    nested_photo.write_bytes(b"placeholder")
+
+    with (
+        patch("photools3.cli.process_photo") as process_photo,
+        patch("photools3.cli.process_batch") as process_batch,
+    ):
+        result = runner.invoke(app, ["metadata-magic", str(root)])
+
+    assert result.exit_code == 0
+    process_photo.assert_called_once_with(top_level_photo, ProcessingMode.BOTH)
+    process_batch.assert_not_called()
+
+
+def test_metadata_magic_command_returns_failure_if_any_batch_item_fails(tmp_path):
+    paths = [tmp_path / "one.jpg", tmp_path / "two.jpg"]
+    for path in paths:
+        path.write_bytes(b"test image")
+
+    with patch("photools3.cli.process_batch") as process_batch:
+        process_batch.return_value = [
+            _success_outcome(paths[0]),
+            SimpleNamespace(
+                path=paths[1], succeeded=False, error="RuntimeError: write failed"
+            ),
+        ]
+        result = runner.invoke(app, ["metadata-magic", *(str(path) for path in paths)])
+
+    assert result.exit_code == 1
+
+
+def test_metadata_magic_command_returns_failure_for_single_photo_error(tmp_path):
+    image_path = tmp_path / "photo.jpg"
+    image_path.write_bytes(b"test image")
+
+    with patch("photools3.cli.process_photo", side_effect=RuntimeError("LLM error")):
+        result = runner.invoke(app, ["metadata-magic", str(image_path)])
+
+    assert result.exit_code == 1
+
+
+def test_keywords_alias_is_not_registered():
+    result = runner.invoke(app, ["keywords", "photo.jpg"])
+
+    assert result.exit_code == 2
+    assert "No such command 'keywords'" in result.output
+
+
+def test_old_process_command_name_is_not_registered():
+    result = runner.invoke(app, ["process", "photo.jpg"])
+
+    assert result.exit_code == 2
+    assert "No such command 'process'" in result.output
+
+
+def test_metadata_magic_command_rejects_empty_directory(tmp_path):
+    with (
+        patch("photools3.cli.process_photo") as process_photo,
+        patch("photools3.cli.process_batch") as process_batch,
+    ):
+        result = runner.invoke(app, ["metadata-magic", str(tmp_path)])
+
+    assert result.exit_code == 1
+    process_photo.assert_not_called()
+    process_batch.assert_not_called()
+
+
+def test_quality_command_closes_photo_and_session(tmp_path):
+    image_path = tmp_path / "photo.jpg"
+    image_path.write_bytes(b"test image")
+
+    with (
+        patch("photools3.cli.LlmSessionQuality") as session_class,
+        patch("photools3.cli.Photo") as photo_class,
+    ):
+        result = runner.invoke(app, ["quality", str(image_path)])
+
+    assert result.exit_code == 0
+    session_class.return_value.__enter__.return_value.call.assert_called_once_with(
+        [photo_class.return_value.__enter__.return_value]
+    )
+    session_class.return_value.__exit__.assert_called_once()
+    photo_class.return_value.__exit__.assert_called_once()
+
+
+def test_quality_command_reports_error_and_exits(tmp_path):
+    image_path = tmp_path / "photo.jpg"
+
+    with patch("photools3.cli.Photo", side_effect=FileNotFoundError(image_path)):
+        result = runner.invoke(app, ["quality", str(image_path)])
+
+    assert result.exit_code == 1
+
+
+def test_version_command():
+    result = runner.invoke(app, ["--version"])
+
+    assert result.exit_code == 0
+    assert "photools3 version 0.1.0" in result.stdout
@@ -0,0 +1,27 @@
+import pytest
+
+from photools3.llm_session import LlmSession
+
+
+@pytest.mark.skipif(
+    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
+)
+def test_llm_session(llm_session, test_photo):
+    llm_session.system_prompt = "Return only JSON format: {'text': '...'}"
+    result = llm_session.call(
+        prompt="keywords for the attached picture", photos=[test_photo]
+    )
+    assert result is not None
+
+
+@pytest.mark.skipif(
+    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
+)
+def test_llm_session_captions_and_keywords(
+    llm_session_captions_and_keywords, test_photo
+):
+    result = llm_session_captions_and_keywords.call(photos=[test_photo])
+    assert result.caption
+    assert result.tags
+    assert isinstance(result.caption, str)
+    assert isinstance(result.tags, list)
@@ -0,0 +1,303 @@
+from unittest.mock import MagicMock, patch
+
+import openai
+import pytest
+
+from photools3.llm_session import LlmSession, LlmSessionCaptionsAndKeywords
+from photools3.models import LlmResponseCaptionsAndKeywords
+
+
+def test_llm_session_mocked(test_photo):
+    # We want to mock OpenAI client's chat.completions.create
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        # Setup the mock response structure
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+
+        # Mock the models list to return a proper object with an ID string
+        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        # Create a response object: response.choices[0].message.content
+        mock_response = MagicMock()
+        mock_choice = MagicMock()
+        mock_message = MagicMock()
+
+        mock_message.content = '{"text": "some text"}'
+        mock_choice.message = mock_message
+        mock_response.choices = [mock_choice]
+
+        # Configure the client to return our mock response
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Create a new LlmSession instance that will use the mocked OpenAI client
+        mocked_session = LlmSession(system_prompt="You are a helper.")
+
+        # Run the test
+        result = mocked_session.call(
+            prompt="keywords for the attached picture", photos=[test_photo]
+        )
+
+        # Verify the results
+        assert result.text == "some text"
+
+        # Verify that the mock was enough
+        assert mock_client.chat.completions.create.called
+
+
+def test_llm_session_captions_and_keywords_mocked(test_photo):
+    # We want to mock OpenAI client's chat.completions.create
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        # Setup the mock response structure
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+
+        # Mock the models list to return a proper object with an ID string
+        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        # Create a response object: response.choices[0].message.content
+        mock_response = MagicMock()
+        mock_choice = MagicMock()
+        mock_message = MagicMock()
+
+        mock_message.content = (
+            '{"caption": "A mocked caption", "tags": ["architektur"]}'
+        )
+        mock_choice.message = mock_message
+        mock_response.choices = [mock_choice]
+
+        # Configure the client to return our mock response
+        mock_client.chat.completions.create.return_value = mock_response
+
+        # Create a new LlmSessionCaptionsAndKeywords instance that will use the mocked OpenAI client
+        from photools3.llm_session import LlmSessionCaptionsAndKeywords
+
+        mocked_session = LlmSessionCaptionsAndKeywords()
+
+        # Run the test
+        result = mocked_session.call(photos=[test_photo])
+
+        # Verify the results
+        assert result.caption == "A mocked caption"
+        assert "architektur" in result.tags
+
+        # Verify that the mock was called
+        assert mock_client.chat.completions.create.called
+
+
+def test_llm_session_retries_from_original_request_without_accumulating_messages():
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        def response(content):
+            message = MagicMock()
+            message.content = content
+            choice = MagicMock()
+            choice.message = message
+            result = MagicMock()
+            result.choices = [choice]
+            return result
+
+        mock_client.chat.completions.create.side_effect = [
+            response('{"wrong": "first"}'),
+            response('{"wrong": "second"}'),
+            response('{"text": "correct"}'),
+        ]
+
+        session = LlmSession(system_prompt="You are a helper.")
+        result = session.call(photos=[], prompt="Return JSON")
+
+    assert result.text == "correct"
+    requests = mock_client.chat.completions.create.call_args_list
+    messages_per_attempt = [call.kwargs["messages"] for call in requests]
+    assert [len(messages) for messages in messages_per_attempt] == [2, 3, 3]
+    final_correction = messages_per_attempt[2][-1]["content"]
+    assert "Field required" in final_correction
+    assert '"wrong"' not in final_correction
+
+
+def test_llm_session_uses_configured_retry_count(monkeypatch):
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 1)
+
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        def response(content):
+            message = MagicMock()
+            message.content = content
+            choice = MagicMock()
+            choice.message = message
+            result = MagicMock()
+            result.choices = [choice]
+            return result
+
+        mock_client.chat.completions.create.side_effect = [
+            response('{"wrong": "first"}'),
+            response('{"wrong": "second"}'),
+            response('{"text": "would be a third call"}'),
+        ]
+        session = LlmSession(system_prompt="You are a helper.")
+
+        with (
+            patch.object(session, "flush_model") as flush_model,
+            pytest.raises(ValueError, match="after 1 retries"),
+        ):
+            session.call(photos=[])
+
+    assert mock_client.chat.completions.create.call_count == 2
+    flush_model.assert_called_once()
+
+
+def test_llm_session_reloads_after_timeout_and_retries(monkeypatch):
+    monkeypatch.setattr(
+        "photools3.llm_session.settings.llm_request_timeout_seconds", 10
+    )
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_completion_tokens", 768)
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 1)
+
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        response_message = MagicMock(content='{"text": "recovered"}')
+        response_choice = MagicMock(message=response_message)
+        success_response = MagicMock(choices=[response_choice])
+        mock_client.chat.completions.create.side_effect = [
+            openai.APITimeoutError(request=MagicMock()),
+            success_response,
+        ]
+
+        session = LlmSession(system_prompt="You are a helper.")
+        with patch.object(session, "flush_model") as flush_model:
+            result = session.call(photos=[])
+
+    assert result.text == "recovered"
+    flush_model.assert_called_once()
+    calls = mock_client.chat.completions.create.call_args_list
+    assert len(calls) == 2
+    assert all(call.kwargs["timeout"] == 10 for call in calls)
+    assert all(call.kwargs["max_tokens"] == 768 for call in calls)
+    mocked_openai.assert_called_once_with(
+        base_url=session.base_url,
+        api_key=session.api_key,
+        max_retries=0,
+    )
+
+
+@pytest.mark.parametrize("failure_kind", ["invalid_response", "timeout"])
+def test_llm_session_reloads_when_retry_limit_is_exhausted(monkeypatch, failure_kind):
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 0)
+
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        if failure_kind == "timeout":
+            mock_client.chat.completions.create.side_effect = openai.APITimeoutError(
+                request=MagicMock()
+            )
+        else:
+            response_message = MagicMock(content='{"wrong": "shape"}')
+            response_choice = MagicMock(message=response_message)
+            mock_client.chat.completions.create.return_value = MagicMock(
+                choices=[response_choice]
+            )
+
+        session = LlmSession(system_prompt="You are a helper.")
+        with (
+            patch.object(session, "flush_model") as flush_model,
+            pytest.raises(ValueError),
+        ):
+            session.call(photos=[])
+
+    flush_model.assert_called_once()
+    assert mock_client.chat.completions.create.call_count == 1
+
+
+def test_invalid_keywords_get_one_correction_then_invalid_tags_are_dropped(monkeypatch):
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_keywords", 3)
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 5)
+
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+
+        def response(content):
+            message = MagicMock(content=content)
+            choice = MagicMock(message=message)
+            return MagicMock(choices=[choice])
+
+        mock_client.chat.completions.create.side_effect = [
+            response(
+                '{"caption": "A caption", "tags": ["not_allowed", "architektur"]}'
+            ),
+            response(
+                '{"caption": "A caption", '
+                '"tags": ["still_invalid", "architektur", "personen"]}'
+            ),
+        ]
+        session = LlmSessionCaptionsAndKeywords()
+        with patch.object(session, "flush_model") as flush_model:
+            result = session.call(photos=[])
+
+    assert result.tags == ["architektur", "personen"]
+    assert mock_client.chat.completions.create.call_count == 2
+    flush_model.assert_not_called()
+    correction = mock_client.chat.completions.create.call_args_list[1].kwargs[
+        "messages"
+    ][-1]["content"]
+    assert "not_allowed" in correction
+    assert '"caption"' not in correction
+
+
+def test_excess_keywords_are_truncated_to_configured_limit(monkeypatch):
+    monkeypatch.setattr("photools3.llm_session.settings.llm_max_keywords", 2)
+
+    with patch("photools3.llm_session.OpenAI") as mocked_openai:
+        mock_client = MagicMock()
+        mocked_openai.return_value = mock_client
+        mock_model = MagicMock()
+        mock_model.id = "gpt-4o"
+        mock_client.models.list.return_value.data = [mock_model]
+        message = MagicMock(
+            content=(
+                '{"caption": "A caption", '
+                '"tags": ["architektur", "personen", "landschaft"]}'
+            )
+        )
+        mock_client.chat.completions.create.return_value = MagicMock(
+            choices=[MagicMock(message=message)]
+        )
+
+        result = LlmSessionCaptionsAndKeywords().call(photos=[])
+
+    assert result.tags == ["architektur", "personen"]
+    assert mock_client.chat.completions.create.call_count == 1
+
+
+def test_keyword_response_schema_includes_configured_max_items():
+    schema = LlmResponseCaptionsAndKeywords.model_json_schema()
+
+    assert schema["properties"]["tags"]["maxItems"] == 15
@@ -0,0 +1,102 @@
+import json
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from photools3.lm_studio import LMStudioError, LMStudioModelManager
+
+
+def _http_response(payload: dict):
+    response = MagicMock()
+    response.__enter__.return_value = response
+    response.read.return_value = json.dumps(payload).encode("utf-8")
+    return response
+
+
+def test_reload_model_unloads_and_reloads_preserving_context_config():
+    model_response = {
+        "models": [
+            {
+                "key": "qwen/qwen3-vl-4b",
+                "loaded_instances": [
+                    {
+                        "id": "qwen/qwen3-vl-4b-instance",
+                        "config": {
+                            "context_length": 16384,
+                            "eval_batch_size": 256,
+                            "flash_attention": True,
+                            "offload_kv_cache_to_gpu": False,
+                            "parallel": 2,
+                        },
+                    }
+                ],
+            }
+        ]
+    }
+    with patch(
+        "photools3.lm_studio.urlopen",
+        side_effect=[
+            _http_response(model_response),
+            _http_response({"instance_id": "qwen/qwen3-vl-4b-instance"}),
+            _http_response({"instance_id": "qwen/qwen3-vl-4b-instance"}),
+        ],
+    ) as mocked_urlopen:
+        manager = LMStudioModelManager(
+            "http://localhost:1234/v1", "test-token", timeout=12
+        )
+        manager.reload_model("qwen/qwen3-vl-4b")
+
+    requests = [call.args[0] for call in mocked_urlopen.call_args_list]
+    assert requests[0].full_url == "http://localhost:1234/api/v1/models"
+    assert requests[0].get_method() == "GET"
+    assert requests[0].get_header("Authorization") == "Bearer test-token"
+
+    assert requests[1].full_url.endswith("/models/unload")
+    assert json.loads(requests[1].data) == {"instance_id": "qwen/qwen3-vl-4b-instance"}
+
+    assert requests[2].full_url.endswith("/models/load")
+    assert json.loads(requests[2].data) == {
+        "model": "qwen/qwen3-vl-4b",
+        "context_length": 16384,
+        "eval_batch_size": 256,
+        "flash_attention": True,
+        "offload_kv_cache_to_gpu": False,
+    }
+
+
+def test_reload_model_loads_by_key_if_no_instance_is_loaded():
+    model_response = {"models": [{"key": "qwen/model", "loaded_instances": []}]}
+    with patch(
+        "photools3.lm_studio.urlopen",
+        side_effect=[
+            _http_response(model_response),
+            _http_response({"status": "loaded"}),
+        ],
+    ) as mocked_urlopen:
+        manager = LMStudioModelManager("http://localhost:1234/v1", "token")
+        manager.reload_model("qwen/model")
+
+    assert mocked_urlopen.call_count == 2
+    load_request = mocked_urlopen.call_args.args[0]
+    assert load_request.full_url.endswith("/models/load")
+    assert json.loads(load_request.data) == {"model": "qwen/model"}
+
+
+def test_reload_model_rejects_ambiguous_model_selection():
+    model_response = {
+        "models": [
+            {
+                "key": "qwen/model",
+                "loaded_instances": [
+                    {"id": "instance-1", "config": {}},
+                    {"id": "instance-2", "config": {}},
+                ],
+            }
+        ]
+    }
+    with patch(
+        "photools3.lm_studio.urlopen", return_value=_http_response(model_response)
+    ):
+        manager = LMStudioModelManager("http://localhost:1234/v1", "token")
+        with pytest.raises(LMStudioError, match="Multiple loaded instances"):
+            manager.reload_model("qwen/model")
@@ -0,0 +1,243 @@
+import shutil
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from photools3.metadata_writer import (
+    MetadataWriter,
+    _is_prefixed,
+    _prefixed,
+)
+from photools3.settings import settings
+from test.test_photo import PATHS_DNG_EXAMPLES
+
+PREFIX = settings.metadata_prefix
+
+
+@pytest.fixture
+def make_dng_file(tmp_path):
+    def _make(src: Path) -> Path:
+        dst = tmp_path / f"copy_{src.name}"
+        shutil.copy(src, dst)
+        return dst
+
+    return _make
+
+
+@pytest.fixture
+def metadata_writer_on_existing(make_dng_file):
+    metadata_writer = MetadataWriter(make_dng_file(PATHS_DNG_EXAMPLES[0]))
+    metadata_writer.write_caption(
+        "Let's pretend this is the caption of the original image",
+        include_prefix=False,
+    )
+    return metadata_writer
+
+
+@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
+def test_read_metadata(make_dng_file, pic):
+    """primitive test for `MetadataWriter`"""
+    file_path = make_dng_file(pic)
+    result = MetadataWriter(file_path)
+    assert result
+    assert result.metadata
+    assert isinstance(result.metadata, dict)
+
+
+@pytest.mark.parametrize(
+    "pic, expected_keywords",
+    [
+        (PATHS_DNG_EXAMPLES[0], ["architektur", "zeit|abend"]),
+        (PATHS_DNG_EXAMPLES[1], ["architektur"]),
+        (PATHS_DNG_EXAMPLES[2], ["morgen", "tiere|vögel"]),
+        (
+            PATHS_DNG_EXAMPLES[3],
+            [
+                "abend",
+                "architektur",
+                "bewegungsunschärfe",
+                "lichter",
+                "mainz",
+                "spiegelung",
+                "wasser",
+            ],
+        ),
+        (
+            PATHS_DNG_EXAMPLES[4],
+            ["grau", "landschaft", "landschaft|weinberge", "personen", "tiefe", "weg"],
+        ),
+    ],
+)
+def test_get_keywords(make_dng_file, pic, expected_keywords):
+    path = make_dng_file(pic)
+    result = MetadataWriter(path).get_keywords()
+    assert sorted(result) == sorted(expected_keywords)
+
+
+def test_prefixed():
+    txt = "bla_bla"
+    txt_prefixed = _prefixed(txt)
+    assert txt_prefixed == f"{PREFIX}bla_bla"
+
+
+@pytest.mark.parametrize(
+    "txt, expected_result",
+    [
+        (f"{PREFIX}some text", True),
+        ("prefix is missing here", False),
+        ("some_perfix|but not the right one", False),
+    ],
+)
+def test_is_prefixed(txt, expected_result):
+    assert _is_prefixed(txt) == expected_result
+
+
+@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
+def test_read_metadata_sidecar_path(make_dng_file, pic):
+    file_path = make_dng_file(pic)
+    metadata_writer = MetadataWriter(file_path)
+    assert isinstance(metadata_writer.sidecar_path, Path)
+    assert str(metadata_writer.sidecar_path).lower().endswith(".dng.xmp")
+
+    # file does not exits yet, only the name
+    assert not metadata_writer.sidecar_path.exists()
+
+
+@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
+def test_set_caption(make_dng_file, pic):
+    metadata_writer = MetadataWriter(make_dng_file(pic))
+    assert not metadata_writer.sidecar_path.exists()  # sidecar does not exist yet ...
+    metadata_writer.write_caption("This is a test")
+    assert metadata_writer.sidecar_path.exists()  # ... but now!
+    result = metadata_writer.get_caption()
+    assert result
+    assert result == f"{PREFIX}This is a test"
+
+
+def test_set_caption_on_existing(metadata_writer_on_existing):
+    assert (
+        metadata_writer_on_existing.get_caption()
+        == "Let's pretend this is the caption of the original image"
+    )
+
+    metadata_writer_on_existing.write_caption("Another caption")
+    assert (
+        metadata_writer_on_existing.get_caption()
+        == f"Let's pretend this is the caption of the original image. {PREFIX}Another caption"
+    )
+
+    metadata_writer_on_existing.write_caption("Updated caption")
+    assert (
+        metadata_writer_on_existing.get_caption()
+        == f"Let's pretend this is the caption of the original image. {PREFIX}Updated caption"
+    )
+
+    metadata_writer_on_existing.write_caption("Don't do this!", override=False)
+    assert (
+        metadata_writer_on_existing.get_caption()
+        == f"Let's pretend this is the caption of the original image. {PREFIX}Updated caption"
+    )
+
+
+@pytest.mark.parametrize(
+    "pic, expected_keywords",
+    [
+        (
+            PATHS_DNG_EXAMPLES[0],
+            [
+                "architektur",
+                "zeit|abend",
+                f"{PREFIX}hund",
+                f"{PREFIX}katze",
+                f"{PREFIX}maus",
+            ],
+        ),
+        (
+            PATHS_DNG_EXAMPLES[1],
+            ["architektur", f"{PREFIX}hund", f"{PREFIX}katze", f"{PREFIX}maus"],
+        ),
+        (
+            PATHS_DNG_EXAMPLES[2],
+            [
+                "morgen",
+                "tiere|vögel",
+                f"{PREFIX}hund",
+                f"{PREFIX}katze",
+                f"{PREFIX}maus",
+            ],
+        ),
+        (
+            PATHS_DNG_EXAMPLES[3],
+            [
+                "abend",
+                "architektur",
+                "bewegungsunschärfe",
+                "lichter",
+                "mainz",
+                "spiegelung",
+                "wasser",
+                f"{PREFIX}hund",
+                f"{PREFIX}katze",
+                f"{PREFIX}maus",
+            ],
+        ),
+        (
+            PATHS_DNG_EXAMPLES[4],
+            [
+                "grau",
+                "landschaft",
+                "landschaft|weinberge",
+                "personen",
+                "tiefe",
+                "weg",
+                f"{PREFIX}hund",
+                f"{PREFIX}katze",
+                f"{PREFIX}maus",
+            ],
+        ),
+    ],
+)
+def test_set_keywords(make_dng_file, pic, expected_keywords):
+    metadata_writer = MetadataWriter(make_dng_file(pic))
+    metadata_writer.write_keywords(["hund", "katze", "maus"])
+    result = metadata_writer.get_keywords()
+    assert result
+    assert isinstance(result, list)
+    assert f"{PREFIX}hund" in result
+    assert f"{PREFIX}katze" in result
+    assert f"{PREFIX}maus" in result
+
+
+def test_set_keywords_on_existing():
+    # TODO
+    ...
+
+
+def test_write_caption_and_keywords_in_one_metadata_update(tmp_path):
+    writer = MetadataWriter(tmp_path / "photo.jpg")
+    with (
+        patch.object(writer, "get_caption", return_value="Original caption"),
+        patch.object(writer, "get_keywords", return_value=["existing"]),
+        patch.object(writer, "set_metadata") as set_metadata,
+    ):
+        writer.write(caption="Generated caption", keywords=["architektur"])
+
+    set_metadata.assert_called_once()
+    metadata_tags = set_metadata.call_args.args[0]
+    assert metadata_tags["XMP:Description"] == (
+        f"Original caption. {PREFIX}Generated caption"
+    )
+    assert set(metadata_tags["XMP:Subject"]) == {
+        "existing",
+        f"{PREFIX}architektur",
+    }
+
+
+def test_set_metadata_surfaces_write_failures(tmp_path):
+    writer = MetadataWriter(tmp_path / "photo.jpg")
+    with patch("photools3.metadata_writer.exiftool.ExifToolHelper") as exif:
+        exif_instance = exif.return_value.__enter__.return_value
+        exif_instance.execute.side_effect = OSError("ExifTool failed")
+        with pytest.raises(RuntimeError, match="Could not write metadata"):
+            writer.set_metadata({"XMP:Description": "caption"})
@@ -0,0 +1,285 @@
+import base64
+from io import BytesIO
+
+import pytest
+from PIL import Image
+
+from photools3.photo import Photo
+from photools3.settings import DNG_EXAMPLES_DIR
+
+PATHS_DNG_EXAMPLES = [
+    DNG_EXAMPLES_DIR / "example_dng_01.dng",
+    DNG_EXAMPLES_DIR / "example_dng_02.dng",
+    DNG_EXAMPLES_DIR / "example_dng_03.dng",
+    DNG_EXAMPLES_DIR / "example_dng_04.dng",
+    DNG_EXAMPLES_DIR / "example_dng_05.dng",
+]
+
+# (PIL mode) -> file format used to persist the test image
+MODE_TO_FORMAT = {
+    "RGB": "PNG",
+    "L": "PNG",
+    "P": "PNG",
+    "RGBA": "PNG",
+    "LA": "PNG",
+    "CMYK": "TIFF",
+    "1": "PNG",
+    "I": "TIFF",
+    "F": "TIFF",
+}
+ALL_MODES = list(MODE_TO_FORMAT)
+JPEG_COMPATIBLE_MODES = ["RGB", "L", "CMYK", "1"]
+JPEG_INCOMPATIBLE_MODES = ["P", "RGBA", "LA", "I", "F"]
+
+
+def _make_image_file(tmp_path, mode: str, size=(64, 48)):
+    """Create an image in the given mode, save it to disk and return its path."""
+    fmt = MODE_TO_FORMAT[mode]
+    img = Image.new(mode, size)
+    path = tmp_path / f"test_{mode}.{fmt.lower()}"
+    img.save(path, format=fmt)
+    return path
+
+
+def test_load_photo():
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    assert photo
+    assert photo.path == PATHS_DNG_EXAMPLES[0]
+    assert isinstance(photo.image, Image.Image)
+
+
+def test_photo_context_manager_closes_image(tmp_path):
+    path = _make_image_file(tmp_path, "RGB")
+    with Photo(path) as photo:
+        image = photo.image
+        assert image.fp is not None
+
+    assert image.fp is None
+
+
+def test_resize_method_exists():
+    """Test that resize method exists and returns a Photo instance"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    resized_photo = photo.resize()
+    assert isinstance(resized_photo, Photo)
+    assert resized_photo is photo  # Should return self for chaining
+
+
+def test_resize_with_custom_max_size():
+    """Test resize with a custom maximum size"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    original_width, original_height = photo.image.size
+    assert original_width > 100 or original_height > 100  # Assume it's larger
+
+    resized_photo = photo.resize(image_max_size=50)
+
+    new_width, new_height = resized_photo.image.size
+    max_dim = max(new_width, new_height)
+    assert max_dim <= 50, f"Max dimension {max_dim} exceeds limit of 50"
+
+
+def test_resize_maintains_aspect_ratio():
+    """Test that resize maintains the original aspect ratio"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    original_width, original_height = photo.image.size
+
+    resized_photo = photo.resize(image_max_size=100)
+    new_width, new_height = resized_photo.image.size
+
+    original_ratio = original_width / original_height
+    resized_ratio = new_width / new_height
+
+    assert (
+        abs(original_ratio - resized_ratio) < 0.01
+    ), f"Aspect ratio changed: {original_ratio} -> {resized_ratio}"
+
+
+def test_resize_image_thumbnail_called():
+    """Test that the image was actually resized using thumbnail"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    original_size = photo.image.size
+
+    # Resize to a smaller dimension
+    resized_photo = photo.resize(image_max_size=10)
+
+    new_size = resized_photo.image.size
+    assert (
+        new_size != original_size
+    ), "Image should be resized when max_size is smaller than original"
+
+
+def test_resize_chaining():
+    """Test that resize returns self for method chaining"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+
+    # Should be able to chain multiple operations
+    result = photo.resize().resize(image_max_size=50)
+    assert result is photo
+
+
+def test_resize_default_parameter():
+    """Test resize with default parameter (no arguments)"""
+    photo = Photo(PATHS_DNG_EXAMPLES[0])
+    resized_photo = photo.resize()
+
+    # Should return a valid Photo instance
+    assert isinstance(resized_photo, Photo)
+    assert resized_photo.path == photo.path
+
+
+# --- to_rgb() ---
+
+
+@pytest.mark.parametrize("mode", ALL_MODES)
+def test_to_rgb_converts_to_rgb_mode(tmp_path, mode):
+    """to_rgb() must leave the image in RGB mode regardless of the initial mode"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    result = photo.to_rgb()
+    assert result.image.mode == "RGB"
+
+
+@pytest.mark.parametrize("mode", ALL_MODES)
+def test_to_rgb_returns_self_for_chaining(tmp_path, mode):
+    """to_rgb() returns self so it can be chained"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    assert photo.to_rgb() is photo
+
+
+def test_to_rgb_keeps_rgb_image_unmodified(tmp_path):
+    """RGB images are not re-converted: the same image object is kept"""
+    path = _make_image_file(tmp_path, "RGB")
+    photo = Photo(path)
+    original_image = photo.image
+    photo.to_rgb()
+    assert photo.image is original_image
+
+
+@pytest.mark.parametrize("mode", ["L", "P", "RGBA", "LA", "CMYK", "1", "I", "F"])
+def test_to_rgb_replaces_non_rgb_image(tmp_path, mode):
+    """Non-RGB images are converted: a new image object is assigned"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    original_image = photo.image
+    photo.to_rgb()
+    assert photo.image is not original_image
+    assert photo.image.mode == "RGB"
+
+
+@pytest.mark.parametrize("mode", ["RGBA", "LA"])
+def test_to_rgb_drops_alpha_channel(tmp_path, mode):
+    """Converting alpha-bearing modes to RGB drops the alpha band"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    assert "A" in photo.image.getbands()
+    photo.to_rgb()
+    assert photo.image.getbands() == ("R", "G", "B")
+
+
+def test_to_rgb_on_dng_examples(tmp_path):
+    """DNG examples are already RGB; to_rgb() is a safe no-op on every file"""
+    for path in PATHS_DNG_EXAMPLES:
+        photo = Photo(path)
+        original_image = photo.image
+        assert photo.to_rgb() is photo
+        assert photo.image is original_image
+        assert photo.image.mode == "RGB"
+
+
+# --- get_encoded() ---
+
+
+@pytest.mark.parametrize("mode", JPEG_COMPATIBLE_MODES)
+def test_get_encoded_returns_valid_base64(tmp_path, mode):
+    """get_encoded() returns a non-empty, decodable base64 string"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    encoded = photo.get_encoded()
+    assert isinstance(encoded, str)
+    assert len(encoded) > 0
+    raw = base64.b64decode(encoded, validate=True)
+    assert len(raw) > 0
+
+
+@pytest.mark.parametrize("mode", JPEG_COMPATIBLE_MODES)
+def test_get_encoded_produces_openable_jpeg(tmp_path, mode):
+    """The decoded payload must be a valid JPEG with the original dimensions"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    original_size = photo.image.size
+    encoded = photo.get_encoded()
+    img = Image.open(BytesIO(base64.b64decode(encoded)))
+    assert img.format == "JPEG"
+    assert img.size == original_size
+
+
+def test_get_encoded_starts_with_jpeg_magic_bytes(tmp_path):
+    """JPEG files start with the FFD8FF magic bytes"""
+    path = _make_image_file(tmp_path, "RGB")
+    photo = Photo(path)
+    raw = base64.b64decode(photo.get_encoded())
+    assert raw[:3] == b"\xff\xd8\xff"
+
+
+def test_get_encoded_reencodes_instead_of_passing_raw_file_bytes(tmp_path):
+    """The output must be a fresh JPEG encoding, not the source file bytes"""
+    path = _make_image_file(tmp_path, "RGB")
+    photo = Photo(path)
+    raw = base64.b64decode(photo.get_encoded())
+    source_bytes = path.read_bytes()
+    assert raw != source_bytes
+    assert source_bytes[:4] == b"\x89PNG"  # source is a PNG...
+    assert raw[:3] == b"\xff\xd8\xff"  # ...but the encoding is a JPEG
+
+
+def test_get_encoded_is_deterministic(tmp_path):
+    """Encoding the same image twice yields identical base64"""
+    path = _make_image_file(tmp_path, "RGB")
+    assert Photo(path).get_encoded() == Photo(path).get_encoded()
+
+
+@pytest.mark.parametrize("mode", JPEG_INCOMPATIBLE_MODES)
+def test_get_encoded_raises_for_non_jpeg_modes(tmp_path, mode):
+    """Modes JPEG cannot store (palette, alpha, integer/float) must raise OSError"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    with pytest.raises(OSError):
+        photo.get_encoded()
+
+
+@pytest.mark.parametrize("mode", JPEG_INCOMPATIBLE_MODES)
+def test_to_rgb_then_get_encoded_works_around_unsupported_modes(tmp_path, mode):
+    """Chaining to_rgb().get_encoded() fixes modes that JPEG cannot store"""
+    path = _make_image_file(tmp_path, mode)
+    photo = Photo(path)
+    with pytest.raises(OSError):
+        photo.get_encoded()
+    encoded = photo.to_rgb().get_encoded()
+    assert isinstance(encoded, str)
+    img = Image.open(BytesIO(base64.b64decode(encoded)))
+    assert img.format == "JPEG"
+
+
+@pytest.mark.parametrize("path", PATHS_DNG_EXAMPLES)
+def test_get_encoded_works_on_all_dng_examples(path):
+    """Every DNG example (all different files) encodes to a valid JPEG"""
+    photo = Photo(path)
+    encoded = photo.get_encoded()
+    assert isinstance(encoded, str)
+    raw = base64.b64decode(encoded, validate=True)
+    assert raw[:3] == b"\xff\xd8\xff"
+    img = Image.open(BytesIO(raw))
+    assert img.format == "JPEG"
+    assert img.size == photo.image.size
+
+
+def test_full_chain_resize_to_rgb_get_encoded(tmp_path):
+    """The intended workflow: resize -> to_rgb -> get_encoded"""
+    path = _make_image_file(tmp_path, "RGBA")
+    photo = Photo(path)
+    photo.resize(32).to_rgb()
+    assert photo.image.mode == "RGB"
+    img = Image.open(BytesIO(base64.b64decode(photo.get_encoded())))
+    assert img.format == "JPEG"
+    assert max(img.size) <= 32
@@ -0,0 +1,129 @@
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from PIL import Image
+
+from photools3.models import (
+    LlmResponseCaption,
+    LlmResponseCaptionsAndKeywords,
+    LlmResponseKeywords,
+)
+from photools3.processing import (
+    ProcessingMode,
+    _create_session,
+    process_batch,
+    process_photo,
+)
+
+
+@pytest.fixture
+def image_path(tmp_path):
+    path = tmp_path / "photo.png"
+    Image.new("RGB", (20, 20)).save(path)
+    return path
+
+
+@pytest.mark.parametrize(
+    ("mode", "response", "expected_metadata"),
+    [
+        (
+            ProcessingMode.KEYWORDS,
+            LlmResponseKeywords(tags=["architektur"]),
+            {"caption": None, "keywords": ["architektur"]},
+        ),
+        (
+            ProcessingMode.CAPTIONS,
+            LlmResponseCaption(caption="Ein Haus"),
+            {"caption": "Ein Haus", "keywords": None},
+        ),
+        (
+            ProcessingMode.BOTH,
+            LlmResponseCaptionsAndKeywords(caption="Ein Haus", tags=["architektur"]),
+            {"caption": "Ein Haus", "keywords": ["architektur"]},
+        ),
+    ],
+)
+def test_process_photo_writes_only_requested_metadata(
+    image_path, mode, response, expected_metadata
+):
+    session = MagicMock()
+    session.call.return_value = response
+
+    with (
+        patch("photools3.processing._create_session", return_value=session),
+        patch("photools3.processing.MetadataWriter") as writer_class,
+    ):
+        result = process_photo(image_path, mode)
+
+    assert result == response
+    session.call.assert_called_once()
+    session.close.assert_called_once()
+    writer_class.assert_called_once_with(image_path)
+    writer_class.return_value.write.assert_called_once_with(**expected_metadata)
+
+
+def test_process_photo_does_not_close_a_caller_owned_session(image_path):
+    session = MagicMock()
+    session.call.return_value = LlmResponseCaption(caption="Ein Haus")
+
+    with patch("photools3.processing.MetadataWriter"):
+        process_photo(image_path, ProcessingMode.CAPTIONS, session=session)
+
+    session.close.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    ("mode", "constructor"),
+    [
+        (ProcessingMode.KEYWORDS, "LlmSessionKeywords"),
+        (ProcessingMode.CAPTIONS, "LlmSessionCaption"),
+        (ProcessingMode.BOTH, "LlmSessionCaptionsAndKeywords"),
+    ],
+)
+def test_create_session_selects_mode_specific_response_model(mode, constructor):
+    with patch(f"photools3.processing.{constructor}") as session_class:
+        assert _create_session(mode) is session_class.return_value
+
+    session_class.assert_called_once_with()
+
+
+def test_process_batch_is_sequential_reuses_session_and_continues_after_error():
+    active_photos = 0
+    max_active_photos = 0
+
+    class TrackingPhoto:
+        def __init__(self, path):
+            self.path = path
+
+        def __enter__(self):
+            nonlocal active_photos, max_active_photos
+            active_photos += 1
+            max_active_photos = max(max_active_photos, active_photos)
+            return self
+
+        def __exit__(self, exc_type, exc_value, traceback):
+            nonlocal active_photos
+            active_photos -= 1
+
+    session = MagicMock()
+    response = LlmResponseCaptionsAndKeywords(caption="Ein Haus", tags=["architektur"])
+    session.call.side_effect = [response, RuntimeError("temporary error"), response]
+    paths = [Path("one.jpg"), Path("two.jpg"), Path("three.jpg")]
+
+    with (
+        patch("photools3.processing._create_session", return_value=session),
+        patch("photools3.processing.Photo", TrackingPhoto),
+        patch("photools3.processing.MetadataWriter") as writer_class,
+    ):
+        outcomes = process_batch(paths, ProcessingMode.BOTH)
+
+    assert [outcome.succeeded for outcome in outcomes] == [True, False, True]
+    assert [outcome.path for outcome in outcomes] == paths
+    assert outcomes[1].error == "RuntimeError: temporary error"
+    assert max_active_photos == 1
+    assert active_photos == 0
+    assert session.call.call_count == 3
+    assert all(len(call.args[0]) == 1 for call in session.call.call_args_list)
+    assert writer_class.return_value.write.call_count == 2
+    session.close.assert_called_once()
@@ -1,6 +1,35 @@
-from photools3.settings import Settings
+from pathlib import Path

-settings = Settings()
+from photools3.settings import (
+    DNG_EXAMPLES_DIR,
+    EXAMPLES_DIR,
+    PHOTOOLS3_DIR,
+    ROOT_DIR,
+    SRC_DIR,
+    settings,
+)
+
+
+def test_photools3_dir():
+    assert PHOTOOLS3_DIR == Path(__file__).parent.parent.resolve() / "src" / "photools3"
+
+
+def test_src_dir():
+    assert SRC_DIR == Path(__file__).parent.parent.resolve() / "src"
+
+
+def test_root_dir():
+    assert ROOT_DIR == Path(__file__).parent.parent.resolve()
+
+
+def test_examples_dir():
+    assert EXAMPLES_DIR == Path(__file__).parent.parent.resolve() / "examples"
+
+
+def test_dng_examples_dir():
+    assert (
+        DNG_EXAMPLES_DIR == Path(__file__).parent.parent.resolve() / "examples" / "dng"
+    )


 def test_settings_base():
@@ -1,6 +1,19 @@
 version = 1
-revision = 3
+revision = 5
 requires-python = ">=3.14"
+resolution-markers = [
+    "python_full_version >= '3.15'",
+    "python_full_version < '3.15'",
+]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
+]

 [[package]]
 name = "annotated-types"
@@ -24,6 +37,235 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/12/b8/4bd346e22b28902df4d651910f5242c28d84e4a5c2435ca5c3f797ed7e2e/anyio-4.15.1-py3-none-any.whl", hash = "sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101", size = 132079, upload-time = "2026-09-05T10:42:37.923Z" },
 ]

+[[package]]
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Author	SHA1	Message	Date
mathias2784	0d6272b4c3	E2e workflow (#21 ) Tests / test_and_lint (push) Successful in 49s Details Reviewed-on: #21 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-10-18 12:02:18 +00:00
mathias2784	ad7dbfdd11	E2e workflow (#20 ) Tests / test_and_lint (push) Successful in 34s Details Reviewed-on: #20 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-10-18 07:24:49 +00:00
mathias2784	ff588f899e	Llm session refactor (#19 ) Tests / test_and_lint (push) Successful in 34s Details Reviewed-on: #19 Co-authored-by: Mathias <mathias2784@web.de>	2026-10-18 06:29:37 +00:00
mathias2784	b6be043426	Mypy integration (#18 ) Tests / test_and_lint (push) Successful in 32s Details Reviewed-on: #18 Co-authored-by: Mathias <mathias2784@web.de>	2026-10-10 18:37:09 +00:00
mathias2784	b9632d46b2	Metadata writer (#17 ) Tests / test_and_lint (push) Successful in 27s Details Reviewed-on: #17 Co-authored-by: Mathias <mathias2784@web.de>	2026-10-10 12:22:15 +00:00
mathias2784	6a12144e39	Logging (#16 ) Tests / test_and_lint (push) Successful in 12s Details Reviewed-on: #16 Co-authored-by: Mathias <mathias2784@web.de>	2026-10-03 08:24:17 +00:00
mathias2784	401488fa7e	Llm client quality (#14 ) Tests / test_and_lint (push) Successful in 14s Details Reviewed-on: #14 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-10-02 21:31:21 +00:00
mathias2784	419fb46c3d	refactor: simplify CLI command logic by extracting helper functions (#13 ) Tests / test_and_lint (push) Successful in 14s Details Reviewed-on: #13 Co-authored-by: Mathias <mathias2784@web.de>	2026-10-02 12:03:20 +00:00
mathias2784	ce2da9d953	Add tests for LlmSession and LlmSessionCaptionsAndKeywords in both integration and mocked scenarios (#12 ) Tests / test_and_lint (push) Successful in 13s Details Reviewed-on: #12 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-10-02 09:21:38 +00:00
mathias2784	906707de55	first successful run of photo llm (#11 ) Tests / test_and_lint (push) Successful in 11s Details Reviewed-on: #11 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-10-02 08:16:20 +00:00
mathias2784	0c33153c98	Photo basics (#10 ) Tests / test_and_lint (push) Successful in 10s Details Reviewed-on: #10 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-09-30 21:01:20 +00:00
mathias2784	d9758c598d	add ci yaml (#9 ) Tests / test_and_lint (push) Successful in 7s Details Reviewed-on: #9 Co-authored-by: Mathias Scheider <mathias2784@web.de>	2026-09-30 18:49:48 +00:00