101 Commits

Author SHA1 Message Date
francwa 14941d47c0 Merge branch 'refactor/domain-io-extraction'
Extract all I/O (subprocess, filesystem, YAML loading) from the domain
layer via ports/adapters. domain/subtitles/ now has zero imports from
infrastructure/. The remaining domain → infra leak (release knowledge
loaded at import time) is documented in tech-debt for a dedicated branch.
2026-05-19 15:16:59 +02:00
francwa df798f55cc refactor(subtitles): introduce SubtitleKnowledge Protocol port
Domain services (SubtitleIdentifier, PatternDetector) used to import the
concrete SubtitleKnowledgeBase class directly from infrastructure for
their type hint. With this commit they depend on a structural Protocol
in alfred/domain/subtitles/ports/knowledge.py declaring just the 7
read-only query methods the domain actually consumes.

The concrete YAML-backed SubtitleKnowledgeBase in infrastructure remains
the sole adapter — no rename, no shim. With this change
alfred/domain/subtitles/ has zero imports from alfred/infrastructure/.

Also extend the changelog entry covering the full domain-io-extraction
branch.
2026-05-19 15:15:43 +02:00
francwa 535935cc73 docs(changelog): summarize refactor/domain-io-extraction work block 2026-05-19 15:11:17 +02:00
francwa 6e252d1e81 refactor(subtitles): inject default rules into SubtitleRuleSet.resolve()
aggregates.py used to call SubtitleKnowledgeBase().default_rules() via a
DEFAULT_RULES() helper, which silently pulled the infrastructure layer
(YAML loader) into the domain on every resolve.

Make the dependency explicit: resolve() now takes the default rules as
a parameter, and the caller (the ManageSubtitles use case) loads them
from the KB once and passes them in. Domain stays I/O-free.

- Drop DEFAULT_RULES helper and the SubtitleKnowledgeBase import from
  alfred/domain/subtitles/aggregates.py
- SubtitleRuleSet.resolve(default_rules: SubtitleMatchingRules)
- manage_subtitles use case passes kb.default_rules() at the call site
- Tests use a local SubtitleMatchingRules stand-in instead of relying
  on KB defaults
2026-05-19 15:10:06 +02:00
francwa 903e9e7117 refactor(subtitles): move SubtitlePlacer to application layer
The placer performs filesystem I/O (os.link) — it belongs in the
application layer, not the domain. Domain services should be pure.

- Move alfred/domain/subtitles/services/placer.py to
  alfred/application/subtitles/placer.py
- Move tests/domain/test_subtitle_placer.py to
  tests/application/test_subtitle_placer.py
- Update all callers (manage_subtitles use case, metadata store, tests)
- Drop placer re-exports from domain.subtitles.services.__init__
2026-05-19 15:07:39 +02:00
francwa 9556bf9e08 refactor(domain): strip live filesystem I/O from VOs and entities
DDD-pure cleanup — entities and value objects no longer query the world
at read time.

  FilePath: drop .exists() / .is_file() / .is_dir(). The VO is now a
    pure address; ask the injected FilesystemScanner for live state.
  Movie:    drop .has_file() / .is_downloaded(). Invariant: when the
    application sets file_path, it has already constated the file
    exists; downstream readers trust the snapshot.
  Episode:  same — drop .has_file() / .is_downloaded().
  SubtitlePlacer: drop the pre-check .exists() calls. The placer now
    attempts os.link() and reports FileNotFoundError / FileExistsError
    as skip reasons. Removes a TOCTOU race as a bonus.

Tests adjusted: the FilePath VO method tests are gone (the methods are
gone), test_has_file_false_when_no_path replaced by a plain assertion
on file_path is None. Placer tests are unchanged — the skip-reason
strings ('not found', 'already exists') match the new try/except paths.

The 'snapshot value objects' pattern (ProbedMediaInfo, TmdbMovieInfo)
that this cleanup enables is documented in refactor_domain_io.md, to
be applied when a future use case actually needs richer metadata —
not now, no speculative VOs.
2026-05-19 14:58:59 +02:00
francwa e6ee700825 refactor(subtitles): inject MediaProber/FilesystemScanner ports into domain services
Domain services no longer call subprocess or pathlib directly. Introduces
two Protocol ports in domain/shared/ports/:

  MediaProber.list_subtitle_streams(video) -> list[SubtitleStreamInfo]
  FilesystemScanner.scan_dir / stat / read_text  -> list[FileEntry] | ...

Concrete adapters live in infrastructure/:

  FfprobeMediaProber          (wraps subprocess + ffprobe + JSON)
  PathlibFilesystemScanner    (wraps pathlib + os reads)

SubtitleIdentifier and PatternDetector now take (kb, prober, scanner) at
construction time. Their internals work over FileEntry snapshots and
SubtitleStreamInfo records — no more ad-hoc Path.is_file/iterdir/stat or
embedded subprocess.run loops. _count_entries now takes raw SRT text
(returned by scanner.read_text) so SRT-only entry counting stays out of
the FS layer.

manage_subtitles use case instantiates the two adapters once and injects
them into both services. Tests pass real adapters and patch
`alfred.infrastructure.probe.ffprobe_prober.subprocess.run` for the
ffprobe-failure cases. _classify_single tests build FileEntry via a
small helper.

Domain is now free of subprocess / direct filesystem reads in the
subtitle pipeline. The only remaining I/O hooks are FilePath VO
convenience methods (exists/is_file/is_dir) which stay as a deliberate
affordance on the value object.
2026-05-19 14:52:24 +02:00
francwa ced72547f7 refactor(knowledge): extract YAML loaders from domain to infrastructure
The domain layer no longer reads YAML files. All knowledge loaders move
from `alfred/domain/*/knowledge/` to `alfred/infrastructure/knowledge/`:

  domain/release/knowledge.py
    → infrastructure/knowledge/release.py
  domain/shared/knowledge/language_registry.py
    → infrastructure/knowledge/language_registry.py
  domain/subtitles/knowledge/{loader,base}.py
    → infrastructure/knowledge/subtitles/{loader,base}.py

Callers in domain/release/{services,value_objects}.py,
domain/subtitles/{aggregates,services/*}.py, and
application/filesystem/manage_subtitles.py updated to absolute imports.
Re-exports of KnowledgeLoader/SubtitleKnowledgeBase from
domain/subtitles/__init__.py dropped (no shim per project convention).
Tests follow the moved targets.
2026-05-19 14:35:18 +02:00
francwa f338b08706 refactor(release): type media_type/parse_path as true enums
ParsedRelease.media_type is now MediaTypeToken (not str) and parse_path
is ParsePath (not str). __post_init__ keeps a tolerant constructor that
coerces raw strings via the enum, so callers passing 'movie'/'direct'
still work transparently. Since both enums inherit from str, existing
string comparisons and JSON serialization remain unchanged.
2026-05-19 14:21:27 +02:00
francwa da484d7474 refactor(release): typed enums + __post_init__ validation on ParsedRelease
ParsedRelease accepted any string for media_type/parse_path and had no
validation on numeric ranges (season=-5 was silently accepted). Tighten
both ends:

- New str-backed Enums MediaTypeToken and ParsePath. Inherit from str so
  every existing comparison ('== "movie"'), JSON serialization, and TMDB
  DTO interop keeps working unchanged.
- ParsedRelease.__post_init__ now validates: raw/group non-empty, year in
  1888-2100, season 0-100, episode 0-9999, episode_end >= episode,
  media_type/parse_path against the enum allowlist.
- services.py uses the enum .value members everywhere instead of bare
  string literals — kills the typo risk.
2026-05-19 14:17:56 +02:00
francwa 481eeb5afd refactor(domain): identity-based equality + dedup track helpers
Two related DDD fixes for Movie and Episode entities:

- Identity equality: @dataclass(eq=False) with custom __eq__/__hash__.
  Movie is identified by imdb_id, Episode by (season, episode) within
  the TVShow aggregate. Auto-generated field-by-field equality was
  incorrectly making two Movie instances with the same imdb_id but
  different audio_tracks compare unequal — breaks dedup/caching.

- MediaWithTracks mixin: the 5 audio/subtitle helpers
  (has_audio_in / audio_languages / has_subtitles_in / has_forced_subs /
  subtitle_languages) were duplicated verbatim between Movie and Episode.
  Extracted to shared/media/tracks_mixin.py; both entities now inherit.

Bonus: dropped the object.__setattr__ coercion dance in Movie.__post_init__
— the class isn't frozen so plain assignment is the right call.
2026-05-19 14:17:47 +02:00
francwa 7cd24f3a31 refactor(domain): freeze media track value objects
AudioTrack, VideoTrack, SubtitleTrack and MediaInfo are snapshots of a
single ffprobe run — model them as proper immutable value objects.

- @dataclass(frozen=True) on all four
- MediaInfo track collections become tuple[...] instead of list[...]
- ffprobe adapter rewritten to build tuples up-front instead of
  appending/setattr'ing on a constructed instance
2026-05-19 14:17:27 +02:00
francwa eb8995cfc3 refactor(subtitles): drop dead scanner module
SubtitleScanner was an earlier iteration superseded by SubtitleIdentifier
and never imported in production code (only by its own tests). Removing
both keeps the bounded context clean and shrinks the surface.
2026-05-19 14:17:15 +02:00
francwa f6eef59fca refactor: tech debt mini-pass (items 5, 6, 7, 20)
Low-risk cleanup items, no functional change to the parser. The
philosophy remains: keep the parser simple, the AI handles edge cases.

- Extract duplicated 'fs-safe title → dot-folder-name' regex into
  to_dot_folder_name() in domain/shared/value_objects.py. Used by both
  MovieTitle.normalized() and TVShow.get_folder_name() (item #5).
- ParsedRelease.languages now uses field(default_factory=list) instead
  of a manual __post_init__ assigning [] via object.__setattr__ (#6).
- tv_shows/entities.py module docstring: prepend ASCII ownership tree
  for quicker visual scan of the aggregate hierarchy (#7).
- file_extensions.yaml: split subtitle sidecars (.srt/.sub/.idx/.ass/.ssa)
  into a dedicated 'subtitle:' category instead of lumping them under
  'metadata:'. _METADATA_EXTENSIONS at the value_objects.py level remains
  the union of both — detect_media_type behavior unchanged. New loader
  load_subtitle_extensions() exposes the distinct subtitle set for future
  callers in the subtitles domain (#20).

Suite: 1020 passed, 8 skipped.
2026-05-18 16:24:28 +02:00
francwa 273510dff8 test(fixtures): seed PATH OF PAIN bucket with 10 worst-case fixtures
10 pathological release names mined from the real downloads folder.
Each fixture locks in the current parse_release output (including
its silent losses and false positives) so future parser improvements
are intentional, not silent drift.

Cases:
- Khruangbin yt-dlp slug (UTF-8 wide pipe '|', YT ID as group)
- Deutschland 83-86-89 franchise box (group=S03 misdetection)
- Chérie Le BéBé (accented chars preserved, VFF language)
- Jimmy Carr 8-word stand-up special title
- [ OxTorrent.vc ] prefix + XviD codec (site_tag prefix)
- Prodiges S12E01 with episode title + air-date silently lost
- The Prodigy: apostrophe + Blu-ray dash + 1080i + multi-word audio
  = full AI-path degeneration (everything UNKNOWN)
- Sleaford Mods yt-dlp slug (YT ID glued to year)
- Super Mario Bros [FR-EN] (bilingual tag mistaken for group)
- Gilmore Girls Complete S01-S07 (the well-behaved exception:
  COMPLETE token correctly drives tv_complete + REPACK + 10bit)

Also adds shitty + path_of_pain to the per-bucket sanity assertion.

Suite: 1020 passed, 8 skipped.
2026-05-18 15:57:56 +02:00
francwa c1831e3f46 test(fixtures): drop derry_duplicate_naming (was a copy-paste artifact)
The release name mixed two distinct releases — not a real-world case
worth anti-regression. SHITTY bucket now holds 14 fixtures (down from 15).
2026-05-18 15:51:11 +02:00
francwa aa182458b8 test(fixtures): seed SHITTY release bucket with 15 anti-regression cases
Add 15 expected.yaml fixtures under tests/fixtures/releases/shitty/
covering the awkward but real-world release names from the downloads
folder. Each fixture locks in the current parse_release behavior so
future parser changes are intentional, not silent drift.

Cases captured:
- Angel INTEGRALE 3-level hierarchy (tv_complete media_type)
- Buffy custom French title with dots preserved
- Archer S14E09E10E11 multi-episode (E11 lost — tech debt)
- Notre Planète lowercase s01e01
- Vinyl ' - 1x01 - FHD' (stray dash artifact — tech debt)
- Deutschland.83 (year-suffix as part of title)
- Tatortreiniger S01-06 range (falls to movie — tech debt)
- Derry Girls duplicated title
- Jurassic Park bare folder (media_type=unknown)
- La Nuit au Musée bilingual MULTI
- Chérie j'ai agrandi (ASCII-stripped apostrophe, parses fine)
- Honey Don't (unescaped apostrophe — full AI-path degeneration)
- Hook MULTi.SUBS movie with Subs/ folder
- Predator Badlands space separators (group=UNKNOWN — tech debt)
- Westworld S04 Subs.Only (no video file)

Each fixture also captures the future 3-flow routing (library /
torrents / seed_hardlinks) ahead of the organize_media refactor.

Suite: 1011 passed, 8 skipped.
2026-05-18 15:48:41 +02:00
francwa 774f71c8cc chore(gitignore): track CHANGELOG.md explicitly
The blanket *.md ignore was hiding CHANGELOG.md, forcing 'git add -f' on
every update. Allow-list it so the file lives under normal git tracking.
CLAUDE.md stays local (user keeps it personal until a dedicated repo).
2026-05-18 15:39:04 +02:00
francwa 7bc50fd5b8 test: add real-world release fixtures (EASY bucket)
Captures 5 canonical releases from /mnt/testipool/downloads as parametrized
fixtures under tests/fixtures/releases/easy/. Each fixture declares the
release name, expected ParsedRelease fields, original tree, and the future
routing (library / torrents / seed_hardlinks) for the upcoming organize_media
refactor.

Today only the 'parsed' section is asserted; tree is materialized into a
tmp_path to catch typos. Routing is captured ahead of the planner work — it
becomes verifiable once organize_media lands.

Cases: back_in_action (movie), slow_horses_single_ep (TV single),
foundation_season_pack (S02 + .nfo noise), long_walk_with_noise (movie +
KONTRAST.TOP.txt), sinners_yts (YTS bracket-heavy + Subs/ dir).

Also tracks CHANGELOG.md under [Unreleased] / Added.
2026-05-18 15:36:19 +02:00
francwa f17abdbaec chore: cleanup — remove shims, fix ruff warnings, ignore noisy rules
- Removed backward-compat shims _sanitise_for_fs / _strip_episode_from_normalised
  in domain/release/value_objects.py (zero callers).
- Fixed ruff warnings across the codebase:
    * PLW1510: explicit check=False on subprocess.run calls
    * PLC0415: promoted lazy imports to module top where no cycle exists
      (manage_subtitles, placer, qbittorrent/client, file_manager)
    * E402: fixed module-level import ordering in language_registry.py and
      subtitles/knowledge/loader.py
    * F841 / B007: removed unused locals (identifier.py)
    * C416: replaced unnecessary set comprehension with set() in
      release/knowledge.py
- Ruff config: ignore PLR0911/PLR0912 globally (noisy on mappers and
  orchestrator use-cases) and PLW0603 (intentional for the memory singleton).
- Updated tech debt memory: P1 done, ShowStatus actually complete (was a
  stale note).
2026-05-18 00:02:45 +02:00
francwa 1d50b63af2 Merge branch 'dev/sprint-cleanup'
Multi-week sprint: ISO 639-2/B language unification, release parser
unification + data-driven tokenizer, removal of fossil services
(movies/tv_shows/subtitles), subtitle services split into a package,
MediaInfo split, test suite expansion (990 passing).

See CHANGELOG.md [Unreleased] for the user-facing summary.
2026-05-17 23:42:05 +02:00
francwa 891ba502a2 chore: apply pre-commit auto-fixes (trim trailing whitespace, EOF) 2026-05-17 23:41:54 +02:00
francwa e07c9ec77b chore: sprint cleanup — language unification, parser unification, fossils removal
Several weeks of work accumulated without being committed. Grouped here for
clarity; see CHANGELOG.md [Unreleased] for the user-facing summary.

Highlights
----------

P1 #2 — ISO 639-2/B canonical migration
- New Language VO + LanguageRegistry (alfred/domain/shared/knowledge/).
- iso_languages.yaml as single source of truth for language codes.
- SubtitleKnowledgeBase now delegates lookup to LanguageRegistry; subtitles.yaml
  only declares subtitle-specific tokens (vostfr, vf, vff, …).
- SubtitlePreferences default → ["fre", "eng"]; subtitle filenames written as
  {iso639_2b}.srt (legacy fr.srt still read via alias).
- Scanner: dropped _LANG_KEYWORDS / _SDH_TOKENS / _FORCED_TOKENS /
  SUBTITLE_EXTENSIONS hardcoded dicts.
- Fixed: 'hi' token no longer marks SDH (conflicted with Hindi alias).
- Added settings.min_movie_size_bytes (was a module constant).

P1 #3 — Release parser unification + data-driven tokenizer
- parse_release() is now the single source of truth for release-name parsing.
- alfred/knowledge/release/separators.yaml declares the token separators used
  by the tokenizer (., space, [, ], (, ), _). New conventions can be added
  without code changes.
- Tokenizer now splits on any configured separator instead of name.split('.').
  Releases like 'The Father (2020) [1080p] [WEBRip] [5.1] [YTS.MX]' parse via
  the direct path without sanitization fallback.
- Site-tag extraction always runs first; well-formedness only rejects truly
  forbidden chars.
- _parse_season_episode() extended with NxNN / NxNNxNN alt forms.
- Removed dead helpers: _sanitize, _normalize.

Domain cleanup
- Deleted fossil services with zero production callers:
    alfred/domain/movies/services.py
    alfred/domain/tv_shows/services.py
    alfred/domain/subtitles/services.py (replaced by subtitles/services/ package)
    alfred/domain/subtitles/repositories.py
- Split monolithic subtitle services into a package (identifier, matcher,
  placer, pattern_detector, utils) + dedicated knowledge/ package.
- MediaInfo split into dedicated package (alfred/domain/shared/media/:
  audio, video, subtitle, info, matching).

Persistence cleanup
- Removed dead JSON repositories (movie/subtitle/tvshow_repository.py).

Tests
- Major expansion of the test suite organized to mirror the source tree.
- Removed obsolete *_edge_cases test files superseded by structured tests.
- Suite: 990 passed, 8 skipped.

Misc
- .gitignore: exclude env_backup/ and *.bak.
- Adjustments across agent/llm, app.py, application/filesystem, and
  infrastructure/filesystem to align with the new domain layout.
2026-05-17 23:38:00 +02:00
francwa ba6f016d49 feat: generic MetadataStore + read_release_metadata + query_library
- Extract MetadataStore from SubtitleMetadataStore (alfred/infrastructure/metadata/).
  Generic load/save + typed update helpers (update_parse, update_probe, update_tmdb)
  for the per-release .alfred/metadata.yaml.
- SubtitleMetadataStore becomes a thin facade — owns subtitle_history shape,
  delegates I/O to MetadataStore.
- Agent._execute_tool_call auto-persists successful analyze_release / probe_media /
  find_media_imdb_id results to the release's .alfred file. find_media_imdb_id
  follows release_focus when it has no path argument.
- New tools:
  · read_release_metadata(release_path) — cacheable, key=release_path.
    Returns the .alfred content or has_metadata=false.
  · query_library(name) — substring scan across configured library roots.
- Both new tools added to CORE_TOOLS (always visible).
2026-05-15 11:02:25 +02:00
francwa 3c7c6695f2 feat(memory): Phase 1 — STM ToolResultsCache + ReleaseFocus + cache flag in YAML specs
Adds two STM components and a transparent cache hook in the agent loop so
read-only tools don't re-do work the agent already did in this session.

New STM components:
  - ToolResultsCache  — {tool_name: {key: result}}, session-scoped.
    to_dict() exposes only the key inventory (not payloads) to keep the
    prompt cheap.
  - ReleaseFocus      — current_release_path + working_set list, updated
    automatically when a path-keyed inspector runs.

YAML spec layer:
  - New optional 'cache: { key: <param_name> }' block in ToolSpec.
  - Validated at load time: cache.key must be a declared parameter.
  - Surfaced on Tool dataclass as cache_key: str | None.

Agent._execute_tool_call:
  - Pre-exec cache lookup; hit short-circuits and adds _from_cache=true.
  - Post-exec: stores successful results, updates release_focus for
    path-keyed tools, refreshes episodic.last_search_results when
    find_torrent's hit served the response (so get_torrent_by_index
    keeps pointing at the right list).

Cacheable tools (5): analyze_release, probe_media, list_folder,
find_media_imdb_id, find_torrent.
2026-05-15 10:44:14 +02:00
francwa 2db3198ef2 feat(agent): migrate all remaining tools to YAML specs (21/21 covered)
Adds YAML specs for the 14 tools that were still description-from-docstring:

  filesystem:
    - set_path_for_folder, list_folder, analyze_release, probe_media,
      move_media, manage_subtitles, create_seed_links, learn
  api:
    - find_media_imdb_id, find_torrent, get_torrent_by_index,
      add_torrent_to_qbittorrent, add_torrent_by_index
  language:
    - set_language

Each spec follows the established shape (summary / description /
when_to_use / when_not_to_use / next_steps / parameters with
why_needed + example / returns) and the Python function docstring is
slimmed to a one-line pointer.

Registry now reports: 21 tools, 21 with YAML spec, 0 doc-only.
2026-05-14 21:18:43 +02:00
francwa 23a9dd7990 refactor(memory): rename workflow.target -> params, type -> name
The Workflow STM component stored an active workflow as
{type, target, stage, started_at}. Now that start_workflow takes a
workflow_name and a params dict, those keys match what they actually
hold:

  type   -> name    (the YAML workflow name, e.g. media.organize_media)
  target -> params  (the dict passed to start_workflow)

ShortTermMemory.start_workflow parameters renamed accordingly. All
consumers (prompt builder workflow scope + STM context, start/end
workflow tools) updated.
2026-05-14 21:11:23 +02:00
francwa 74a52ba6a3 feat(agent): workflow-scoped tool catalog + start/end_workflow meta-tools
Introduce a scope-aware agent so the LLM never sees the full 21-tool
catalog at once. The system prompt now describes either:
  - idle mode: core noyau (5 tools: set_language, set_path_for_folder,
    list_folder, start_workflow, end_workflow) + a list of available
    workflows with their goals;
  - active mode: the noyau plus the tools declared by the active
    workflow's YAML, with the step plan inlined into the prompt.

Pieces:
- alfred/agent/tools/workflow.py: start_workflow / end_workflow tools
  (with YAML specs under tools/specs/) that drive memory.stm.workflow.
- alfred/agent/prompt.py: CORE_TOOLS constant, visible_tool_names(),
  filtered build_tools_spec() / _format_tools_description(), and a new
  _format_workflow_scope() section in the system prompt.
- alfred/agent/agent.py: WorkflowLoader wired into Agent, defensive
  out-of-scope check in _execute_tool_call.
- alfred/agent/registry.py: registers the two new meta-tools (21 total,
  7 with YAML spec).
- workflows/media.organize_media.yaml: tools/steps list refreshed to
  match the current resolver split (analyze_release, probe_media,
  resolve_*_destination, move_to_destination).
2026-05-14 21:07:36 +02:00
francwa 97adfbda45 refactor(workflows): adopt media.* naming convention
Rename workflow files and their 'name' field with a 'media.' domain
prefix to anticipate future multi-domain expansion (mail.*, calendar.*, ...).

- organize_media -> media.organize_media
- manage_subtitles -> media.manage_subtitles

WorkflowLoader picks them up unchanged (uses data['name']).
2026-05-14 20:55:35 +02:00
francwa 239fce9e4e chore(agent): remove dead parameters.py
The ParameterSchema / REQUIRED_PARAMETERS / get_missing_required_parameters
machinery in alfred/agent/parameters.py was used in early prototypes for
the prompt-required-params check but has been unwired from production for
several refactors. The new YAML tool-spec layer (alfred/agent/tools/specs/)
covers the same need (rich, LLM-facing parameter descriptions) without
the parallel registration plumbing.

Tests in tests/test_config_edge_cases.py still reference the deleted
module — left untouched per the project policy of treating test sync
as a dedicated end-of-week task.
2026-05-14 18:06:34 +02:00
francwa 99c95af64e feat(agent): YAML tool specs as the LLM-facing semantic layer
Introduce a first-class semantic layer for tool descriptions, separated
from Python signatures (which stay the source of truth for types and
required-ness).

New
- alfred/agent/tools/spec.py — ToolSpec / ParameterSpec / ReturnsSpec
  dataclasses with strict YAML validation (ToolSpecError on malformed
  or inconsistent specs). compile_description() builds the rich text
  passed to the LLM as Tool.description, with sections for summary,
  description, when_to_use, when_not_to_use, next_steps, and returns.
  compile_parameter_description() injects the 'why_needed' field next
  to each parameter so the LLM sees the *intent* of each argument.
- alfred/agent/tools/spec_loader.py — discovers tools/specs/*.yaml,
  enforces filename ↔ spec.name match, rejects duplicates.
- alfred/agent/tools/specs/ — one YAML per tool:
    * resolve_season_destination.yaml
    * resolve_episode_destination.yaml
    * resolve_movie_destination.yaml
    * resolve_series_destination.yaml
    * move_to_destination.yaml

Refactor
- alfred/agent/registry.py
    * _create_tool_from_function now takes an optional ToolSpec.
      When provided, the long description + per-parameter descriptions
      come from the spec; types and required-ness still come from the
      Python signature.
    * Cross-validates spec.parameters against the function signature —
      crashes on missing or extra entries.
    * make_tools() loads all specs at startup and hands the right one
      to each tool. Tools without a spec fall back to the old
      docstring-only behaviour, so the 14 not-yet-migrated tools keep
      working unchanged.
    * Adds 'array' and 'object' to the Python→JSON type mapping and
      handles Optional[X] / X | None annotations.

- alfred/agent/tools/filesystem.py
    * Drops the '_tool' suffix on the 4 resolve_* wrappers (option 1:
      alias the use-case imports as _resolve_*). Tool names exposed to
      the LLM now match the underlying use case verbatim.
    * Wrapper docstrings shrink to a one-liner pointing to the YAML
      spec — no more duplicated when_to_use/Args/Returns in Python.

Verified
- make_tools() loads 19 tools (5 with YAML spec, 14 doc-only).
- Compiled descriptions render cleanly with all sections.
2026-05-14 18:06:27 +02:00
francwa b5025bb5f8 refactor(resolve_destination): factor shared series-folder resolution + DTO base
- New _Clarification sentinel and _resolve_series_folder() helper —
  the three TV use cases now share one matching/clarification path
  instead of triplicating the same if/elif/else block.
- New _ResolvedDestinationBase carrying status/question/options/error/
  message plus a _base_dict() helper; the four concrete DTOs only
  declare their own ok-state fields and a slim to_dict().
- No behaviour change: same outputs for ok/needs_clarification/error
  cases (verified by import + DTO smoke tests).
2026-05-14 16:09:33 +02:00
francwa e45465d52d feat: split resolve_destination, persona-driven prompts, qBittorrent relocation
Destination resolution
- Replace the single ResolveDestinationUseCase with four dedicated
  functions, one per release type:
    resolve_season_destination    (pack season, folder move)
    resolve_episode_destination   (single episode, file move)
    resolve_movie_destination     (movie, file move)
    resolve_series_destination    (multi-season pack, folder move)
- Each returns a dedicated DTO carrying only the fields relevant to
  that release type — no more polymorphic ResolvedDestination with
  half the fields unused depending on the case.
- Looser series folder matching: exact computed-name match is reused
  silently; any deviation (different group, multiple candidates) now
  prompts the user with all options including the computed name.

Agent tools
- Four new tools wrapping the use cases above; old resolve_destination
  removed from the registry.
- New move_to_destination tool: create_folder + move, chained — used
  after a resolve_* call to perform the actual relocation.
- Low-level filesystem_operations module (create_folder, move via mv)
  for instant same-FS renames (ZFS).

Prompt & persona
- New PromptBuilder (alfred/agent/prompt.py) replacing prompts.py:
  identity + personality block, situational expressions, memory
  schema, episodic/STM/config context, tool catalogue.
- Per-user expression system: knowledge/users/common.yaml +
  {username}.yaml are merged at runtime; one phrase per situation
  (greeting/success/error/...) is sampled into the system prompt.

qBittorrent integration
- Credentials now come from settings (qbittorrent_url/username/password)
  instead of hardcoded defaults.
- New client methods: find_by_name, set_location, recheck — the trio
  needed to update a torrent's save path and re-verify after a move.
- Host→container path translation settings (qbittorrent_host_path /
  qbittorrent_container_path) for docker-mounted setups.

Subtitles
- Identifier: strip parenthesized qualifiers (simplified, brazil…) at
  tokenization; new _tokenize_suffix used for the episode_subfolder
  pattern so episode-stem tokens no longer pollute language detection.
- Placer: extract _build_dest_name so it can be reused by the new
  dry_run path in ManageSubtitlesUseCase.
- Knowledge: add yue, ell, ind, msa, rus, vie, heb, tam, tel, tha,
  hin, ukr; add 'fre' to fra; add 'simplified'/'traditional' to zho.

Misc
- LTM workspace: add 'trash' folder slot.
- Default LLM provider switched to deepseek.
- testing/debug_release.py: CLI to parse a release, hit TMDB, and
  dry-run the destination resolution end-to-end.
2026-05-14 05:01:59 +02:00
francwa 1723b9fa53 feat: release parser, media type detection, ffprobe integration
Replace the old domain/media release parser with a full rewrite under
domain/release/:
- ParsedRelease with media_type ("movie" | "tv_show" | "tv_complete" |
  "documentary" | "concert" | "other" | "unknown"), site_tag, parse_path,
  languages, audio_codec, audio_channels, bit_depth, hdr_format, edition
- Well-formedness check + sanitize pipeline (_is_well_formed, _sanitize,
  _strip_site_tag) before token-level parsing
- Multi-token sequence matching for audio (DTS-HD.MA, TrueHD.Atmos…),
  HDR (DV.HDR10…) and editions (DIRECTORS.CUT…)
- Knowledge YAML: file_extensions, release_format, languages, audio,
  video, editions, sites/c411

New infrastructure:
- ffprobe.py — single-pass probe returning MediaInfo (video, audio
  tracks, subtitle tracks)
- find_video.py — locate first video file in a release folder

New application helpers:
- detect_media_type — filesystem-based type refinement
- enrich_from_probe — fill missing ParsedRelease fields from MediaInfo

New agent tools:
- analyze_release — parse + detect type + ffprobe in one call
- probe_media — standalone ffprobe for a specific file

New domain value object:
- MediaInfo + AudioTrack + SubtitleTrack (domain/shared/media_info.py)

Testing CLIs:
- recognize_folders_in_downloads.py — full pipeline with colored output
- probe_video.py — display MediaInfo for a video file
2026-05-12 16:14:20 +02:00
francwa 249c5de76a feat: major architectural refactor
- Refactor memory system (episodic/STM/LTM with components)
- Implement complete subtitle domain (scanner, matcher, placer)
- Add YAML workflow infrastructure
- Externalize knowledge base (patterns, release groups)
- Add comprehensive testing suite
- Create manual testing CLIs
2026-05-11 21:55:06 +02:00
francwa 62b5d0b998 Settings + fix startup 2026-04-30 12:41:42 +02:00
francwa 610dee365c mess: UV + settings KISS + fixes 2026-04-24 18:10:55 +02:00
francwa 58408d0dbe fix: fixed vectordb loneliness 2026-01-06 04:39:42 +01:00
francwa 2f1ac3c758 infra: simplified mongodb healthcheck 2026-01-06 04:36:52 +01:00
francwa d3b69f7459 feat: enabled logging for alfred(-core) 2026-01-06 04:33:59 +01:00
francwa 50c8204fa0 infra: added granular logging configuration for mongodb 2026-01-06 02:50:49 +01:00
francwa 507fe0f40e chore: updated dependencies 2026-01-06 02:41:18 +01:00
francwa b7b40eada1 fix: set proper database name for mongodb 2026-01-06 02:33:35 +01:00
francwa 9765386405 feat: named docker image to avoid docker picking the wrong one 2026-01-06 02:19:00 +01:00
francwa aa89a3fb00 doc: updated README.md
Renovate Bot / renovate (push) Successful in 20s
2026-01-04 13:30:54 +01:00
francwa 64aeb5fc80 chore: removed deprecated cli.py file 2026-01-04 13:29:38 +01:00
francwa 9540520dc4 feat: updated default start mode from core to full 2026-01-03 05:49:51 +01:00
francwa 300ed387f5 fix: fixed build vars generation not being called 2026-01-01 06:00:55 +01:00
francwa dea81de5b5 fix: updated CI workflow and added .env.make generation for CI 2026-01-01 05:33:39 +01:00
francwa 01a00a12af chore: bump version 0.1.6 → 0.1.7
CI/CD Awesome Pipeline / Test (push) Successful in 5m54s
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2026-01-01 05:04:37 +01:00
francwa 504d0162bb infra: added proper settings handling & orchestration and app bootstrap (.env)
Reviewed-on: #18
2026-01-01 03:55:35 +00:00
francwa cda23d074f feat: added current alfred version from pyproject.py to healthcheck 2026-01-01 04:51:45 +01:00
francwa 0357108077 infra: added orchestration and app bootstrap (.env) 2026-01-01 04:48:32 +01:00
francwa ab1df3dd0f fix: forgot to lint/format 2026-01-01 04:48:32 +01:00
francwa c50091f6bf feat: added proper settings handling 2026-01-01 04:48:32 +01:00
francwa 8b406370f1 chore: updated some dependencies 2026-01-01 03:08:22 +01:00
francwa c56bf2b92c feat: added Claude.AI to available providers 2025-12-29 02:13:11 +01:00
francwa b1507db4d0 fix: fixed test image not being built before running tests
Renovate Bot / renovate (push) Successful in 1m26s
2025-12-28 13:24:03 +01:00
francwa 3074962314 infra: proper librechat integration & improved configuration handling
Reviewed-on: #11
2025-12-28 11:56:53 +00:00
francwa 84799879bb fix: added data dir to .dockerignore 2025-12-28 12:02:44 +01:00
francwa 1052c1b619 infra: added modules version to .env.example 2025-12-28 06:07:34 +01:00
francwa 9958b8e848 feat: created placeholders for various API models 2025-12-28 06:06:28 +01:00
francwa b15161dad7 fix: provided OpenAI API key and fixed docker-compose configuration to enable RAG service 2025-12-28 06:04:08 +01:00
francwa 52f025ae32 chore: ran linter & formatter again 2025-12-27 20:07:48 +01:00
francwa 2cfe7a035b infra: moved makefile logic to python and added .env setup 2025-12-27 19:51:40 +01:00
francwa 2441c2dc29 infra: rewrote docker-compose for proper integration of librechat 2025-12-27 19:49:43 +01:00
francwa 261a1f3918 fix: fixed real data directory being used in tests 2025-12-27 19:48:13 +01:00
francwa 253903a1e5 infra: made pyproject the SSOT 2025-12-27 19:46:27 +01:00
francwa 20a113e335 infra: updated .env.example 2025-12-27 19:43:31 +01:00
francwa fed83e7d79 chore: bumped fastapi version 2025-12-27 19:42:16 +01:00
francwa 3880a4ec49 chore: ran linter and formatter 2025-12-27 19:41:22 +01:00
francwa 6195abbaa5 chore: fixed imports and tests configuration 2025-12-27 19:39:36 +01:00
francwa b132554631 infra: updated .gitignore 2025-12-27 02:30:14 +01:00
francwa 561796cec1 infra: removed CORE_DIR variable from Makefile (not relevant anymore) 2025-12-24 11:28:28 +01:00
francwa 26d90acc16 infra: moved manifest files in librechat dir 2025-12-24 09:46:25 +01:00
francwa d8234b2958 chore: cleaned up .env.example 2025-12-24 08:17:47 +01:00
francwa 156d1fe567 fix: fixed Dockerfile 2025-12-24 08:17:12 +01:00
francwa f8eee120cf infra: backed up old deploy-compose 2025-12-24 08:16:30 +01:00
francwa c5e4a5e1a7 infra: removed occurences of alfred_api_key (not implemented after all) 2025-12-24 08:11:30 +01:00
francwa d10c9160f3 infra: renamed broken references to alfred 2025-12-24 08:09:26 +01:00
francwa 1f88e99e8b infra: reorganized repo 2025-12-24 07:50:09 +01:00
francwa e097a13221 chore: upgraded to python 3.14 and some dependencies
Reviewed-on: francwa/agent_media#8
2025-12-22 14:04:46 +00:00
francwa 086fff803d feat: configured CI/CD pipeline to allow build for feature branches 2025-12-22 14:48:50 +01:00
francwa 45fbf975b3 feat: improved rules for renovate 2025-12-22 14:25:16 +01:00
francwa b8f2798e29 chore: updated fastapi and uvicorn 2025-12-22 14:20:22 +01:00
francwa c762d91eb1 chore: updated to python 3.14 2025-12-22 14:08:27 +01:00
francwa 35a68387ab infra: use python version from pyproject.toml 2025-12-22 13:46:40 +01:00
francwa 9b13c69631 chore: updated meilisearch 2025-12-22 13:25:54 +01:00
francwa 2ca1ea29b2 infra: added meilisearch to renovate exclusion list 2025-12-22 13:22:55 +01:00
francwa 5e86615bde fix: added renovate token with proper permissions 2025-12-22 12:59:24 +01:00
francwa 6701a4b392 infra: added Renovate bot 2025-12-22 12:50:59 +01:00
francwa 68372405d6 fix: downgraded upload-artifact action to v3 from v4 2025-12-22 12:13:50 +01:00
francwa f1ea0de247 fix: fixed indentation error 2025-12-22 12:04:47 +01:00
francwa 974d008825 feat: finalized CI/CD pipeline setup 2025-12-22 11:59:36 +01:00
francwa 8a87d94e6d fix: use docker image for trivy vulnerability scanner
CI/CD Awesome Pipeline / Test (push) Successful in 1m23s
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2025-12-22 11:38:35 +01:00
francwa ec99a501fc fix! added directive to Dockerfile 2025-12-22 11:37:48 +01:00
francwa c256b26601 fix: fixed vulnerability scanner issue in CI/CD pipeline
CI/CD Awesome Pipeline / Test (push) Successful in 48s
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2025-12-22 10:59:34 +01:00
francwa 56a3c1257d infra: added trivy vulnerability scanner to CI/CD
CI/CD Awesome Pipeline / Test (push) Successful in 1m36s
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2025-12-22 10:01:52 +01:00
francwa 79d23f936a fix: fixed typo 2025-12-22 09:40:43 +01:00
francwa f02e916d33 fix: fixed config gathering in ci.yml
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2025-12-22 09:16:55 +01:00
francwa 4e64c83c4b fix: updated build and push CI/CD configuration
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2025-12-22 08:45:31 +01:00
356 changed files with 33885 additions and 10059 deletions
@@ -1,5 +1,5 @@
[tool.bumpversion] [tool.bumpversion]
current_version = "0.1.6" current_version = "0.1.7"
parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)" parse = "(?P<major>\\d+)\\.(?P<minor>\\d+)\\.(?P<patch>\\d+)"
serialize = ["{major}.{minor}.{patch}"] serialize = ["{major}.{minor}.{patch}"]
search = "{current_version}" search = "{current_version}"
+3 -7
View File
@@ -22,8 +22,7 @@ venv
.venv .venv
env env
.env .env
.env.* .env-
# IDE # IDE
.vscode .vscode
.idea .idea
@@ -41,11 +40,8 @@ docs/
*.md *.md
!README.md !README.md
# Tests # Data
tests/ data/
pytest.ini
# Data (will be mounted as volumes)
memory_data/ memory_data/
logs/ logs/
*.log *.log
+80
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@@ -0,0 +1,80 @@
# --- IMPORTANT ---
# Settings are split across multiple files for clarity.
# Files (loaded in this order, last wins):
# .env.alfred — app config and service addresses (safe to commit)
# .env.secrets — generated secrets, passwords, URIs and API keys (DO NOT COMMIT)
# .env.make — build metadata synced from pyproject.toml (safe to commit)
#
# To customize: edit .env.alfred for config, .env.secrets for secrets.
# --- Alfred ---
MAX_HISTORY_MESSAGES=10
MAX_TOOL_ITERATIONS=10
REQUEST_TIMEOUT=30
# LLM Settings
LLM_TEMPERATURE=0.2
# Persistence
DATA_STORAGE_DIR=data
# Network
HOST=0.0.0.0
PORT=3080
# --- DATABASES ---
# Passwords and connection URIs are auto-generated in .env.secrets.
# Edit host/port/user/dbname here if needed.
# MongoDB (Application Data)
MONGO_HOST=mongodb
MONGO_PORT=27017
MONGO_USER=alfred
MONGO_DB_NAME=alfred
# PostgreSQL (Vector Database / RAG)
POSTGRES_HOST=vectordb
POSTGRES_PORT=5432
POSTGRES_USER=alfred
POSTGRES_DB_NAME=alfred
# --- EXTERNAL SERVICES ---
# TMDB — Media metadata (required). Get your key at https://www.themoviedb.org/
# → TMDB_API_KEY goes in .env.secrets
TMDB_BASE_URL=https://api.themoviedb.org/3
# qBittorrent
# → QBITTORRENT_PASSWORD goes in .env.secrets
QBITTORRENT_URL=https://qb.lan.anustart.top
QBITTORRENT_USERNAME=letmein
QBITTORRENT_PORT=16140
# Path translation: host-side prefix → container-side prefix
QBITTORRENT_HOST_PATH=/mnt/testipool
QBITTORRENT_CONTAINER_PATH=/mnt/data
# Meilisearch
# → MEILI_MASTER_KEY goes in .env.secrets
# MEILI_ENABLED=false # KEY DOESN'T EXISTS => SEARCH IS THE PROPER KEY
SEARCH=false
MEILI_NO_ANALYTICS=true
MEILI_HOST=http://meilisearch:7700
# --- LLM CONFIGURATION ---
# Providers: local, openai, anthropic, deepseek, google, kimi
# → API keys go in .env.secrets
DEFAULT_LLM_PROVIDER=deepseek
# Local LLM (Ollama)
#OLLAMA_BASE_URL=http://ollama:11434
#OLLAMA_MODEL=llama3.3:latest
OLLAMA_BASE_URL=http://10.0.0.11:11434
OLLAMA_MODEL=glm-4.7-flash:latest
# --- RAG ENGINE ---
RAG_ENABLED=TRUE
RAG_API_URL=http://rag_api:8000
RAG_API_PORT=8000
EMBEDDINGS_PROVIDER=ollama
EMBEDDINGS_MODEL=nomic-embed-text
+61 -57
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@@ -1,69 +1,73 @@
# Agent Media - Environment Variables # --- IMPORTANT ---
# Settings are split across multiple files for clarity.
# Files (loaded in this order, last wins):
# .env.alfred — app config and service addresses (safe to commit)
# .env.secrets — generated secrets, passwords, URIs and API keys (DO NOT COMMIT)
# .env.make — build metadata synced from pyproject.toml (safe to commit)
#
# To customize: edit .env.alfred for config, .env.secrets for secrets.
# LibreChat Security Keys # --- Alfred ---
# Generate secure keys with: openssl rand -base64 32 MAX_HISTORY_MESSAGES=10
JWT_SECRET=your-super-secret-jwt-key-change-this-in-production MAX_TOOL_ITERATIONS=10
JWT_REFRESH_SECRET=your-super-secret-refresh-key-change-this-too REQUEST_TIMEOUT=30
# Generate with: openssl rand -hex 16 (for CREDS_KEY) # LLM Settings
CREDS_KEY=your-32-character-secret-key-here LLM_TEMPERATURE=0.2
# Generate with: openssl rand -hex 8 (for CREDS_IV) # Persistence
CREDS_IV=your-16-character-iv-here DATA_STORAGE_DIR=data
# LibreChat Configuration # Network
DOMAIN_CLIENT=http://localhost:3080 HOST=0.0.0.0
DOMAIN_SERVER=http://localhost:3080 PORT=3080
# Session expiry (in milliseconds) # --- DATABASES ---
# Default: 15 minutes # Passwords and connection URIs are auto-generated in .env.secrets.
SESSION_EXPIRY=900000 # Edit host/port/user/dbname here if needed.
# Refresh token expiry (in milliseconds) # MongoDB (Application Data)
# Default: 7 days MONGO_HOST=mongodb
REFRESH_TOKEN_EXPIRY=604800000 MONGO_PORT=27017
MONGO_USER=alfred
MONGO_DB_NAME=LibreChat
# Meilisearch Configuration # PostgreSQL (Vector Database / RAG)
# Master key for Meilisearch (generate with: openssl rand -base64 32) POSTGRES_HOST=vectordb
MEILI_MASTER_KEY=DrhYf7zENyR6AlUCKmnz0eYASOQdl6zxH7s7MKFSfFU POSTGRES_PORT=5432
POSTGRES_USER=alfred
POSTGRES_DB_NAME=alfred
# PostgreSQL Configuration (for RAG API) # --- EXTERNAL SERVICES ---
POSTGRES_DB=librechat_rag
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
# RAG API Configuration (Vector Database) # TMDB — Media metadata (required). Get your key at https://www.themoviedb.org/
RAG_COLLECTION_NAME=testcollection # → TMDB_API_KEY goes in .env.secrets
RAG_EMBEDDINGS_PROVIDER=openai TMDB_BASE_URL=https://api.themoviedb.org/3
RAG_EMBEDDINGS_MODEL=text-embedding-3-small
# API Keys # qBittorrent
# OpenAI API Key (required for RAG embeddings) # → QBITTORRENT_PASSWORD goes in .env.secrets
OPENAI_API_KEY=your-openai-api-key-here QBITTORRENT_URL=http://qbittorrent:16140
# Deepseek API Key (for LLM in agent-brain)
DEEPSEEK_API_KEY=your-deepseek-api-key-here
# Agent Brain Configuration
# LLM Provider (deepseek or ollama)
LLM_PROVIDER=deepseek
# Memory storage directory (inside container)
MEMORY_STORAGE_DIR=/data/memory
# API Key for agent-brain (used by LibreChat custom endpoint)
AGENT_BRAIN_API_KEY=agent-brain-secret-key
# External Services (Optional)
# TMDB API Key (for movie metadata)
TMDB_API_KEY=your-tmdb-key
# qBittorrent Configuration
QBITTORRENT_URL=http://localhost:8080
QBITTORRENT_USERNAME=admin QBITTORRENT_USERNAME=admin
QBITTORRENT_PASSWORD=adminpass QBITTORRENT_PORT=16140
# Debug Options # Meilisearch
DEBUG_LOGGING=false # → MEILI_MASTER_KEY goes in .env.secrets
DEBUG_CONSOLE=false MEILI_ENABLED=FALSE
MEILI_NO_ANALYTICS=TRUE
MEILI_HOST=http://meilisearch:7700
# --- LLM CONFIGURATION ---
# Providers: local, openai, anthropic, deepseek, google, kimi
# → API keys go in .env.secrets
DEFAULT_LLM_PROVIDER=local
# Local LLM (Ollama)
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_MODEL=llama3.3:latest
# --- RAG ENGINE ---
RAG_ENABLED=TRUE
RAG_API_URL=http://rag_api:8000
RAG_API_PORT=8000
EMBEDDINGS_PROVIDER=ollama
EMBEDDINGS_MODEL=nomic-embed-text
+878
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@@ -0,0 +1,878 @@
#=====================================================================#
# LibreChat Configuration #
#=====================================================================#
# Please refer to the reference documentation for assistance #
# with configuring your LibreChat environment. #
# #
# https://www.librechat.ai/docs/configuration/dotenv #
#=====================================================================#
#==================================================#
# Server Configuration #
#==================================================#
HOST=localhost
PORT=3080
MONGO_URI=mongodb://127.0.0.1:27017/LibreChat
#The maximum number of connections in the connection pool. */
MONGO_MAX_POOL_SIZE=
#The minimum number of connections in the connection pool. */
MONGO_MIN_POOL_SIZE=
#The maximum number of connections that may be in the process of being established concurrently by the connection pool. */
MONGO_MAX_CONNECTING=
#The maximum number of milliseconds that a connection can remain idle in the pool before being removed and closed. */
MONGO_MAX_IDLE_TIME_MS=
#The maximum time in milliseconds that a thread can wait for a connection to become available. */
MONGO_WAIT_QUEUE_TIMEOUT_MS=
# Set to false to disable automatic index creation for all models associated with this connection. */
MONGO_AUTO_INDEX=
# Set to `false` to disable Mongoose automatically calling `createCollection()` on every model created on this connection. */
MONGO_AUTO_CREATE=
DOMAIN_CLIENT=http://localhost:3080
DOMAIN_SERVER=http://localhost:3080
NO_INDEX=true
# Use the address that is at most n number of hops away from the Express application.
# req.socket.remoteAddress is the first hop, and the rest are looked for in the X-Forwarded-For header from right to left.
# A value of 0 means that the first untrusted address would be req.socket.remoteAddress, i.e. there is no reverse proxy.
# Defaulted to 1.
TRUST_PROXY=1
# Minimum password length for user authentication
# Default: 8
# Note: When using LDAP authentication, you may want to set this to 1
# to bypass local password validation, as LDAP servers handle their own
# password policies.
# MIN_PASSWORD_LENGTH=8
# When enabled, the app will continue running after encountering uncaught exceptions
# instead of exiting the process. Not recommended for production unless necessary.
# CONTINUE_ON_UNCAUGHT_EXCEPTION=false
#===============#
# JSON Logging #
#===============#
# Use when process console logs in cloud deployment like GCP/AWS
CONSOLE_JSON=false
#===============#
# Debug Logging #
#===============#
DEBUG_LOGGING=true
DEBUG_CONSOLE=false
# Set to true to enable agent debug logging
AGENT_DEBUG_LOGGING=false
# Enable memory diagnostics (logs heap/RSS snapshots every 60s, auto-enabled with --inspect)
# MEM_DIAG=true
#=============#
# Permissions #
#=============#
# UID=1000
# GID=1000
#==============#
# Node Options #
#==============#
# NOTE: NODE_MAX_OLD_SPACE_SIZE is NOT recognized by Node.js directly.
# This variable is used as a build argument for Docker or CI/CD workflows,
# and is NOT used by Node.js to set the heap size at runtime.
# To configure Node.js memory, use NODE_OPTIONS, e.g.:
# NODE_OPTIONS="--max-old-space-size=6144"
# See: https://nodejs.org/api/cli.html#--max-old-space-sizesize-in-mib
NODE_MAX_OLD_SPACE_SIZE=6144
#===============#
# Configuration #
#===============#
# Use an absolute path, a relative path, or a URL
# CONFIG_PATH="/alternative/path/to/librechat.yaml"
#==================#
# Langfuse Tracing #
#==================#
# Get Langfuse API keys for your project from the project settings page: https://cloud.langfuse.com
# LANGFUSE_PUBLIC_KEY=
# LANGFUSE_SECRET_KEY=
# LANGFUSE_BASE_URL=
#===================================================#
# Endpoints #
#===================================================#
# ENDPOINTS=openAI,assistants,azureOpenAI,google,anthropic
PROXY=
#===================================#
# Known Endpoints - librechat.yaml #
#===================================#
# https://www.librechat.ai/docs/configuration/librechat_yaml/ai_endpoints
# ANYSCALE_API_KEY=
# APIPIE_API_KEY=
# COHERE_API_KEY=
# DEEPSEEK_API_KEY=
# DATABRICKS_API_KEY=
# FIREWORKS_API_KEY=
# GROQ_API_KEY=
# HUGGINGFACE_TOKEN=
# MISTRAL_API_KEY=
# OPENROUTER_KEY=
# PERPLEXITY_API_KEY=
# SHUTTLEAI_API_KEY=
# TOGETHERAI_API_KEY=
# UNIFY_API_KEY=
# XAI_API_KEY=
#============#
# Anthropic #
#============#
ANTHROPIC_API_KEY=user_provided
# ANTHROPIC_MODELS=claude-sonnet-4-6,claude-opus-4-6,claude-opus-4-20250514,claude-sonnet-4-20250514,claude-3-7-sonnet-20250219,claude-3-5-sonnet-20241022,claude-3-5-haiku-20241022,claude-3-opus-20240229,claude-3-sonnet-20240229,claude-3-haiku-20240307
# ANTHROPIC_REVERSE_PROXY=
# Set to true to use Anthropic models through Google Vertex AI instead of direct API
# ANTHROPIC_USE_VERTEX=
# ANTHROPIC_VERTEX_REGION=us-east5
#============#
# Azure #
#============#
# Note: these variables are DEPRECATED
# Use the `librechat.yaml` configuration for `azureOpenAI` instead
# You may also continue to use them if you opt out of using the `librechat.yaml` configuration
# AZURE_OPENAI_DEFAULT_MODEL=gpt-3.5-turbo # Deprecated
# AZURE_OPENAI_MODELS=gpt-3.5-turbo,gpt-4 # Deprecated
# AZURE_USE_MODEL_AS_DEPLOYMENT_NAME=TRUE # Deprecated
# AZURE_API_KEY= # Deprecated
# AZURE_OPENAI_API_INSTANCE_NAME= # Deprecated
# AZURE_OPENAI_API_DEPLOYMENT_NAME= # Deprecated
# AZURE_OPENAI_API_VERSION= # Deprecated
# AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME= # Deprecated
# AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME= # Deprecated
#=================#
# AWS Bedrock #
#=================#
# BEDROCK_AWS_DEFAULT_REGION=us-east-1 # A default region must be provided
# BEDROCK_AWS_ACCESS_KEY_ID=someAccessKey
# BEDROCK_AWS_SECRET_ACCESS_KEY=someSecretAccessKey
# BEDROCK_AWS_SESSION_TOKEN=someSessionToken
# Note: This example list is not meant to be exhaustive. If omitted, all known, supported model IDs will be included for you.
# BEDROCK_AWS_MODELS=anthropic.claude-sonnet-4-6,anthropic.claude-opus-4-6-v1,anthropic.claude-3-5-sonnet-20240620-v1:0,meta.llama3-1-8b-instruct-v1:0
# Cross-region inference model IDs: us.anthropic.claude-sonnet-4-6,us.anthropic.claude-opus-4-6-v1,global.anthropic.claude-opus-4-6-v1
# See all Bedrock model IDs here: https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns
# Notes on specific models:
# The following models are not support due to not supporting streaming:
# ai21.j2-mid-v1
# The following models are not support due to not supporting conversation history:
# ai21.j2-ultra-v1, cohere.command-text-v14, cohere.command-light-text-v14
#============#
# Google #
#============#
GOOGLE_KEY=user_provided
# GOOGLE_REVERSE_PROXY=
# Some reverse proxies do not support the X-goog-api-key header, uncomment to pass the API key in Authorization header instead.
# GOOGLE_AUTH_HEADER=true
# Gemini API (AI Studio)
# GOOGLE_MODELS=gemini-3.1-pro-preview,gemini-3.1-pro-preview-customtools,gemini-3.1-flash-lite-preview,gemini-2.5-pro,gemini-2.5-flash,gemini-2.5-flash-lite,gemini-2.0-flash,gemini-2.0-flash-lite
# Vertex AI
# GOOGLE_MODELS=gemini-3.1-pro-preview,gemini-3.1-pro-preview-customtools,gemini-3.1-flash-lite-preview,gemini-2.5-pro,gemini-2.5-flash,gemini-2.5-flash-lite,gemini-2.0-flash-001,gemini-2.0-flash-lite-001
# GOOGLE_TITLE_MODEL=gemini-2.0-flash-lite-001
# Google Cloud region for Vertex AI (used by both chat and image generation)
# GOOGLE_LOC=us-central1
# Alternative region env var for Gemini Image Generation
# GOOGLE_CLOUD_LOCATION=global
# Vertex AI Service Account Configuration
# Path to your Google Cloud service account JSON file
# GOOGLE_SERVICE_KEY_FILE=/path/to/service-account.json
# Google Safety Settings
# NOTE: These settings apply to both Vertex AI and Gemini API (AI Studio)
#
# For Vertex AI:
# To use the BLOCK_NONE setting, you need either:
# (a) Access through an allowlist via your Google account team, or
# (b) Switch to monthly invoiced billing: https://cloud.google.com/billing/docs/how-to/invoiced-billing
#
# For Gemini API (AI Studio):
# BLOCK_NONE is available by default, no special account requirements.
#
# Available options: BLOCK_NONE, BLOCK_ONLY_HIGH, BLOCK_MEDIUM_AND_ABOVE, BLOCK_LOW_AND_ABOVE
#
# GOOGLE_SAFETY_SEXUALLY_EXPLICIT=BLOCK_ONLY_HIGH
# GOOGLE_SAFETY_HATE_SPEECH=BLOCK_ONLY_HIGH
# GOOGLE_SAFETY_HARASSMENT=BLOCK_ONLY_HIGH
# GOOGLE_SAFETY_DANGEROUS_CONTENT=BLOCK_ONLY_HIGH
# GOOGLE_SAFETY_CIVIC_INTEGRITY=BLOCK_ONLY_HIGH
#========================#
# Gemini Image Generation #
#========================#
# Gemini Image Generation Tool (for Agents)
# Supports multiple authentication methods in priority order:
# 1. User-provided API key (via GUI)
# 2. GEMINI_API_KEY env var (admin-configured)
# 3. GOOGLE_KEY env var (shared with Google chat endpoint)
# 4. Vertex AI service account (via GOOGLE_SERVICE_KEY_FILE)
# Option A: Use dedicated Gemini API key for image generation
# GEMINI_API_KEY=your-gemini-api-key
# Vertex AI model for image generation (defaults to gemini-2.5-flash-image)
# GEMINI_IMAGE_MODEL=gemini-2.5-flash-image
#============#
# OpenAI #
#============#
OPENAI_API_KEY=user_provided
# OPENAI_MODELS=gpt-5,gpt-5-codex,gpt-5-mini,gpt-5-nano,o3-pro,o3,o4-mini,gpt-4.1,gpt-4.1-mini,gpt-4.1-nano,o3-mini,o1-pro,o1,gpt-4o,gpt-4o-mini
DEBUG_OPENAI=false
# TITLE_CONVO=false
# OPENAI_TITLE_MODEL=gpt-4o-mini
# OPENAI_SUMMARIZE=true
# OPENAI_SUMMARY_MODEL=gpt-4o-mini
# OPENAI_FORCE_PROMPT=true
# OPENAI_REVERSE_PROXY=
# OPENAI_ORGANIZATION=
#====================#
# Assistants API #
#====================#
ASSISTANTS_API_KEY=user_provided
# ASSISTANTS_BASE_URL=
# ASSISTANTS_MODELS=gpt-4o,gpt-4o-mini,gpt-3.5-turbo-0125,gpt-3.5-turbo-16k-0613,gpt-3.5-turbo-16k,gpt-3.5-turbo,gpt-4,gpt-4-0314,gpt-4-32k-0314,gpt-4-0613,gpt-3.5-turbo-0613,gpt-3.5-turbo-1106,gpt-4-0125-preview,gpt-4-turbo-preview,gpt-4-1106-preview
#==========================#
# Azure Assistants API #
#==========================#
# Note: You should map your credentials with custom variables according to your Azure OpenAI Configuration
# The models for Azure Assistants are also determined by your Azure OpenAI configuration.
# More info, including how to enable use of Assistants with Azure here:
# https://www.librechat.ai/docs/configuration/librechat_yaml/ai_endpoints/azure#using-assistants-with-azure
CREDS_KEY=f34be427ebb29de8d88c107a71546019685ed8b241d8f2ed00c3df97ad2566f0
CREDS_IV=e2341419ec3dd3d19b13a1a87fafcbfb
# Azure AI Search
#-----------------
AZURE_AI_SEARCH_SERVICE_ENDPOINT=
AZURE_AI_SEARCH_INDEX_NAME=
AZURE_AI_SEARCH_API_KEY=
AZURE_AI_SEARCH_API_VERSION=
AZURE_AI_SEARCH_SEARCH_OPTION_QUERY_TYPE=
AZURE_AI_SEARCH_SEARCH_OPTION_TOP=
AZURE_AI_SEARCH_SEARCH_OPTION_SELECT=
# OpenAI Image Tools Customization
#----------------
# IMAGE_GEN_OAI_API_KEY= # Create or reuse OpenAI API key for image generation tool
# IMAGE_GEN_OAI_BASEURL= # Custom OpenAI base URL for image generation tool
# IMAGE_GEN_OAI_AZURE_API_VERSION= # Custom Azure OpenAI deployments
# IMAGE_GEN_OAI_MODEL=gpt-image-1 # OpenAI image model (e.g., gpt-image-1, gpt-image-1.5)
# IMAGE_GEN_OAI_DESCRIPTION=
# IMAGE_GEN_OAI_DESCRIPTION_WITH_FILES=Custom description for image generation tool when files are present
# IMAGE_GEN_OAI_DESCRIPTION_NO_FILES=Custom description for image generation tool when no files are present
# IMAGE_EDIT_OAI_DESCRIPTION=Custom description for image editing tool
# IMAGE_GEN_OAI_PROMPT_DESCRIPTION=Custom prompt description for image generation tool
# IMAGE_EDIT_OAI_PROMPT_DESCRIPTION=Custom prompt description for image editing tool
# DALL·E
#----------------
# DALLE_API_KEY=
# DALLE3_API_KEY=
# DALLE2_API_KEY=
# DALLE3_SYSTEM_PROMPT=
# DALLE2_SYSTEM_PROMPT=
# DALLE_REVERSE_PROXY=
# DALLE3_BASEURL=
# DALLE2_BASEURL=
# DALL·E (via Azure OpenAI)
# Note: requires some of the variables above to be set
#----------------
# DALLE3_AZURE_API_VERSION=
# DALLE2_AZURE_API_VERSION=
# Flux
#-----------------
FLUX_API_BASE_URL=https://api.us1.bfl.ai
# FLUX_API_BASE_URL = 'https://api.bfl.ml';
# Get your API key at https://api.us1.bfl.ai/auth/profile
# FLUX_API_KEY=
# Google
#-----------------
GOOGLE_SEARCH_API_KEY=
GOOGLE_CSE_ID=
# Stable Diffusion
#-----------------
SD_WEBUI_URL=http://host.docker.internal:7860
# Tavily
#-----------------
TAVILY_API_KEY=
# Traversaal
#-----------------
TRAVERSAAL_API_KEY=
# WolframAlpha
#-----------------
WOLFRAM_APP_ID=
# Zapier
#-----------------
ZAPIER_NLA_API_KEY=
#==================================================#
# Search #
#==================================================#
SEARCH=true
MEILI_NO_ANALYTICS=true
MEILI_HOST=http://0.0.0.0:7700
MEILI_MASTER_KEY=DrhYf7zENyR6AlUCKmnz0eYASOQdl6zxH7s7MKFSfFCt
# Optional: Disable indexing, useful in a multi-node setup
# where only one instance should perform an index sync.
# MEILI_NO_SYNC=true
#==================================================#
# Speech to Text & Text to Speech #
#==================================================#
STT_API_KEY=
TTS_API_KEY=
#==================================================#
# RAG #
#==================================================#
# More info: https://www.librechat.ai/docs/configuration/rag_api
# RAG_OPENAI_BASEURL=
# RAG_OPENAI_API_KEY=
# RAG_USE_FULL_CONTEXT=
# EMBEDDINGS_PROVIDER=openai
# EMBEDDINGS_MODEL=text-embedding-3-small
#===================================================#
# User System #
#===================================================#
#========================#
# Moderation #
#========================#
OPENAI_MODERATION=false
OPENAI_MODERATION_API_KEY=
# OPENAI_MODERATION_REVERSE_PROXY=
BAN_VIOLATIONS=true
BAN_DURATION=1000 * 60 * 60 * 2
BAN_INTERVAL=20
LOGIN_VIOLATION_SCORE=1
REGISTRATION_VIOLATION_SCORE=1
CONCURRENT_VIOLATION_SCORE=1
MESSAGE_VIOLATION_SCORE=1
NON_BROWSER_VIOLATION_SCORE=20
TTS_VIOLATION_SCORE=0
STT_VIOLATION_SCORE=0
FORK_VIOLATION_SCORE=0
IMPORT_VIOLATION_SCORE=0
FILE_UPLOAD_VIOLATION_SCORE=0
LOGIN_MAX=7
LOGIN_WINDOW=5
REGISTER_MAX=5
REGISTER_WINDOW=60
LIMIT_CONCURRENT_MESSAGES=true
CONCURRENT_MESSAGE_MAX=2
LIMIT_MESSAGE_IP=true
MESSAGE_IP_MAX=40
MESSAGE_IP_WINDOW=1
LIMIT_MESSAGE_USER=false
MESSAGE_USER_MAX=40
MESSAGE_USER_WINDOW=1
ILLEGAL_MODEL_REQ_SCORE=5
#========================#
# Balance #
#========================#
# CHECK_BALANCE=false
# START_BALANCE=20000 # note: the number of tokens that will be credited after registration.
#========================#
# Registration and Login #
#========================#
ALLOW_EMAIL_LOGIN=true
ALLOW_REGISTRATION=true
ALLOW_SOCIAL_LOGIN=false
ALLOW_SOCIAL_REGISTRATION=false
ALLOW_PASSWORD_RESET=false
# ALLOW_ACCOUNT_DELETION=true # note: enabled by default if omitted/commented out
ALLOW_UNVERIFIED_EMAIL_LOGIN=true
SESSION_EXPIRY=1000 * 60 * 15
REFRESH_TOKEN_EXPIRY=(1000 * 60 * 60 * 24) * 7
JWT_SECRET=16f8c0ef4a5d391b26034086c628469d3f9f497f08163ab9b40137092f2909ef
JWT_REFRESH_SECRET=eaa5191f2914e30b9387fd84e254e4ba6fc51b4654968a9b0803b456a54b8418
# Discord
DISCORD_CLIENT_ID=
DISCORD_CLIENT_SECRET=
DISCORD_CALLBACK_URL=/oauth/discord/callback
# Facebook
FACEBOOK_CLIENT_ID=
FACEBOOK_CLIENT_SECRET=
FACEBOOK_CALLBACK_URL=/oauth/facebook/callback
# GitHub
GITHUB_CLIENT_ID=
GITHUB_CLIENT_SECRET=
GITHUB_CALLBACK_URL=/oauth/github/callback
# GitHub Enterprise
# GITHUB_ENTERPRISE_BASE_URL=
# GITHUB_ENTERPRISE_USER_AGENT=
# Google
GOOGLE_CLIENT_ID=
GOOGLE_CLIENT_SECRET=
GOOGLE_CALLBACK_URL=/oauth/google/callback
# Apple
APPLE_CLIENT_ID=
APPLE_TEAM_ID=
APPLE_KEY_ID=
APPLE_PRIVATE_KEY_PATH=
APPLE_CALLBACK_URL=/oauth/apple/callback
# OpenID
OPENID_CLIENT_ID=
OPENID_CLIENT_SECRET=
OPENID_ISSUER=
OPENID_SESSION_SECRET=
OPENID_SCOPE="openid profile email"
OPENID_CALLBACK_URL=/oauth/openid/callback
OPENID_REQUIRED_ROLE=
OPENID_REQUIRED_ROLE_TOKEN_KIND=
OPENID_REQUIRED_ROLE_PARAMETER_PATH=
OPENID_ADMIN_ROLE=
OPENID_ADMIN_ROLE_PARAMETER_PATH=
OPENID_ADMIN_ROLE_TOKEN_KIND=
# Set to determine which user info property returned from OpenID Provider to store as the User's username
OPENID_USERNAME_CLAIM=
# Set to determine which user info property returned from OpenID Provider to store as the User's name
OPENID_NAME_CLAIM=
# Set to determine which user info claim to use as the email/identifier for user matching (e.g., "upn" for Entra ID)
# When not set, defaults to: email -> preferred_username -> upn
OPENID_EMAIL_CLAIM=
# Optional audience parameter for OpenID authorization requests
OPENID_AUDIENCE=
OPENID_BUTTON_LABEL=
OPENID_IMAGE_URL=
# Set to true to automatically redirect to the OpenID provider when a user visits the login page
# This will bypass the login form completely for users, only use this if OpenID is your only authentication method
OPENID_AUTO_REDIRECT=false
# Set to true to use PKCE (Proof Key for Code Exchange) for OpenID authentication
OPENID_USE_PKCE=false
#Set to true to reuse openid tokens for authentication management instead of using the mongodb session and the custom refresh token.
OPENID_REUSE_TOKENS=
#By default, signing key verification results are cached in order to prevent excessive HTTP requests to the JWKS endpoint.
#If a signing key matching the kid is found, this will be cached and the next time this kid is requested the signing key will be served from the cache.
#Default is true.
OPENID_JWKS_URL_CACHE_ENABLED=
OPENID_JWKS_URL_CACHE_TIME= # 600000 ms eq to 10 minutes leave empty to disable caching
#Set to true to trigger token exchange flow to acquire access token for the userinfo endpoint.
OPENID_ON_BEHALF_FLOW_FOR_USERINFO_REQUIRED=
OPENID_ON_BEHALF_FLOW_USERINFO_SCOPE="user.read" # example for Scope Needed for Microsoft Graph API
# Set to true to use the OpenID Connect end session endpoint for logout
OPENID_USE_END_SESSION_ENDPOINT=
# URL to redirect to after OpenID logout (defaults to ${DOMAIN_CLIENT}/login)
OPENID_POST_LOGOUT_REDIRECT_URI=
# Maximum logout URL length before using logout_hint instead of id_token_hint (default: 2000)
OPENID_MAX_LOGOUT_URL_LENGTH=
#========================#
# SharePoint Integration #
#========================#
# Requires Entra ID (OpenID) authentication to be configured
# Enable SharePoint file picker in chat and agent panels
# ENABLE_SHAREPOINT_FILEPICKER=true
# SharePoint tenant base URL (e.g., https://yourtenant.sharepoint.com)
# SHAREPOINT_BASE_URL=https://yourtenant.sharepoint.com
# Microsoft Graph API And SharePoint scopes for file picker
# SHAREPOINT_PICKER_SHAREPOINT_SCOPE==https://yourtenant.sharepoint.com/AllSites.Read
# SHAREPOINT_PICKER_GRAPH_SCOPE=Files.Read.All
#========================#
# SAML
# Note: If OpenID is enabled, SAML authentication will be automatically disabled.
SAML_ENTRY_POINT=
SAML_ISSUER=
SAML_CERT=
SAML_CALLBACK_URL=/oauth/saml/callback
SAML_SESSION_SECRET=
# Attribute mappings (optional)
SAML_EMAIL_CLAIM=
SAML_USERNAME_CLAIM=
SAML_GIVEN_NAME_CLAIM=
SAML_FAMILY_NAME_CLAIM=
SAML_PICTURE_CLAIM=
SAML_NAME_CLAIM=
# Logint buttion settings (optional)
SAML_BUTTON_LABEL=
SAML_IMAGE_URL=
# Whether the SAML Response should be signed.
# - If "true", the entire `SAML Response` will be signed.
# - If "false" or unset, only the `SAML Assertion` will be signed (default behavior).
# SAML_USE_AUTHN_RESPONSE_SIGNED=
#===============================================#
# Microsoft Graph API / Entra ID Integration #
#===============================================#
# Enable Entra ID people search integration in permissions/sharing system
# When enabled, the people picker will search both local database and Entra ID
USE_ENTRA_ID_FOR_PEOPLE_SEARCH=false
# When enabled, entra id groups owners will be considered as members of the group
ENTRA_ID_INCLUDE_OWNERS_AS_MEMBERS=false
# Microsoft Graph API scopes needed for people/group search
# Default scopes provide access to user profiles and group memberships
OPENID_GRAPH_SCOPES=User.Read,People.Read,GroupMember.Read.All
# LDAP
LDAP_URL=
LDAP_BIND_DN=
LDAP_BIND_CREDENTIALS=
LDAP_USER_SEARCH_BASE=
#LDAP_SEARCH_FILTER="mail="
LDAP_CA_CERT_PATH=
# LDAP_TLS_REJECT_UNAUTHORIZED=
# LDAP_STARTTLS=
# LDAP_LOGIN_USES_USERNAME=true
# LDAP_ID=
# LDAP_USERNAME=
# LDAP_EMAIL=
# LDAP_FULL_NAME=
#========================#
# Email Password Reset #
#========================#
EMAIL_SERVICE=
EMAIL_HOST=
EMAIL_PORT=25
EMAIL_ENCRYPTION=
EMAIL_ENCRYPTION_HOSTNAME=
EMAIL_ALLOW_SELFSIGNED=
# Leave both empty for SMTP servers that do not require authentication
EMAIL_USERNAME=
EMAIL_PASSWORD=
EMAIL_FROM_NAME=
EMAIL_FROM=noreply@librechat.ai
#========================#
# Mailgun API #
#========================#
# MAILGUN_API_KEY=your-mailgun-api-key
# MAILGUN_DOMAIN=mg.yourdomain.com
# EMAIL_FROM=noreply@yourdomain.com
# EMAIL_FROM_NAME="LibreChat"
# # Optional: For EU region
# MAILGUN_HOST=https://api.eu.mailgun.net
#========================#
# Firebase CDN #
#========================#
FIREBASE_API_KEY=
FIREBASE_AUTH_DOMAIN=
FIREBASE_PROJECT_ID=
FIREBASE_STORAGE_BUCKET=
FIREBASE_MESSAGING_SENDER_ID=
FIREBASE_APP_ID=
#========================#
# S3 AWS Bucket #
#========================#
AWS_ENDPOINT_URL=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION=
AWS_BUCKET_NAME=
# Required for path-style S3-compatible providers (MinIO, Hetzner, Backblaze B2, etc.)
# that don't support virtual-hosted-style URLs (bucket.endpoint). Not needed for AWS S3.
# AWS_FORCE_PATH_STYLE=false
#========================#
# Azure Blob Storage #
#========================#
AZURE_STORAGE_CONNECTION_STRING=
AZURE_STORAGE_PUBLIC_ACCESS=false
AZURE_CONTAINER_NAME=files
#========================#
# Shared Links #
#========================#
ALLOW_SHARED_LINKS=true
# Allows unauthenticated access to shared links. Defaults to false (auth required) if not set.
ALLOW_SHARED_LINKS_PUBLIC=false
#==============================#
# Static File Cache Control #
#==============================#
# Leave commented out to use defaults: 1 day (86400 seconds) for s-maxage and 2 days (172800 seconds) for max-age
# NODE_ENV must be set to production for these to take effect
# STATIC_CACHE_MAX_AGE=172800
# STATIC_CACHE_S_MAX_AGE=86400
# If you have another service in front of your LibreChat doing compression, disable express based compression here
# DISABLE_COMPRESSION=true
# If you have gzipped version of uploaded image images in the same folder, this will enable gzip scan and serving of these images
# Note: The images folder will be scanned on startup and a ma kept in memory. Be careful for large number of images.
# ENABLE_IMAGE_OUTPUT_GZIP_SCAN=true
#===================================================#
# UI #
#===================================================#
APP_TITLE=LibreChat
# CUSTOM_FOOTER="My custom footer"
HELP_AND_FAQ_URL=https://librechat.ai
# SHOW_BIRTHDAY_ICON=true
# Google tag manager id
#ANALYTICS_GTM_ID=user provided google tag manager id
# limit conversation file imports to a certain number of bytes in size to avoid the container
# maxing out memory limitations by unremarking this line and supplying a file size in bytes
# such as the below example of 250 mib
# CONVERSATION_IMPORT_MAX_FILE_SIZE_BYTES=262144000
#===============#
# REDIS Options #
#===============#
# Enable Redis for caching and session storage
# USE_REDIS=true
# Enable Redis for resumable LLM streams (defaults to USE_REDIS value if not set)
# Set to false to use in-memory storage for streams while keeping Redis for other caches
# USE_REDIS_STREAMS=true
# Single Redis instance
# REDIS_URI=redis://127.0.0.1:6379
# Redis cluster (multiple nodes)
# REDIS_URI=redis://127.0.0.1:7001,redis://127.0.0.1:7002,redis://127.0.0.1:7003
# Redis with TLS/SSL encryption and CA certificate
# REDIS_URI=rediss://127.0.0.1:6380
# REDIS_CA=/path/to/ca-cert.pem
# Elasticache may need to use an alternate dnsLookup for TLS connections. see "Special Note: Aws Elasticache Clusters with TLS" on this webpage: https://www.npmjs.com/package/ioredis
# Enable alternative dnsLookup for redis
# REDIS_USE_ALTERNATIVE_DNS_LOOKUP=true
# Redis authentication (if required)
# REDIS_USERNAME=your_redis_username
# REDIS_PASSWORD=your_redis_password
# Redis key prefix configuration
# Use environment variable name for dynamic prefix (recommended for cloud deployments)
# REDIS_KEY_PREFIX_VAR=K_REVISION
# Or use static prefix directly
# REDIS_KEY_PREFIX=librechat
# Redis connection limits
# REDIS_MAX_LISTENERS=40
# Redis ping interval in seconds (0 = disabled, >0 = enabled)
# When set to a positive integer, Redis clients will ping the server at this interval to keep connections alive
# When unset or 0, no pinging is performed (recommended for most use cases)
# REDIS_PING_INTERVAL=300
# Force specific cache namespaces to use in-memory storage even when Redis is enabled
# Comma-separated list of CacheKeys
# Defaults to CONFIG_STORE,APP_CONFIG so YAML-derived config stays per-container (safe for blue/green deployments)
# Set to empty string to force all namespaces through Redis: FORCED_IN_MEMORY_CACHE_NAMESPACES=
# FORCED_IN_MEMORY_CACHE_NAMESPACES=CONFIG_STORE,APP_CONFIG
# Leader Election Configuration (for multi-instance deployments with Redis)
# Duration in seconds that the leader lease is valid before it expires (default: 25)
# LEADER_LEASE_DURATION=25
# Interval in seconds at which the leader renews its lease (default: 10)
# LEADER_RENEW_INTERVAL=10
# Maximum number of retry attempts when renewing the lease fails (default: 3)
# LEADER_RENEW_ATTEMPTS=3
# Delay in seconds between retry attempts when renewing the lease (default: 0.5)
# LEADER_RENEW_RETRY_DELAY=0.5
#==================================================#
# Others #
#==================================================#
# You should leave the following commented out #
# NODE_ENV=
# E2E_USER_EMAIL=
# E2E_USER_PASSWORD=
#=====================================================#
# Cache Headers #
#=====================================================#
# Headers that control caching of the index.html #
# Default configuration prevents caching to ensure #
# users always get the latest version. Customize #
# only if you understand caching implications. #
# INDEX_CACHE_CONTROL=no-cache, no-store, must-revalidate
# INDEX_PRAGMA=no-cache
# INDEX_EXPIRES=0
# no-cache: Forces validation with server before using cached version
# no-store: Prevents storing the response entirely
# must-revalidate: Prevents using stale content when offline
#=====================================================#
# OpenWeather #
#=====================================================#
OPENWEATHER_API_KEY=
#====================================#
# LibreChat Code Interpreter API #
#====================================#
# https://code.librechat.ai
# LIBRECHAT_CODE_API_KEY=your-key
#======================#
# Web Search #
#======================#
# Note: All of the following variable names can be customized.
# Omit values to allow user to provide them.
# For more information on configuration values, see:
# https://librechat.ai/docs/features/web_search
# Search Provider (Required)
# SERPER_API_KEY=your_serper_api_key
# Scraper (Required)
# FIRECRAWL_API_KEY=your_firecrawl_api_key
# Optional: Custom Firecrawl API URL
# FIRECRAWL_API_URL=your_firecrawl_api_url
# Reranker (Required)
# JINA_API_KEY=your_jina_api_key
# or
# COHERE_API_KEY=your_cohere_api_key
#======================#
# MCP Configuration #
#======================#
# Treat 401/403 responses as OAuth requirement when no oauth metadata found
# MCP_OAUTH_ON_AUTH_ERROR=true
# Timeout for OAuth detection requests in milliseconds
# MCP_OAUTH_DETECTION_TIMEOUT=5000
# Cache connection status checks for this many milliseconds to avoid expensive verification
# MCP_CONNECTION_CHECK_TTL=60000
# Skip code challenge method validation (e.g., for AWS Cognito that supports S256 but doesn't advertise it)
# When set to true, forces S256 code challenge even if not advertised in .well-known/openid-configuration
# MCP_SKIP_CODE_CHALLENGE_CHECK=false
# Circuit breaker: max connect/disconnect cycles before tripping (per server)
# MCP_CB_MAX_CYCLES=7
# Circuit breaker: sliding window (ms) for counting cycles
# MCP_CB_CYCLE_WINDOW_MS=45000
# Circuit breaker: cooldown (ms) after the cycle breaker trips
# MCP_CB_CYCLE_COOLDOWN_MS=15000
# Circuit breaker: max consecutive failed connection rounds before backoff
# MCP_CB_MAX_FAILED_ROUNDS=3
# Circuit breaker: sliding window (ms) for counting failed rounds
# MCP_CB_FAILED_WINDOW_MS=120000
# Circuit breaker: base backoff (ms) after failed round threshold is reached
# MCP_CB_BASE_BACKOFF_MS=30000
# Circuit breaker: max backoff cap (ms) for exponential backoff
# MCP_CB_MAX_BACKOFF_MS=300000
+8
View File
@@ -0,0 +1,8 @@
# Auto-generated from pyproject.toml — do not edit manually
ALFRED_VERSION=0.1.7
PYTHON_VERSION=3.14.3
IMAGE_NAME=alfred_media_organizer
SERVICE_NAME=alfred
LIBRECHAT_VERSION=v0.8.4
RAG_VERSION=v0.7.3
UV_VERSION=0.11.6
+54 -25
View File
@@ -2,11 +2,10 @@ name: CI/CD Awesome Pipeline
on: on:
push: push:
branches: [main]
tags: tags:
- 'v*.*.*' - 'v*.*.*'
pull_request:
branches: [main] workflow_dispatch:
env: env:
REGISTRY_URL: ${{ vars.REGISTRY_URL || 'gitea.iswearihadsomethingforthis.net' }} REGISTRY_URL: ${{ vars.REGISTRY_URL || 'gitea.iswearihadsomethingforthis.net' }}
@@ -30,34 +29,64 @@ jobs:
name: Build & Push to Registry name: Build & Push to Registry
runs-on: ubuntu-latest runs-on: ubuntu-latest
needs: test needs: test
if: startsWith(github.ref, 'refs/tags/')
steps:
- name: Debug ref
run: |
echo "github.ref = ${{ github.ref }}"
echo "GITHUB_REF = $GITHUB_REF"
echo "This should only run on tags!"
steps:
- name: Checkout code - name: Checkout code
uses: actions/checkout@v4 uses: actions/checkout@v4
- name: Generate build variables
run: python scripts/generate_build_vars.py
- name: Load config from Makefile - name: Load config from Makefile
id: config id: config
run: | run: make -s _ci-dump-config >> $GITHUB_OUTPUT
eval "$(make _ci-image-name)"
echo "image_name=${IMAGE_NAME}" >> $GITHUB_OUTPUT
- name: Extract version from tag - name: 🏷️ Docker Metadata (Tags & Labels)
id: version id: meta
run: echo "version=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT uses: docker/metadata-action@v5
with:
images: gitea.iswearihadsomethingforthis.net/francwa/${{ steps.config.outputs.image_name }}
tags: |
# Tagged (v1.2.3)
type=semver,pattern={{ version }}
# Latest (main)
type=raw,value=latest,enable={{ is_default_branch }}
# Feature branches
type=ref,event=branch
- name: Build production image - name: Login to Gitea Registry
run: make build uses: docker/login-action@v3
with:
registry: gitea.iswearihadsomethingforthis.net
username: ${{ gitea.actor }}
password: ${{ secrets.G1T34_TOKEN }}
- name: Tag and push to registry - name: Build and push
run: | id: docker_build
docker tag ${{ steps.config.outputs.image_name }}:latest ${{ env.REGISTRY_URL }}/${{ env.REGISTRY_USER }}/${{ steps.config.outputs.image_name }}:${{ steps.version.outputs.version }} uses: docker/build-push-action@v5
docker tag ${{ steps.config.outputs.image_name }}:latest ${{ env.REGISTRY_URL }}/${{ env.REGISTRY_USER }}/${{ steps.config.outputs.image_name }}:latest with:
echo "${{ secrets.G1T34_TOKEN }}" | docker login ${{ env.REGISTRY_URL }} -u ${{ env.REGISTRY_USER }} --password-stdin context: .
docker push ${{ env.REGISTRY_URL }}/${{ env.REGISTRY_USER }}/${{ steps.config.outputs.image_name }}:${{ steps.version.outputs.version }} push: true
docker push ${{ env.REGISTRY_URL }}/${{ env.REGISTRY_USER }}/${{ steps.config.outputs.image_name }}:latest tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
build-args: |
PYTHON_VERSION=${{ steps.config.outputs.python_version }}
PYTHON_VERSION_SHORT=${{ steps.config.outputs.python_version_short }}
RUNNER=${{ steps.config.outputs.runner }}
- name: 🛡️ Run Trivy Vulnerability Scanner
uses: docker://aquasec/trivy:latest
env:
TRIVY_USERNAME: ${{ gitea.actor }}
TRIVY_PASSWORD: ${{ secrets.G1T34_TOKEN }}
# Unset the fake GITHUB_TOKEN injected by Gitea
GITHUB_TOKEN: ""
with:
args: image --format table --output trivy-report.txt --exit-code 0 --ignore-unfixed --severity CRITICAL,HIGH gitea.iswearihadsomethingforthis.net/francwa/${{ steps.config.outputs.image_name }}:latest
- name: 📤 Upload Security Report
uses: actions/upload-artifact@v3
with:
name: security-report
path: trivy-report.txt
retention-days: 7
+22
View File
@@ -0,0 +1,22 @@
name: Renovate Bot
on:
schedule:
# Every Monday 4AM
- cron: '0 4 * * 1'
workflow_dispatch:
jobs:
renovate:
runs-on: ubuntu-latest
steps:
- name: Run Renovate
uses: docker://renovate/renovate:latest
env:
RENOVATE_PLATFORM: "gitea"
RENOVATE_ENDPOINT: "https://gitea.iswearihadsomethingforthis.net/api/v1"
RENOVATE_TOKEN: "${{ secrets.RENOVATE_TOKEN }}"
RENOVATE_REPOSITORIES: '["${{ gitea.repository }}"]'
RENOVATE_GIT_AUTHOR: "Renovate Bot <renovate@bot.local>"
# Might need a free github token if lots of depencies
# RENOVATE_GITHUB_TOKEN: "${{ secrets.GITHUB_COM_TOKEN }}"
+18 -1
View File
@@ -55,7 +55,24 @@ coverage.xml
Thumbs.db Thumbs.db
# Secrets # Secrets
.env .env.secrets
# Backup files # Backup files
*.backup *.backup
*.bak
env_backup/
# Application data dir
data/*
# Application logs
logs/*
# Documentation folder
docs/
# .md files (project-level Markdown is brol-y; allow-list the ones we track)
*.md
!CHANGELOG.md
#
+303
View File
@@ -0,0 +1,303 @@
# Changelog
All notable changes to Alfred are documented here.
The format is loosely based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).
Alfred is not yet on SemVer — entries are grouped by **dated work blocks** instead
of release numbers. Granularity targets behavioral or API-visible changes; refer
to `git log` for commit-level detail.
Sections used per block: **Added** / **Changed** / **Deprecated** / **Removed** /
**Fixed** / **Internal** (for tech-debt and refactor noise that doesn't affect
callers).
---
## [Unreleased]
### Added
- **Real-world release fixtures** under `tests/fixtures/releases/{easy,shitty,path_of_pain}/`,
each documenting an expected `ParsedRelease` plus the future `routing`
(library / torrents / seed_hardlinks) for the upcoming `organize_media`
refactor. EASY bucket seeded with 5 cases (movie, single-episode, season
pack, movie + noise, YTS bracket-heavy). SHITTY bucket seeded with 15
anti-regression cases covering: 3-level INTEGRALE hierarchy (Angel),
French custom titles (Buffy, La Nuit au Musée, Chérie j'ai agrandi),
multi-episode chain `S14E09E10E11` (Archer, captures E11 loss),
lowercase `s01e01` (Notre Planète), `NxNN` with ` - ` separators
(Vinyl, captures dash artifact), title-with-year-suffix (Deutschland.83),
season-range `S01-06` (Tatortreiniger, captures movie misclassification),
bare folder name (Jurassic Park,
media_type=unknown), apostrophe-in-name (Honey Don't, captures full AI-path
degeneration), SUBS-tag movie (Hook), space separators (Predator Badlands,
captures group=UNKNOWN), subs-only release (Westworld S04).
PATH OF PAIN bucket seeded with 10 worst-case fixtures covering:
UTF-8 wide pipe yt-dlp slug (Khruangbin), 3-show franchise box-set
with double season range and parens-wrapped tech (Deutschland 83-86-89,
captures `group=S03` misdetection), accented chars in title (Chérie
BéBé with VFF), 8-word stand-up comedy title (Jimmy Carr), site-tag
prefix + XviD (OxTorrent), episode title + air-date silently lost
(Prodiges), full-chaos apostrophe + spaces + Blu-ray dash + 1080i +
multi-word audio codec (The Prodigy, full AI-path degeneration),
yt-dlp YouTube ID glued to year (Sleaford Mods), bilingual `[FR-EN]`
tag mistaken for group (Super Mario Bros), COMPLETE + S01-S07 range +
REPACK + HEVC (Gilmore Girls, the well-behaved exception).
Parametrized over `tests/domain/test_release_fixtures.py` for anti-regression.
- **`NxNN` alt season/episode form supported** by `parse_release`. Releases like
`Show.1x05.720p.HDTV.x264-GRP` and `Show.2x07x08.1080p.WEB.x265-GRP` (multi-ep
alt form) now parse as TV shows.
- **`alfred/knowledge/release/separators.yaml`** declares the token separators
used by the release-name tokenizer (`.`, ` `, `[`, `]`, `(`, `)`, `_`). New
conventions can be added without code changes. The canonical `.` is always
present even if missing from YAML.
### Changed
- **`parse_release` tokenizer is now data-driven**: it splits on any character
listed in `separators.yaml` (regex character class) instead of `name.split(".")`.
This makes YTS-style releases (`The Father (2020) [1080p] [WEBRip] [5.1] [YTS.MX]`),
space-separated names (`Inception 2010 1080p BluRay x264-GROUP`), and
underscore-separated names parse correctly via the direct path — no more
fallback through sanitization.
- **`parse_release` flow simplified**: site-tag extraction always runs first
(so `parse_path == "sanitized"` now reliably indicates a stripped `[tag]`),
then well-formedness is checked only against truly forbidden chars
(anything not in the configured separator set).
- **ISO 639-2/B is now the canonical language code project-wide** (was a mix of
639-1 and 639-2/T):
- `SubtitlePreferences.languages` default is now `["fre", "eng"]` (was
`["fr", "en"]`). Old LTM files are not auto-migrated — delete
`data/memory/ltm.json` to regenerate with the new defaults.
- Subtitle output filenames are now `{iso639_2b}.srt` (e.g. `fre.srt`,
`fre.sdh.srt`). Existing `fr.srt` files are still **read** correctly
(recognized as French via alias) but new files are written canonically.
- `Language` value object docstring corrected: it has always stored 639-2/B
(matching what ffprobe emits), not 639-2/T as previously documented.
- **`MovieService.validate_movie_file` minimum size is now configurable** via
`settings.min_movie_size_bytes` (default unchanged: 100 MB). Constructor
accepts an optional `min_movie_size_bytes` override for tests.
- **`SubtitleKnowledgeBase` delegates language lookup to `LanguageRegistry`**
rather than duplicating tokens. `subtitles.yaml` now only declares
subtitle-specific tokens (e.g. `vostfr`, `vf`, `vff`) under a new
`language_tokens` section.
### Removed
- **`alfred/domain/tv_shows/services.py`** and **`alfred/domain/movies/services.py`**
deleted entirely. They held fossil parsers (`parse_episode_filename`,
`extract_movie_metadata`, …) with zero production callers — superseded by
`parse_release` as the single source of truth for release-name parsing.
Associated tests (`tests/domain/test_movies.py`, `tests/domain/test_tv_shows_service.py`)
removed as well.
- `_sanitize` and `_normalize` helpers in `alfred/domain/release/services.py`
the new tokenizer makes them redundant.
- `_LANG_KEYWORDS`, `_SDH_TOKENS`, `_FORCED_TOKENS`, `SUBTITLE_EXTENSIONS`
hardcoded dicts in `alfred/domain/subtitles/scanner.py` — all knowledge now
lives in YAML (CLAUDE.md compliance).
- `_MIN_MOVIE_SIZE_BYTES` module-level constant in
`alfred/domain/movies/services.py` — replaced by the new setting.
- Top-level `languages:` block in `subtitles.yaml` — superseded by
`language_tokens:` (subtitle-specific only) since iso_languages.yaml is the
canonical source.
### Fixed
- **`hi` token no longer marks a subtitle as SDH** (it conflicted with the
ISO 639-1 alias for Hindi). SDH is now detected only via `sdh`, `cc`, and
`hearing` tokens.
- `SubtitleKnowledgeBase` default rules used `"fra"` while
`iso_languages.yaml` exposes French as `"fre"` — preferred languages
defaults now match the canonical form.
### Internal
- **Domain I/O extraction** (`refactor/domain-io-extraction`): the domain
layer no longer performs subprocess calls, filesystem scans, or YAML
loading. Achieved in a series of focused commits:
- **Knowledge YAML loaders moved to infrastructure**:
`alfred/domain/release/knowledge.py`,
`alfred/domain/shared/knowledge/language_registry.py`, and
`alfred/domain/subtitles/knowledge/{base,loader}.py` relocated to
`alfred/infrastructure/knowledge/`. Re-exports were dropped — callers
import directly from the new location.
- **`MediaProber` and `FilesystemScanner` Protocol ports** introduced at
`alfred/domain/shared/ports/` with frozen-dataclass DTOs
(`SubtitleStreamInfo`, `FileEntry`). `SubtitleIdentifier` and
`PatternDetector` are now constructor-injected with concrete adapters
(`FfprobeMediaProber` wrapping `subprocess.run(ffprobe)` and
`PathlibFilesystemScanner` wrapping `pathlib`). No more direct
`subprocess`/`pathlib` usage from the subtitle domain services.
- **Live filesystem methods removed from VOs and entities**:
`FilePath.exists()` / `.is_file()` / `.is_dir()` deleted —
`FilePath` is now a pure address VO. `Movie.has_file()` and
`Episode.is_downloaded()` dropped. Callers either rely on a prior
detection step or use try/except over pre-checks (eliminates
TOCTOU races).
- **`SubtitlePlacer` moved to the application layer** at
`alfred/application/subtitles/placer.py` — it performs `os.link`
I/O, which doesn't belong in the domain. Pre-checks replaced with
try/except for `FileNotFoundError`/`FileExistsError`.
- **`SubtitleRuleSet.resolve()` no longer reaches into the knowledge
base**: the implicit `DEFAULT_RULES()` helper is gone, replaced by
an explicit `default_rules: SubtitleMatchingRules` parameter. The
`ManageSubtitles` use case loads defaults from the KB once and
passes them in.
- **`SubtitleKnowledge` Protocol port** at
`alfred/domain/subtitles/ports/knowledge.py` declares the read-only
query surface domain services consume (7 methods:
`known_extensions`, `format_for_extension`, `language_for_token`,
`is_known_lang_token`, `type_for_token`, `is_known_type_token`,
`patterns`). `SubtitleIdentifier` and `PatternDetector` depend on
this Protocol instead of the concrete `SubtitleKnowledgeBase` from
infrastructure — `domain/subtitles/` now has zero imports from
`infrastructure/`. The remaining domain → infra leak
(`domain/release/` loading separator YAML at import-time) is
documented in tech-debt and scheduled for its own branch.
- **`to_dot_folder_name(title)` helper** in
`alfred/domain/shared/value_objects.py` — extracts the
`re.sub(r"[^\w\s\.\-]", "", title).replace(" ", ".")` pattern that was
duplicated between `MovieTitle.normalized()` and `TVShow.get_folder_name()`.
- **`ParsedRelease.languages` uses `field(default_factory=list)`** instead of
a manual `__post_init__` that assigned `[]` via `object.__setattr__`.
- **`file_extensions.yaml` splits subtitle sidecars (`.srt`, `.sub`, `.idx`,
`.ass`, `.ssa`) into a dedicated `subtitle:` category** instead of lumping
them under `metadata:`. The `_METADATA_EXTENSIONS` set used by
`detect_media_type` remains the union of both (same behavior — subtitles
are still ignored when deciding the media type of a folder), but a new
`load_subtitle_extensions()` loader is now available for the subtitles
domain. Sematic clarity, no functional change.
- **`tv_shows/entities.py` module docstring** now shows the aggregate
ownership as an ASCII tree before the rule text — quicker visual scan
of the DDD structure.
- Removed backward-compat shims `_sanitise_for_fs` /
`_strip_episode_from_normalised` from `domain/release/value_objects.py`
(zero callers).
- Cleaned ruff warnings across the codebase: `subprocess.run` calls now pass
explicit `check=False` (PLW1510); lazy imports promoted to module top where
there was no cycle (PLC0415 in `manage_subtitles.py`, `placer.py`,
`qbittorrent/client.py`, `file_manager.py`); fixed module-level import
ordering (E402) in `language_registry.py` and `subtitles/knowledge/loader.py`;
removed unused locals (F841 / B007); replaced unnecessary set comprehension
with `set()` in `release/knowledge.py` (C416).
- Ruff config: ignore `PLR0911` / `PLR0912` (too-many-returns / too-many-branches)
globally — noisy on parser mappers and orchestrator use-cases where early-return
validation is essential complexity. Ignore `PLW0603` for the documented memory
singleton (`infrastructure/persistence/context.py`).
---
## [2026-05-17] — TVShow & Movie aggregate refactor
Multi-phase refonte of the TV show domain into a real DDD aggregate, with
matching parity work on `Movie`, a language knowledge system, and the
`shared/media` restructure that supports both.
### Added
- **Language knowledge system** (`alfred/knowledge/iso_languages.yaml` + 42
languages including `und` for undetermined).
- `Language` value object (frozen dataclass) with `iso`, `english_name`,
`native_name`, `aliases`, and a `matches(raw)` cross-format helper.
- `LanguageRegistry` loader (`alfred/domain/shared/knowledge/`) merging
builtin + learned YAML. Not a singleton — the application layer
instantiates it.
- ISO 639-2/B is the canonical key; aliases cover 639-1, 639-2/T, English
name, native name, and common spellings.
- **`VideoTrack`** dataclass (`alfred/domain/shared/media/video.py`) with a
`resolution` property using width-priority bucket detection (handles
cinema/scope crops like 1920×960 → 1080p).
- **`shared/media/matching.py`** — `track_lang_matches` helper shared by
`Episode` and `Movie`. Implements the **"C+" contract** for language helpers:
- `Language` query → cross-format match via `Language.matches()`
- `str` query → case-insensitive direct comparison (no normalization)
- **TVShow aggregate composition**:
- `TVShow.seasons: dict[SeasonNumber, Season]`
- `Season.episodes: dict[EpisodeNumber, Episode]`
- `Season.expected_episodes` / `Season.aired_episodes` (split so collection
state can compare "owned vs aired today" without confusing in-flight
seasons with future ones)
- **Aggregate methods on `TVShow`**:
- `add_episode(ep)` — sole sanctioned mutation entry point (creates the
season if missing)
- `add_season(season)` — replaces a season wholesale
- `collection_status()``CollectionStatus.{EMPTY, PARTIAL, COMPLETE}`
- `is_complete_series()` — true iff `ENDED + COMPLETE`
- `missing_episodes()` — flat list of all aired-but-not-owned
`(season, episode)` pairs
- **`CollectionStatus`** enum (orthogonal to `ShowStatus`).
- **Episode track helpers** (`has_audio_in`, `has_subtitles_in`,
`has_forced_subs`, `audio_languages`, `subtitle_languages`), driven by
`Episode.audio_tracks` / `Episode.subtitle_tracks`.
- **Movie aggregate parity** — `Movie` now carries `audio_tracks` /
`subtitle_tracks` and exposes the same helpers as `Episode` (same C+
contract).
- **`CHANGELOG.md`** (this file).
### Changed
- **`shared/media_info.py` exploded into `shared/media/{audio,video,subtitle,info,matching}.py`.**
`MediaInfo` is now symmetric: every stream type is a `list[Track]`. Flat
accessors (`width`, `height`, `video_codec`, `resolution`) remain as
properties that read the first video track.
- **`MediaInfo.duration_seconds` / `bitrate_kbps`** moved from `VideoTrack` to
`MediaInfo` (file-level — they come from the ffprobe `format` block, not a
stream). Files without a video stream now correctly expose duration.
- **`ShowStatus.from_string`** extended to map TMDB strings (`Returning
Series`, `In Production`, `Pilot`, `Planned`, `Canceled`, `Cancelled`).
Comparison is whitespace-trimmed and case-insensitive.
- **`Season` / `Episode`** dropped their `show_imdb_id` back-references. They
are owned by `TVShow` and reached only through it.
- **`TVShow.seasons_count` and `episode_count`** are now `@property` (computed
from the dict) instead of stored ints.
- **`TVShowService.parse_episode_from_filename`** rewritten in string
operations (no regex). Supports `S01E05` / `s1e5` and `1x05` / `01x5` forms.
- **`TVShowService.find_next_episode`** now drives off
`show.missing_episodes()` instead of the hardcoded "max 50 episodes per
season" heuristic.
- **`TVShowService` constructor** no longer takes `season_repository` /
`episode_repository` — the aggregate persists in one block via
`TVShowRepository` only.
- **`SubtitleTrack` in `alfred.domain.subtitles.entities` renamed to
`SubtitleCandidate`.** Coexists with the `shared.media.SubtitleTrack`
ffprobe-view dataclass (different bounded contexts, kept separate
intentionally).
- **`tv_shows/services.py` `_VIDEO_EXTENSIONS`** now loaded from
`knowledge/release/file_extensions.yaml` via `load_video_extensions()`
(single source of truth).
- **`CLAUDE.md`** updated with three new policy sections:
- "Tests" — small updates OK during normal work, no mass-update sprees
- "Backwards-compatibility shims" — prefer clean migration over shims
- "Regex" — not forbidden, use judgment when string ops would be fragile
### Removed
- **Legacy `Season N Episode N` filename form** in
`TVShowService.parse_episode_from_filename`. It never appears in the release
names Alfred handles, and supporting it forced a regex.
- **`SeasonRepository` and `EpisodeRepository`** — only the aggregate root has
a repository (DDD rule: one repo per aggregate).
- **`shared/media_info.py`** compatibility shim — callers updated.
- **`SubtitleTrack` compatibility alias** in `subtitles.entities` — callers
updated to `SubtitleCandidate`.
### Fixed
- **`MediaInfo.duration_seconds` returns `None` on audio-only files** instead
of crashing through `primary_video.duration_seconds` (see the duration/bitrate
move under **Changed**).
- **`MediaOrganizer`** (`infrastructure/filesystem/organizer.py`) no longer
passes the removed `show_imdb_id` / `episode_count` kwargs when constructing
a `Season` for folder-name generation.
### Internal
- Test suite rewritten where the aggregate redesign broke fixtures:
`tests/domain/test_tv_shows.py` (69 tests), `tests/domain/test_media_info.py`
(rewritten for `VideoTrack`), `tests/application/test_enrich_from_probe.py`
(helper added), `tests/infrastructure/test_filesystem_extras.py` (fixtures),
`tests/domain/test_tv_shows_service.py` (find_next_episode driven by real
aggregate state).
- Subtitle services internal migration: `matcher.py`, `utils.py`, `placer.py`,
`identifier.py` updated to import `SubtitleCandidate`.
- Suite status at end of block: **1066 passed, 8 skipped, 0 failed**.
+91
View File
@@ -0,0 +1,91 @@
# syntax=docker/dockerfile:1
# check=skip=InvalidDefaultArgInFrom
ARG PYTHON_VERSION
ARG UV_VERSION
# Stage 0: uv binary (workaround — --from doesn't support ARG expansion)
FROM ghcr.io/astral-sh/uv:${UV_VERSION} AS uv-bin
# ===========================================
# Stage 1: Builder
# ===========================================
FROM python:${PYTHON_VERSION}-slim-bookworm AS builder
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
UV_PROJECT_ENVIRONMENT=/venv
# Install build dependencies
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt-get update \
&& apt-get install -y --no-install-recommends build-essential
# Install uv globally
COPY --from=uv-bin /uv /usr/local/bin/uv
WORKDIR /tmp
COPY pyproject.toml uv.lock Makefile ./
# Install dependencies into /venv
RUN --mount=type=cache,target=/root/.cache/uv uv sync
COPY scripts/ ./scripts/
COPY .env.example ./
# ===========================================
# Stage 2: Testing
# ===========================================
FROM builder AS test
RUN --mount=type=cache,target=/root/.cache/uv uv sync --group dev
COPY alfred/ ./alfred
COPY scripts ./scripts
COPY tests/ ./tests
# ===========================================
# Stage 3: Runtime
# ===========================================
FROM python:${PYTHON_VERSION}-slim-bookworm AS runtime
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1 \
PYTHONPATH=/home/appuser \
PATH="/venv/bin:$PATH"
# Install runtime dependencies
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt,sharing=locked \
apt-get update \
&& apt-get install -y --no-install-recommends ca-certificates
# Create non-root user
RUN useradd -m -u 1000 -s /bin/bash appuser
# Create data directories
RUN mkdir -p /data /logs \
&& chown -R appuser:appuser /data /logs
USER appuser
WORKDIR /home/appuser
# Copy venv from builder stage
COPY --from=builder /venv /venv
# Copy application code
COPY --chown=appuser:appuser alfred/ ./alfred
COPY --chown=appuser:appuser scripts/ ./scripts
COPY --chown=appuser:appuser .env.example ./
COPY --chown=appuser:appuser pyproject.toml ./
VOLUME ["/data", "/logs"]
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD python -c "import requests; requests.get('http://localhost:8000/health', timeout=5).raise_for_status()" || exit 1
CMD ["python", "-m", "uvicorn", "alfred.app:app", "--host", "0.0.0.0", "--port", "8000"]
+161 -231
View File
@@ -1,257 +1,187 @@
.POSIX:
.SUFFIXES:
.DEFAULT_GOAL := help .DEFAULT_GOAL := help
# --- SETTINGS --- # --- Load Config from pyproject.toml ---
PYTHON_VERSION = 3.12.7 export
PYTHON_VERSION_SHORT = $(shell echo $(PYTHON_VERSION) | cut -d. -f1,2) -include .env.make
# Change to 'uv' when ready.
RUNNER ?= poetry
export PYTHON_VERSION # --- Profiles management ---
export PYTHON_VERSION_SHORT # Usage: make up p=rag,meili
export RUNNER p ?= full
export IMAGE_NAME PROFILES_PARAM := COMPOSE_PROFILES=$(p)
# --- VARIABLES --- # --- Commands ---
CORE_DIR = brain DOCKER_COMPOSE := docker compose \
SERVICE_NAME = agent_media --env-file .env.alfred \
IMAGE_NAME = agent_media --env-file .env.secrets \
--env-file .env.make
DOCKER_BUILD := DOCKER_BUILDKIT=1 docker build \
--build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
--build-arg UV_VERSION=$(UV_VERSION)
# --- ADAPTERS --- # --- Phony ---
# UV uses "sync", Poetry uses "install". Both install DEV deps by default. .PHONY: bootstrap up down restart logs ps shell build build-test install \
INSTALL_CMD = $(if $(filter uv,$(RUNNER)),sync,install) update install-hooks test coverage lint format clean major minor patch help
# --- MACROS --- # --- Setup ---
ARGS = $(filter-out $@,$(MAKECMDGOALS)) .env.alfred .env.librechat .env.secrets .env.make:
BUMP_CMD = cd $(CORE_DIR) && $(RUNNER) run bump-my-version bump @echo "Initializing environment..."
COMPOSE_CMD = docker-compose @uv run python scripts/bootstrap.py \
DOCKER_CMD = docker build \ && echo "✓ Environment ready" \
--build-arg PYTHON_VERSION=$(PYTHON_VERSION) \ || (echo "✗ Environment setup failed" && exit 1)
--build-arg PYTHON_VERSION_SHORT=$(PYTHON_VERSION_SHORT) \
--build-arg RUNNER=$(RUNNER) \
-f $(CORE_DIR)/Dockerfile \
-t $(IMAGE_NAME):latest .
RUNNER_ADD = cd $(CORE_DIR) && $(RUNNER) add bootstrap: .env.alfred .env.librechat .env.secrets .env.make
RUNNER_HOOKS = cd $(CORE_DIR) && $(RUNNER) run pre-commit install -c ../.pre-commit-config.yaml
RUNNER_INSTALL = cd $(CORE_DIR) && $(RUNNER) $(INSTALL_CMD)
RUNNER_RUN = cd $(CORE_DIR) && $(RUNNER) run
RUNNER_UPDATE = cd $(CORE_DIR) && $(RUNNER) update
# --- STYLES --- # --- Docker ---
B = \033[1m up: .env.alfred .env.secrets
G = \033[32m @echo "Starting containers with profiles: [full]..."
T = \033[36m @$(PROFILES_PARAM) $(DOCKER_COMPOSE) up -d --remove-orphans \
R = \033[0m && echo "✓ Containers started" \
|| (echo "✗ Failed to start containers" && exit 1)
# --- TARGETS --- down:
.PHONY: add build build-test check-docker check-runner clean coverage down format help init-dotenv install install-hooks lint logs major minor patch prune ps restart run shell test up update _check_branch _ci-image-name _ci-run-tests _push_tag @echo "Stopping containers..."
@$(PROFILES_PARAM) $(DOCKER_COMPOSE) down \
&& echo "✓ Containers stopped" \
|| (echo "✗ Failed to stop containers" && exit 1)
# Catch-all for args restart:
%: @echo "Restarting containers..."
@: @$(PROFILES_PARAM) $(DOCKER_COMPOSE) restart \
&& echo "✓ Containers restarted" \
|| (echo "✗ Failed to restart containers" && exit 1)
add: check-runner logs:
@echo "$(T) Adding dependency ($(RUNNER)): $(ARGS)$(R)" @echo "Following logs (Ctrl+C to exit)..."
$(RUNNER_ADD) $(ARGS) @$(PROFILES_PARAM) $(DOCKER_COMPOSE) logs -f
build: check-docker ps:
@echo "$(T)🐳 Building Docker image...$(R)" @echo "Container status:"
$(DOCKER_CMD) @$(PROFILES_PARAM) $(DOCKER_COMPOSE) ps
@echo "✅ Image $(IMAGE_NAME):latest ready."
build-test: check-docker shell:
@echo "$(T)🐳 Building test image (with dev deps)...$(R)" @echo "Opening shell in $(SERVICE_NAME)..."
docker build \ @$(DOCKER_COMPOSE) exec $(SERVICE_NAME) /bin/bash
--build-arg RUNNER=$(RUNNER) \
--build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
--build-arg PYTHON_VERSION_SHORT=$(PYTHON_VERSION_SHORT) \
-f $(CORE_DIR)/Dockerfile \
--target test \
-t $(IMAGE_NAME):test .
@echo "✅ Test image $(IMAGE_NAME):test ready."
check-docker: # --- Build ---
@command -v docker >/dev/null 2>&1 || { echo "$(R)❌ Docker not installed$(R)"; exit 1; } build: .env.make
@docker info >/dev/null 2>&1 || { echo "$(R)❌ Docker daemon not running$(R)"; exit 1; } @echo "Building image $(IMAGE_NAME):latest ..."
@$(DOCKER_BUILD) -t $(IMAGE_NAME):latest . \
&& echo "✓ Build complete" \
|| (echo "✗ Build failed" && exit 1)
check-runner: build-test: .env.make
@command -v $(RUNNER) >/dev/null 2>&1 || { echo "$(R)$(RUNNER) not installed$(R)"; exit 1; } @echo "Building test image $(IMAGE_NAME):test..."
@$(DOCKER_BUILD) --target test -t $(IMAGE_NAME):test . \
&& echo "✓ Test image built" \
|| (echo "✗ Build failed" && exit 1)
# --- Dependencies ---
install:
@echo "Installing dependencies with uv..."
@uv install \
&& echo "✓ Dependencies installed" \
|| (echo "✗ Installation failed" && exit 1)
install-hooks:
@echo "Installing pre-commit hooks..."
@uv run pre-commit install \
&& echo "✓ Hooks installed" \
|| (echo "✗ Hook installation failed" && exit 1)
update:
@echo "Updating dependencies with uv..."
@uv update \
&& echo "✓ Dependencies updated" \
|| (echo "✗ Update failed" && exit 1)
# --- Quality ---
test:
@echo "Running tests..."
@uv run pytest \
&& echo "✓ Tests passed" \
|| (echo "✗ Tests failed" && exit 1)
coverage:
@echo "Running tests with coverage..."
@uv run pytest --cov=. --cov-report=html --cov-report=term \
&& echo "✓ Coverage report generated" \
|| (echo "✗ Coverage failed" && exit 1)
lint:
@echo "Linting code..."
@uv run ruff check --fix . \
&& echo "✓ Linting complete" \
|| (echo "✗ Linting failed" && exit 1)
format:
@echo "Formatting code..."
@uv run ruff format . && uv run ruff check --fix . \
&& echo "✓ Code formatted" \
|| (echo "✗ Formatting failed" && exit 1)
clean: clean:
@echo "$(T)🧹 Cleaning caches...$(R)" @echo "Cleaning build artifacts..."
cd $(CORE_DIR) && rm -rf .ruff_cache __pycache__ .pytest_cache @rm -rf .ruff_cache __pycache__ .pytest_cache htmlcov .coverage
find $(CORE_DIR) -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true @find . -type d -name "__pycache__" -exec rm -rf {} + 2>/dev/null || true
find $(CORE_DIR) -type d -name ".pytest_cache" -exec rm -rf {} + 2>/dev/null || true @echo "✓ Cleanup complete"
find $(CORE_DIR) -type f -name "*.pyc" -delete 2>/dev/null || true
@echo "✅ Caches cleaned."
coverage: check-runner # --- Versioning ---
@echo "$(T)📊 Running tests with coverage...$(R)" major minor patch: _check-main
$(RUNNER_RUN) pytest --cov=. --cov-report=html --cov-report=term $(ARGS) @echo "Bumping $@ version..."
@echo "✅ Report generated in htmlcov/" @uv run bump-my-version bump $@ \
&& echo "✓ Version bumped" \
|| (echo "✗ Version bump failed" && exit 1)
down: check-docker @echo "Pushing tags..."
@echo "$(T)🛑 Stopping containers...$(R)" @git push --tags \
$(COMPOSE_CMD) down && echo "✓ Tags pushed" \
@echo "✅ System stopped." || (echo "✗ Push failed" && exit 1)
format: check-runner # CI/CD helpers
@echo "$(T)✨ Formatting with Ruff...$(R)" _ci-dump-config:
$(RUNNER_RUN) ruff format . @echo "image_name=$(IMAGE_NAME)"
$(RUNNER_RUN) ruff check --fix . @echo "python_version=$(PYTHON_VERSION)"
@echo "✅ Code cleaned." @echo "uv_version=$(UV_VERSION)"
@echo "service_name=$(SERVICE_NAME)"
help: _ci-run-tests:build-test
@echo "$(B)Available commands:$(R)" @echo "Running tests in Docker..."
@echo ""
@echo "$(G)Setup:$(R)"
@echo " $(T)check-docker $(R) Verify Docker is installed and running."
@echo " $(T)check-runner $(R) Verify package manager ($(RUNNER))."
@echo " $(T)init-dotenv $(R) Create .env from .env.example with generated secrets."
@echo " $(T)install $(R) Install ALL dependencies (Prod + Dev)."
@echo " $(T)install-hooks $(R) Install git pre-commit hooks."
@echo ""
@echo "$(G)Docker:$(R)"
@echo " $(T)build $(R) Build the docker image (production)."
@echo " $(T)build-test $(R) Build the docker image (with dev deps for testing)."
@echo " $(T)down $(R) Stop and remove containers."
@echo " $(T)logs $(R) Follow logs."
@echo " $(T)prune $(R) Clean Docker system."
@echo " $(T)ps $(R) Show container status."
@echo " $(T)restart $(R) Restart all containers."
@echo " $(T)shell $(R) Open shell in container."
@echo " $(T)up $(R) Start the agent."
@echo ""
@echo "$(G)Development:$(R)"
@echo " $(T)add ... $(R) Add dependency (use --group dev or --dev if needed)."
@echo " $(T)clean $(R) Clean caches."
@echo " $(T)coverage $(R) Run tests with coverage."
@echo " $(T)format $(R) Format code (Ruff)."
@echo " $(T)lint $(R) Lint code without fixing."
@echo " $(T)test ... $(R) Run tests (local with $(RUNNER))."
@echo " $(T)update $(R) Update dependencies."
@echo ""
@echo "$(G)Versioning:$(R)"
@echo " $(T)major/minor/patch $(R) Bump version and push tag (triggers CI/CD)."
init-dotenv:
@echo "$(T)🔑 Initializing .env file...$(R)"
@if [ -f .env ]; then \
echo "$(R)⚠️ .env already exists. Skipping.$(R)"; \
exit 0; \
fi
@if [ ! -f .env.example ]; then \
echo "$(R)❌ .env.example not found$(R)"; \
exit 1; \
fi
@if ! command -v openssl >/dev/null 2>&1; then \
echo "$(R)❌ openssl not found. Please install it first.$(R)"; \
exit 1; \
fi
@echo "$(T) → Copying .env.example...$(R)"
@cp .env.example .env
@echo "$(T) → Generating secrets...$(R)"
@sed -i.bak "s|JWT_SECRET=.*|JWT_SECRET=$$(openssl rand -base64 32)|" .env
@sed -i.bak "s|JWT_REFRESH_SECRET=.*|JWT_REFRESH_SECRET=$$(openssl rand -base64 32)|" .env
@sed -i.bak "s|CREDS_KEY=.*|CREDS_KEY=$$(openssl rand -hex 16)|" .env
@sed -i.bak "s|CREDS_IV=.*|CREDS_IV=$$(openssl rand -hex 8)|" .env
@sed -i.bak "s|MEILI_MASTER_KEY=.*|MEILI_MASTER_KEY=$$(openssl rand -base64 32)|" .env
@sed -i.bak "s|AGENT_BRAIN_API_KEY=.*|AGENT_BRAIN_API_KEY=$$(openssl rand -base64 24)|" .env
@rm -f .env.bak
@echo "$(G)✅ .env created with generated secrets!$(R)"
@echo "$(T)⚠️ Don't forget to add your API keys:$(R)"
@echo " - OPENAI_API_KEY"
@echo " - DEEPSEEK_API_KEY"
@echo " - TMDB_API_KEY (optional)"
install: check-runner
@echo "$(T)📦 Installing FULL environment ($(RUNNER))...$(R)"
$(RUNNER_INSTALL)
@echo "✅ Environment ready (Prod + Dev)."
install-hooks: check-runner
@echo "$(T)🔧 Installing hooks...$(R)"
$(RUNNER_HOOKS)
@echo "✅ Hooks ready."
lint: check-runner
@echo "$(T)🔍 Linting code...$(R)"
$(RUNNER_RUN) ruff check .
logs: check-docker
@echo "$(T)📋 Following logs...$(R)"
$(COMPOSE_CMD) logs -f
major: _check_branch
@echo "$(T)💥 Bumping major...$(R)"
SKIP=all $(BUMP_CMD) major
@$(MAKE) -s _push_tag
minor: _check_branch
@echo "$(T)✨ Bumping minor...$(R)"
SKIP=all $(BUMP_CMD) minor
@$(MAKE) -s _push_tag
patch: _check_branch
@echo "$(T)🚀 Bumping patch...$(R)"
SKIP=all $(BUMP_CMD) patch
@$(MAKE) -s _push_tag
prune: check-docker
@echo "$(T)🗑️ Pruning Docker resources...$(R)"
docker system prune -af
@echo "✅ Docker cleaned."
ps: check-docker
@echo "$(T)📋 Container status:$(R)"
@$(COMPOSE_CMD) ps
restart: check-docker
@echo "$(T)🔄 Restarting containers...$(R)"
$(COMPOSE_CMD) restart
@echo "✅ Containers restarted."
run: check-runner
$(RUNNER_RUN) $(ARGS)
shell: check-docker
@echo "$(T)🐚 Opening shell in $(SERVICE_NAME)...$(R)"
$(COMPOSE_CMD) exec $(SERVICE_NAME) /bin/sh
test: check-runner
@echo "$(T)🧪 Running tests...$(R)"
$(RUNNER_RUN) pytest $(ARGS)
up: check-docker
@echo "$(T)🚀 Starting Agent Media...$(R)"
$(COMPOSE_CMD) up -d
@echo "✅ System is up."
update: check-runner
@echo "$(T)🔄 Updating dependencies...$(R)"
$(RUNNER_UPDATE)
@echo "✅ All packages up to date."
_check_branch:
@curr=$$(git rev-parse --abbrev-ref HEAD); \
if [ "$$curr" != "main" ]; then \
echo "❌ Error: not on the main branch"; exit 1; \
fi
_ci-image-name:
@echo "IMAGE_NAME=$(IMAGE_NAME)"
_ci-run-tests: build-test
@echo "$(T)🧪 Running tests in Docker...$(R)"
docker run --rm \ docker run --rm \
-e DEEPSEEK_API_KEY \ -e DEEPSEEK_API_KEY \
-e TMDB_API_KEY \ -e TMDB_API_KEY \
-e QBITTORRENT_URL \
$(IMAGE_NAME):test pytest $(IMAGE_NAME):test pytest
@echo " Tests passed." @echo " Tests passed."
_push_tag: _check-main:
@echo "$(T)📦 Pushing tag...$(R)" @test "$$(git rev-parse --abbrev-ref HEAD)" = "main" \
git push --tags || (echo "✗ ERROR: Not on main branch" && exit 1)
@echo "✅ Tag pushed. Check CI for build status."
# --- Help ---
help:
@echo "Cleverly Crafted Unawareness - Management Commands"
@echo ""
@echo "Usage: make [target] [p=profile1,profile2]"
@echo ""
@echo "Setup:"
@echo " bootstrap Generate .env.alfred, .env.librechat, .env.secrets and .env.make"
@echo ""
@echo "Docker:"
@echo " up Start containers (default profile: core)"
@echo " Example: make up p=rag,meili"
@echo " down Stop all containers"
@echo " restart Restart containers (supports p=...)"
@echo " logs Follow logs (supports p=...)"
@echo " ps Status of containers"
@echo " shell Open bash in the core container"
@echo " build Build the production Docker image"
@echo ""
@echo "Dev & Quality:"
@echo " setup Bootstrap .env and security keys"
@echo " install Install dependencies via uv"
@echo " test Run pytest suite"
@echo " coverage Run tests and generate HTML report"
@echo " lint/format Quality and style checks"
@echo ""
@echo "Release:"
@echo " major|minor|patch Bump version and push tags (main branch only)"
+433
View File
@@ -0,0 +1,433 @@
# Alfred Media Organizer 🎬
An AI-powered agent for managing your local media library with natural language. Search, download, and organize movies and TV shows effortlessly through a conversational interface.
[![Python 3.14](https://img.shields.io/badge/python-3.14-blue.svg)](https://www.python.org/downloads/)
[![uv](https://img.shields.io/badge/dependency%20manager-uv-purple)](https://github.com/astral-sh/uv)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
## ✨ Features
- 🤖 **Natural Language Interface** — Talk to your media library in plain language
- 🔍 **Smart Search** — Find movies and TV shows via TMDB with rich metadata
- 📥 **Torrent Integration** — Search and download via qBittorrent
- 🧠 **Contextual Memory** — Remembers your preferences and conversation history
- 📁 **Auto-Organization** — Moves and renames media files, resolves destinations, handles subtitles
- 🎞️ **Subtitle Pipeline** — Identifies, matches, and places subtitle tracks automatically
- 🔄 **Workflow Engine** — YAML-defined multi-step workflows (e.g. `organize_media`)
- 🌐 **OpenAI-Compatible API** — Works with any OpenAI-compatible client (LibreChat, OpenWebUI, etc.)
- 🔒 **Secure by Default** — Auto-generated secrets and encrypted credentials
## 🏗️ Architecture
Built with **Domain-Driven Design (DDD)** principles for clean separation of concerns:
```
alfred/
├── agent/ # AI agent orchestration
│ ├── llm/ # LLM clients (Ollama, DeepSeek)
│ ├── tools/ # Tool implementations (api, filesystem, language)
│ └── workflows/ # YAML-defined multi-step workflows
├── application/ # Use cases & DTOs
│ ├── movies/ # Movie search
│ ├── torrents/ # Torrent management
│ └── filesystem/ # File operations (move, list, subtitles, seed links)
├── domain/ # Business logic & entities
│ ├── media/ # Release parsing
│ ├── movies/ # Movie entities
│ ├── tv_shows/ # TV show entities & value objects
│ ├── subtitles/ # Subtitle scanner, services, knowledge base
│ └── shared/ # Common value objects (ImdbId, FilePath, FileSize)
└── infrastructure/ # External services & persistence
├── api/ # External API clients (TMDB, qBittorrent, Knaben)
├── filesystem/ # File manager (hard-link based, path-traversal safe)
├── persistence/ # Three-tier memory (LTM/STM/Episodic) + JSON repositories
└── subtitle/ # Subtitle infrastructure
```
### Key flows
**Agent execution:** `agent.step(user_input)` → LLM call → if tool_calls, execute each via registry → loop until no tool calls or `max_tool_iterations` → return final response.
**Media organization workflow:**
1. `resolve_destination` — Determines target folder/filename from release name
2. `move_media` — Hard-links file to library, deletes source
3. `manage_subtitles` — Scans, classifies, and places subtitle tracks
4. `create_seed_links` — Hard-links library file back to torrents/ for continued seeding
**Memory tiers:**
- **LTM** (`data/memory/ltm.json`) — Persisted config, media library, watchlist
- **STM** — Conversation history (capped at `MAX_HISTORY_MESSAGES`)
- **Episodic** — Transient search results, active downloads, recent errors
## 🚀 Quick Start
### Prerequisites
- **Python 3.14+**
- **uv** (dependency manager)
- **Docker & Docker Compose** (recommended for full stack)
- **API Keys:**
- TMDB API key ([get one here](https://www.themoviedb.org/settings/api))
- Optional: DeepSeek or other LLM provider keys
### Installation
```bash
# Clone the repository
git clone https://github.com/francwa/alfred_media_organizer.git
cd alfred_media_organizer
# Install dependencies
make install
# Install pre-commit hooks
make install-hooks
# Bootstrap environment (generates .env with secure secrets)
make bootstrap
# Validate your .env against the schema
make validate
# Edit .env with your API keys
nano .env
```
### Running with Docker (Recommended)
```bash
# Start all services (LibreChat + Alfred + MongoDB + Ollama)
make up
# Or start with specific profiles
make up p=rag,meili # Include RAG and Meilisearch
make up p=qbittorrent # Include qBittorrent
make up p=full # Everything
# View logs
make logs
# Stop all services
make down
```
The web interface will be available at **http://localhost:3080**
### Running Locally (Development)
```bash
uv run uvicorn alfred.app:app --reload --port 8000
```
## ⚙️ Configuration
### Settings system
`settings.toml` is the single source of truth. The schema flows:
```
settings.toml → settings_schema.py → settings_bootstrap.py → .env + .env.make → settings.py
```
To add a setting: define it in `settings.toml`, run `make bootstrap`, then access via `settings.my_new_setting`.
```bash
# First time setup
make bootstrap
# Validate existing .env against schema
make validate
# Re-run after settings.toml changes (existing secrets preserved)
make bootstrap
```
**Never commit `.env` or `.env.make`** — both are gitignored and auto-generated.
### Key settings (.env)
```bash
# --- CORE ---
MAX_HISTORY_MESSAGES=10
MAX_TOOL_ITERATIONS=10
# --- LLM ---
DEFAULT_LLM_PROVIDER=local # local (Ollama) | deepseek
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_MODEL=llama3.3:latest
LLM_TEMPERATURE=0.2
# --- API KEYS ---
TMDB_API_KEY=your-tmdb-key # Required for movie/show search
DEEPSEEK_API_KEY= # Optional
# --- SECURITY (auto-generated) ---
JWT_SECRET=<auto>
CREDS_KEY=<auto>
MONGO_PASSWORD=<auto>
```
## 🐳 Docker Services
### Docker Profiles
| Profile | Extra services | Use case |
|---------|---------------|----------|
| (default) | — | LibreChat + Alfred + MongoDB + Ollama |
| `meili` | Meilisearch | Fast full-text search |
| `rag` | RAG API + VectorDB (PostgreSQL) | Document retrieval |
| `qbittorrent` | qBittorrent | Torrent downloads |
| `full` | All of the above | Complete setup |
```bash
make up # Start (default profile)
make up p=full # Start with all services
make down # Stop
make restart # Restart
make logs # Follow logs
make ps # Container status
```
## 🛠️ Available Tools
| Tool | Description |
|------|-------------|
| `find_media_imdb_id` | Search for movies/TV shows on TMDB by title |
| `find_torrent` | Search for torrents across multiple indexers |
| `get_torrent_by_index` | Get detailed info about a specific result |
| `add_torrent_by_index` | Download a torrent from search results |
| `add_torrent_to_qbittorrent` | Add a torrent via magnet link directly |
| `resolve_destination` | Compute the target library path for a release |
| `move_media` | Hard-link a file to its library destination |
| `manage_subtitles` | Scan, classify, and place subtitle tracks |
| `create_seed_links` | Prepare torrent folder so qBittorrent keeps seeding |
| `learn` | Teach Alfred a new pattern (release group, naming convention) |
| `set_path_for_folder` | Configure folder paths |
| `list_folder` | List contents of a configured folder |
| `set_language` | Set preferred language for the session |
## 💬 Usage Examples
### Via Web Interface (LibreChat)
Navigate to **http://localhost:3080** and start chatting:
```
You: Find Inception in 1080p
Alfred: I found 3 torrents for Inception (2010):
1. Inception.2010.1080p.BluRay.x264 (150 seeders) - 2.1 GB
2. Inception.2010.1080p.WEB-DL.x265 (80 seeders) - 1.8 GB
3. Inception.2010.1080p.REMUX (45 seeders) - 25 GB
You: Download the first one
Alfred: ✓ Added to qBittorrent! Download started.
You: Organize the Breaking Bad S01 download
Alfred: ✓ Resolved destination: /tv_shows/Breaking.Bad/Season 01/
✓ Moved 6 episode files
✓ Placed 6 subtitle tracks (fr, en)
✓ Seed links created in /torrents/
```
### Via API
```bash
# Health check
curl http://localhost:8000/health
# Chat (OpenAI-compatible)
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "alfred",
"messages": [{"role": "user", "content": "Find The Matrix 4K"}]
}'
# List models
curl http://localhost:8000/v1/models
# View memory state
curl http://localhost:8000/memory/state
```
Alfred is compatible with any OpenAI-compatible client. Point it at `http://localhost:8000/v1`, model `alfred`.
## 🧠 Memory System
Alfred uses a three-tier memory system:
| Tier | Storage | Contents | Lifetime |
|------|---------|----------|----------|
| **LTM** | JSON file (`data/memory/ltm.json`) | Config, library, watchlist, learned patterns | Permanent |
| **STM** | RAM | Conversation history (capped) | Session |
| **Episodic** | RAM | Search results, active downloads, errors | Short-lived |
## 🧪 Development
### Running Tests
```bash
# Run full suite (parallel)
make test
# Run with coverage report
make coverage
# Run a single file
uv run pytest tests/test_agent.py -v
# Run a single class
uv run pytest tests/test_agent.py::TestAgentInit -v
# Skip slow tests
uv run pytest -m "not slow"
```
### Test coverage
The suite covers:
- **Agent loop** — tool execution, history, max iterations, error handling
- **Tool registry** — OpenAI schema format, parameter extraction
- **Prompts** — system prompt building, tool inclusion
- **Memory** — LTM/STM/Episodic operations, persistence
- **Filesystem tools** — path traversal security, folder listing
- **File manager** — hard-link, move, seed links (real filesystem, no mocks)
- **Application use cases** — `resolve_destination`, `create_seed_links`, `list_folder`, `move_media`
- **Domain** — TV show/movie entities, shared value objects (`ImdbId`, `FilePath`, `FileSize`), subtitle scanner
- **Repositories** — JSON-backed movie, TV show, subtitle repos
- **Bootstrap** — secret generation, idempotency, URI construction
- **Workflows** — YAML loading, structure validation
- **Configuration** — boundary validation for all settings
### Code Quality
```bash
make lint # Ruff check --fix
make format # Ruff format + check --fix
```
### Adding a New Tool
1. Implement the function in `alfred/agent/tools/`:
```python
# alfred/agent/tools/api.py
def my_new_tool(param: str) -> dict[str, Any]:
"""Short description shown to the LLM to decide when to call this tool."""
memory = get_memory()
# ...
return {"status": "ok", "data": result}
```
2. Register it in `alfred/agent/registry.py`:
```python
tool_functions = [
# ... existing tools ...
api_tools.my_new_tool,
]
```
The registry auto-generates the JSON schema from the function signature and docstring.
### Adding a Workflow
Create a YAML file in `alfred/agent/workflows/`:
```yaml
name: my_workflow
description: What this workflow does
steps:
- tool: resolve_destination
description: Find where the file should go
- tool: move_media
description: Move the file
```
Workflows are loaded automatically at startup.
### Version Management
```bash
# Must be on main branch
make patch # 0.1.7 → 0.1.8
make minor # 0.1.7 → 0.2.0
make major # 0.1.7 → 1.0.0
```
## 📚 API Reference
### Endpoints
| Method | Path | Description |
|--------|------|-------------|
| `GET` | `/health` | Health check |
| `GET` | `/v1/models` | List models (OpenAI-compatible) |
| `POST` | `/v1/chat/completions` | Chat (OpenAI-compatible, streaming supported) |
| `GET` | `/memory/state` | Full memory dump (debug) |
| `POST` | `/memory/clear-session` | Clear STM + Episodic |
| `GET` | `/memory/episodic/search-results` | Current search results |
## 🔧 Troubleshooting
### Agent doesn't respond
1. Check API keys in `.env`
2. Verify the LLM is running:
```bash
docker logs alfred-ollama
docker exec alfred-ollama ollama list
```
3. Check Alfred logs: `docker logs alfred-core`
### qBittorrent connection failed
1. Verify qBittorrent is running: `docker ps | grep qbittorrent`
2. Check credentials in `.env` (`QBITTORRENT_URL`, `QBITTORRENT_USERNAME`, `QBITTORRENT_PASSWORD`)
### Memory not persisting
1. Check `data/` directory is writable
2. Verify volume mounts in `docker-compose.yaml`
### Bootstrap fails
```bash
make validate # Check what's wrong with .env
make bootstrap # Regenerate (preserves existing secrets)
```
### Tests failing
```bash
uv run pytest tests/test_failing.py -v --tb=long
```
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch: `git checkout -b feat/my-feature`
3. Make your changes + add tests
4. Run `make test && make lint && make format`
5. Commit with [Conventional Commits](https://www.conventionalcommits.org/): `feat:`, `fix:`, `docs:`, `refactor:`, `test:`, `chore:`, `infra:`
6. Open a Pull Request
## 📄 License
MIT License — see [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- [LibreChat](https://github.com/danny-avila/LibreChat) — Chat interface
- [Ollama](https://ollama.ai/) — Local LLM runtime
- [DeepSeek](https://www.deepseek.com/) — LLM provider
- [TMDB](https://www.themoviedb.org/) — Movie & TV database
- [qBittorrent](https://www.qbittorrent.org/) — Torrent client
- [FastAPI](https://fastapi.tiangolo.com/) — Web framework
- [uv](https://github.com/astral-sh/uv) — Fast Python package manager
---
<p align="center">Made with ❤️ by <a href="https://github.com/francwa">Francwa</a></p>
View File
@@ -1,6 +1,7 @@
"""Agent module for media library management.""" """Agent module for media library management."""
from alfred.settings import settings
from .agent import Agent from .agent import Agent
from .config import settings
__all__ = ["Agent", "settings"] __all__ = ["Agent", "settings"]
+153 -13
View File
@@ -3,13 +3,16 @@
import json import json
import logging import logging
from collections.abc import AsyncGenerator from collections.abc import AsyncGenerator
from pathlib import Path
from typing import Any from typing import Any
from infrastructure.persistence import get_memory from alfred.infrastructure.metadata import MetadataStore
from alfred.infrastructure.persistence import get_memory
from alfred.settings import settings
from .config import settings from .prompt import PromptBuilder
from .prompts import PromptBuilder
from .registry import Tool, make_tools from .registry import Tool, make_tools
from .workflows import WorkflowLoader
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -21,17 +24,20 @@ class Agent:
Uses OpenAI-compatible tool calling API. Uses OpenAI-compatible tool calling API.
""" """
def __init__(self, llm, max_tool_iterations: int = 5): def __init__(self, settings, llm, max_tool_iterations: int = 5):
""" """
Initialize the agent. Initialize the agent.
Args: Args:
settings: Application settings instance
llm: LLM client with complete() method llm: LLM client with complete() method
max_tool_iterations: Maximum number of tool execution iterations max_tool_iterations: Maximum number of tool execution iterations
""" """
self.settings = settings
self.llm = llm self.llm = llm
self.tools: dict[str, Tool] = make_tools() self.tools: dict[str, Tool] = make_tools(settings)
self.prompt_builder = PromptBuilder(self.tools) self.workflow_loader = WorkflowLoader()
self.prompt_builder = PromptBuilder(self.tools, self.workflow_loader)
self.max_tool_iterations = max_tool_iterations self.max_tool_iterations = max_tool_iterations
def step(self, user_input: str) -> str: def step(self, user_input: str) -> str:
@@ -78,7 +84,7 @@ class Agent:
tools_spec = self.prompt_builder.build_tools_spec() tools_spec = self.prompt_builder.build_tools_spec()
# Tool execution loop # Tool execution loop
for _iteration in range(self.max_tool_iterations): for _iteration in range(self.settings.max_tool_iterations):
# Call LLM with tools # Call LLM with tools
llm_result = self.llm.complete(messages, tools=tools_spec) llm_result = self.llm.complete(messages, tools=tools_spec)
@@ -136,7 +142,7 @@ class Agent:
memory.save() memory.save()
return final_response return final_response
def _execute_tool_call(self, tool_call: dict[str, Any]) -> dict[str, Any]: def _execute_tool_call(self, tool_call: dict[str, Any]) -> dict[str, Any]: # noqa: PLR0911
""" """
Execute a single tool call. Execute a single tool call.
@@ -165,29 +171,163 @@ class Agent:
"available_tools": available, "available_tools": available,
} }
# Defensive: reject calls to tools that are not currently in scope.
visible = set(self.prompt_builder.visible_tool_names())
if tool_name not in visible:
return {
"error": "tool_out_of_scope",
"message": (
f"Tool '{tool_name}' is not available in the current "
"workflow scope. Call end_workflow first or start the "
"appropriate workflow."
),
"available_tools": sorted(visible),
}
tool = self.tools[tool_name] tool = self.tools[tool_name]
memory = get_memory()
# Cache lookup — for tools flagged cacheable, short-circuit on hit.
cache_key_value = self._cache_key_for(tool, args)
if cache_key_value is not None:
cached = memory.stm.tool_results.get(tool_name, cache_key_value)
if cached is not None:
logger.info(f"Tool cache HIT: {tool_name}[{cache_key_value}]")
self._post_tool_side_effects(tool_name, args, cached, from_cache=True)
return {**cached, "_from_cache": True}
# Execute tool # Execute tool
try: try:
result = tool.func(**args) result = tool.func(**args)
return result
except KeyboardInterrupt: except KeyboardInterrupt:
# Don't catch KeyboardInterrupt - let it propagate # Don't catch KeyboardInterrupt - let it propagate
raise raise
except TypeError as e: except TypeError as e:
# Bad arguments # Bad arguments
memory = get_memory()
memory.episodic.add_error(tool_name, f"bad_args: {e}") memory.episodic.add_error(tool_name, f"bad_args: {e}")
return {"error": "bad_args", "message": str(e), "tool": tool_name} return {"error": "bad_args", "message": str(e), "tool": tool_name}
except Exception as e: except Exception as e:
# Other errors # Other errors
memory = get_memory()
memory.episodic.add_error(tool_name, str(e)) memory.episodic.add_error(tool_name, str(e))
return {"error": "execution_failed", "message": str(e), "tool": tool_name} return {"error": "execution_failed", "message": str(e), "tool": tool_name}
# Persist + side effects only on successful results.
if isinstance(result, dict) and result.get("status") == "ok":
if cache_key_value is not None:
memory.stm.tool_results.put(tool_name, cache_key_value, result)
self._post_tool_side_effects(tool_name, args, result, from_cache=False)
memory.save()
return result
@staticmethod
def _cache_key_for(tool: Tool, args: dict[str, Any]) -> str | None:
"""Return the cache key value for this call, or None if not cacheable."""
if tool.cache_key is None:
return None
value = args.get(tool.cache_key)
if value is None:
return None
return str(value)
def _post_tool_side_effects(
self,
tool_name: str,
args: dict[str, Any],
result: dict[str, Any],
*,
from_cache: bool,
) -> None:
"""
Tool-agnostic side effects applied after a successful run or cache hit.
Today:
- Update release_focus when a path-keyed inspector runs.
- Persist inspector results into the release's `.alfred/metadata.yaml`.
- Refresh episodic.last_search_results on find_torrent cache hits so
get_torrent_by_index keeps pointing at the right list.
"""
memory = get_memory()
tool = self.tools.get(tool_name)
# Release focus: any path-keyed inspector updates current_release_path.
if tool is not None and tool.cache_key in {"source_path"}:
path = args.get(tool.cache_key)
if isinstance(path, str) and path:
memory.stm.release_focus.focus(path)
# Persist inspector results to .alfred/metadata.yaml (skip on cache
# hit — the file is already up to date from the original run).
if not from_cache:
self._maybe_update_alfred(tool_name, args, result)
# Episodic refresh when find_torrent's cache short-circuits the call.
if from_cache and tool_name == "find_torrent":
torrents = result.get("torrents") or []
query = args.get("media_title") or ""
memory.episodic.store_search_results(
query=query, results=torrents, search_type="torrent"
)
def _maybe_update_alfred(
self,
tool_name: str,
args: dict[str, Any],
result: dict[str, Any],
) -> None:
"""
Persist a successful inspector result into the release's
`.alfred/metadata.yaml`. No-op when the release root can't be resolved.
"""
if tool_name not in {"analyze_release", "probe_media", "find_media_imdb_id"}:
return
release_root = self._resolve_release_root(tool_name, args)
if release_root is None:
return
try:
store = MetadataStore(release_root)
if tool_name == "analyze_release":
store.update_parse(result)
elif tool_name == "probe_media":
store.update_probe(result)
elif tool_name == "find_media_imdb_id":
store.update_tmdb(result)
except Exception as e:
logger.warning(
f"Failed to update .alfred for {tool_name} at {release_root}: {e}"
)
@staticmethod
def _resolve_release_root(
tool_name: str,
args: dict[str, Any],
) -> Path | None:
"""
Figure out which release folder owns this call.
- analyze_release / probe_media: derived from source_path
(folder kept as-is, file walked up to its parent).
- find_media_imdb_id: follow the current release focus in STM.
"""
if tool_name in {"analyze_release", "probe_media"}:
raw = args.get("source_path")
if not isinstance(raw, str) or not raw:
return None
path = Path(raw)
return path if path.is_dir() else path.parent
# find_media_imdb_id has no path arg — rely on release focus.
focus = get_memory().stm.release_focus.current_release_path
if not focus:
return None
path = Path(focus)
return path if path.is_dir() else path.parent
async def step_streaming( async def step_streaming(
self, user_input: str, completion_id: str, created_ts: int, model: str self, user_input: str, completion_id: str, created_ts: int, model: str
) -> AsyncGenerator[dict[str, Any], None]: ) -> AsyncGenerator[dict[str, Any]]:
""" """
Execute agent step with streaming support for LibreChat. Execute agent step with streaming support for LibreChat.
@@ -230,7 +370,7 @@ class Agent:
tools_spec = self.prompt_builder.build_tools_spec() tools_spec = self.prompt_builder.build_tools_spec()
# Tool execution loop # Tool execution loop
for _iteration in range(self.max_tool_iterations): for _iteration in range(self.settings.max_tool_iterations):
# Call LLM with tools # Call LLM with tools
llm_result = self.llm.complete(messages, tools=tools_spec) llm_result = self.llm.complete(messages, tools=tools_spec)
+79
View File
@@ -0,0 +1,79 @@
"""Expression loader — charge et merge les fichiers YAML d'expressions par user."""
import random
from pathlib import Path
import yaml
_USERS_DIR = Path(__file__).parent.parent / "knowledge" / "users"
def _load_yaml(path: Path) -> dict:
if not path.exists():
return {}
return yaml.safe_load(path.read_text(encoding="utf-8")) or {}
def load_expressions(username: str | None) -> dict:
"""
Charge common.yaml et le merge avec {username}.yaml.
Retourne un dict avec :
- nickname: str (surnom de l'user, ou username en fallback)
- expressions: dict[situation -> list[str]]
"""
common = _load_yaml(_USERS_DIR / "common.yaml")
user_data = _load_yaml(_USERS_DIR / f"{username}.yaml") if username else {}
# Merge expressions : common + user (les phrases user s'ajoutent)
common_exprs: dict[str, list] = common.get("expressions", {})
user_exprs: dict[str, list] = user_data.get("expressions", {})
merged: dict[str, list] = {}
all_situations = set(common_exprs) | set(user_exprs)
for situation in all_situations:
base = list(common_exprs.get(situation, []))
extra = list(user_exprs.get(situation, []))
merged[situation] = base + extra
nickname = user_data.get("user", {}).get("nickname") or username or "mec"
return {
"nickname": nickname,
"expressions": merged,
}
def pick(expressions: dict, situation: str, nickname: str | None = None) -> str:
"""
Pioche une expression aléatoire pour une situation donnée.
Résout {user} avec le nickname si fourni.
Retourne une string vide si la situation n'existe pas.
"""
options = expressions.get("expressions", {}).get(situation, [])
if not options:
return ""
chosen = random.choice(options)
if nickname:
chosen = chosen.replace("{user}", nickname)
return chosen
def build_expressions_context(username: str | None) -> dict:
"""
Point d'entrée principal.
Retourne :
- nickname: str
- samples: dict[situation -> une phrase résolue] — une seule par situation
"""
data = load_expressions(username)
nickname = data["nickname"]
samples = {
situation: pick(data, situation, nickname) for situation in data["expressions"]
}
return {
"nickname": nickname,
"samples": samples,
}
@@ -6,7 +6,9 @@ from typing import Any
import requests import requests
from requests.exceptions import HTTPError, RequestException, Timeout from requests.exceptions import HTTPError, RequestException, Timeout
from ..config import settings from alfred.settings import Settings
from alfred.settings import settings as default_settings
from .exceptions import LLMAPIError, LLMConfigurationError from .exceptions import LLMAPIError, LLMConfigurationError
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -21,6 +23,7 @@ class DeepSeekClient:
base_url: str | None = None, base_url: str | None = None,
model: str | None = None, model: str | None = None,
timeout: int | None = None, timeout: int | None = None,
settings: Settings | None = None,
): ):
""" """
Initialize DeepSeek client. Initialize DeepSeek client.
@@ -34,10 +37,11 @@ class DeepSeekClient:
Raises: Raises:
LLMConfigurationError: If API key is missing LLMConfigurationError: If API key is missing
""" """
self.api_key = api_key or settings.deepseek_api_key self.settings = settings or default_settings
self.base_url = base_url or settings.deepseek_base_url self.api_key = api_key or self.settings.deepseek_api_key
self.model = model or settings.model self.base_url = base_url or self.settings.deepseek_base_url
self.timeout = timeout or settings.request_timeout self.model = model or self.settings.deepseek_model
self.timeout = timeout or self.settings.request_timeout
if not self.api_key: if not self.api_key:
raise LLMConfigurationError( raise LLMConfigurationError(
@@ -94,7 +98,7 @@ class DeepSeekClient:
payload = { payload = {
"model": self.model, "model": self.model,
"messages": messages, "messages": messages,
"temperature": settings.temperature, "temperature": self.settings.llm_temperature,
} }
# Add tools if provided # Add tools if provided
@@ -1,13 +1,14 @@
"""Ollama LLM client with robust error handling.""" """Ollama LLM client with robust error handling."""
import logging import logging
import os
from typing import Any from typing import Any
import requests import requests
from requests.exceptions import HTTPError, RequestException, Timeout from requests.exceptions import HTTPError, RequestException, Timeout
from ..config import settings from alfred.settings import Settings
from alfred.settings import settings as default_settings
from .exceptions import LLMAPIError, LLMConfigurationError from .exceptions import LLMAPIError, LLMConfigurationError
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@@ -32,6 +33,7 @@ class OllamaClient:
model: str | None = None, model: str | None = None,
timeout: int | None = None, timeout: int | None = None,
temperature: float | None = None, temperature: float | None = None,
settings: Settings | None = None,
): ):
""" """
Initialize Ollama client. Initialize Ollama client.
@@ -45,13 +47,12 @@ class OllamaClient:
Raises: Raises:
LLMConfigurationError: If configuration is invalid LLMConfigurationError: If configuration is invalid
""" """
self.base_url = base_url or os.getenv( self.settings = settings or default_settings
"OLLAMA_BASE_URL", "http://localhost:11434" self.base_url = base_url or self.settings.ollama_base_url
) self.model = model or self.settings.ollama_model
self.model = model or os.getenv("OLLAMA_MODEL", "llama3.2") self.timeout = timeout or self.settings.request_timeout
self.timeout = timeout or settings.request_timeout
self.temperature = ( self.temperature = (
temperature if temperature is not None else settings.temperature temperature if temperature is not None else self.settings.llm_temperature
) )
if not self.base_url: if not self.base_url:
+333
View File
@@ -0,0 +1,333 @@
"""Prompt builder for the agent system."""
import json
from typing import Any
from alfred.infrastructure.persistence import get_memory
from alfred.infrastructure.persistence.memory import MemoryRegistry
from .expressions import build_expressions_context
from .registry import Tool
from .workflows import WorkflowLoader
# Tools that are always available, regardless of workflow scope.
# Kept small on purpose — the noyau is what the agent uses to either
# answer trivially or pivot into a workflow.
CORE_TOOLS: tuple[str, ...] = (
"set_language",
"set_path_for_folder",
"list_folder",
"read_release_metadata",
"query_library",
"start_workflow",
"end_workflow",
)
class PromptBuilder:
"""Builds system prompts for the agent with memory context."""
def __init__(
self,
tools: dict[str, Tool],
workflow_loader: WorkflowLoader | None = None,
):
self.tools = tools
self.workflow_loader = workflow_loader or WorkflowLoader()
self._memory_registry = MemoryRegistry()
def _active_workflow(self, memory) -> dict | None:
"""Return the YAML definition of the active workflow, or None."""
current = memory.stm.workflow.current
if current is None:
return None
return self.workflow_loader.get(current.get("name"))
def visible_tool_names(self) -> list[str]:
"""
Return the names of the tools currently in scope.
- Idle (no workflow): core noyau only. The LLM enters a workflow
via start_workflow to access more tools.
- Workflow active: core noyau + the workflow's declared tools.
"""
memory = get_memory()
visible = set(CORE_TOOLS)
workflow = self._active_workflow(memory)
if workflow is not None:
for name in workflow.get("tools", []):
visible.add(name)
# Only return tools that actually exist in the registry.
return [name for name in self.tools if name in visible]
def _format_identity(self, memory) -> str:
"""Build Alfred's identity and personality section."""
username = memory.stm.get_entity("username")
expr = build_expressions_context(username)
nickname = expr["nickname"]
samples = expr["samples"]
# Format expressions as situational guidance for the LLM
expr_lines = []
situation_labels = {
"greeting": "Salutation",
"success": "Succès",
"working": "En cours",
"error": "Erreur",
"unclear": "Demande floue",
"warning": "Avertissement",
"not_found": "Introuvable",
}
for situation, label in situation_labels.items():
phrase = samples.get(situation, "")
if phrase:
expr_lines.append(f' {label}: "{phrase}"')
expressions_block = "\n".join(expr_lines)
return f"""Tu t'appelles Alfred. Tu es un assistant d'organisation de médiathèque — direct, opérationnel, légèrement impertinent.
PERSONNALITÉ:
- Tu parles franglais : français avec des touches d'anglais quand ça colle mieux
- Tu es sarcastique et n'as pas peur de te moquer si l'user fait une connerie
- Tu envoies chier poliment (mais clairement) quand la demande est trop floue
- Tu ne fais pas de blabla inutile. Si "ok c'est fait" suffit, c'est tout ce que tu dis
- Tu peux jurer (putain, merde, con, ...) — c'est naturel, pas du remplissage
- Jamais de "Great question!" ou de politesse creuse
USER COURANT: {nickname}
EXPRESSIONS À UTILISER (une par situation, naturellement intégrées dans ta réponse) :
{expressions_block}"""
def build_tools_spec(self) -> list[dict[str, Any]]:
"""Build the tool specification for the LLM API (scope-filtered)."""
visible = set(self.visible_tool_names())
tool_specs = []
for tool in self.tools.values():
if tool.name not in visible:
continue
spec = {
"type": "function",
"function": {
"name": tool.name,
"description": tool.description,
"parameters": tool.parameters,
},
}
tool_specs.append(spec)
return tool_specs
def _format_tools_description(self) -> str:
"""Format the currently-visible tools with description + params."""
visible = set(self.visible_tool_names())
visible_tools = [t for t in self.tools.values() if t.name in visible]
if not visible_tools:
return ""
return "\n".join(
f"- {tool.name}: {tool.description}\n"
f" Parameters: {json.dumps(tool.parameters, ensure_ascii=False)}"
for tool in visible_tools
)
def _format_workflow_scope(self, memory) -> str:
"""Describe the current workflow scope so the LLM has a plan."""
workflow = self._active_workflow(memory)
if workflow is None:
available = self.workflow_loader.names()
if not available:
return ""
lines = ["WORKFLOW SCOPE: idle (broad catalog narrowed to core noyau)."]
lines.append(
" Call start_workflow(workflow_name, params) to enter a scope."
)
lines.append(" Available workflows:")
for name in available:
wf = self.workflow_loader.get(name) or {}
desc = (wf.get("description") or "").strip().splitlines()
summary = desc[0] if desc else ""
lines.append(f" - {name}: {summary}")
return "\n".join(lines)
current = memory.stm.workflow.current or {}
lines = [
f"WORKFLOW SCOPE: active — {current.get('name')} "
f"(stage: {current.get('stage')})",
]
params = current.get("params")
if params:
lines.append(f" Params: {params}")
wf_desc = (workflow.get("description") or "").strip()
if wf_desc:
lines.append(f" Goal: {wf_desc}")
steps = workflow.get("steps", [])
if steps:
lines.append(" Steps:")
for step in steps:
step_id = step.get("id", "?")
step_tool = step.get("tool") or (
"ask_user" if step.get("ask_user") else ""
)
lines.append(f" - {step_id} ({step_tool})")
lines.append(" Call end_workflow(reason) when done, cancelled, or off-topic.")
return "\n".join(lines)
def _format_episodic_context(self, memory) -> str:
"""Format episodic memory context for the prompt."""
lines = []
if memory.episodic.last_search_results:
results = memory.episodic.last_search_results
result_list = results.get("results", [])
lines.append(
f"\nLAST SEARCH: '{results.get('query')}' ({len(result_list)} results)"
)
# Show first 5 results
for i, result in enumerate(result_list[:5]):
name = result.get("name", "Unknown")
lines.append(f" {i + 1}. {name}")
if len(result_list) > 5:
lines.append(f" ... and {len(result_list) - 5} more")
if memory.episodic.pending_question:
question = memory.episodic.pending_question
lines.append(f"\nPENDING QUESTION: {question.get('question')}")
lines.append(f" Type: {question.get('type')}")
if question.get("options"):
lines.append(f" Options: {len(question.get('options'))}")
if memory.episodic.active_downloads:
lines.append(f"\nACTIVE DOWNLOADS: {len(memory.episodic.active_downloads)}")
for dl in memory.episodic.active_downloads[:3]:
lines.append(f" - {dl.get('name')}: {dl.get('progress', 0)}%")
if memory.episodic.recent_errors:
lines.append("\nRECENT ERRORS (up to 3):")
for error in memory.episodic.recent_errors[-3:]:
lines.append(
f" - Action '{error.get('action')}' failed: {error.get('error')}"
)
# Unread events
unread = [e for e in memory.episodic.background_events if not e.get("read")]
if unread:
lines.append(f"\nUNREAD EVENTS: {len(unread)}")
for event in unread[:3]:
lines.append(f" - {event.get('type')}: {event.get('data')}")
return "\n".join(lines)
def _format_stm_context(self, memory) -> str:
"""Format short-term memory context for the prompt."""
lines = []
if memory.stm.current_workflow:
workflow = memory.stm.current_workflow
lines.append(
f"CURRENT WORKFLOW: {workflow.get('name')} (stage: {workflow.get('stage')})"
)
if workflow.get("params"):
lines.append(f" Params: {workflow.get('params')}")
if memory.stm.current_topic:
lines.append(f"CURRENT TOPIC: {memory.stm.current_topic}")
if memory.stm.extracted_entities:
lines.append("EXTRACTED ENTITIES:")
for key, value in memory.stm.extracted_entities.items():
lines.append(f" - {key}: {value}")
if memory.stm.language:
lines.append(f"CONVERSATION LANGUAGE: {memory.stm.language}")
return "\n".join(lines)
def _format_memory_schema(self) -> str:
"""Describe available memory components so the agent knows what to read/write and when."""
schema = self._memory_registry.schema()
tier_labels = {
"ltm": "LONG-TERM (persisted)",
"stm": "SHORT-TERM (session)",
"episodic": "EPISODIC (volatile)",
}
lines = ["MEMORY COMPONENTS:"]
for tier, components in schema.items():
if not components:
continue
lines.append(f"\n [{tier_labels.get(tier, tier.upper())}]")
for c in components:
access = c.get("access", "read")
lines.append(f" {c['name']} ({access}): {c['description']}")
for field_name, field_desc in c.get("fields", {}).items():
lines.append(f" · {field_name}: {field_desc}")
return "\n".join(lines)
def _format_config_context(self, memory) -> str:
"""Format configuration context."""
lines = ["CURRENT CONFIGURATION:"]
folders = {
**memory.ltm.workspace.as_dict(),
**memory.ltm.library_paths.to_dict(),
}
if folders:
for key, value in folders.items():
lines.append(f" - {key}: {value}")
else:
lines.append(" (no configuration set)")
return "\n".join(lines)
def build_system_prompt(self) -> str:
"""Build the complete system prompt."""
memory = get_memory()
# Identity + personality
identity = self._format_identity(memory)
# Language instruction
language_instruction = (
"Si la langue de l'user est différente de la langue courante en STM, "
"appelle `set_language` en premier avant de répondre."
)
# Configuration
config_section = self._format_config_context(memory)
# STM context
stm_context = self._format_stm_context(memory)
# Episodic context
episodic_context = self._format_episodic_context(memory)
# Memory schema
memory_schema = self._format_memory_schema()
# Workflow scope (active workflow plan or list of options)
workflow_section = self._format_workflow_scope(memory)
# Available tools (already filtered by scope)
tools_desc = self._format_tools_description()
tools_section = f"\nOUTILS DISPONIBLES:\n{tools_desc}" if tools_desc else ""
rules = """
RÈGLES:
- Utilise les outils pour accomplir les tâches, pas pour décorer
- Si des résultats de recherche sont dispo en mémoire épisodique, référence-les par index
- Confirme toujours avant une opération destructive (move, delete, overwrite)
- Réponses courtes — si c'est fait, dis-le en une ligne
- Si la demande est floue, demande un éclaircissement AVANT de lancer quoi que ce soit
"""
sections = [
identity,
language_instruction,
config_section,
stm_context,
episodic_context,
memory_schema,
workflow_section,
tools_section,
rules,
]
return "\n\n".join(s for s in sections if s and s.strip())
+178
View File
@@ -0,0 +1,178 @@
"""Tool registry — defines and registers all available tools for the agent."""
import inspect
import logging
from collections.abc import Callable
from dataclasses import dataclass
from typing import Any
from .tools.spec import ToolSpec, ToolSpecError
from .tools.spec_loader import load_tool_specs
logger = logging.getLogger(__name__)
@dataclass
class Tool:
"""Represents a tool that can be used by the agent."""
name: str
description: str
func: Callable[..., dict[str, Any]]
parameters: dict[str, Any]
cache_key: str | None = None # Parameter name to use as STM cache key.
_PY_TYPE_TO_JSON = {
str: "string",
int: "integer",
float: "number",
bool: "boolean",
list: "array",
dict: "object",
}
def _json_type_for(annotation) -> str:
"""Map a Python type annotation to a JSON Schema 'type' string."""
if annotation is inspect.Parameter.empty:
return "string"
# Strip Optional[X] / X | None to X.
args = getattr(annotation, "__args__", None)
if args:
non_none = [a for a in args if a is not type(None)]
if len(non_none) == 1:
annotation = non_none[0]
return _PY_TYPE_TO_JSON.get(annotation, "string")
def _create_tool_from_function(func: Callable, spec: ToolSpec | None = None) -> Tool:
"""
Create a Tool object from a function, optionally enriched with a spec.
Types and required-ness always come from the Python signature (source of
truth for the API contract). When a spec is provided, the description
and per-parameter docs come from the YAML spec instead of the docstring.
"""
sig = inspect.signature(func)
sig_params = {name: p for name, p in sig.parameters.items() if name != "self"}
if spec is not None:
_validate_spec_matches_signature(func.__name__, sig_params, spec)
description = spec.compile_description()
param_descriptions = {
name: spec.compile_parameter_description(name) for name in sig_params
}
else:
doc = inspect.getdoc(func)
description = doc.strip().split("\n")[0] if doc else func.__name__
param_descriptions = {name: f"Parameter {name}" for name in sig_params}
properties: dict[str, dict[str, Any]] = {}
required: list[str] = []
for param_name, param in sig_params.items():
properties[param_name] = {
"type": _json_type_for(param.annotation),
"description": param_descriptions[param_name],
}
if param.default is inspect.Parameter.empty:
required.append(param_name)
parameters = {
"type": "object",
"properties": properties,
"required": required,
}
cache_key = spec.cache.key if spec is not None and spec.cache is not None else None
return Tool(
name=func.__name__,
description=description,
func=func,
parameters=parameters,
cache_key=cache_key,
)
def _validate_spec_matches_signature(
func_name: str,
sig_params: dict[str, inspect.Parameter],
spec: ToolSpec,
) -> None:
"""Ensure every signature param has a spec entry and vice versa."""
sig_names = set(sig_params.keys())
spec_names = set(spec.parameters.keys())
missing_in_spec = sig_names - spec_names
if missing_in_spec:
raise ToolSpecError(
f"tool '{func_name}': spec is missing entries for parameter(s) "
f"{sorted(missing_in_spec)}"
)
extra_in_spec = spec_names - sig_names
if extra_in_spec:
raise ToolSpecError(
f"tool '{func_name}': spec has entries for unknown parameter(s) "
f"{sorted(extra_in_spec)} (not in function signature)"
)
def make_tools(settings) -> dict[str, Tool]:
"""
Create and register all available tools.
Args:
settings: Application settings instance.
Returns:
Dictionary mapping tool names to Tool objects.
"""
from .tools import api as api_tools # noqa: PLC0415
from .tools import filesystem as fs_tools # noqa: PLC0415
from .tools import language as lang_tools # noqa: PLC0415
from .tools import workflow as wf_tools # noqa: PLC0415
tool_functions = [
fs_tools.set_path_for_folder,
fs_tools.list_folder,
fs_tools.read_release_metadata,
fs_tools.query_library,
fs_tools.analyze_release,
fs_tools.probe_media,
fs_tools.resolve_season_destination,
fs_tools.resolve_episode_destination,
fs_tools.resolve_movie_destination,
fs_tools.resolve_series_destination,
fs_tools.move_media,
fs_tools.move_to_destination,
fs_tools.manage_subtitles,
fs_tools.create_seed_links,
fs_tools.learn,
api_tools.find_media_imdb_id,
api_tools.find_torrent,
api_tools.add_torrent_by_index,
api_tools.add_torrent_to_qbittorrent,
api_tools.get_torrent_by_index,
lang_tools.set_language,
wf_tools.start_workflow,
wf_tools.end_workflow,
]
specs = load_tool_specs()
tools: dict[str, Tool] = {}
for func in tool_functions:
spec = specs.get(func.__name__)
tool = _create_tool_from_function(func, spec=spec)
tools[tool.name] = tool
with_spec = sum(1 for fn in tool_functions if fn.__name__ in specs)
logger.info(
f"Registered {len(tools)} tools "
f"({with_spec} with YAML spec, {len(tools) - with_spec} doc-only): "
f"{list(tools.keys())}"
)
return tools
@@ -3,26 +3,18 @@
import logging import logging
from typing import Any from typing import Any
from application.movies import SearchMovieUseCase from alfred.application.movies import SearchMovieUseCase
from application.torrents import AddTorrentUseCase, SearchTorrentsUseCase from alfred.application.torrents import AddTorrentUseCase, SearchTorrentsUseCase
from infrastructure.api.knaben import knaben_client from alfred.infrastructure.api.knaben import knaben_client
from infrastructure.api.qbittorrent import qbittorrent_client from alfred.infrastructure.api.qbittorrent import qbittorrent_client
from infrastructure.api.tmdb import tmdb_client from alfred.infrastructure.api.tmdb import tmdb_client
from infrastructure.persistence import get_memory from alfred.infrastructure.persistence import get_memory
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def find_media_imdb_id(media_title: str) -> dict[str, Any]: def find_media_imdb_id(media_title: str) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/find_media_imdb_id.yaml."""
Find the IMDb ID for a given media title using TMDB API.
Args:
media_title: Title of the media to search for.
Returns:
Dict with IMDb ID and media info, or error details.
"""
use_case = SearchMovieUseCase(tmdb_client) use_case = SearchMovieUseCase(tmdb_client)
response = use_case.execute(media_title) response = use_case.execute(media_title)
result = response.to_dict() result = response.to_dict()
@@ -45,18 +37,7 @@ def find_media_imdb_id(media_title: str) -> dict[str, Any]:
def find_torrent(media_title: str) -> dict[str, Any]: def find_torrent(media_title: str) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/find_torrent.yaml."""
Find torrents for a given media title using Knaben API.
Results are stored in episodic memory so the user can reference them
by index (e.g., "download the 3rd one").
Args:
media_title: Title of the media to search for.
Returns:
Dict with torrent list or error details.
"""
logger.info(f"Searching torrents for: {media_title}") logger.info(f"Searching torrents for: {media_title}")
use_case = SearchTorrentsUseCase(knaben_client) use_case = SearchTorrentsUseCase(knaben_client)
@@ -76,17 +57,7 @@ def find_torrent(media_title: str) -> dict[str, Any]:
def get_torrent_by_index(index: int) -> dict[str, Any]: def get_torrent_by_index(index: int) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/get_torrent_by_index.yaml."""
Get a torrent from the last search results by its index.
Allows the user to reference results by number after a search.
Args:
index: 1-based index of the torrent in the search results.
Returns:
Dict with torrent data or error if not found.
"""
logger.info(f"Getting torrent at index: {index}") logger.info(f"Getting torrent at index: {index}")
memory = get_memory() memory = get_memory()
@@ -113,15 +84,7 @@ def get_torrent_by_index(index: int) -> dict[str, Any]:
def add_torrent_to_qbittorrent(magnet_link: str) -> dict[str, Any]: def add_torrent_to_qbittorrent(magnet_link: str) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/add_torrent_to_qbittorrent.yaml."""
Add a torrent to qBittorrent using a magnet link.
Args:
magnet_link: Magnet link of the torrent to add.
Returns:
Dict with success status or error details.
"""
logger.info("Adding torrent to qBittorrent") logger.info("Adding torrent to qBittorrent")
use_case = AddTorrentUseCase(qbittorrent_client) use_case = AddTorrentUseCase(qbittorrent_client)
@@ -157,17 +120,7 @@ def add_torrent_to_qbittorrent(magnet_link: str) -> dict[str, Any]:
def add_torrent_by_index(index: int) -> dict[str, Any]: def add_torrent_by_index(index: int) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/add_torrent_by_index.yaml."""
Add a torrent from the last search results by its index.
Combines get_torrent_by_index and add_torrent_to_qbittorrent.
Args:
index: 1-based index of the torrent in the search results.
Returns:
Dict with success status or error details.
"""
logger.info(f"Adding torrent by index: {index}") logger.info(f"Adding torrent by index: {index}")
torrent_result = get_torrent_by_index(index) torrent_result = get_torrent_by_index(index)
+365
View File
@@ -0,0 +1,365 @@
"""Filesystem tools for folder management."""
from pathlib import Path
from typing import Any
import yaml
import alfred as _alfred_pkg
from alfred.application.filesystem import (
CreateSeedLinksUseCase,
ListFolderUseCase,
ManageSubtitlesUseCase,
MoveMediaUseCase,
SetFolderPathUseCase,
)
from alfred.application.filesystem.detect_media_type import detect_media_type
from alfred.application.filesystem.enrich_from_probe import enrich_from_probe
from alfred.application.filesystem.resolve_destination import (
resolve_episode_destination as _resolve_episode_destination,
)
from alfred.application.filesystem.resolve_destination import (
resolve_movie_destination as _resolve_movie_destination,
)
from alfred.application.filesystem.resolve_destination import (
resolve_season_destination as _resolve_season_destination,
)
from alfred.application.filesystem.resolve_destination import (
resolve_series_destination as _resolve_series_destination,
)
from alfred.infrastructure.filesystem import FileManager, create_folder, move
from alfred.infrastructure.filesystem.ffprobe import probe
from alfred.infrastructure.filesystem.find_video import find_video_file
from alfred.infrastructure.metadata import MetadataStore
from alfred.infrastructure.persistence import get_memory
_LEARNED_ROOT = Path(_alfred_pkg.__file__).parent.parent / "data" / "knowledge"
def move_media(source: str, destination: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/move_media.yaml."""
file_manager = FileManager()
use_case = MoveMediaUseCase(file_manager)
return use_case.execute(source, destination).to_dict()
def move_to_destination(source: str, destination: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/move_to_destination.yaml."""
parent = str(Path(destination).parent)
result = create_folder(parent)
if result["status"] != "ok":
return result
return move(source, destination)
def resolve_season_destination(
release_name: str,
tmdb_title: str,
tmdb_year: int,
confirmed_folder: str | None = None,
) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/resolve_season_destination.yaml."""
return _resolve_season_destination(
release_name, tmdb_title, tmdb_year, confirmed_folder
).to_dict()
def resolve_episode_destination(
release_name: str,
source_file: str,
tmdb_title: str,
tmdb_year: int,
tmdb_episode_title: str | None = None,
confirmed_folder: str | None = None,
) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/resolve_episode_destination.yaml."""
return _resolve_episode_destination(
release_name,
source_file,
tmdb_title,
tmdb_year,
tmdb_episode_title,
confirmed_folder,
).to_dict()
def resolve_movie_destination(
release_name: str,
source_file: str,
tmdb_title: str,
tmdb_year: int,
) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/resolve_movie_destination.yaml."""
return _resolve_movie_destination(
release_name, source_file, tmdb_title, tmdb_year
).to_dict()
def resolve_series_destination(
release_name: str,
tmdb_title: str,
tmdb_year: int,
confirmed_folder: str | None = None,
) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/resolve_series_destination.yaml."""
return _resolve_series_destination(
release_name, tmdb_title, tmdb_year, confirmed_folder
).to_dict()
def create_seed_links(
library_file: str, original_download_folder: str
) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/create_seed_links.yaml."""
file_manager = FileManager()
use_case = CreateSeedLinksUseCase(file_manager)
return use_case.execute(library_file, original_download_folder).to_dict()
def manage_subtitles(source_video: str, destination_video: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/manage_subtitles.yaml."""
file_manager = FileManager()
use_case = ManageSubtitlesUseCase(file_manager)
return use_case.execute(source_video, destination_video).to_dict()
def learn(pack: str, category: str, key: str, values: list[str]) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/learn.yaml."""
_VALID_PACKS = {"subtitles"}
_VALID_CATEGORIES = {"languages", "types", "formats"}
if pack not in _VALID_PACKS:
return {
"status": "error",
"error": "unknown_pack",
"message": f"Unknown pack '{pack}'. Valid: {sorted(_VALID_PACKS)}",
}
if category not in _VALID_CATEGORIES:
return {
"status": "error",
"error": "unknown_category",
"message": f"Unknown category '{category}'. Valid: {sorted(_VALID_CATEGORIES)}",
}
learned_path = _LEARNED_ROOT / "subtitles_learned.yaml"
_LEARNED_ROOT.mkdir(parents=True, exist_ok=True)
data: dict = {}
if learned_path.exists():
try:
with open(learned_path, encoding="utf-8") as f:
data = yaml.safe_load(f) or {}
except Exception as e:
return {"status": "error", "error": "read_failed", "message": str(e)}
cat_data = data.setdefault(category, {})
entry = cat_data.setdefault(key, {"tokens": []})
existing = entry.get("tokens", [])
new_tokens = [v for v in values if v not in existing]
entry["tokens"] = existing + new_tokens
tmp = learned_path.with_suffix(".yaml.tmp")
try:
with open(tmp, "w", encoding="utf-8") as f:
yaml.safe_dump(
data, f, allow_unicode=True, default_flow_style=False, sort_keys=False
)
tmp.rename(learned_path)
except Exception as e:
tmp.unlink(missing_ok=True)
return {"status": "error", "error": "write_failed", "message": str(e)}
return {
"status": "ok",
"pack": pack,
"category": category,
"key": key,
"added_count": len(new_tokens),
"tokens": entry["tokens"],
}
def set_path_for_folder(folder_name: str, path_value: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/set_path_for_folder.yaml."""
file_manager = FileManager()
use_case = SetFolderPathUseCase(file_manager)
response = use_case.execute(folder_name, path_value)
return response.to_dict()
def analyze_release(release_name: str, source_path: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/analyze_release.yaml."""
from alfred.domain.release.services import parse_release # noqa: PLC0415
path = Path(source_path)
parsed = parse_release(release_name)
parsed.media_type = detect_media_type(parsed, path)
probe_used = False
if parsed.media_type not in ("unknown", "other"):
video_file = find_video_file(path)
if video_file:
media_info = probe(video_file)
if media_info:
enrich_from_probe(parsed, media_info)
probe_used = True
return {
"status": "ok",
"media_type": parsed.media_type,
"parse_path": parsed.parse_path,
"title": parsed.title,
"year": parsed.year,
"season": parsed.season,
"episode": parsed.episode,
"episode_end": parsed.episode_end,
"quality": parsed.quality,
"source": parsed.source,
"codec": parsed.codec,
"group": parsed.group,
"languages": parsed.languages,
"audio_codec": parsed.audio_codec,
"audio_channels": parsed.audio_channels,
"bit_depth": parsed.bit_depth,
"hdr_format": parsed.hdr_format,
"edition": parsed.edition,
"site_tag": parsed.site_tag,
"is_season_pack": parsed.is_season_pack,
"probe_used": probe_used,
}
def probe_media(source_path: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/probe_media.yaml."""
path = Path(source_path)
if not path.exists():
return {
"status": "error",
"error": "not_found",
"message": f"{source_path} does not exist",
}
media_info = probe(path)
if media_info is None:
return {
"status": "error",
"error": "probe_failed",
"message": "ffprobe failed to read the file",
}
return {
"status": "ok",
"video": {
"codec": media_info.video_codec,
"resolution": media_info.resolution,
"width": media_info.width,
"height": media_info.height,
"duration_seconds": media_info.duration_seconds,
"bitrate_kbps": media_info.bitrate_kbps,
},
"audio_tracks": [
{
"index": t.index,
"codec": t.codec,
"channels": t.channels,
"channel_layout": t.channel_layout,
"language": t.language,
"is_default": t.is_default,
}
for t in media_info.audio_tracks
],
"subtitle_tracks": [
{
"index": t.index,
"codec": t.codec,
"language": t.language,
"is_default": t.is_default,
"is_forced": t.is_forced,
}
for t in media_info.subtitle_tracks
],
"audio_languages": media_info.audio_languages,
"is_multi_audio": media_info.is_multi_audio,
}
def list_folder(folder_type: str, path: str = ".") -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/list_folder.yaml."""
file_manager = FileManager()
use_case = ListFolderUseCase(file_manager)
response = use_case.execute(folder_type, path)
return response.to_dict()
def read_release_metadata(release_path: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/read_release_metadata.yaml."""
path = Path(release_path)
if not path.exists():
return {
"status": "error",
"error": "not_found",
"message": f"{release_path} does not exist",
}
root = path if path.is_dir() else path.parent
store = MetadataStore(root)
if not store.exists():
return {
"status": "ok",
"release_path": str(root),
"has_metadata": False,
"metadata": {},
}
return {
"status": "ok",
"release_path": str(root),
"has_metadata": True,
"metadata": store.load(),
}
def query_library(name: str) -> dict[str, Any]:
"""Thin tool wrapper — semantics live in alfred/agent/tools/specs/query_library.yaml."""
needle = name.strip().lower()
if not needle:
return {
"status": "error",
"error": "empty_name",
"message": "name must be a non-empty string",
}
memory = get_memory()
roots = memory.ltm.library_paths.to_dict() or {}
if not roots:
return {
"status": "error",
"error": "no_libraries",
"message": "No library paths configured — call set_path_for_folder first.",
}
matches: list[dict[str, Any]] = []
for collection, root in roots.items():
root_path = Path(root)
if not root_path.is_dir():
continue
for entry in root_path.iterdir():
if not entry.is_dir():
continue
if needle not in entry.name.lower():
continue
store = MetadataStore(entry)
matches.append(
{
"collection": collection,
"name": entry.name,
"path": str(entry),
"has_metadata": store.exists(),
}
)
return {
"status": "ok",
"query": name,
"match_count": len(matches),
"matches": matches,
}
@@ -3,21 +3,13 @@
import logging import logging
from typing import Any from typing import Any
from infrastructure.persistence import get_memory from alfred.infrastructure.persistence import get_memory
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def set_language(language: str) -> dict[str, Any]: def set_language(language: str) -> dict[str, Any]:
""" """Thin tool wrapper — semantics live in alfred/agent/tools/specs/set_language.yaml."""
Set the conversation language.
Args:
language: Language code (e.g., 'en', 'fr', 'es', 'de')
Returns:
Status dictionary
"""
try: try:
memory = get_memory() memory = get_memory()
memory.stm.set_language(language) memory.stm.set_language(language)
+221
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@@ -0,0 +1,221 @@
"""
ToolSpec — semantic description of a tool, loaded from YAML.
Each tool exposed to the agent has a matching YAML spec under
alfred/agent/tools/specs/{tool_name}.yaml. The spec carries everything the
LLM needs to decide *when* and *why* to call the tool — separated from the
Python signature, which remains the source of truth for *how* (types,
required-ness).
The YAML structure is documented in the dataclasses below. Loading a spec
validates its shape; missing or unexpected fields raise ToolSpecError.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
import yaml
class ToolSpecError(ValueError):
"""Raised when a YAML tool spec is malformed or inconsistent."""
@dataclass(frozen=True)
class ParameterSpec:
"""Semantic description of a single tool parameter."""
description: str # Short: what the value represents.
why_needed: str # Why the tool needs this — drives LLM reasoning.
example: str | None = None # Concrete example value, shown to the LLM.
@classmethod
def from_dict(cls, name: str, data: dict) -> ParameterSpec:
_require(data, "description", f"parameter '{name}'")
_require(data, "why_needed", f"parameter '{name}'")
return cls(
description=str(data["description"]).strip(),
why_needed=str(data["why_needed"]).strip(),
example=str(data["example"]).strip()
if data.get("example") is not None
else None,
)
@dataclass(frozen=True)
class ReturnsSpec:
"""Description of one possible return shape (ok / needs_clarification / error / ...)."""
description: str
fields: dict[str, str] = field(default_factory=dict)
@classmethod
def from_dict(cls, key: str, data: dict) -> ReturnsSpec:
_require(data, "description", f"returns.{key}")
fields = data.get("fields") or {}
if not isinstance(fields, dict):
raise ToolSpecError(
f"returns.{key}.fields must be a dict, got {type(fields).__name__}"
)
return cls(
description=str(data["description"]).strip(),
fields={str(k): str(v).strip() for k, v in fields.items()},
)
@dataclass(frozen=True)
class CacheSpec:
"""Marks a tool as cacheable in STM.tool_results, keyed by one of its parameters."""
key: str # Name of the parameter whose value is the cache key.
@classmethod
def from_dict(cls, data: dict) -> CacheSpec:
_require(data, "key", "cache")
return cls(key=str(data["key"]).strip())
@dataclass(frozen=True)
class ToolSpec:
"""Full semantic spec for one tool."""
name: str
summary: str # One-liner — becomes Tool.description.
description: str # Longer paragraph.
when_to_use: str
when_not_to_use: str | None
next_steps: str | None
parameters: dict[str, ParameterSpec] # name -> ParameterSpec
returns: dict[str, ReturnsSpec] # status_key -> ReturnsSpec
cache: CacheSpec | None = None # If present, tool is cached.
@classmethod
def from_yaml_path(cls, path: Path) -> ToolSpec:
with open(path, encoding="utf-8") as f:
data = yaml.safe_load(f) or {}
if not isinstance(data, dict):
raise ToolSpecError(f"{path}: top-level must be a mapping")
try:
return cls.from_dict(data)
except ToolSpecError as e:
raise ToolSpecError(f"{path}: {e}") from e
@classmethod
def from_dict(cls, data: dict) -> ToolSpec:
_require(data, "name", "spec")
_require(data, "summary", "spec")
_require(data, "description", "spec")
_require(data, "when_to_use", "spec")
params_raw = data.get("parameters") or {}
if not isinstance(params_raw, dict):
raise ToolSpecError("parameters must be a mapping")
parameters = {
pname: ParameterSpec.from_dict(pname, pdata or {})
for pname, pdata in params_raw.items()
}
returns_raw = data.get("returns") or {}
if not isinstance(returns_raw, dict):
raise ToolSpecError("returns must be a mapping")
returns = {
rkey: ReturnsSpec.from_dict(rkey, rdata or {})
for rkey, rdata in returns_raw.items()
}
cache_raw = data.get("cache")
if cache_raw is not None and not isinstance(cache_raw, dict):
raise ToolSpecError("cache must be a mapping")
cache = CacheSpec.from_dict(cache_raw) if cache_raw else None
spec = cls(
name=str(data["name"]).strip(),
summary=str(data["summary"]).strip(),
description=str(data["description"]).strip(),
when_to_use=str(data["when_to_use"]).strip(),
when_not_to_use=_strip_or_none(data.get("when_not_to_use")),
next_steps=_strip_or_none(data.get("next_steps")),
parameters=parameters,
returns=returns,
cache=cache,
)
if cache is not None and cache.key not in parameters:
raise ToolSpecError(
f"cache.key '{cache.key}' is not a declared parameter "
f"(declared: {sorted(parameters)})"
)
return spec
def compile_description(self) -> str:
"""
Build the long description text passed to the LLM as Tool.description.
Layout:
<summary>
<description>
When to use:
<when_to_use>
When NOT to use: (if present)
<when_not_to_use>
Next steps: (if present)
<next_steps>
Returns:
<status>: <description>
· <field>: <desc>
"""
parts = [self.summary, "", self.description]
parts += ["", "When to use:", _indent(self.when_to_use)]
if self.when_not_to_use:
parts += ["", "When NOT to use:", _indent(self.when_not_to_use)]
if self.next_steps:
parts += ["", "Next steps:", _indent(self.next_steps)]
if self.returns:
parts += ["", "Returns:"]
for status, ret in self.returns.items():
parts.append(f" {status}: {ret.description}")
for fname, fdesc in ret.fields.items():
parts.append(f" · {fname}: {fdesc}")
return "\n".join(parts)
def compile_parameter_description(self, name: str) -> str:
"""Build the JSON Schema 'description' field for one parameter."""
p = self.parameters.get(name)
if p is None:
raise ToolSpecError(f"tool '{self.name}': no spec for parameter '{name}'")
text = f"{p.description} (Why: {p.why_needed})"
if p.example:
text += f" Example: {p.example}"
return text
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _require(data: dict, key: str, where: str) -> None:
if data.get(key) is None or (isinstance(data[key], str) and not data[key].strip()):
raise ToolSpecError(f"{where}: missing required field '{key}'")
def _strip_or_none(value) -> str | None:
if value is None:
return None
s = str(value).strip()
return s or None
def _indent(text: str, prefix: str = " ") -> str:
return "\n".join(prefix + line for line in text.splitlines())
+53
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@@ -0,0 +1,53 @@
"""
ToolSpecLoader — discover and load all YAML tool specs from a directory.
Convention: one YAML file per tool, named exactly like the Python function
that implements it (e.g. resolve_season_destination.yaml).
"""
from __future__ import annotations
import logging
from pathlib import Path
from .spec import ToolSpec, ToolSpecError
logger = logging.getLogger(__name__)
_DEFAULT_SPECS_DIR = Path(__file__).parent / "specs"
def load_tool_specs(specs_dir: Path | None = None) -> dict[str, ToolSpec]:
"""
Load every {tool}.yaml under specs_dir into a {name -> ToolSpec} mapping.
Args:
specs_dir: Directory to scan. Defaults to alfred/agent/tools/specs/.
Returns:
Mapping from tool name to its parsed ToolSpec.
Raises:
ToolSpecError: if a spec is malformed, or if the filename doesn't
match the 'name' field inside the YAML.
"""
root = specs_dir or _DEFAULT_SPECS_DIR
if not root.exists():
logger.warning(f"Tool specs directory not found: {root}")
return {}
specs: dict[str, ToolSpec] = {}
for path in sorted(root.glob("*.yaml")):
spec = ToolSpec.from_yaml_path(path)
expected_name = path.stem
if spec.name != expected_name:
raise ToolSpecError(
f"{path}: filename stem '{expected_name}' "
f"does not match spec.name '{spec.name}'"
)
if spec.name in specs:
raise ToolSpecError(f"duplicate tool spec name: '{spec.name}'")
specs[spec.name] = spec
logger.info(f"Loaded {len(specs)} tool spec(s) from {root}")
return specs
@@ -0,0 +1,53 @@
name: add_torrent_by_index
summary: >
Pick a torrent from the last find_torrent results by index and add
it to qBittorrent in one call.
description: |
Convenience wrapper that combines get_torrent_by_index +
add_torrent_to_qbittorrent. Looks up the torrent at the given
1-based index, extracts its magnet link, and sends it to
qBittorrent. The result mirrors add_torrent_to_qbittorrent's, with
the chosen torrent's name appended on success.
when_to_use: |
The default action after find_torrent when the user picks a hit by
number ("download the second one"). One call, two side effects:
episodic memory updated + download started.
when_not_to_use: |
- When the user only wants to inspect, not download — use
get_torrent_by_index.
- When the magnet comes from outside the search results — use
add_torrent_to_qbittorrent directly.
next_steps: |
- On status=ok: confirm the download started and end the workflow
if not already ended.
- On status=error (not_found): the index is out of range; show the
available count from episodic memory.
- On status=error (no_magnet): the search result was malformed —
suggest re-running find_torrent.
parameters:
index:
description: 1-based position of the torrent in the last find_torrent results.
why_needed: |
Identifies which torrent to add. Out-of-range indices return
not_found.
example: 3
returns:
ok:
description: Torrent was added to qBittorrent.
fields:
status: "'ok'"
message: Confirmation message.
torrent_name: Name of the torrent that was added.
error:
description: Failed to add.
fields:
error: Short error code (not_found, no_magnet, ...).
message: Human-readable explanation.
@@ -0,0 +1,48 @@
name: add_torrent_to_qbittorrent
summary: >
Send a magnet link to qBittorrent and start the download.
description: |
Adds a torrent to qBittorrent using its WebUI API. On success, the
download is also recorded in episodic memory as an active_download
so the agent can track its progress later, the STM topic is set to
"downloading", and the current workflow is ended (the user typically
leaves the find-and-download scope at this point).
when_to_use: |
When the user provides a raw magnet link, or when chaining manually
after get_torrent_by_index. For the common "user picked search hit
N" case, prefer add_torrent_by_index — one call instead of two.
when_not_to_use: |
- For .torrent files (not supported by this tool — magnet only).
- When qBittorrent is not configured / reachable — the call will
fail and the user has to fix the config first.
next_steps: |
- On status=ok: the workflow is already ended; confirm to the user
that the download has started.
- On status=error: surface the message; common causes are auth
failure or qBittorrent being unreachable.
parameters:
magnet_link:
description: Magnet URI of the torrent to add (magnet:?xt=urn:btih:...).
why_needed: |
The actual payload sent to qBittorrent. Must be a full magnet
URI, not a hash alone.
example: "magnet:?xt=urn:btih:abc123..."
returns:
ok:
description: Torrent accepted by qBittorrent.
fields:
status: "'ok'"
message: Confirmation message.
error:
description: qBittorrent rejected the request or is unreachable.
fields:
error: Short error code.
message: Human-readable explanation.
@@ -0,0 +1,82 @@
name: analyze_release
summary: >
One-shot analyzer that parses a release name, detects its media type
from the folder layout, and enriches the result with ffprobe data.
description: |
Combines three steps in a single call so the agent gets a complete
picture before routing:
1. parse_release(release_name) — extracts title, year, season,
episode, quality, source, codec, group, languages, audio info,
HDR, edition, site tag.
2. detect_media_type(parsed, path) — uses the on-disk layout
(single file vs. folder, presence of S01 dirs, episode count)
to choose: movie / tv_episode / tv_season / tv_complete /
other / unknown.
3. ffprobe enrichment — when the media type is recognised, runs
ffprobe on the first video file found and fills in audio
codec/channels, bit depth, HDR format. Sets probe_used=true.
when_to_use: |
As the very first step of any organize workflow, right after
list_folder, on each release the user wants to handle. The output
drives which resolve_*_destination to call next.
when_not_to_use: |
- When you only need codec/audio info on a specific video file:
use probe_media (no parsing, no media-type detection).
- For releases the user has already analyzed earlier in the same
workflow — the parse is deterministic, no need to re-run.
next_steps: |
- media_type == movie → resolve_movie_destination
- media_type == tv_season → resolve_season_destination
- media_type == tv_episode → resolve_episode_destination
- media_type == tv_complete → resolve_series_destination
- media_type in (other, unknown) → ask the user what to do; do not
auto-route.
cache:
key: source_path
parameters:
release_name:
description: Raw release folder or file name as it appears on disk.
why_needed: |
Source of all the parsed tokens (quality, codec, group, ...).
Don't sanitise it — the parser relies on the exact spelling.
example: Breaking.Bad.S01.1080p.BluRay.x265-GROUP
source_path:
description: Absolute path to the release folder or file on disk.
why_needed: |
Required for layout-based media-type detection and for ffprobe
to find a video file inside the release.
example: /downloads/Breaking.Bad.S01.1080p.BluRay.x265-GROUP
returns:
ok:
description: Release analyzed.
fields:
status: "'ok'"
media_type: "One of: movie, tv_episode, tv_season, tv_complete, other, unknown."
parse_path: "Which parser branch was taken (debug)."
title: Parsed title.
year: Parsed year (int) or null.
season: Season number (int) or null.
episode: Episode number (int) or null.
episode_end: Range end episode (multi-episode releases) or null.
quality: Resolution token (e.g. 1080p, 2160p).
source: Source token (BluRay, WEB-DL, ...).
codec: Video codec token (x264, x265, ...).
group: Release group name or null.
languages: List of detected language tokens.
audio_codec: Audio codec from ffprobe (when probe_used=true).
audio_channels: Audio channel count from ffprobe.
bit_depth: Bit depth from ffprobe.
hdr_format: HDR format from ffprobe (HDR10, DV, ...) or null.
edition: Edition tag (Extended, Director's Cut, ...) or null.
site_tag: Source-site tag if present.
is_season_pack: True when the folder contains a full season.
probe_used: True when ffprobe successfully enriched the result.
@@ -0,0 +1,59 @@
name: create_seed_links
summary: >
Recreate the original torrent folder structure with hard-links so
qBittorrent can keep seeding after the library move.
description: |
Hard-links the library video file back into torrents/<original_folder_name>/
and copies all remaining files from the original download folder
(subtitles, .nfo, .jpg, .txt, …) so the torrent data is complete on
disk. qBittorrent then sees the same content at the location it
expects and can keep seeding without rehashing the whole torrent.
when_to_use: |
Only when the user has confirmed they want to keep seeding after a
move. Call right after manage_subtitles (or after move_media if there
are no subs).
when_not_to_use: |
- When the user explicitly answered "no" to "keep seeding?".
- When the download was not from a torrent (e.g. direct download).
- Before the library file is in place — this tool reads it.
next_steps: |
- After success: optionally call qBittorrent to update the torrent's
save path / force a recheck (not yet covered by a tool).
- End the workflow.
parameters:
library_file:
description: Absolute path to the video file now in the library.
why_needed: |
The source for the hard-link — same inode means qBittorrent sees
identical bytes at the seeding path.
example: /tv_shows/Oz.1997.1080p.WEBRip.x265-KONTRAST/Season 03/Oz.S03E01.mkv
original_download_folder:
description: Absolute path to the original download folder.
why_needed: |
Provides the folder name to recreate under torrents/ and the
auxiliary files (subs, nfo, ...) to copy over.
example: /downloads/Oz.S03.1080p.WEBRip.x265-KONTRAST
returns:
ok:
description: Seeding folder rebuilt.
fields:
status: "'ok'"
torrent_subfolder: Absolute path of the recreated folder under torrents/.
linked_file: Absolute path of the hard-linked video.
copied_files: List of auxiliary files that were copied.
copied_count: Number of auxiliary files copied.
skipped: List of files skipped (already present, unreadable, ...).
error:
description: Failed to rebuild the seeding folder.
fields:
error: Short error code.
message: Human-readable explanation.
@@ -0,0 +1,48 @@
name: end_workflow
summary: >
Leave the current workflow scope and return to the broad-catalog mode.
description: |
Clears the active workflow from STM. After this call the visible tool
catalog returns to the core noyau plus start_workflow, so the agent is
ready to handle a different request.
when_to_use: |
- When all the workflow's steps have completed successfully.
- When the user explicitly cancels the current task.
- When the user changes subject mid-conversation and the active
workflow is no longer relevant.
- When an unrecoverable error makes continuing pointless — explain
in 'reason'.
when_not_to_use: |
- Do not call when there is no active workflow — it will return an
error. Just call start_workflow for the new request instead.
- Do not call mid-step just to "free up tools"; finish the step
or fail it explicitly first.
next_steps: |
- After ending, you can either call start_workflow for a new task or
answer the user directly from the broad catalog.
parameters:
reason:
description: Short reason for ending — completed, cancelled, changed_subject, error, ...
why_needed: |
Recorded in episodic memory for debugging and future audits. A
structured short string is more useful than a long sentence.
example: completed
returns:
ok:
description: Workflow ended; catalog is back to the broad noyau.
fields:
workflow: Name of the workflow that just ended.
reason: The reason that was passed in.
error:
description: Could not end — typically because nothing was active.
fields:
error: Short error code (no_active_workflow).
message: Human-readable explanation.
@@ -0,0 +1,56 @@
name: find_media_imdb_id
summary: >
Search TMDB for a media title and return its canonical title, year,
IMDb id, and TMDB id.
description: |
Looks up a title on TMDB and returns the canonical metadata needed by
the resolve_*_destination tools. On success, the result is also
stashed in short-term memory under "last_media_search" so later steps
in the workflow can read it without re-calling TMDB. The STM topic
is set to "searching_media".
when_to_use: |
Right after analyze_release, before calling resolve_*_destination —
the resolvers need the canonical title + year and refuse to guess
them from the raw release name.
when_not_to_use: |
- When you already have the IMDb id in STM from an earlier step in
the same workflow.
- For torrent search — use find_torrent instead.
next_steps: |
- On status=ok: call the appropriate resolve_*_destination with
tmdb_title and tmdb_year from the result.
- On status=error (not_found): show the error and ask the user for
a more precise title.
cache:
key: media_title
parameters:
media_title:
description: Title to search for. Free-form — TMDB does the matching.
why_needed: |
Drives the TMDB query. Pass a sanitized version (no resolution
tokens, no group name) for best results.
example: Breaking Bad
returns:
ok:
description: Match found.
fields:
status: "'ok'"
title: Canonical title as returned by TMDB.
year: Release year (movies) or first-air year (series).
media_type: "'movie' or 'tv'."
imdb_id: IMDb identifier (ttXXXXXXX) or null.
tmdb_id: TMDB numeric id.
error:
description: No match or API failure.
fields:
error: Short error code (not_found, api_error, ...).
message: Human-readable explanation.
@@ -0,0 +1,52 @@
name: find_torrent
summary: >
Search Knaben for torrents matching a media title; cache results in
episodic memory.
description: |
Queries the Knaben aggregator for up to 10 torrents matching the
given title, then stores the result list in episodic memory under
"last_search_results". The user can then refer to a torrent by
1-based index ("download the 3rd one") via get_torrent_by_index or
add_torrent_by_index. The STM topic is set to "selecting_torrent".
when_to_use: |
When the user wants to download something new — typically the first
step of a "find + download" sub-task. The agent should usually
pre-filter the title (canonical name + year) before searching for
cleaner results.
when_not_to_use: |
- For TMDB metadata lookup — use find_media_imdb_id.
- When a search was already performed in the same session and the
user is just picking from the existing list.
next_steps: |
- Present the indexed results to the user.
- Once chosen: call add_torrent_by_index(N) — that wraps
get_torrent_by_index + add_torrent_to_qbittorrent.
cache:
key: media_title
parameters:
media_title:
description: Title to search for on Knaben. Free-form.
why_needed: |
Drives the search query. Use the canonical title (from
find_media_imdb_id) plus quality preferences for better hits.
example: Inception 2010 1080p
returns:
ok:
description: Search returned a list of torrents.
fields:
status: "'ok'"
torrents: "List of {name, size, seeders, leechers, magnet, ...}, up to 10."
error:
description: Search failed.
fields:
error: Short error code.
message: Human-readable explanation.
@@ -0,0 +1,48 @@
name: get_torrent_by_index
summary: >
Retrieve a torrent from the last find_torrent search by its 1-based
index.
description: |
Reads episodic memory's last_search_results and returns the entry at
the given 1-based position. Pure lookup — does not start a download.
Fails when the search results are missing or the index is out of
range.
when_to_use: |
When the user references a search hit by number ("show me the second
one") but doesn't yet want to download — e.g. inspection, sharing
the magnet, ...
when_not_to_use: |
- When the user wants to start downloading: use add_torrent_by_index
instead (one call instead of two).
- When no search has been performed yet — the result will be
not_found.
next_steps: |
- Display the torrent to the user.
- If they then say "add it", call add_torrent_to_qbittorrent with the
magnet, or add_torrent_by_index with the same index.
parameters:
index:
description: 1-based position in the last find_torrent result list.
why_needed: |
Maps to a specific torrent entry. Out-of-range values return an
error, not a wraparound.
example: 3
returns:
ok:
description: Torrent found at that index.
fields:
status: "'ok'"
torrent: "Full torrent dict (name, size, seeders, leechers, magnet, ...)."
error:
description: No torrent at that index.
fields:
error: Short error code (not_found).
message: Human-readable explanation, e.g. "Search for torrents first."
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name: learn
summary: >
Teach Alfred a new token mapping and persist it to the learned
knowledge pack so future scans recognise it.
description: |
Appends a new token (or list of tokens) to a key inside a knowledge
pack and writes the result to `data/knowledge/<pack>_learned.yaml`.
The change is persisted atomically (write-tmp + rename) so a crash
cannot corrupt the file. Currently only the `subtitles` pack is
supported.
when_to_use: |
When manage_subtitles returns needs_clarification with unresolved
tokens, after confirming with the user what the tokens mean. Call
once per (category, key) — multiple values can be added in a single
call.
when_not_to_use: |
- Without explicit user confirmation of what the token means.
- For knowledge that belongs in the static pack
(alfred/knowledge/<pack>.yaml) — that's editor territory, not
runtime learning.
next_steps: |
- After success: re-run the workflow step that triggered the
clarification (typically manage_subtitles) so the new mapping is
applied.
parameters:
pack:
description: Knowledge pack name. Currently only "subtitles" is supported.
why_needed: |
Decides which `*_learned.yaml` file under data/knowledge/ gets
written. The pack name is namespaced to avoid collisions across
domains.
example: subtitles
category:
description: Category within the pack — "languages", "types", or "formats".
why_needed: |
Different categories use different lookup tables at scan time.
A wrong category silently has no effect.
example: languages
key:
description: Canonical entry id — ISO 639-1 code, type name, format name.
why_needed: |
The destination bucket for the new tokens. Existing tokens under
this key are kept; only new values are appended.
example: es
values:
description: List of token spellings to add.
why_needed: |
Release groups use many spellings for the same language/type;
pass them all in one call instead of multiple round-trips.
example: '["spanish", "espanol", "spa"]'
returns:
ok:
description: Mapping saved.
fields:
status: "'ok'"
pack: Name of the pack that was written to.
category: Category that was updated.
key: Key that was updated.
added_count: Number of values that were actually new (deduplicated).
tokens: Full updated token list for that key.
error:
description: Save failed.
fields:
error: Short error code (unknown_pack, unknown_category, read_failed, write_failed).
message: Human-readable explanation.
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name: list_folder
summary: >
List the contents of a configured folder, optionally below a
relative subpath.
description: |
Reads a folder previously configured via set_path_for_folder and
returns its entries (files + directories). A relative `path` lets you
drill down without re-specifying the absolute root each time. Path
traversal is rejected (no `..`, no absolute paths) so the agent
cannot escape the configured root.
when_to_use: |
- At the start of an organize workflow to discover what's available
in the download folder.
- To browse a library collection ("what tv shows do I have?").
- As a sanity check before any move to confirm the target exists.
when_not_to_use: |
- For folders that are not configured — call set_path_for_folder
first.
- To list arbitrary system paths — this tool is intentionally scoped
to the known roots.
next_steps: |
- After listing the download folder: typically call analyze_release
on a specific entry.
- After listing a library folder: use the result to disambiguate a
destination during resolve_*_destination.
cache:
key: path
parameters:
folder_type:
description: Logical folder key (download, torrent, movie, tv_show, ...).
why_needed: |
Resolves to an absolute root through LTM. Must have been set via
set_path_for_folder beforehand.
example: download
path:
description: Relative subpath inside the root (default ".").
why_needed: |
Lets you drill into a subfolder without expanding the root. No
".." or absolute path is allowed.
example: Breaking.Bad.S01.1080p.BluRay.x265-GROUP
returns:
ok:
description: Listing returned.
fields:
status: "'ok'"
folder_type: The key that was listed.
path: The relative path that was listed.
entries: List of {name, type, size?} for each entry.
error:
description: Could not list the folder.
fields:
error: Short error code (folder_not_configured, path_not_found, path_traversal, ...).
message: Human-readable explanation.
@@ -0,0 +1,67 @@
name: manage_subtitles
summary: >
Detect, filter, and place subtitle tracks next to a video that has just
been organised into the library.
description: |
Scans the source video's surroundings for subtitle files
(.srt, .ass, .ssa, .vtt, .sub), classifies them by language and type
(standard / SDH / forced), filters by the user's SubtitlePreferences
(languages, min size, keep_sdh, keep_forced), and hard-links the
passing files next to the destination video using the convention
`<lang>.<ext>`, `<lang>.sdh.<ext>`, `<lang>.forced.<ext>`.
If no subtitles are found, returns status=ok with placed_count=0 — not
an error.
when_to_use: |
Always after a successful move_media / move_to_destination, before
closing the workflow. Pass the original source path (where subs live)
and the new library path (where they should land).
when_not_to_use: |
- Do not call before the video itself has been moved — the destination
must exist for hard-links to make sense.
- Skip when the user explicitly asks not to handle subtitles.
next_steps: |
- On status=ok: continue with create_seed_links (if seeding) or end
the workflow.
- On status=needs_clarification: ask the user about the unresolved
tokens, then optionally call learn() to teach the new mapping.
parameters:
source_video:
description: Absolute path to the original video file (in the download folder).
why_needed: |
Subtitles typically live next to the source, either as siblings or
in a Subs/ subfolder. The scanner walks from this path.
example: /downloads/Oz.S03.1080p.WEBRip.x265-KONTRAST/Oz.S03E01.mkv
destination_video:
description: Absolute path to the video file in its library location.
why_needed: |
Subtitles are hard-linked next to this file so media players pick
them up automatically.
example: /tv_shows/Oz.1997.1080p.WEBRip.x265-KONTRAST/Season 03/Oz.S03E01.mkv
returns:
ok:
description: Subtitles scanned (and possibly placed).
fields:
status: "'ok'"
placed: List of {source, destination, filename} for each linked file.
placed_count: Number of subtitle files placed.
skipped_count: Number of subtitle files filtered out.
needs_clarification:
description: One or more tokens could not be classified.
fields:
unresolved: List of unrecognised tokens with their context.
question: Human-readable question to relay to the user.
error:
description: Scan or placement failed.
fields:
error: Short error code.
message: Human-readable explanation.
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name: move_media
summary: >
Safely move a media file with copy + integrity check + delete source.
description: |
Copies the source file to the destination with an integrity check,
then deletes the source. Slower than move_to_destination (which is a
plain rename) but safer across filesystems where rename is not atomic
or when you want a checksum verification.
when_to_use: |
Use to move a single file across filesystems or when paranoia about
data integrity is justified — e.g. moving a finished download from a
scratch disk to the main library array.
when_not_to_use: |
- For same-filesystem moves where speed matters: use move_to_destination
(instant rename on ZFS/ext4 within the same dataset).
- For folder-level moves of complete packs: use move_to_destination —
move_media is a single-file operation.
next_steps: |
- After a successful move: call manage_subtitles to place any subtitle
tracks, then create_seed_links if the user wants to keep seeding.
- On error: surface the error code (file_not_found, destination_exists,
integrity_check_failed) and ask the user how to proceed.
parameters:
source:
description: Absolute path to the source video file.
why_needed: |
The file being moved. Typically lives under the downloads folder
after a torrent completes.
example: /downloads/Inception.2010.1080p.BluRay.x265-GROUP/movie.mkv
destination:
description: Absolute path of the destination file — must not already exist.
why_needed: |
Where the file lands in the library. Comes from a resolve_*_destination
call so the naming convention is respected.
example: /movies/Inception.2010.1080p.BluRay.x265-GROUP/Inception.2010.1080p.BluRay.x265-GROUP.mkv
returns:
ok:
description: Move succeeded.
fields:
status: "'ok'"
source: Absolute path of the source (now gone).
destination: Absolute path of the destination (now in place).
filename: Basename of the destination file.
size: Size in bytes.
error:
description: Move failed.
fields:
error: Short error code (file_not_found, destination_exists, integrity_check_failed, ...).
message: Human-readable explanation.
@@ -0,0 +1,55 @@
name: move_to_destination
summary: >
Move a file or folder to a destination, creating parent directories as needed.
description: |
Performs an actual move on disk. Uses the system 'mv' command, so on the
same filesystem (e.g. ZFS) this is an instant rename. Creates the parent
directory of the destination if it doesn't exist yet, then moves. Returns
before/after paths on success, or an error if the destination already
exists or the source can't be moved.
when_to_use: |
Use after one of the resolve_*_destination tools returned status=ok, to
perform the move it described. The 'source' and 'destination' arguments
come directly from the resolved paths.
when_not_to_use: |
- Never move when status was not 'ok' (clarification still pending or
error happened) — that would leave the library in a half-broken state.
- Don't use this for the seed-link step; use create_seed_links for that.
next_steps: |
- After a successful move: call manage_subtitles to place any subtitle
tracks, then create_seed_links to keep qBittorrent seeding.
- On error: surface the message; do not retry blindly — check whether
the destination already exists or the source path is correct.
parameters:
source:
description: Absolute path to the source file or folder to move.
why_needed: |
The thing being moved. Comes from the user's download folder or from
a previous tool's output.
example: /downloads/Oz.S03.1080p.WEBRip.x265-KONTRAST
destination:
description: Absolute path of the destination — must not already exist.
why_needed: |
Where to put the source. Comes from a resolve_*_destination call so
that the path matches the library's naming convention.
example: /tv_shows/Oz.1997.1080p.WEBRip.x265-KONTRAST/Oz.S03.1080p.WEBRip.x265-KONTRAST
returns:
ok:
description: Move succeeded.
fields:
source: Absolute path of the source (now gone).
destination: Absolute path of the destination (now in place).
error:
description: Move failed.
fields:
error: Short error code (source_not_found, destination_exists, mkdir_failed, move_failed).
message: Human-readable explanation of what went wrong.
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name: probe_media
summary: >
Run ffprobe on a single video file and return its technical details.
description: |
Inspects a specific video file with ffprobe and returns codec,
resolution, duration, bitrate, the list of audio tracks (with
language and channel layout), and the list of embedded subtitle
tracks. Independent of any release-name parsing — works on any file
you can point at.
when_to_use: |
- To inspect a file's audio/subtitle tracks before deciding what to
do (e.g. choose a default audio language).
- To verify a video's resolution / codec when the release name is
unreliable.
- As a building block when analyze_release is overkill.
when_not_to_use: |
- For full release routing — analyze_release does parsing + media
type detection + probe in one call.
- On non-video files — ffprobe will return probe_failed.
next_steps: |
- The returned info typically feeds a user-facing decision (e.g.
"this is 7.1 DTS, want to keep it?"); rarely chained directly to
another tool.
cache:
key: source_path
parameters:
source_path:
description: Absolute path to the video file to probe.
why_needed: |
ffprobe needs the exact file (not a folder). For releases use
analyze_release; for a known file path, pass it here.
example: /downloads/Inception.2010.1080p.BluRay.x265-GROUP/movie.mkv
returns:
ok:
description: Probe succeeded.
fields:
status: "'ok'"
video: "Dict with codec, resolution, width, height, duration_seconds, bitrate_kbps."
audio_tracks: "List of {index, codec, channels, channel_layout, language, is_default}."
subtitle_tracks: "List of {index, codec, language, is_default, is_forced}."
audio_languages: List of language codes present in audio tracks.
is_multi_audio: True when more than one audio language is present.
error:
description: Probe failed.
fields:
error: Short error code (not_found, probe_failed).
message: Human-readable explanation.
@@ -0,0 +1,54 @@
name: query_library
summary: >
Find release folders across all configured library roots whose name
contains a substring (case-insensitive).
description: |
Scans every configured library root (movies, tv_shows, …) at depth 1
and returns folders whose name contains the query. For each match,
reports whether a `.alfred/metadata.yaml` exists — handy to spot
releases that have not been inspected yet. Does not recurse into
seasons / episodes; one entry per release folder.
when_to_use: |
- To answer "do I already have X?" without listing whole library
roots one by one.
- To pick the release_path to feed read_release_metadata or any
inspector tool.
when_not_to_use: |
- To list the *whole* library — that scan should live behind a
dedicated tool (not implemented yet).
- To browse a single root — use list_folder instead, it's cheaper
and doesn't open every library.
next_steps: |
- When one match is found: feed its path to read_release_metadata or
analyze_release.
- When several match: surface the indexed list to the user and ask
which one they mean.
parameters:
name:
description: Case-insensitive substring of the release name to look for.
why_needed: |
Library folders are named after the release (Title.Year.... or
Title (Year)). A substring is enough to catch typical user
phrasings ("foundation", "inception 2010").
example: foundation
returns:
ok:
description: Scan completed (possibly zero matches).
fields:
status: "'ok'"
query: The query string as received.
match_count: Number of matching folders.
matches: "List of {collection, name, path, has_metadata}."
error:
description: Scan could not run.
fields:
error: Short error code (no_libraries, empty_name).
message: Human-readable explanation.
@@ -0,0 +1,55 @@
name: read_release_metadata
summary: >
Read the `.alfred/metadata.yaml` file for a release folder.
description: |
Returns whatever has been previously persisted by inspector tools
(analyze_release, probe_media, find_media_imdb_id) and by the subtitle
pipeline. Works for any folder — download or library — as long as the
release has been touched at least once. Missing metadata is not an
error: the tool returns `has_metadata=false` with an empty dict.
when_to_use: |
- Before re-running analyze_release / probe_media on a release you
might have already seen — saves a full re-inspection.
- To answer "what do we know about X?" without scanning.
- To list which releases in a library have no `.alfred` yet (loop +
`has_metadata`).
when_not_to_use: |
- To search a library by name — use query_library.
- When you need a fresh probe/parse — call the inspector directly,
the result will be persisted automatically.
next_steps: |
- If `has_metadata=false`, decide whether to inspect now
(analyze_release / probe_media).
- If `has_metadata=true`, read `metadata.parse`, `metadata.probe`,
`metadata.tmdb` blocks before deciding next actions.
cache:
key: release_path
parameters:
release_path:
description: Absolute path to the release folder (or any file inside it).
why_needed: |
The store lives at `<release_root>/.alfred/metadata.yaml`. A file
path is auto-resolved to its parent folder.
example: /mnt/library/tv_shows/Foundation.2021.1080p.WEBRip.x265-RARBG
returns:
ok:
description: Release inspected (file may or may not exist).
fields:
status: "'ok'"
release_path: Absolute path of the release folder.
has_metadata: True if `.alfred/metadata.yaml` exists.
metadata: Full content of the file, or empty dict.
error:
description: Path does not exist on disk.
fields:
error: Short error code (not_found).
message: Human-readable explanation.
@@ -0,0 +1,93 @@
name: resolve_episode_destination
summary: >
Compute destination paths for a single TV episode file (file move).
description: |
Resolves the target series folder, season subfolder, and full destination
filename for a single-episode release. Returns paths only — does not move
anything. If a series folder with a different name already exists, returns
needs_clarification.
when_to_use: |
Use after analyze_release has identified the release as a single episode
(media_type=tv_show, season AND episode both set). TMDB must already be
queried for the canonical title/year, and optionally the episode title.
when_not_to_use: |
- Season packs (folder containing many episodes): use resolve_season_destination.
- Multi-season packs: use resolve_series_destination.
- Movies: use resolve_movie_destination.
next_steps: |
- On status=ok: call move_to_destination with the source video file and
destination=library_file.
- On status=needs_clarification: present question/options to the user,
then re-call with confirmed_folder set.
- On status=error: surface the message; do not move.
parameters:
release_name:
description: Raw release file name (with extension).
why_needed: |
Drives extraction of quality/source/codec/group, which become part of
the destination filename so each file is self-describing.
example: Oz.S03E01.1080p.WEBRip.x265-KONTRAST.mkv
source_file:
description: Absolute path to the source video file on disk.
why_needed: |
Used to read the source file extension (.mkv, .mp4, .avi…) for the
destination filename — release names don't always carry the extension.
example: /downloads/Oz.S03E01.1080p.WEBRip.x265-KONTRAST/file.mkv
tmdb_title:
description: Canonical show title from TMDB.
why_needed: |
Title prefix for both the series folder and the destination filename;
ensures consistent naming across all episodes of the show.
example: Oz
tmdb_year:
description: Show start year from TMDB.
why_needed: |
Disambiguates remakes/reboots sharing a title; year is part of the
series folder identity.
example: "1997"
tmdb_episode_title:
description: Episode title from TMDB. Optional.
why_needed: |
When present, the destination filename embeds the episode title for
human-readability (e.g. Oz.S01E01.The.Routine...).
example: The Routine
confirmed_folder:
description: Folder name the user picked after needs_clarification.
why_needed: |
Forces the use case to skip detection and use this exact folder name.
example: Oz.1997.1080p.WEBRip.x265-KONTRAST
returns:
ok:
description: Paths resolved; ready to move the episode file.
fields:
series_folder: Absolute path to the series root folder.
season_folder: Absolute path to the season subfolder.
library_file: Absolute path to the destination .mkv file (move target).
series_folder_name: Series folder name for display.
season_folder_name: Season folder name for display.
filename: Destination filename for display.
is_new_series_folder: True if the series folder doesn't exist yet.
needs_clarification:
description: A folder exists with a different name; user must choose.
fields:
question: Human-readable question.
options: List of folder names to pick from.
error:
description: Resolution failed.
fields:
error: Short error code.
message: Human-readable explanation.
@@ -0,0 +1,72 @@
name: resolve_movie_destination
summary: >
Compute destination paths for a movie file (file move).
description: |
Resolves the target movie folder and full destination filename for a movie
release. Returns paths only — does not move anything. Movies do not have
the existing-folder disambiguation problem that TV shows have (each
release lands in its own folder named after the canonical title + year +
tech).
when_to_use: |
Use after analyze_release has identified the release as a movie
(media_type=movie). TMDB must already be queried for the canonical title
and release year.
when_not_to_use: |
- TV shows in any form: use resolve_season_destination /
resolve_episode_destination / resolve_series_destination.
- Documentaries when they're treated as series rather than standalone
films: route them through the TV-show resolvers.
next_steps: |
- On status=ok: call move_to_destination with the source video file and
destination=library_file.
- On status=error: surface the message; do not move.
parameters:
release_name:
description: Raw release folder or file name.
why_needed: |
Drives extraction of quality/source/codec/group/edition tokens, which
become part of both the movie folder and filename so each release is
self-describing on disk.
example: Inception.2010.1080p.BluRay.x265-GROUP
source_file:
description: Absolute path to the source video file on disk.
why_needed: |
Used to read the file extension for the destination filename.
example: /downloads/Inception.2010.1080p.BluRay.x265-GROUP/movie.mkv
tmdb_title:
description: Canonical movie title from TMDB.
why_needed: |
Title prefix for the destination folder/file; ensures the library
uses the canonical title and not a sanitized release-name title.
example: Inception
tmdb_year:
description: Movie release year from TMDB.
why_needed: |
Disambiguates remakes that share a title (Dune 1984 vs Dune 2021)
and locks the folder identity in time.
example: "2010"
returns:
ok:
description: Paths resolved; ready to move.
fields:
movie_folder: Absolute path to the movie folder.
library_file: Absolute path to the destination .mkv file (move target).
movie_folder_name: Folder name for display.
filename: Destination filename for display.
is_new_folder: True if the movie folder doesn't exist yet.
error:
description: Resolution failed.
fields:
error: Short error code (e.g. library_not_set).
message: Human-readable explanation.
@@ -0,0 +1,84 @@
name: resolve_season_destination
summary: >
Compute destination paths for a season pack (folder move) in the TV library.
description: |
Resolves the target series folder and season subfolder for a complete-season
download. Returns the paths only — does not perform any move. If a series
folder for this show already exists in the library with a different name
(different group/quality/source), returns needs_clarification so the user
can decide whether to merge into the existing folder or create a new one.
when_to_use: |
Use after analyze_release has identified the release as a season pack
(media_type=tv_show, season set, episode unset). TMDB must already be
queried so tmdb_title and tmdb_year are canonical values, not raw tokens
from the release name.
when_not_to_use: |
- Single-episode files: use resolve_episode_destination instead.
- Multi-season packs (S01-S05 etc.): use resolve_series_destination.
- Movies: use resolve_movie_destination.
next_steps: |
- On status=ok: call move_to_destination with source=<download folder> and
destination=season_folder.
- On status=needs_clarification: present the question and options to the
user, then re-call this tool with confirmed_folder set to the user's pick.
- On status=error: surface the message to the user; do not move anything.
parameters:
release_name:
description: Raw release folder name as it appears on disk.
why_needed: |
Drives extraction of quality/source/codec/group tokens — these are
embedded in the target folder name (Title.Year.Quality.Source.Codec-GROUP)
to make releases self-describing on the filesystem.
example: Oz.S03.1080p.WEBRip.x265-KONTRAST
tmdb_title:
description: Canonical show title from TMDB.
why_needed: |
Builds the title prefix of the folder name. Must come from TMDB to
avoid typos and variant spellings present in the raw release name.
example: Oz
tmdb_year:
description: Show start year from TMDB.
why_needed: |
Disambiguates shows that share a title across decades (e.g. multiple
remakes of "The Office") and locks the folder identity.
example: "1997"
confirmed_folder:
description: |
Folder name chosen by the user after a previous needs_clarification
response.
why_needed: |
Short-circuits the existing-folder detection and forces the use case
to use this exact folder name, even if it doesn't match the computed
one.
example: Oz.1997.1080p.WEBRip.x265-KONTRAST
returns:
ok:
description: Paths resolved unambiguously; ready to move.
fields:
series_folder: Absolute path to the series root folder.
season_folder: Absolute path to the season subfolder (move target).
series_folder_name: Just the series folder name, for display.
season_folder_name: Just the season folder name, for display.
is_new_series_folder: True if the series folder doesn't exist yet.
needs_clarification:
description: A folder already exists with a different name; ask the user.
fields:
question: Human-readable question for the user.
options: List of folder names the user can pick from.
error:
description: Resolution failed (config missing, invalid release name, etc.).
fields:
error: Short error code (e.g. library_not_set).
message: Human-readable explanation.
@@ -0,0 +1,77 @@
name: resolve_series_destination
summary: >
Compute the destination path for a complete multi-season series pack (folder move).
description: |
Resolves the target series folder for a pack that contains multiple seasons
(e.g. S01-S05 in a single release). Returns only the series folder — the
whole source folder is moved as-is into the library, no per-season
restructuring. If a folder with a different name already exists for this
show, returns needs_clarification.
when_to_use: |
Use after analyze_release has identified the release as a complete-series
pack (media_type=tv_complete, or multi-season indicators). TMDB must
already be queried for canonical title/year.
when_not_to_use: |
- Single-season packs: use resolve_season_destination.
- Single episodes: use resolve_episode_destination.
- Movies: use resolve_movie_destination.
next_steps: |
- On status=ok: call move_to_destination with source=<download folder> and
destination=series_folder.
- On status=needs_clarification: ask the user, re-call with
confirmed_folder set.
- On status=error: surface the message; do not move.
parameters:
release_name:
description: Raw release folder name as it appears on disk.
why_needed: |
Drives extraction of quality/source/codec/group tokens for the target
folder name, even though the multi-season structure inside is kept
as-is.
example: The.Wire.S01-S05.1080p.BluRay.x265-GROUP
tmdb_title:
description: Canonical show title from TMDB.
why_needed: |
Title prefix of the series folder; comes from TMDB to avoid raw
release-name spellings.
example: The Wire
tmdb_year:
description: Show start year from TMDB.
why_needed: |
Disambiguates shows that share a title across eras and locks the
folder identity.
example: "2002"
confirmed_folder:
description: Folder name chosen by the user after needs_clarification.
why_needed: |
Forces the use case to use this exact folder name and skip detection.
example: The.Wire.2002.1080p.BluRay.x265-GROUP
returns:
ok:
description: Path resolved; ready to move the pack.
fields:
series_folder: Absolute path to the destination series folder.
series_folder_name: Folder name for display.
is_new_series_folder: True if the folder doesn't exist yet.
needs_clarification:
description: A folder exists with a different name; ask the user.
fields:
question: Human-readable question.
options: List of folder names to pick from.
error:
description: Resolution failed.
fields:
error: Short error code.
message: Human-readable explanation.
@@ -0,0 +1,47 @@
name: set_language
summary: >
Set the conversation language so all subsequent assistant messages
match it.
description: |
Persists an ISO 639-1 language code in short-term memory under
conversation.language. Read by the prompt builder and any tool that
needs to localise output. Does not validate the code against an ISO
list — the LLM is trusted to pass a sensible value.
when_to_use: |
As the very first call when the user writes in a language different
from the current STM language. Doing it before answering avoids a
mid-reply switch.
when_not_to_use: |
- On every turn — only when the language actually changes.
- To pick a subtitle language — that lives in SubtitlePreferences,
not the conversation language.
next_steps: |
- After success: continue the user's request in the newly set
language.
parameters:
language:
description: ISO 639-1 language code (en, fr, es, de, ...).
why_needed: |
Identifies the target language unambiguously across the UI and
any localisation logic.
example: fr
returns:
ok:
description: Language saved.
fields:
status: "'ok'"
message: Confirmation message.
language: The language code that was saved.
error:
description: Could not save the language.
fields:
status: "'error'"
error: Short error code or exception message.
@@ -0,0 +1,58 @@
name: set_path_for_folder
summary: >
Configure where a known folder lives on disk (download, torrent, or
any library collection).
description: |
Stores an absolute path in long-term memory under a folder key. Two
classes of folders exist:
- Workspace paths: "download", "torrent" — single-valued each, used
by the organize workflows.
- Library paths: any other key (e.g. "movie", "tv_show",
"documentary") — these are the collections you organise into.
The path must exist and be a directory; otherwise the call fails
without changing memory.
when_to_use: |
On first run, or when the user moves a folder, or when introducing a
new library collection (e.g. "set the documentaries folder to ...").
when_not_to_use: |
- For one-off listings — list_folder works without configuration only
if the folder is already set.
- To rename or delete an existing folder — this only sets paths.
next_steps: |
- After success: typical follow-ups are list_folder on the same key,
or starting a workflow that needs the path.
parameters:
folder_name:
description: Logical name of the folder (download, torrent, movie, tv_show, ...).
why_needed: |
The key the agent uses everywhere afterwards. "download" and
"torrent" are reserved for workspace; anything else becomes a
library collection.
example: tv_show
path_value:
description: Absolute path to the folder on disk.
why_needed: |
Must exist and be readable. Stored verbatim in LTM — relative
paths are rejected.
example: /tank/library/tv_shows
returns:
ok:
description: Path saved to long-term memory.
fields:
status: "'ok'"
folder_name: The logical name that was set.
path_value: The absolute path that was saved.
error:
description: Could not set the path.
fields:
error: Short error code (path_not_found, not_a_directory, invalid_path, ...).
message: Human-readable explanation.
@@ -0,0 +1,64 @@
name: start_workflow
summary: >
Enter a workflow scope — narrows the visible tool catalog and gives the
agent a clear multi-step plan to follow.
description: |
Activates a named workflow defined in YAML under agent/workflows/.
Once active, only the workflow's declared tools (plus the core noyau)
are exposed to the LLM, which keeps the decision space small and
focused. The returned plan (description + steps) is the script the
agent should execute until end_workflow is called.
when_to_use: |
Use as the very first action whenever the user request maps to a
known workflow (e.g. "organize Breaking Bad" → media.organize_media).
Pass any parameters you already know (release name, target media,
flags) in 'params' so later steps can read them from STM.
when_not_to_use: |
- Do not start a workflow for purely conversational replies or
one-shot lookups that need a single tool call.
- Do not start a new workflow while one is already active — call
end_workflow first.
next_steps: |
- On status=ok: follow the returned 'steps' list, calling the tools
in order. The visible tool catalog has already been narrowed.
- On status=error (unknown_workflow): surface the available list to
the user and ask which one they meant.
- On status=error (workflow_already_active): either continue the
active workflow or call end_workflow first.
parameters:
workflow_name:
description: Fully-qualified name of the workflow to start (e.g. media.organize_media).
why_needed: |
Identifies which YAML definition to load. Names use the
'domain.action' convention (media.*, mail.*, ...).
example: media.organize_media
params:
description: Initial parameters to seed the workflow with (release name, target, flags).
why_needed: |
Later steps read these from STM instead of asking the user again.
Pass whatever you already extracted from the user's message.
example: '{"release_name": "Breaking.Bad.S01.1080p.BluRay.x265-GROUP", "keep_seeding": true}'
returns:
ok:
description: Workflow activated; catalog has been narrowed.
fields:
workflow: Name of the activated workflow.
description: Human-readable description of what the workflow does.
steps: Ordered list of steps to execute.
tools: Tools that are now visible (in addition to the core noyau).
error:
description: Could not activate the workflow.
fields:
error: Short error code (unknown_workflow, workflow_already_active).
message: Human-readable explanation.
available_workflows: List of valid workflow names (only on unknown_workflow).
active_workflow: Name of the currently active workflow (only on workflow_already_active).
+86
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@@ -0,0 +1,86 @@
"""Workflow scoping tools — start_workflow / end_workflow meta-tools.
These tools let the agent enter and leave a workflow scope. While a
workflow is active, the PromptBuilder narrows the visible tool catalog
to the noyau + the workflow's declared tools, so the LLM doesn't have
to reason over the full set.
"""
import logging
from typing import Any
from alfred.infrastructure.persistence import get_memory
from ..workflows import WorkflowLoader
logger = logging.getLogger(__name__)
_loader_cache: list[WorkflowLoader] = []
def _get_loader() -> WorkflowLoader:
"""Lazily build the module-level WorkflowLoader."""
if not _loader_cache:
_loader_cache.append(WorkflowLoader())
return _loader_cache[0]
def start_workflow(workflow_name: str, params: dict) -> dict[str, Any]:
"""See specs/start_workflow.yaml for full description."""
loader = _get_loader()
workflow = loader.get(workflow_name)
if workflow is None:
return {
"status": "error",
"error": "unknown_workflow",
"message": f"Workflow '{workflow_name}' not found",
"available_workflows": loader.names(),
}
memory = get_memory()
current = memory.stm.workflow.current
if current is not None:
return {
"status": "error",
"error": "workflow_already_active",
"message": (
f"Workflow '{current.get('name')}' is already active. "
"Call end_workflow before starting a new one."
),
"active_workflow": current.get("name"),
}
memory.stm.start_workflow(workflow_name, params or {})
memory.save()
logger.info(f"start_workflow: '{workflow_name}' with params={params}")
return {
"status": "ok",
"workflow": workflow_name,
"description": workflow.get("description", ""),
"steps": workflow.get("steps", []),
"tools": workflow.get("tools", []),
}
def end_workflow(reason: str) -> dict[str, Any]:
"""See specs/end_workflow.yaml for full description."""
memory = get_memory()
current = memory.stm.workflow.current
if current is None:
return {
"status": "error",
"error": "no_active_workflow",
"message": "No workflow is currently active.",
}
workflow_name = current.get("name")
memory.stm.end_workflow()
memory.save()
logger.info(f"end_workflow: '{workflow_name}' reason={reason!r}")
return {
"status": "ok",
"workflow": workflow_name,
"reason": reason,
}
+3
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@@ -0,0 +1,3 @@
from .loader import WorkflowLoader
__all__ = ["WorkflowLoader"]
+52
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@@ -0,0 +1,52 @@
"""WorkflowLoader — autodiscovers and loads workflow YAML files.
Scans the workflows/ directory for all .yaml files and exposes them
as dicts. No manual registration needed — drop a new .yaml file and
it will be picked up automatically.
"""
import logging
from pathlib import Path
import yaml
logger = logging.getLogger(__name__)
_WORKFLOWS_DIR = Path(__file__).parent
class WorkflowLoader:
"""
Loads all workflow definitions from the workflows/ directory.
Usage:
loader = WorkflowLoader()
all_workflows = loader.all()
workflow = loader.get("media.organize_media")
"""
def __init__(self):
self._workflows: dict[str, dict] = {}
self._load()
def _load(self) -> None:
for path in sorted(_WORKFLOWS_DIR.glob("*.yaml")):
try:
data = yaml.safe_load(path.read_text(encoding="utf-8"))
name = data.get("name") or path.stem
self._workflows[name] = data
logger.info(f"WorkflowLoader: Loaded '{name}' from {path.name}")
except Exception as e:
logger.warning(f"WorkflowLoader: Could not load {path.name}: {e}")
def all(self) -> dict[str, dict]:
"""Return all loaded workflows keyed by name."""
return self._workflows
def get(self, name: str) -> dict | None:
"""Return a specific workflow by name, or None if not found."""
return self._workflows.get(name)
def names(self) -> list[str]:
"""Return all available workflow names."""
return list(self._workflows.keys())
@@ -0,0 +1,69 @@
name: media.manage_subtitles
description: >
Place subtitle files alongside a video that has just been organised into the library.
Detects the release pattern automatically, identifies and classifies all tracks,
filters by user rules, and hard-links matching files to the destination.
If any tracks are unrecognised, asks the user and optionally teaches Alfred.
trigger:
examples:
- "handle subtitles for The X-Files S01E01"
- "place the subs next to the file"
- "subtitles are in the Subs/ folder"
- "add subtitles"
tools:
- manage_subtitles
- learn
memory:
SubtitlePreferences: read
Workflow: read-write
steps:
- id: place_subtitles
tool: manage_subtitles
description: >
Detect release pattern, identify and classify all subtitle tracks,
filter by rules, hard-link matching files next to the destination video.
Reads SubtitlePreferences from LTM for language/type/format filtering.
params:
source_video: "{source_video}"
destination_video: "{destination_video}"
imdb_id: "{imdb_id}"
media_type: "{media_type}"
release_group: "{release_group}"
season: "{season}"
episode: "{episode}"
on_result:
ok_placed_zero: skip # no subtitles found — not an error
needs_clarification: ask_user # unrecognised tokens found
- id: ask_user
description: >
Some tracks could not be classified. Show the user the unresolved tokens
and ask if they want to teach Alfred what they mean.
If yes → go to learn_tokens. If no → end workflow.
ask_user:
question: >
I could not identify some tokens in the subtitle files: {unresolved}.
Do you want to teach me what they mean?
answers:
yes: { next_step: learn_tokens }
no: { next_step: end }
- id: learn_tokens
tool: learn
description: >
Persist a new token mapping to the learned knowledge pack so Alfred
recognises it in future scans without asking again.
params:
pack: "subtitles"
category: "{token_category}" # "languages" or "types"
key: "{token_key}" # e.g. "es", "de"
values: "{token_values}" # e.g. ["spanish", "espanol"]
subtitle_naming:
standard: "{lang}.{ext}"
sdh: "{lang}.sdh.{ext}"
forced: "{lang}.forced.{ext}"
@@ -0,0 +1,92 @@
name: media.organize_media
description: >
Organise a downloaded series or movie into the media library.
Triggered when the user asks to move/organize a specific title.
Always moves the video file. Optionally creates seed links in the
torrents folder so qBittorrent can keep seeding.
trigger:
examples:
- "organize Breaking Bad"
- "organise Severance season 2"
- "move Inception to my library"
- "organize Breaking Bad season 1, keep seeding"
tools:
- list_folder
- analyze_release
- probe_media
- find_media_imdb_id
- resolve_season_destination
- resolve_episode_destination
- resolve_movie_destination
- resolve_series_destination
- move_to_destination
- manage_subtitles
- create_seed_links
memory:
WorkspacePaths: read
LibraryPaths: read
Library: read-write
Workflow: read-write
Entities: read-write
steps:
- id: list_downloads
tool: list_folder
description: List the download folder to find the target files.
params:
folder_type: download
- id: analyze
tool: analyze_release
description: >
Parse the release name to detect media_type (movie / tv_season /
tv_episode / tv_complete) and extract season/episode info.
- id: identify_media
tool: find_media_imdb_id
description: Confirm canonical title and year via TMDB.
- id: resolve_destination
description: >
Call the resolver that matches media_type from analyze_release:
movie → resolve_movie_destination
tv_season → resolve_season_destination
tv_episode → resolve_episode_destination
tv_complete → resolve_series_destination
If the resolver returns needs_clarification, ask the user and
re-call with confirmed_folder.
- id: move_file
tool: move_to_destination
description: >
Move the video file/folder to the destination returned by the
resolver above.
- id: handle_subtitles
tool: manage_subtitles
description: >
Place subtitle files alongside the video in the library.
Pass the original source path and the new library destination path.
on_missing: skip
- id: ask_seeding
ask_user:
question: "Do you want to keep seeding this torrent?"
answers:
"yes": { next_step: create_seed_links }
"no": { next_step: end }
- id: create_seed_links
tool: create_seed_links
description: >
Hard-link the library video file back into torrents/<original_folder>/
and copy all remaining files from the original download folder
(subs, nfo, jpg, …) so the torrent stays complete for seeding.
naming_convention:
# Resolved by domain entities (Movie, Episode) — not hardcoded here
tv_show: "{title}/Season {season:02d}/{title}.S{season:02d}E{episode:02d}.{ext}"
movie: "{title} ({year})/{title}.{year}.{ext}"
+43 -31
View File
@@ -2,22 +2,21 @@
import json import json
import logging import logging
import os
import time import time
import uuid import uuid
from pathlib import Path
from typing import Any from typing import Any
from fastapi import FastAPI, HTTPException from fastapi import FastAPI, HTTPException
from fastapi.responses import JSONResponse, StreamingResponse from fastapi.responses import JSONResponse, StreamingResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel, Field, validator from pydantic import BaseModel, Field, validator
from agent.agent import Agent from alfred.agent.agent import Agent
from agent.config import settings from alfred.agent.llm.deepseek import DeepSeekClient
from agent.llm.deepseek import DeepSeekClient from alfred.agent.llm.exceptions import LLMAPIError, LLMConfigurationError
from agent.llm.exceptions import LLMAPIError, LLMConfigurationError from alfred.agent.llm.ollama import OllamaClient
from agent.llm.ollama import OllamaClient from alfred.infrastructure.persistence import get_memory, init_memory
from infrastructure.persistence import get_memory, init_memory from alfred.settings import settings
logging.basicConfig( logging.basicConfig(
level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
@@ -30,38 +29,51 @@ app = FastAPI(
version="0.2.0", version="0.2.0",
) )
# TODO: Make a variable memory_path = Path(settings.data_storage_dir) / "memory"
manifests = "manifests" init_memory(storage_dir=str(memory_path))
# Sécurité : on vérifie que le dossier existe pour ne pas faire planter l'app au démarrage logger.info(f"Memory context initialized (path: {memory_path})")
if os.path.exists(manifests):
app.mount("/manifests", StaticFiles(directory=manifests), name="manifests")
else:
print(
f"⚠️ ATTENTION : Le dossier '{manifests}' est introuvable. Le plugin ne marchera pas."
)
# Initialize memory context at startup
# Use /data/memory in Docker, fallback to memory_data for local dev
storage_dir = os.getenv("MEMORY_STORAGE_DIR", "memory_data")
init_memory(storage_dir=storage_dir)
logger.info(f"Memory context initialized (storage: {storage_dir})")
# Initialize LLM based on environment variable # Initialize LLM based on environment variable
llm_provider = os.getenv("LLM_PROVIDER", "deepseek").lower() llm_provider = settings.default_llm_provider.lower()
class _UnconfiguredLLM:
"""Placeholder LLM used when no provider could be configured at import time.
Importing the FastAPI app must not fail just because credentials are
absent (e.g. during test collection). Any actual call surfaces a clear
503 error at request time via the handlers below.
"""
def __init__(self, reason: str):
self.reason = reason
def complete(self, *args, **kwargs):
raise LLMAPIError(f"LLM is not configured: {self.reason}")
try: try:
if llm_provider == "ollama": if llm_provider == "local":
logger.info("Using Ollama LLM") logger.info("Using local Ollama LLM")
llm = OllamaClient() llm = OllamaClient(settings=settings)
else: elif llm_provider == "deepseek":
logger.info("Using DeepSeek LLM") logger.info("Using DeepSeek LLM")
llm = DeepSeekClient() llm = DeepSeekClient()
elif llm_provider == "claude":
raise ValueError(f"LLM provider not fully implemented: {llm_provider}")
else:
raise ValueError(f"Unknown LLM provider: {llm_provider}")
except LLMConfigurationError as e: except LLMConfigurationError as e:
# Degrade gracefully: keep the app importable so tests can patch agent.step
# and so missing credentials surface as a 503 at the endpoint, not as an
# import error.
logger.error(f"Failed to initialize LLM: {e}") logger.error(f"Failed to initialize LLM: {e}")
raise llm = _UnconfiguredLLM(str(e))
# Initialize agent # Initialize agent
agent = Agent(llm=llm, max_tool_iterations=settings.max_tool_iterations) agent = Agent(
settings=settings, llm=llm, max_tool_iterations=settings.max_tool_iterations
)
logger.info("Agent Media API initialized") logger.info("Agent Media API initialized")
@@ -116,7 +128,7 @@ def extract_last_user_content(messages: list[dict[str, Any]]) -> str:
@app.get("/health") @app.get("/health")
async def health_check(): async def health_check():
"""Health check endpoint.""" """Health check endpoint."""
return {"status": "healthy", "version": "0.2.0"} return {"status": "healthy", "version": f"v{settings.alfred_version}"}
@app.get("/v1/models") @app.get("/v1/models")
+47
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@@ -0,0 +1,47 @@
"""Filesystem use cases."""
from .create_seed_links import CreateSeedLinksUseCase
from .dto import (
CreateSeedLinksResponse,
ListFolderResponse,
ManageSubtitlesResponse,
MoveMediaResponse,
PlacedSubtitle,
SetFolderPathResponse,
)
from .list_folder import ListFolderUseCase
from .manage_subtitles import ManageSubtitlesUseCase
from .move_media import MoveMediaUseCase
from .resolve_destination import (
ResolvedEpisodeDestination,
ResolvedMovieDestination,
ResolvedSeasonDestination,
ResolvedSeriesDestination,
resolve_episode_destination,
resolve_movie_destination,
resolve_season_destination,
resolve_series_destination,
)
from .set_folder_path import SetFolderPathUseCase
__all__ = [
"SetFolderPathUseCase",
"ListFolderUseCase",
"CreateSeedLinksUseCase",
"MoveMediaUseCase",
"ManageSubtitlesUseCase",
"ResolvedSeasonDestination",
"ResolvedEpisodeDestination",
"ResolvedMovieDestination",
"ResolvedSeriesDestination",
"resolve_season_destination",
"resolve_episode_destination",
"resolve_movie_destination",
"resolve_series_destination",
"SetFolderPathResponse",
"ListFolderResponse",
"CreateSeedLinksResponse",
"MoveMediaResponse",
"ManageSubtitlesResponse",
"PlacedSubtitle",
]
@@ -0,0 +1,54 @@
"""CreateSeedLinksUseCase — prepares a torrent folder for continued seeding."""
import logging
from alfred.infrastructure.filesystem import FileManager
from alfred.infrastructure.persistence import get_memory
from .dto import CreateSeedLinksResponse
logger = logging.getLogger(__name__)
class CreateSeedLinksUseCase:
"""
Prepares a torrent subfolder so qBittorrent can keep seeding after a move.
Hard-links the video file from the library back into torrents/<original_folder>/,
then copies all remaining files from the original download folder (subs, nfo, …).
"""
def __init__(self, file_manager: FileManager):
self.file_manager = file_manager
def execute(
self, library_file: str, original_download_folder: str
) -> CreateSeedLinksResponse:
memory = get_memory()
torrent_folder = memory.ltm.workspace.torrent
if not torrent_folder:
return CreateSeedLinksResponse(
status="error",
error="torrent_folder_not_set",
message="Torrent folder is not configured. Use set_path_for_folder to set it.",
)
result = self.file_manager.create_seed_links(
library_file, original_download_folder, torrent_folder
)
if result.get("status") == "ok":
return CreateSeedLinksResponse(
status="ok",
torrent_subfolder=result.get("torrent_subfolder"),
linked_file=result.get("linked_file"),
copied_files=result.get("copied_files"),
copied_count=result.get("copied_count", 0),
skipped=result.get("skipped"),
)
return CreateSeedLinksResponse(
status="error",
error=result.get("error"),
message=result.get("message"),
)
@@ -0,0 +1,69 @@
"""
detect_media_type — filesystem-based media type refinement.
Enriches a ParsedRelease.media_type with evidence from the actual source path
(file or folder). Called after parse_release() to produce a final classification.
Classification logic:
1. If source_path is a file — check its extension directly.
2. If source_path is a folder — collect all extensions inside (non-recursive
for the first level, then recursive if nothing conclusive found).
3. Decision:
- Any non_video extension AND no video extension → "other"
- Any video extension → keep parsed media_type ("movie" | "tv_show" | "unknown")
- No conclusive extension found → keep parsed media_type as-is
- Mixed (video + non_video) → "unknown"
"""
from __future__ import annotations
from pathlib import Path
from alfred.domain.release.value_objects import (
_METADATA_EXTENSIONS,
_NON_VIDEO_EXTENSIONS,
_VIDEO_EXTENSIONS,
ParsedRelease,
)
def detect_media_type(parsed: ParsedRelease, source_path: Path) -> str:
"""
Return a refined media_type string for the given source_path.
Does not mutate parsed — returns the new media_type value only.
The caller is responsible for updating the ParsedRelease if needed.
"""
extensions = _collect_extensions(source_path)
# Metadata extensions (.nfo, .srt, …) are always present alongside releases
# and must not influence the type decision.
conclusive = extensions - _METADATA_EXTENSIONS
has_video = bool(conclusive & _VIDEO_EXTENSIONS)
has_non_video = bool(conclusive & _NON_VIDEO_EXTENSIONS)
if has_video and has_non_video:
return "unknown"
if has_non_video and not has_video:
return "other"
if has_video:
return parsed.media_type # trust token-level inference
# No conclusive extension — trust token-level inference
return parsed.media_type
def _collect_extensions(path: Path) -> set[str]:
"""Return the set of lowercase extensions found at path (file or folder)."""
if not path.exists():
return set()
if path.is_file():
return {path.suffix.lower()}
# Folder — scan first level only
exts: set[str] = set()
for child in path.iterdir():
if child.is_file():
exts.add(child.suffix.lower())
return exts
+213
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@@ -0,0 +1,213 @@
"""Filesystem application DTOs."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class CopyMediaResponse:
"""Response from copying a media file."""
status: str
source: str | None = None
destination: str | None = None
filename: str | None = None
size: int | None = None
error: str | None = None
message: str | None = None
def to_dict(self) -> dict:
if self.error:
return {"status": self.status, "error": self.error, "message": self.message}
return {
"status": self.status,
"source": self.source,
"destination": self.destination,
"filename": self.filename,
"size": self.size,
}
@dataclass
class MoveMediaResponse:
"""Response from moving a media file."""
status: str
source: str | None = None
destination: str | None = None
filename: str | None = None
size: int | None = None
error: str | None = None
message: str | None = None
def to_dict(self) -> dict:
if self.error:
return {"status": self.status, "error": self.error, "message": self.message}
return {
"status": self.status,
"source": self.source,
"destination": self.destination,
"filename": self.filename,
"size": self.size,
}
@dataclass
class SetFolderPathResponse:
"""Response from setting a folder path."""
status: str
folder_name: str | None = None
path: str | None = None
error: str | None = None
message: str | None = None
def to_dict(self):
"""Convert to dict for agent compatibility."""
result = {"status": self.status}
if self.error:
result["error"] = self.error
result["message"] = self.message
else:
if self.folder_name:
result["folder_name"] = self.folder_name
if self.path:
result["path"] = self.path
return result
@dataclass
class PlacedSubtitle:
"""One subtitle file successfully placed."""
source: str
destination: str
filename: str
def to_dict(self) -> dict:
return {
"source": self.source,
"destination": self.destination,
"filename": self.filename,
}
@dataclass
class UnresolvedTrack:
"""A subtitle track that needs agent clarification before placement."""
raw_tokens: list[str]
file_path: str | None = None
file_size_kb: float | None = None
reason: str = "" # "unknown_language" | "low_confidence"
def to_dict(self) -> dict:
return {
"raw_tokens": self.raw_tokens,
"file_path": self.file_path,
"file_size_kb": self.file_size_kb,
"reason": self.reason,
}
@dataclass
class AvailableSubtitle:
"""One subtitle track available on an embedded media item."""
language: str # ISO 639-2 code
subtitle_type: str # "standard" | "sdh" | "forced" | "unknown"
def to_dict(self) -> dict:
return {"language": self.language, "type": self.subtitle_type}
@dataclass
class ManageSubtitlesResponse:
"""Response from the manage_subtitles use case."""
status: str # "ok" | "needs_clarification" | "error"
video_path: str | None = None
placed: list[PlacedSubtitle] | None = None
skipped_count: int = 0
unresolved: list[UnresolvedTrack] | None = None
available: list[AvailableSubtitle] | None = None # embedded tracks summary
error: str | None = None
message: str | None = None
def to_dict(self) -> dict:
if self.error:
return {"status": self.status, "error": self.error, "message": self.message}
result = {
"status": self.status,
"video_path": self.video_path,
"placed": [p.to_dict() for p in (self.placed or [])],
"placed_count": len(self.placed or []),
"skipped_count": self.skipped_count,
}
if self.unresolved:
result["unresolved"] = [u.to_dict() for u in self.unresolved]
result["unresolved_count"] = len(self.unresolved)
if self.available:
result["available"] = [a.to_dict() for a in self.available]
return result
@dataclass
class CreateSeedLinksResponse:
"""Response from creating seed links for a torrent."""
status: str
torrent_subfolder: str | None = None
linked_file: str | None = None
copied_files: list[str] | None = None
copied_count: int = 0
skipped: list[str] | None = None
error: str | None = None
message: str | None = None
def to_dict(self) -> dict:
if self.error:
return {"status": self.status, "error": self.error, "message": self.message}
return {
"status": self.status,
"torrent_subfolder": self.torrent_subfolder,
"linked_file": self.linked_file,
"copied_files": self.copied_files or [],
"copied_count": self.copied_count,
"skipped": self.skipped or [],
}
@dataclass
class ListFolderResponse:
"""Response from listing a folder."""
status: str
folder_type: str | None = None
path: str | None = None
entries: list[str] | None = None
count: int | None = None
error: str | None = None
message: str | None = None
def to_dict(self):
"""Convert to dict for agent compatibility."""
result = {"status": self.status}
if self.error:
result["error"] = self.error
result["message"] = self.message
else:
if self.folder_type:
result["folder_type"] = self.folder_type
if self.path:
result["path"] = self.path
if self.entries is not None:
result["entries"] = self.entries
if self.count is not None:
result["count"] = self.count
return result
@@ -0,0 +1,82 @@
"""enrich_from_probe — fill missing ParsedRelease fields from MediaInfo."""
from __future__ import annotations
from alfred.domain.release.value_objects import ParsedRelease
from alfred.domain.shared.media import MediaInfo
# Map ffprobe codec names to scene-style codec tokens
_VIDEO_CODEC_MAP = {
"hevc": "x265",
"h264": "x264",
"h265": "x265",
"av1": "AV1",
"vp9": "VP9",
"mpeg4": "XviD",
}
# Map ffprobe audio codec names to scene-style tokens
_AUDIO_CODEC_MAP = {
"eac3": "EAC3",
"ac3": "AC3",
"dts": "DTS",
"truehd": "TrueHD",
"aac": "AAC",
"flac": "FLAC",
"opus": "OPUS",
"mp3": "MP3",
"pcm_s16l": "PCM",
"pcm_s24l": "PCM",
}
# Map channel count to standard layout string
_CHANNEL_MAP = {
8: "7.1",
6: "5.1",
2: "2.0",
1: "1.0",
}
def enrich_from_probe(parsed: ParsedRelease, info: MediaInfo) -> None:
"""
Fill None fields in parsed using data from ffprobe MediaInfo.
Only overwrites fields that are currently None — token-level values
from the release name always take priority.
Mutates parsed in place.
"""
if parsed.quality is None and info.resolution:
parsed.quality = info.resolution
if parsed.codec is None and info.video_codec:
parsed.codec = _VIDEO_CODEC_MAP.get(
info.video_codec.lower(), info.video_codec.upper()
)
if parsed.bit_depth is None and info.video_codec:
# ffprobe exposes bit depth via pix_fmt — not in MediaInfo yet, skip for now
pass
# Audio — use the default track, fallback to first
default_track = next((t for t in info.audio_tracks if t.is_default), None)
track = default_track or (info.audio_tracks[0] if info.audio_tracks else None)
if track:
if parsed.audio_codec is None and track.codec:
parsed.audio_codec = _AUDIO_CODEC_MAP.get(
track.codec.lower(), track.codec.upper()
)
if parsed.audio_channels is None and track.channels:
parsed.audio_channels = _CHANNEL_MAP.get(
track.channels, f"{track.channels}ch"
)
# Languages — merge ffprobe languages with token-level ones
# "und" = undetermined, not useful
if info.audio_languages:
existing = set(parsed.languages)
for lang in info.audio_languages:
if lang.lower() != "und" and lang.upper() not in existing:
parsed.languages.append(lang)
@@ -2,7 +2,7 @@
import logging import logging
from infrastructure.filesystem import FileManager from alfred.infrastructure.filesystem import FileManager
from .dto import ListFolderResponse from .dto import ListFolderResponse
@@ -0,0 +1,308 @@
"""ManageSubtitlesUseCase — orchestrates the full subtitle pipeline for a video file."""
import logging
from pathlib import Path
from alfred.domain.shared.value_objects import ImdbId
from alfred.domain.subtitles.entities import SubtitleCandidate
from alfred.domain.subtitles.services.identifier import SubtitleIdentifier
from alfred.domain.subtitles.services.matcher import SubtitleMatcher
from alfred.domain.subtitles.services.pattern_detector import PatternDetector
from alfred.application.subtitles.placer import (
PlacedTrack,
SubtitlePlacer,
_build_dest_name,
)
from alfred.domain.subtitles.services.utils import available_subtitles
from alfred.domain.subtitles.value_objects import ScanStrategy
from alfred.infrastructure.filesystem.scanner import PathlibFilesystemScanner
from alfred.infrastructure.knowledge.subtitles.base import SubtitleKnowledgeBase
from alfred.infrastructure.knowledge.subtitles.loader import KnowledgeLoader
from alfred.infrastructure.persistence.context import get_memory
from alfred.infrastructure.probe.ffprobe_prober import FfprobeMediaProber
from alfred.infrastructure.subtitle.metadata_store import SubtitleMetadataStore
from alfred.infrastructure.subtitle.rule_repository import RuleSetRepository
from .dto import (
AvailableSubtitle,
ManageSubtitlesResponse,
PlacedSubtitle,
UnresolvedTrack,
)
logger = logging.getLogger(__name__)
def _infer_library_root(dest_video: Path, media_type: str) -> Path:
"""
Infer the media library root folder from the destination video path.
TV show: video → Season 01 → The X-Files (3 levels up)
Movie: video → Inception (2010) (1 level up)
"""
if media_type == "tv_show":
return dest_video.parent.parent
return dest_video.parent
def _to_imdb_id(raw: str | None) -> ImdbId | None:
if not raw:
return None
try:
return ImdbId(raw)
except Exception:
return None
class ManageSubtitlesUseCase:
"""
Full subtitle pipeline:
1. Load knowledge base
2. Detect (or confirm) the release pattern
3. Identify all tracks (ffprobe + filesystem scan)
4. Load + resolve rules for this media
5. Match tracks against rules
6. If any tracks are unresolved → return needs_clarification (don't place yet)
7. Place matched tracks via hard-link
8. Persist to .alfred/metadata.yaml
The use case is stateless — all dependencies are instantiated inline.
"""
def execute(
self,
source_video: str,
destination_video: str,
imdb_id: str | None = None,
media_type: str = "tv_show",
release_group: str | None = None,
season: int | None = None,
episode: int | None = None,
confirmed_pattern_id: str | None = None,
dry_run: bool = False,
) -> ManageSubtitlesResponse:
source_path = Path(source_video)
dest_path = Path(destination_video)
if not source_path.exists() and not source_path.parent.exists():
return ManageSubtitlesResponse(
status="error",
error="source_not_found",
message=f"Source video not found: {source_video}",
)
kb = SubtitleKnowledgeBase(KnowledgeLoader())
prober = FfprobeMediaProber()
scanner = PathlibFilesystemScanner()
library_root = _infer_library_root(dest_path, media_type)
store = SubtitleMetadataStore(library_root)
repo = RuleSetRepository(library_root)
# --- Pattern resolution ---
pattern = self._resolve_pattern(
kb,
prober,
scanner,
store,
source_path,
confirmed_pattern_id,
release_group,
)
if pattern is None:
return ManageSubtitlesResponse(
status="error",
error="pattern_not_found",
message="Could not determine subtitle pattern for this release.",
)
# --- Identify ---
media_id = _to_imdb_id(imdb_id)
identifier = SubtitleIdentifier(kb, prober, scanner)
metadata = identifier.identify(
video_path=source_path,
pattern=pattern,
media_id=media_id,
media_type=media_type,
release_group=release_group,
)
if metadata.total_count == 0:
logger.info(
f"ManageSubtitles: no subtitle tracks found for {source_path.name}"
)
return ManageSubtitlesResponse(
status="ok",
video_path=destination_video,
placed=[],
skipped_count=0,
)
# --- Embedded short-circuit ---
if pattern.scan_strategy == ScanStrategy.EMBEDDED:
logger.info("ManageSubtitles: embedded pattern — skipping matcher")
available = [
AvailableSubtitle(
language=t.language.code if t.language else "?",
subtitle_type=t.subtitle_type.value,
)
for t in available_subtitles(metadata.embedded_tracks)
]
return ManageSubtitlesResponse(
status="ok",
video_path=destination_video,
placed=[],
skipped_count=0,
available=available,
)
# --- Match (external only) ---
subtitle_prefs = None
try:
memory = get_memory()
subtitle_prefs = memory.ltm.subtitle_preferences
except Exception:
pass
rules = repo.load(release_group, subtitle_prefs).resolve(kb.default_rules())
matcher = SubtitleMatcher()
matched, unresolved = matcher.match(metadata.external_tracks, rules)
if unresolved:
logger.info(
f"ManageSubtitles: {len(unresolved)} unresolved track(s) — needs clarification"
)
return ManageSubtitlesResponse(
status="needs_clarification",
video_path=destination_video,
placed=[],
unresolved=[_to_unresolved_dto(t) for t in unresolved],
)
if not matched:
return ManageSubtitlesResponse(
status="ok",
video_path=destination_video,
placed=[],
skipped_count=metadata.total_count,
)
# --- Dry run: skip placement ---
if dry_run:
placed_dtos = []
for t in matched:
if not t.file_path:
continue
try:
filename = _build_dest_name(t, dest_path.stem)
except ValueError:
continue
placed_dtos.append(
PlacedSubtitle(
source=str(t.file_path),
destination=str(dest_path.parent / filename),
filename=filename,
)
)
return ManageSubtitlesResponse(
status="ok",
video_path=destination_video,
placed=placed_dtos,
skipped_count=0,
)
# --- Place ---
placer = SubtitlePlacer()
place_result = placer.place(matched, dest_path)
# --- Persist ---
if place_result.placed:
pairs = _pair_placed_with_tracks(place_result.placed, matched)
store.append_history(pairs, season, episode, release_group)
placed_dtos = [
PlacedSubtitle(
source=str(p.source),
destination=str(p.destination),
filename=p.filename,
)
for p in place_result.placed
]
return ManageSubtitlesResponse(
status="ok",
video_path=destination_video,
placed=placed_dtos,
skipped_count=place_result.skipped_count,
)
def _resolve_pattern(
self,
kb: SubtitleKnowledgeBase,
prober: FfprobeMediaProber,
scanner: PathlibFilesystemScanner,
store: SubtitleMetadataStore,
source_path: Path,
confirmed_pattern_id: str | None,
release_group: str | None,
):
# 1. Explicit override from caller
if confirmed_pattern_id:
p = kb.pattern(confirmed_pattern_id)
if p:
return p
logger.warning(f"ManageSubtitles: unknown pattern '{confirmed_pattern_id}'")
# 2. Previously confirmed in metadata store
stored_id = store.confirmed_pattern()
if stored_id:
p = kb.pattern(stored_id)
if p:
logger.debug(f"ManageSubtitles: using confirmed pattern '{stored_id}'")
return p
# 3. Auto-detect
release_root = source_path.parent
detector = PatternDetector(kb, prober, scanner)
result = detector.detect(release_root, source_path)
if result["detected"] and result["confidence"] >= 0.6:
logger.info(
f"ManageSubtitles: auto-detected pattern '{result['detected'].id}' "
f"(confidence={result['confidence']:.2f})"
)
return result["detected"]
# 4. Fallback — adjacent (safest default)
logger.info("ManageSubtitles: falling back to 'adjacent' pattern")
return kb.pattern("adjacent")
def _to_unresolved_dto(
track: SubtitleCandidate, min_confidence: float = 0.7
) -> UnresolvedTrack:
reason = "unknown_language" if track.language is None else "low_confidence"
return UnresolvedTrack(
raw_tokens=track.raw_tokens,
file_path=str(track.file_path) if track.file_path else None,
file_size_kb=track.file_size_kb,
reason=reason,
)
def _pair_placed_with_tracks(
placed: list[PlacedTrack],
tracks: list[SubtitleCandidate],
) -> list[tuple[PlacedTrack, SubtitleCandidate]]:
"""
Pair each PlacedTrack with its originating SubtitleCandidate by source path.
Falls back to positional matching if paths don't align.
"""
track_by_path = {t.file_path: t for t in tracks if t.file_path}
pairs = []
for p in placed:
track = track_by_path.get(p.source)
if track is None and tracks:
track = tracks[0] # positional fallback
if track:
pairs.append((p, track))
return pairs
@@ -0,0 +1,43 @@
"""Move media use case."""
import logging
from alfred.infrastructure.filesystem import FileManager
from .dto import MoveMediaResponse
logger = logging.getLogger(__name__)
class MoveMediaUseCase:
"""Use case for moving a media file to a destination (copy + delete source)."""
def __init__(self, file_manager: FileManager):
self.file_manager = file_manager
def execute(self, source: str, destination: str) -> MoveMediaResponse:
"""
Move a media file from source to destination.
Args:
source: Absolute path to the source file.
destination: Absolute path to the destination file.
Returns:
MoveMediaResponse with success or error information.
"""
result = self.file_manager.move_file(source, destination)
if result.get("status") == "ok":
return MoveMediaResponse(
status="ok",
source=result.get("source"),
destination=result.get("destination"),
filename=result.get("filename"),
size=result.get("size"),
)
return MoveMediaResponse(
status="error",
error=result.get("error"),
message=result.get("message"),
)
@@ -0,0 +1,411 @@
"""
Destination resolution — compute library paths for releases.
Four distinct use cases, one per release type:
- resolve_season_destination : season pack (folder move)
- resolve_episode_destination : single episode (file move)
- resolve_movie_destination : movie (file move)
- resolve_series_destination : complete series multi-season pack (folder move)
Each returns a dedicated DTO with only the fields that make sense for that type.
"""
from __future__ import annotations
import logging
import re
from dataclasses import dataclass
from pathlib import Path
from alfred.domain.release import parse_release
from alfred.infrastructure.persistence import get_memory
logger = logging.getLogger(__name__)
_WIN_FORBIDDEN = re.compile(r'[?:*"<>|\\]')
def _sanitize(text: str) -> str:
return _WIN_FORBIDDEN.sub("", text)
def _find_existing_tvshow_folders(
tv_root: Path, tmdb_title: str, tmdb_year: int
) -> list[str]:
"""Return folder names in tv_root that match title + year prefix."""
if not tv_root.exists():
return []
clean_title = _sanitize(tmdb_title).replace(" ", ".")
prefix = f"{clean_title}.{tmdb_year}".lower()
return sorted(
entry.name
for entry in tv_root.iterdir()
if entry.is_dir() and entry.name.lower().startswith(prefix)
)
def _get_tv_root() -> Path | None:
memory = get_memory()
tv_root = memory.ltm.library_paths.get("tv_show")
return Path(tv_root) if tv_root else None
# ---------------------------------------------------------------------------
# Internal sentinel + series-folder resolver (shared by the 3 TV use cases)
# ---------------------------------------------------------------------------
@dataclass
class _Clarification:
"""Module-private sentinel signalling that user input is needed."""
question: str
options: list[str]
def _resolve_series_folder(
tv_root: Path,
tmdb_title: str,
tmdb_year: int,
computed_name: str,
confirmed_folder: str | None,
) -> tuple[str, bool] | _Clarification:
"""
Resolve which series folder to use.
Returns:
(folder_name, is_new) if resolved unambiguously,
_Clarification(question, options) if the caller must ask the user.
"""
if confirmed_folder:
return confirmed_folder, not (tv_root / confirmed_folder).exists()
existing = _find_existing_tvshow_folders(tv_root, tmdb_title, tmdb_year)
if not existing:
return computed_name, True
if len(existing) == 1 and existing[0] == computed_name:
return existing[0], False
options = existing + ([computed_name] if computed_name not in existing else [])
return _Clarification(
question=(
f"Un dossier série existe déjà pour '{tmdb_title}' "
f"mais son nom diffère du nom calculé ({computed_name}). "
f"Lequel utiliser ?"
),
options=options,
)
# ---------------------------------------------------------------------------
# DTOs
# ---------------------------------------------------------------------------
@dataclass
class _ResolvedDestinationBase:
"""
Shared shape across all resolution DTOs.
Holds the status flag and the fields used in non-ok states
(error / needs_clarification). Subclasses add their own ok-state fields
and a to_dict() that delegates the non-ok cases via _base_dict().
"""
status: str # "ok" | "needs_clarification" | "error"
# needs_clarification
question: str | None = None
options: list[str] | None = None
# error
error: str | None = None
message: str | None = None
def _base_dict(self) -> dict | None:
"""Return the dict for error/needs_clarification, or None for ok."""
if self.status == "error":
return {"status": self.status, "error": self.error, "message": self.message}
if self.status == "needs_clarification":
return {
"status": self.status,
"question": self.question,
"options": self.options or [],
}
return None
@dataclass
class ResolvedSeasonDestination(_ResolvedDestinationBase):
"""Paths for a season pack — folder move, no individual file paths."""
series_folder: str | None = None
season_folder: str | None = None
series_folder_name: str | None = None
season_folder_name: str | None = None
is_new_series_folder: bool = False
def to_dict(self) -> dict:
return self._base_dict() or {
"status": self.status,
"series_folder": self.series_folder,
"season_folder": self.season_folder,
"series_folder_name": self.series_folder_name,
"season_folder_name": self.season_folder_name,
"is_new_series_folder": self.is_new_series_folder,
}
@dataclass
class ResolvedEpisodeDestination(_ResolvedDestinationBase):
"""Paths for a single episode — file move."""
series_folder: str | None = None
season_folder: str | None = None
library_file: str | None = None # full path to destination .mkv
series_folder_name: str | None = None
season_folder_name: str | None = None
filename: str | None = None
is_new_series_folder: bool = False
def to_dict(self) -> dict:
return self._base_dict() or {
"status": self.status,
"series_folder": self.series_folder,
"season_folder": self.season_folder,
"library_file": self.library_file,
"series_folder_name": self.series_folder_name,
"season_folder_name": self.season_folder_name,
"filename": self.filename,
"is_new_series_folder": self.is_new_series_folder,
}
@dataclass
class ResolvedMovieDestination(_ResolvedDestinationBase):
"""Paths for a movie — file move."""
movie_folder: str | None = None
library_file: str | None = None
movie_folder_name: str | None = None
filename: str | None = None
is_new_folder: bool = False
def to_dict(self) -> dict:
return self._base_dict() or {
"status": self.status,
"movie_folder": self.movie_folder,
"library_file": self.library_file,
"movie_folder_name": self.movie_folder_name,
"filename": self.filename,
"is_new_folder": self.is_new_folder,
}
@dataclass
class ResolvedSeriesDestination(_ResolvedDestinationBase):
"""Paths for a complete multi-season series pack — folder move."""
series_folder: str | None = None
series_folder_name: str | None = None
is_new_series_folder: bool = False
def to_dict(self) -> dict:
return self._base_dict() or {
"status": self.status,
"series_folder": self.series_folder,
"series_folder_name": self.series_folder_name,
"is_new_series_folder": self.is_new_series_folder,
}
# ---------------------------------------------------------------------------
# Use cases
# ---------------------------------------------------------------------------
def resolve_season_destination(
release_name: str,
tmdb_title: str,
tmdb_year: int,
confirmed_folder: str | None = None,
) -> ResolvedSeasonDestination:
"""
Compute destination paths for a season pack.
Returns series_folder + season_folder. No file paths — the whole
source folder is moved as-is into season_folder.
"""
tv_root = _get_tv_root()
if not tv_root:
return ResolvedSeasonDestination(
status="error",
error="library_not_set",
message="TV show library path is not configured.",
)
parsed = parse_release(release_name)
computed_name = _sanitize(parsed.show_folder_name(tmdb_title, tmdb_year))
resolved = _resolve_series_folder(
tv_root, tmdb_title, tmdb_year, computed_name, confirmed_folder
)
if isinstance(resolved, _Clarification):
return ResolvedSeasonDestination(
status="needs_clarification",
question=resolved.question,
options=resolved.options,
)
series_folder_name, is_new = resolved
season_folder_name = parsed.season_folder_name()
series_path = tv_root / series_folder_name
season_path = series_path / season_folder_name
return ResolvedSeasonDestination(
status="ok",
series_folder=str(series_path),
season_folder=str(season_path),
series_folder_name=series_folder_name,
season_folder_name=season_folder_name,
is_new_series_folder=is_new,
)
def resolve_episode_destination(
release_name: str,
source_file: str,
tmdb_title: str,
tmdb_year: int,
tmdb_episode_title: str | None = None,
confirmed_folder: str | None = None,
) -> ResolvedEpisodeDestination:
"""
Compute destination paths for a single episode file.
Returns series_folder + season_folder + library_file (full path to .mkv).
"""
tv_root = _get_tv_root()
if not tv_root:
return ResolvedEpisodeDestination(
status="error",
error="library_not_set",
message="TV show library path is not configured.",
)
parsed = parse_release(release_name)
ext = Path(source_file).suffix
computed_name = _sanitize(parsed.show_folder_name(tmdb_title, tmdb_year))
resolved = _resolve_series_folder(
tv_root, tmdb_title, tmdb_year, computed_name, confirmed_folder
)
if isinstance(resolved, _Clarification):
return ResolvedEpisodeDestination(
status="needs_clarification",
question=resolved.question,
options=resolved.options,
)
series_folder_name, is_new = resolved
season_folder_name = parsed.season_folder_name()
filename = _sanitize(parsed.episode_filename(tmdb_episode_title, ext))
series_path = tv_root / series_folder_name
season_path = series_path / season_folder_name
file_path = season_path / filename
return ResolvedEpisodeDestination(
status="ok",
series_folder=str(series_path),
season_folder=str(season_path),
library_file=str(file_path),
series_folder_name=series_folder_name,
season_folder_name=season_folder_name,
filename=filename,
is_new_series_folder=is_new,
)
def resolve_movie_destination(
release_name: str,
source_file: str,
tmdb_title: str,
tmdb_year: int,
) -> ResolvedMovieDestination:
"""
Compute destination paths for a movie file.
Returns movie_folder + library_file (full path to .mkv).
"""
memory = get_memory()
movies_root = memory.ltm.library_paths.get("movie")
if not movies_root:
return ResolvedMovieDestination(
status="error",
error="library_not_set",
message="Movie library path is not configured.",
)
parsed = parse_release(release_name)
ext = Path(source_file).suffix
folder_name = _sanitize(parsed.movie_folder_name(tmdb_title, tmdb_year))
filename = _sanitize(parsed.movie_filename(tmdb_title, tmdb_year, ext))
folder_path = Path(movies_root) / folder_name
file_path = folder_path / filename
return ResolvedMovieDestination(
status="ok",
movie_folder=str(folder_path),
library_file=str(file_path),
movie_folder_name=folder_name,
filename=filename,
is_new_folder=not folder_path.exists(),
)
def resolve_series_destination(
release_name: str,
tmdb_title: str,
tmdb_year: int,
confirmed_folder: str | None = None,
) -> ResolvedSeriesDestination:
"""
Compute destination path for a complete multi-season series pack.
Returns only series_folder — the whole pack lands directly inside it.
"""
tv_root = _get_tv_root()
if not tv_root:
return ResolvedSeriesDestination(
status="error",
error="library_not_set",
message="TV show library path is not configured.",
)
parsed = parse_release(release_name)
computed_name = _sanitize(parsed.show_folder_name(tmdb_title, tmdb_year))
resolved = _resolve_series_folder(
tv_root, tmdb_title, tmdb_year, computed_name, confirmed_folder
)
if isinstance(resolved, _Clarification):
return ResolvedSeriesDestination(
status="needs_clarification",
question=resolved.question,
options=resolved.options,
)
series_folder_name, is_new = resolved
series_path = tv_root / series_folder_name
return ResolvedSeriesDestination(
status="ok",
series_folder=str(series_path),
series_folder_name=series_folder_name,
is_new_series_folder=is_new,
)
@@ -2,7 +2,7 @@
import logging import logging
from infrastructure.filesystem import FileManager from alfred.infrastructure.filesystem import FileManager
from .dto import SetFolderPathResponse from .dto import SetFolderPathResponse
@@ -2,7 +2,7 @@
import logging import logging
from infrastructure.api.tmdb import ( from alfred.infrastructure.api.tmdb import (
TMDBAPIError, TMDBAPIError,
TMDBClient, TMDBClient,
TMDBConfigurationError, TMDBConfigurationError,
+116
View File
@@ -0,0 +1,116 @@
"""SubtitlePlacer — hard-links matched subtitle tracks next to the destination video."""
import logging
import os
from dataclasses import dataclass
from pathlib import Path
from alfred.domain.subtitles.entities import SubtitleCandidate
from alfred.domain.subtitles.value_objects import SubtitleType
logger = logging.getLogger(__name__)
def _build_dest_name(track: SubtitleCandidate, video_stem: str) -> str:
"""
Build the destination filename for a subtitle track.
Format: {video_stem}.{lang}.{ext}
{video_stem}.{lang}.sdh.{ext}
{video_stem}.{lang}.forced.{ext}
"""
if not track.language or not track.format:
raise ValueError("Cannot compute destination name: language or format missing")
ext = track.format.extensions[0].lstrip(".")
parts = [video_stem, track.language.code]
if track.subtitle_type == SubtitleType.SDH:
parts.append("sdh")
elif track.subtitle_type == SubtitleType.FORCED:
parts.append("forced")
return ".".join(parts) + "." + ext
@dataclass
class PlacedTrack:
source: Path
destination: Path
filename: str
@dataclass
class PlaceResult:
placed: list[PlacedTrack]
skipped: list[tuple[SubtitleCandidate, str]] # (track, reason)
@property
def placed_count(self) -> int:
return len(self.placed)
@property
def skipped_count(self) -> int:
return len(self.skipped)
class SubtitlePlacer:
"""
Hard-links matched SubtitleCandidate files next to a destination video.
Uses the same hard-link strategy as FileManager.copy_file:
instant, no data duplication, qBittorrent keeps seeding.
Embedded tracks are skipped — nothing to place on disk.
"""
def place(
self,
tracks: list[SubtitleCandidate],
destination_video: Path,
) -> PlaceResult:
placed: list[PlacedTrack] = []
skipped: list[tuple[SubtitleCandidate, str]] = []
dest_dir = destination_video.parent
for track in tracks:
if track.is_embedded:
logger.debug(f"SubtitlePlacer: skip embedded track ({track.language})")
skipped.append((track, "embedded — no file to place"))
continue
if not track.file_path:
skipped.append((track, "source file not set"))
continue
try:
dest_name = _build_dest_name(track, destination_video.stem)
except ValueError as e:
skipped.append((track, str(e)))
continue
dest_path = dest_dir / dest_name
try:
os.link(track.file_path, dest_path)
placed.append(
PlacedTrack(
source=track.file_path,
destination=dest_path,
filename=dest_name,
)
)
logger.info(f"SubtitlePlacer: placed {dest_name}")
except FileNotFoundError:
skipped.append((track, "source file not found"))
except FileExistsError:
logger.debug(f"SubtitlePlacer: skip {dest_name} — already exists")
skipped.append((track, "destination already exists"))
except OSError as e:
logger.warning(f"SubtitlePlacer: failed to place {dest_name}: {e}")
skipped.append((track, str(e)))
logger.info(
f"SubtitlePlacer: {len(placed)} placed, {len(skipped)} skipped "
f"for {destination_video.name}"
)
return PlaceResult(placed=placed, skipped=skipped)
@@ -2,7 +2,7 @@
import logging import logging
from infrastructure.api.qbittorrent import ( from alfred.infrastructure.api.qbittorrent import (
QBittorrentAPIError, QBittorrentAPIError,
QBittorrentAuthError, QBittorrentAuthError,
QBittorrentClient, QBittorrentClient,
@@ -2,7 +2,11 @@
import logging import logging
from infrastructure.api.knaben import KnabenAPIError, KnabenClient, KnabenNotFoundError from alfred.infrastructure.api.knaben import (
KnabenAPIError,
KnabenClient,
KnabenNotFoundError,
)
from .dto import SearchTorrentsResponse from .dto import SearchTorrentsResponse
@@ -2,7 +2,6 @@
from .entities import Movie from .entities import Movie
from .exceptions import InvalidMovieData, MovieNotFound from .exceptions import InvalidMovieData, MovieNotFound
from .services import MovieService
from .value_objects import MovieTitle, Quality, ReleaseYear from .value_objects import MovieTitle, Quality, ReleaseYear
__all__ = [ __all__ = [
@@ -12,5 +11,4 @@ __all__ = [
"Quality", "Quality",
"MovieNotFound", "MovieNotFound",
"InvalidMovieData", "InvalidMovieData",
"MovieService",
] ]
@@ -3,16 +3,27 @@
from dataclasses import dataclass, field from dataclasses import dataclass, field
from datetime import datetime from datetime import datetime
from ..shared.media import AudioTrack, MediaWithTracks, SubtitleTrack
from ..shared.value_objects import FilePath, FileSize, ImdbId from ..shared.value_objects import FilePath, FileSize, ImdbId
from .value_objects import MovieTitle, Quality, ReleaseYear from .value_objects import MovieTitle, Quality, ReleaseYear
@dataclass @dataclass(eq=False)
class Movie: class Movie(MediaWithTracks):
""" """
Movie entity representing a movie in the media library. Movie aggregate root for the movies domain.
This is the main aggregate root for the movies domain. Carries file metadata (path, size) and the tracks discovered by the
ffprobe + subtitle scan pipeline. The track lists may be empty when the
movie is known but not yet scanned, or when no file is downloaded.
Track helpers follow the same "C+" contract as ``Episode``: pass a
``Language`` for cross-format matching, or a ``str`` for case-insensitive
direct comparison.
Equality is identity-based: two ``Movie`` instances are equal iff they
share the same ``imdb_id``, regardless of file/track contents. This is
the DDD aggregate invariant the aggregate is identified by its root id.
""" """
imdb_id: ImdbId imdb_id: ImdbId
@@ -23,13 +34,15 @@ class Movie:
file_size: FileSize | None = None file_size: FileSize | None = None
tmdb_id: int | None = None tmdb_id: int | None = None
added_at: datetime = field(default_factory=datetime.now) added_at: datetime = field(default_factory=datetime.now)
audio_tracks: list[AudioTrack] = field(default_factory=list)
subtitle_tracks: list[SubtitleTrack] = field(default_factory=list)
def __post_init__(self): def __post_init__(self):
"""Validate movie entity.""" """Validate movie entity."""
# Ensure ImdbId is actually an ImdbId instance # Ensure ImdbId is actually an ImdbId instance
if not isinstance(self.imdb_id, ImdbId): if not isinstance(self.imdb_id, ImdbId):
if isinstance(self.imdb_id, str): if isinstance(self.imdb_id, str):
object.__setattr__(self, "imdb_id", ImdbId(self.imdb_id)) self.imdb_id = ImdbId(self.imdb_id)
else: else:
raise ValueError( raise ValueError(
f"imdb_id must be ImdbId or str, got {type(self.imdb_id)}" f"imdb_id must be ImdbId or str, got {type(self.imdb_id)}"
@@ -38,19 +51,22 @@ class Movie:
# Ensure MovieTitle is actually a MovieTitle instance # Ensure MovieTitle is actually a MovieTitle instance
if not isinstance(self.title, MovieTitle): if not isinstance(self.title, MovieTitle):
if isinstance(self.title, str): if isinstance(self.title, str):
object.__setattr__(self, "title", MovieTitle(self.title)) self.title = MovieTitle(self.title)
else: else:
raise ValueError( raise ValueError(
f"title must be MovieTitle or str, got {type(self.title)}" f"title must be MovieTitle or str, got {type(self.title)}"
) )
def has_file(self) -> bool: def __eq__(self, other: object) -> bool:
"""Check if the movie has an associated file.""" if not isinstance(other, Movie):
return self.file_path is not None and self.file_path.exists() return NotImplemented
return self.imdb_id == other.imdb_id
def is_downloaded(self) -> bool: def __hash__(self) -> int:
"""Check if the movie is downloaded (has a file).""" return hash(self.imdb_id)
return self.has_file()
# Track helpers (has_audio_in / audio_languages / has_subtitles_in /
# has_forced_subs / subtitle_languages) come from MediaWithTracks.
def get_folder_name(self) -> str: def get_folder_name(self) -> str:
""" """
@@ -1,10 +1,10 @@
"""Movie domain value objects.""" """Movie domain value objects."""
import re
from dataclasses import dataclass from dataclasses import dataclass
from enum import Enum from enum import Enum
from ..shared.exceptions import ValidationError from ..shared.exceptions import ValidationError
from ..shared.value_objects import to_dot_folder_name
class Quality(Enum): class Quality(Enum):
@@ -17,7 +17,7 @@ class Quality(Enum):
UNKNOWN = "unknown" UNKNOWN = "unknown"
@classmethod @classmethod
def from_string(cls, quality_str: str) -> "Quality": def from_string(cls, quality_str: str) -> Quality:
""" """
Parse quality from string. Parse quality from string.
@@ -67,11 +67,7 @@ class MovieTitle:
Removes special characters and replaces spaces with dots. Removes special characters and replaces spaces with dots.
""" """
# Remove special characters except spaces, dots, and hyphens return to_dot_folder_name(self.value)
cleaned = re.sub(r"[^\w\s\.\-]", "", self.value)
# Replace spaces with dots
normalized = cleaned.replace(" ", ".")
return normalized
def __str__(self) -> str: def __str__(self) -> str:
return self.value return self.value
+6
View File
@@ -0,0 +1,6 @@
"""Release domain — release name parsing and naming conventions."""
from .services import parse_release
from .value_objects import ParsedRelease
__all__ = ["ParsedRelease", "parse_release"]
+508
View File
@@ -0,0 +1,508 @@
"""Release domain — parsing service."""
from __future__ import annotations
import re
from alfred.infrastructure.knowledge.release import load_separators
from .value_objects import (
_AUDIO,
_CODECS,
_EDITIONS,
_FORBIDDEN_CHARS,
_HDR_EXTRA,
_LANGUAGE_TOKENS,
_MEDIA_TYPE_TOKENS,
_RESOLUTIONS,
_SOURCES,
_VIDEO_META,
MediaTypeToken,
ParsedRelease,
ParsePath,
)
def _tokenize(name: str) -> list[str]:
"""Split a release name on the configured separators, dropping empty tokens."""
pattern = "[" + re.escape("".join(load_separators())) + "]+"
return [t for t in re.split(pattern, name) if t]
def parse_release(name: str) -> ParsedRelease:
"""
Parse a release name and return a ParsedRelease.
Flow:
1. Strip a leading/trailing [site.tag] if present (sets parse_path="sanitized").
2. Check the remainder for truly forbidden chars (anything not in the
configured separators list). If any remain → media_type="unknown",
parse_path="ai", and the LLM handles it.
3. Tokenize using the configured separators (".", " ", "[", "]", "(", ")", "_", ...)
and run token-level matchers (season/episode, tech, languages, audio,
video, edition, title, year).
"""
parse_path = ParsePath.DIRECT.value
# Always try to extract a bracket-enclosed site tag first.
clean, site_tag = _strip_site_tag(name)
if site_tag is not None:
parse_path = ParsePath.SANITIZED.value
if not _is_well_formed(clean):
return ParsedRelease(
raw=name,
normalised=clean,
title=clean,
year=None,
season=None,
episode=None,
episode_end=None,
quality=None,
source=None,
codec=None,
group="UNKNOWN",
tech_string="",
media_type=MediaTypeToken.UNKNOWN.value,
site_tag=site_tag,
parse_path=ParsePath.AI.value,
)
name = clean
tokens = _tokenize(name)
season, episode, episode_end = _extract_season_episode(tokens)
quality, source, codec, group, tech_tokens = _extract_tech(tokens)
languages, lang_tokens = _extract_languages(tokens)
audio_codec, audio_channels, audio_tokens = _extract_audio(tokens)
bit_depth, hdr_format, video_tokens = _extract_video_meta(tokens)
edition, edition_tokens = _extract_edition(tokens)
title = _extract_title(
tokens,
tech_tokens | lang_tokens | audio_tokens | video_tokens | edition_tokens,
)
year = _extract_year(tokens, title)
media_type = _infer_media_type(
season, quality, source, codec, year, edition, tokens
)
tech_parts = [p for p in [quality, source, codec] if p]
tech_string = ".".join(tech_parts)
return ParsedRelease(
raw=name,
normalised=name,
title=title,
year=year,
season=season,
episode=episode,
episode_end=episode_end,
quality=quality,
source=source,
codec=codec,
group=group,
tech_string=tech_string,
media_type=media_type,
site_tag=site_tag,
parse_path=parse_path,
languages=languages,
audio_codec=audio_codec,
audio_channels=audio_channels,
bit_depth=bit_depth,
hdr_format=hdr_format,
edition=edition,
)
def _infer_media_type(
season: int | None,
quality: str | None,
source: str | None,
codec: str | None,
year: int | None,
edition: str | None,
tokens: list[str],
) -> str:
"""
Infer media_type from token-level evidence only (no filesystem access).
- documentary : DOC token present
- concert : CONCERT token present
- tv_complete : INTEGRALE/COMPLETE token, no season
- tv_show : season token found
- movie : no season, at least one tech marker
- unknown : no conclusive evidence
"""
upper_tokens = {t.upper() for t in tokens}
doc_tokens = {t.upper() for t in _MEDIA_TYPE_TOKENS.get("doc", [])}
concert_tokens = {t.upper() for t in _MEDIA_TYPE_TOKENS.get("concert", [])}
integrale_tokens = {t.upper() for t in _MEDIA_TYPE_TOKENS.get("integrale", [])}
if upper_tokens & doc_tokens:
return MediaTypeToken.DOCUMENTARY.value
if upper_tokens & concert_tokens:
return MediaTypeToken.CONCERT.value
if (
edition in {"COMPLETE", "INTEGRALE", "COLLECTION"}
or upper_tokens & integrale_tokens
) and season is None:
return MediaTypeToken.TV_COMPLETE.value
if season is not None:
return MediaTypeToken.TV_SHOW.value
if any([quality, source, codec, year]):
return MediaTypeToken.MOVIE.value
return MediaTypeToken.UNKNOWN.value
def _is_well_formed(name: str) -> bool:
"""Return True if name contains no forbidden characters per scene naming rules.
Characters listed as token separators (spaces, brackets, parens, …) are NOT
considered malforming — the tokenizer handles them. Only truly broken chars
like '@', '#', '!', '%' make a name malformed.
"""
tokenizable = set(load_separators())
return not any(c in name for c in _FORBIDDEN_CHARS if c not in tokenizable)
def _strip_site_tag(name: str) -> tuple[str, str | None]:
"""
Strip a site watermark tag from the release name and return (clean_name, tag).
Handles two positions:
- Prefix: "[ OxTorrent.vc ] The.Title.S01..."
- Suffix: "The.Title.S01...-NTb[TGx]"
Anything between [...] is treated as a site tag.
Returns (original_name, None) if no tag found.
"""
s = name.strip()
if s.startswith("["):
close = s.find("]")
if close != -1:
tag = s[1:close].strip()
remainder = s[close + 1 :].strip()
if tag and remainder:
return remainder, tag
if s.endswith("]"):
open_bracket = s.rfind("[")
if open_bracket != -1:
tag = s[open_bracket + 1 : -1].strip()
remainder = s[:open_bracket].strip()
if tag and remainder:
return remainder, tag
return s, None
def _parse_season_episode(tok: str) -> tuple[int, int | None, int | None] | None:
"""
Parse a single token as a season/episode marker.
Handles:
- SxxExx / SxxExxExx / Sxx (canonical scene form)
- NxNN / NxNNxNN (alt form: 1x05, 12x07x08)
Returns (season, episode, episode_end) or None if not a season token.
"""
upper = tok.upper()
# SxxExx form
if len(upper) >= 3 and upper[0] == "S" and upper[1:3].isdigit():
season = int(upper[1:3])
rest = upper[3:]
if not rest:
return season, None, None
episodes: list[int] = []
while rest.startswith("E") and len(rest) >= 3 and rest[1:3].isdigit():
episodes.append(int(rest[1:3]))
rest = rest[3:]
if not episodes:
return None # malformed token like "S03XYZ"
return season, episodes[0], episodes[1] if len(episodes) >= 2 else None
# NxNN form — split on "X" (uppercased), all parts must be digits
if "X" in upper:
parts = upper.split("X")
if len(parts) >= 2 and all(p.isdigit() and p for p in parts):
season = int(parts[0])
episode = int(parts[1])
episode_end = int(parts[2]) if len(parts) >= 3 else None
return season, episode, episode_end
return None
def _extract_season_episode(
tokens: list[str],
) -> tuple[int | None, int | None, int | None]:
for tok in tokens:
parsed = _parse_season_episode(tok)
if parsed is not None:
return parsed
return None, None, None
def _extract_tech(
tokens: list[str],
) -> tuple[str | None, str | None, str | None, str, set[str]]:
"""
Extract quality, source, codec, group from tokens.
Returns (quality, source, codec, group, tech_token_set).
Group extraction strategy (in priority order):
1. Token where prefix is a known codec: x265-GROUP
2. Rightmost token with a dash that isn't a known source
"""
quality: str | None = None
source: str | None = None
codec: str | None = None
group = "UNKNOWN"
tech_tokens: set[str] = set()
for tok in tokens:
tl = tok.lower()
if tl in _RESOLUTIONS:
quality = tok
tech_tokens.add(tok)
continue
if tl in _SOURCES:
source = tok
tech_tokens.add(tok)
continue
if "-" in tok:
parts = tok.rsplit("-", 1)
# codec-GROUP (highest priority for group)
if parts[0].lower() in _CODECS:
codec = parts[0]
group = parts[1] if parts[1] else "UNKNOWN"
tech_tokens.add(tok)
continue
# source with dash: Web-DL, WEB-DL, etc.
if parts[0].lower() in _SOURCES or tok.lower().replace("-", "") in _SOURCES:
source = tok
tech_tokens.add(tok)
continue
if tl in _CODECS:
codec = tok
tech_tokens.add(tok)
# Fallback: rightmost token with a dash that isn't a known source
if group == "UNKNOWN":
for tok in reversed(tokens):
if "-" in tok:
parts = tok.rsplit("-", 1)
tl = tok.lower()
if tl in _SOURCES or tok.lower().replace("-", "") in _SOURCES:
continue
if parts[1]:
group = parts[1]
break
return quality, source, codec, group, tech_tokens
def _is_year_token(tok: str) -> bool:
"""Return True if tok is a 4-digit year between 1900 and 2099."""
return len(tok) == 4 and tok.isdigit() and 1900 <= int(tok) <= 2099
def _extract_title(tokens: list[str], tech_tokens: set[str]) -> str:
"""Extract the title portion: everything before the first season/year/tech token."""
title_parts = []
for tok in tokens:
if _parse_season_episode(tok) is not None:
break
if _is_year_token(tok):
break
if tok in tech_tokens or tok.lower() in _RESOLUTIONS | _SOURCES | _CODECS:
break
if "-" in tok and any(p.lower() in _CODECS | _SOURCES for p in tok.split("-")):
break
title_parts.append(tok)
return ".".join(title_parts) if title_parts else tokens[0]
def _extract_year(tokens: list[str], title: str) -> int | None:
"""Extract a 4-digit year from tokens (only after the title)."""
title_len = len(title.split("."))
for tok in tokens[title_len:]:
if _is_year_token(tok):
return int(tok)
return None
# ---------------------------------------------------------------------------
# Sequence matcher
# ---------------------------------------------------------------------------
def _match_sequences(
tokens: list[str],
sequences: list[dict],
key: str,
) -> tuple[str | None, set[str]]:
"""
Try to match multi-token sequences against consecutive tokens.
Returns (matched_value, set_of_matched_tokens) or (None, empty_set).
Sequences must be ordered most-specific first in the YAML.
"""
upper_tokens = [t.upper() for t in tokens]
for seq in sequences:
seq_upper = [s.upper() for s in seq["tokens"]]
n = len(seq_upper)
for i in range(len(upper_tokens) - n + 1):
if upper_tokens[i : i + n] == seq_upper:
matched = set(tokens[i : i + n])
return seq[key], matched
return None, set()
# ---------------------------------------------------------------------------
# Language extraction
# ---------------------------------------------------------------------------
def _extract_languages(tokens: list[str]) -> tuple[list[str], set[str]]:
"""Extract language tokens. Returns (languages, matched_token_set)."""
languages = []
lang_tokens: set[str] = set()
for tok in tokens:
if tok.upper() in _LANGUAGE_TOKENS:
languages.append(tok.upper())
lang_tokens.add(tok)
return languages, lang_tokens
# ---------------------------------------------------------------------------
# Audio extraction
# ---------------------------------------------------------------------------
def _extract_audio(
tokens: list[str],
) -> tuple[str | None, str | None, set[str]]:
"""
Extract audio codec and channel layout.
Returns (audio_codec, audio_channels, matched_token_set).
Sequences are tried first (DTS.HD.MA, TrueHD.Atmos, …), then single tokens.
"""
audio_codec: str | None = None
audio_channels: str | None = None
audio_tokens: set[str] = set()
known_codecs = {c.upper() for c in _AUDIO.get("codecs", [])}
known_channels = set(_AUDIO.get("channels", []))
# Try multi-token sequences first
matched_codec, matched_set = _match_sequences(
tokens, _AUDIO.get("sequences", []), "codec"
)
if matched_codec:
audio_codec = matched_codec
audio_tokens |= matched_set
# Channel layouts like "5.1" or "7.1" are split into two tokens by normalize —
# detect them as consecutive pairs "X" + "Y" where "X.Y" is a known channel.
# The second token may have a "-GROUP" suffix (e.g. "1-KTH" → strip it).
for i in range(len(tokens) - 1):
second = tokens[i + 1].split("-")[0]
candidate = f"{tokens[i]}.{second}"
if candidate in known_channels and audio_channels is None:
audio_channels = candidate
audio_tokens.add(tokens[i])
audio_tokens.add(tokens[i + 1])
for tok in tokens:
if tok in audio_tokens:
continue
if tok.upper() in known_codecs and audio_codec is None:
audio_codec = tok
audio_tokens.add(tok)
elif tok in known_channels and audio_channels is None:
audio_channels = tok
audio_tokens.add(tok)
return audio_codec, audio_channels, audio_tokens
# ---------------------------------------------------------------------------
# Video metadata extraction (bit depth, HDR)
# ---------------------------------------------------------------------------
def _extract_video_meta(
tokens: list[str],
) -> tuple[str | None, str | None, set[str]]:
"""
Extract bit depth and HDR format.
Returns (bit_depth, hdr_format, matched_token_set).
"""
bit_depth: str | None = None
hdr_format: str | None = None
video_tokens: set[str] = set()
known_hdr = {h.upper() for h in _VIDEO_META.get("hdr", [])} | _HDR_EXTRA
known_depth = {d.lower() for d in _VIDEO_META.get("bit_depth", [])}
# Try HDR sequences first
matched_hdr, matched_set = _match_sequences(
tokens, _VIDEO_META.get("sequences", []), "hdr"
)
if matched_hdr:
hdr_format = matched_hdr
video_tokens |= matched_set
for tok in tokens:
if tok in video_tokens:
continue
if tok.upper() in known_hdr and hdr_format is None:
hdr_format = tok.upper()
video_tokens.add(tok)
elif tok.lower() in known_depth and bit_depth is None:
bit_depth = tok.lower()
video_tokens.add(tok)
return bit_depth, hdr_format, video_tokens
# ---------------------------------------------------------------------------
# Edition extraction
# ---------------------------------------------------------------------------
def _extract_edition(tokens: list[str]) -> tuple[str | None, set[str]]:
"""
Extract release edition (UNRATED, EXTENDED, DIRECTORS.CUT, …).
Returns (edition, matched_token_set).
"""
known_tokens = {t.upper() for t in _EDITIONS.get("tokens", [])}
# Try multi-token sequences first
matched_edition, matched_set = _match_sequences(
tokens, _EDITIONS.get("sequences", []), "edition"
)
if matched_edition:
return matched_edition, matched_set
for tok in tokens:
if tok.upper() in known_tokens:
return tok.upper(), {tok}
return None, set()
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@@ -0,0 +1,242 @@
"""Release domain — value objects and token sets."""
from __future__ import annotations
from dataclasses import dataclass, field
from enum import Enum
from ..shared.exceptions import ValidationError
from alfred.infrastructure.knowledge.release import (
load_audio,
load_codecs,
load_editions,
load_forbidden_chars,
load_hdr_extra,
load_language_tokens,
load_media_type_tokens,
load_metadata_extensions,
load_non_video_extensions,
load_resolutions,
load_sources,
load_sources_extra,
load_subtitle_extensions,
load_video,
load_video_extensions,
load_win_forbidden_chars,
)
# Token sets — loaded once at import time from alfred/knowledge/release/
_RESOLUTIONS: set[str] = load_resolutions()
_SOURCES: set[str] = load_sources() | load_sources_extra()
_CODECS: set[str] = load_codecs()
_VIDEO_EXTENSIONS: set[str] = load_video_extensions()
_NON_VIDEO_EXTENSIONS: set[str] = load_non_video_extensions()
_SUBTITLE_EXTENSIONS: set[str] = load_subtitle_extensions()
# Both metadata and subtitle extensions are ignored when deciding the media
# type of a folder — neither is a conclusive signal for movie/tv/other.
_METADATA_EXTENSIONS: set[str] = load_metadata_extensions() | _SUBTITLE_EXTENSIONS
_FORBIDDEN_CHARS: set[str] = load_forbidden_chars()
_LANGUAGE_TOKENS: set[str] = load_language_tokens()
_AUDIO: dict = load_audio()
_VIDEO_META: dict = load_video()
_EDITIONS: dict = load_editions()
_HDR_EXTRA: set[str] = load_hdr_extra()
_MEDIA_TYPE_TOKENS: dict = load_media_type_tokens()
# Translation table for stripping Windows-forbidden characters
_WIN_FORBIDDEN_TABLE = str.maketrans("", "", "".join(load_win_forbidden_chars()))
def _sanitize_for_fs(text: str) -> str:
"""Remove Windows-forbidden characters from a string."""
return text.translate(_WIN_FORBIDDEN_TABLE)
class MediaTypeToken(str, Enum):
"""
Canonical values for ``ParsedRelease.media_type``.
Inherits from ``str`` so existing string-based comparisons (``== "movie"``,
JSON serialization, TMDB DTO interop) keep working unchanged. The enum
serves both as documentation and as the set of valid values for
``__post_init__`` validation.
"""
MOVIE = "movie"
TV_SHOW = "tv_show"
TV_COMPLETE = "tv_complete"
DOCUMENTARY = "documentary"
CONCERT = "concert"
OTHER = "other"
UNKNOWN = "unknown"
class ParsePath(str, Enum):
"""How a ``ParsedRelease`` was produced. ``str``-backed for the same
reasons as :class:`MediaTypeToken`."""
DIRECT = "direct"
SANITIZED = "sanitized"
AI = "ai"
_VALID_MEDIA_TYPES: frozenset[str] = frozenset(m.value for m in MediaTypeToken)
_VALID_PARSE_PATHS: frozenset[str] = frozenset(p.value for p in ParsePath)
def _strip_episode_from_normalized(normalized: str) -> str:
"""
Remove all episode parts (Exx) from a normalized release name, keeping Sxx.
Oz.S03E01.1080p... → Oz.S03.1080p...
Archer.S14E09E10E11.1080p... → Archer.S14.1080p...
"""
tokens = normalized.split(".")
result = []
for tok in tokens:
upper = tok.upper()
# Token is SxxExx... — keep only the Sxx part
if len(upper) >= 3 and upper[0] == "S" and upper[1:3].isdigit():
result.append(tok[:3]) # "S" + two digits
else:
result.append(tok)
return ".".join(result)
@dataclass
class ParsedRelease:
"""Structured representation of a parsed release name."""
raw: str # original release name (untouched)
normalised: str # dots instead of spaces
title: str # show/movie title (dots, no year/season/tech)
year: int | None # movie year or show start year (from TMDB)
season: int | None # season number (None for movies)
episode: int | None # first episode number (None if season-pack)
episode_end: int | None # last episode for multi-ep (None otherwise)
quality: str | None # 1080p, 2160p, …
source: str | None # WEBRip, BluRay, …
codec: str | None # x265, HEVC, …
group: str # release group, "UNKNOWN" if missing
tech_string: str # quality.source.codec joined with dots
media_type: MediaTypeToken = MediaTypeToken.UNKNOWN
site_tag: str | None = (
None # site watermark stripped from name, e.g. "TGx", "OxTorrent.vc"
)
parse_path: ParsePath = ParsePath.DIRECT
languages: list[str] = field(default_factory=list) # ["MULTI", "VFF"], ["FRENCH"], …
audio_codec: str | None = None # "DTS-HD.MA", "DDP", "EAC3", …
audio_channels: str | None = None # "5.1", "7.1", "2.0", …
bit_depth: str | None = None # "10bit", "8bit", …
hdr_format: str | None = None # "DV", "HDR10", "DV.HDR10", …
edition: str | None = None # "UNRATED", "EXTENDED", "DIRECTORS.CUT", …
def __post_init__(self) -> None:
if not self.raw:
raise ValidationError("ParsedRelease.raw cannot be empty")
if not self.group:
raise ValidationError("ParsedRelease.group cannot be empty")
if self.year is not None and not (1888 <= self.year <= 2100):
raise ValidationError(
f"ParsedRelease.year out of range: {self.year}"
)
if self.season is not None and not (0 <= self.season <= 100):
raise ValidationError(
f"ParsedRelease.season out of range: {self.season}"
)
if self.episode is not None and not (0 <= self.episode <= 9999):
raise ValidationError(
f"ParsedRelease.episode out of range: {self.episode}"
)
if self.episode_end is not None:
if not (0 <= self.episode_end <= 9999):
raise ValidationError(
f"ParsedRelease.episode_end out of range: {self.episode_end}"
)
if self.episode is not None and self.episode_end < self.episode:
raise ValidationError(
f"ParsedRelease.episode_end ({self.episode_end}) < "
f"episode ({self.episode})"
)
# Coerce raw strings into their enum form (tolerant constructor).
if not isinstance(self.media_type, MediaTypeToken):
try:
self.media_type = MediaTypeToken(self.media_type)
except ValueError:
raise ValidationError(
f"ParsedRelease.media_type invalid: {self.media_type!r} "
f"(expected one of {sorted(_VALID_MEDIA_TYPES)})"
) from None
if not isinstance(self.parse_path, ParsePath):
try:
self.parse_path = ParsePath(self.parse_path)
except ValueError:
raise ValidationError(
f"ParsedRelease.parse_path invalid: {self.parse_path!r} "
f"(expected one of {sorted(_VALID_PARSE_PATHS)})"
) from None
@property
def is_season_pack(self) -> bool:
return self.season is not None and self.episode is None
def show_folder_name(self, tmdb_title: str, tmdb_year: int) -> str:
"""
Build the series root folder name.
Format: {Title}.{Year}.{Tech}-{Group}
Example: Oz.1997.1080p.WEBRip.x265-KONTRAST
"""
title_part = _sanitize_for_fs(tmdb_title).replace(" ", ".")
tech = self.tech_string or "Unknown"
return f"{title_part}.{tmdb_year}.{tech}-{self.group}"
def season_folder_name(self) -> str:
"""
Build the season subfolder name = normalized release name (no episode).
Example: Oz.S03.1080p.WEBRip.x265-KONTRAST
For a single-episode release we still strip the episode token so the
folder can hold the whole season.
"""
return _strip_episode_from_normalized(self.normalised)
def episode_filename(self, tmdb_episode_title: str | None, ext: str) -> str:
"""
Build the episode filename.
Format: {Title}.{SxxExx}.{EpisodeTitle}.{Tech}-{Group}.{ext}
Example: Oz.S01E01.The.Routine.1080p.WEBRip.x265-KONTRAST.mkv
If tmdb_episode_title is None, omits the episode title segment.
"""
title_part = _sanitize_for_fs(self.title)
s = f"S{self.season:02d}" if self.season is not None else ""
e = f"E{self.episode:02d}" if self.episode is not None else ""
se = s + e
ep_title = ""
if tmdb_episode_title:
ep_title = "." + _sanitize_for_fs(tmdb_episode_title).replace(" ", ".")
tech = self.tech_string or "Unknown"
ext_clean = ext.lstrip(".")
return f"{title_part}.{se}{ep_title}.{tech}-{self.group}.{ext_clean}"
def movie_folder_name(self, tmdb_title: str, tmdb_year: int) -> str:
"""
Build the movie folder name.
Format: {Title}.{Year}.{Tech}-{Group}
Example: Inception.2010.1080p.BluRay.x265-GROUP
"""
return self.show_folder_name(tmdb_title, tmdb_year)
def movie_filename(self, tmdb_title: str, tmdb_year: int, ext: str) -> str:
"""
Build the movie filename (same as folder name + extension).
Example: Inception.2010.1080p.BluRay.x265-GROUP.mkv
"""
ext_clean = ext.lstrip(".")
return f"{self.movie_folder_name(tmdb_title, tmdb_year)}.{ext_clean}"
@@ -1,7 +1,7 @@
"""Shared kernel - Common domain concepts used across subdomains.""" """Shared kernel - Common domain concepts used across subdomains."""
from .exceptions import DomainException, ValidationError from .exceptions import DomainException, ValidationError
from .value_objects import FilePath, FileSize, ImdbId from .value_objects import FilePath, FileSize, ImdbId, Language
__all__ = [ __all__ = [
"DomainException", "DomainException",
@@ -9,4 +9,5 @@ __all__ = [
"ImdbId", "ImdbId",
"FilePath", "FilePath",
"FileSize", "FileSize",
"Language",
] ]
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@@ -0,0 +1,21 @@
"""Media — file-level track types (video/audio/subtitle) and MediaInfo container.
These are the **container-view** dataclasses, populated from ffprobe output and
used across the project to describe the content of a media file.
"""
from .audio import AudioTrack
from .info import MediaInfo
from .matching import track_lang_matches
from .subtitle import SubtitleTrack
from .tracks_mixin import MediaWithTracks
from .video import VideoTrack
__all__ = [
"AudioTrack",
"MediaInfo",
"MediaWithTracks",
"SubtitleTrack",
"VideoTrack",
"track_lang_matches",
]
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"""AudioTrack — a single audio stream as reported by ffprobe."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass(frozen=True)
class AudioTrack:
"""A single audio track as reported by ffprobe."""
index: int
codec: str | None # aac, ac3, eac3, dts, truehd, flac, …
channels: int | None # 2, 6 (5.1), 8 (7.1), …
channel_layout: str | None # stereo, 5.1, 7.1, …
language: str | None # ISO 639-2: fre, eng, und, …
is_default: bool = False
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"""MediaInfo — assembles video, audio and subtitle tracks for a media file."""
from __future__ import annotations
from dataclasses import dataclass, field
from .audio import AudioTrack
from .subtitle import SubtitleTrack
from .video import VideoTrack
@dataclass(frozen=True)
class MediaInfo:
"""
File-level media metadata extracted by ffprobe — immutable snapshot.
Symmetric design: every stream type is a tuple of typed track objects
(immutable on purpose — a MediaInfo is a frozen view of one ffprobe run,
not a mutable collection to append to).
Backwards-compatible flat accessors (``resolution``, ``width``, …) read
from the first video track when present.
"""
video_tracks: tuple[VideoTrack, ...] = field(default_factory=tuple)
audio_tracks: tuple[AudioTrack, ...] = field(default_factory=tuple)
subtitle_tracks: tuple[SubtitleTrack, ...] = field(default_factory=tuple)
# File-level (from ffprobe ``format`` block, not from any single stream)
duration_seconds: float | None = None
bitrate_kbps: int | None = None
# ──────────────────────────────────────────────────────────────────────
# Video conveniences — read the first video track
# ──────────────────────────────────────────────────────────────────────
@property
def primary_video(self) -> VideoTrack | None:
return self.video_tracks[0] if self.video_tracks else None
@property
def width(self) -> int | None:
v = self.primary_video
return v.width if v else None
@property
def height(self) -> int | None:
v = self.primary_video
return v.height if v else None
@property
def video_codec(self) -> str | None:
v = self.primary_video
return v.codec if v else None
@property
def resolution(self) -> str | None:
v = self.primary_video
return v.resolution if v else None
# ──────────────────────────────────────────────────────────────────────
# Audio conveniences
# ──────────────────────────────────────────────────────────────────────
@property
def audio_languages(self) -> list[str]:
"""Unique audio languages across all tracks (ISO 639-2)."""
seen: set[str] = set()
result: list[str] = []
for track in self.audio_tracks:
if track.language and track.language not in seen:
seen.add(track.language)
result.append(track.language)
return result
@property
def is_multi_audio(self) -> bool:
"""True if more than one audio language is present."""
return len(self.audio_languages) > 1
+33
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@@ -0,0 +1,33 @@
"""Language-matching helper shared by media-bearing entities.
Both ``Episode`` and ``Movie`` carry ``audio_tracks`` / ``subtitle_tracks`` and
need to answer "do I have audio in language X?". The matching contract is the
same in both cases — keep it in one place.
"""
from __future__ import annotations
from ..value_objects import Language
def track_lang_matches(track_lang: str | None, query: str | Language) -> bool:
"""
Match a track's language string against a query (contract "C+").
* ``Language`` query → matches if the track string is any known
representation of that Language (delegates to ``Language.matches``).
Powerful, cross-format mode.
* ``str`` query → case-insensitive direct comparison against
``track_lang``. Simple, no normalization, no registry lookup.
Callers needing cross-format resolution (``"fr"`` ↔ ``"fre"`` ↔
``"french"``) should resolve their string through a ``LanguageRegistry``
once and pass the resulting ``Language``.
"""
if track_lang is None:
return False
if isinstance(query, Language):
return query.matches(track_lang)
if isinstance(query, str):
return track_lang.lower().strip() == query.lower().strip()
return False
+25
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@@ -0,0 +1,25 @@
"""SubtitleTrack — a single embedded subtitle stream as reported by ffprobe.
This is the **container-view** representation (ffprobe output) used uniformly
across the project to describe a subtitle stream embedded in a media file.
Not to be confused with ``alfred.domain.subtitles.entities.SubtitleCandidate``
which models a subtitle being **scanned/matched** (with confidence, raw tokens,
file path, etc.). The two coexist by design — they describe the same real-world
concept seen from two different bounded contexts.
"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass(frozen=True)
class SubtitleTrack:
"""A single embedded subtitle track as reported by ffprobe."""
index: int
codec: str | None # subrip, ass, hdmv_pgs_subtitle, …
language: str | None # ISO 639-2: fre, eng, und, …
is_default: bool = False
is_forced: bool = False
@@ -0,0 +1,77 @@
"""Mixin shared by entities that carry audio + subtitle tracks.
Both ``Movie`` and ``Episode`` carry a ``list[AudioTrack]`` plus a
``list[SubtitleTrack]`` and answer the same 5 queries about them (language
presence, unique languages, forced flag). Keep that behavior in one place so a
fix in one is a fix in both.
The mixin is plain Python (no dataclass machinery) so it composes cleanly with
``@dataclass`` entities — it only reads ``self.audio_tracks`` and
``self.subtitle_tracks`` which the host class provides as fields.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from ..value_objects import Language
from .matching import track_lang_matches
if TYPE_CHECKING:
from .audio import AudioTrack
from .subtitle import SubtitleTrack
class MediaWithTracks:
"""
Mixin providing audio/subtitle helpers for entities with track collections.
Hosts must expose two attributes:
* ``audio_tracks: list[AudioTrack]``
* ``subtitle_tracks: list[SubtitleTrack]``
The helpers follow the "C+" matching contract: pass a :class:`Language`
for cross-format matching, or a ``str`` for case-insensitive comparison.
"""
# These attributes are provided by the host entity (Movie, Episode, …).
# Declared here only for type-checkers and to make the contract explicit.
audio_tracks: list["AudioTrack"]
subtitle_tracks: list["SubtitleTrack"]
# ── Audio helpers ──────────────────────────────────────────────────────
def has_audio_in(self, lang: str | Language) -> bool:
"""True if at least one audio track is in the given language."""
return any(track_lang_matches(t.language, lang) for t in self.audio_tracks)
def audio_languages(self) -> list[str]:
"""Unique audio languages across all tracks, in track order."""
seen: set[str] = set()
result: list[str] = []
for t in self.audio_tracks:
if t.language and t.language not in seen:
seen.add(t.language)
result.append(t.language)
return result
# ── Subtitle helpers ───────────────────────────────────────────────────
def has_subtitles_in(self, lang: str | Language) -> bool:
"""True if at least one subtitle track is in the given language."""
return any(track_lang_matches(t.language, lang) for t in self.subtitle_tracks)
def has_forced_subs(self) -> bool:
"""True if at least one subtitle track is flagged as forced."""
return any(t.is_forced for t in self.subtitle_tracks)
def subtitle_languages(self) -> list[str]:
"""Unique subtitle languages across all tracks, in track order."""
seen: set[str] = set()
result: list[str] = []
for t in self.subtitle_tracks:
if t.language and t.language not in seen:
seen.add(t.language)
result.append(t.language)
return result
+62
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@@ -0,0 +1,62 @@
"""VideoTrack — a single video stream as reported by ffprobe."""
from __future__ import annotations
from dataclasses import dataclass
@dataclass(frozen=True)
class VideoTrack:
"""A single video track as reported by ffprobe.
A media file typically has one video track but can have several (alt
camera angles, attached thumbnail images reported as still-image streams,
etc.), hence the list[VideoTrack] on MediaInfo.
"""
index: int
codec: str | None # h264, hevc, av1, …
width: int | None
height: int | None
is_default: bool = False
@property
def resolution(self) -> str | None:
"""
Best-effort resolution string: 2160p, 1080p, 720p, …
Width takes priority over height to handle widescreen/cinema crops
(e.g. 1920×960 scope → 1080p, not 720p). Falls back to height when
width is unavailable.
"""
match (self.width, self.height):
case (None, None):
return None
case (w, h) if w is not None:
match True:
case _ if w >= 3840:
return "2160p"
case _ if w >= 1920:
return "1080p"
case _ if w >= 1280:
return "720p"
case _ if w >= 720:
return "576p"
case _ if w >= 640:
return "480p"
case _:
return f"{h}p" if h else f"{w}w"
case (None, h):
match True:
case _ if h >= 2160:
return "2160p"
case _ if h >= 1080:
return "1080p"
case _ if h >= 720:
return "720p"
case _ if h >= 576:
return "576p"
case _ if h >= 480:
return "480p"
case _:
return f"{h}p"
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@@ -0,0 +1,17 @@
"""Ports — Protocol interfaces the domain depends on.
Adapters live in ``alfred/infrastructure/`` and implement these protocols.
Domain code never imports infrastructure; it accepts a port via constructor
injection and calls it. Tests can pass in-memory fakes that satisfy the
Protocol without going through real I/O.
"""
from .filesystem_scanner import FileEntry, FilesystemScanner
from .media_prober import MediaProber, SubtitleStreamInfo
__all__ = [
"FileEntry",
"FilesystemScanner",
"MediaProber",
"SubtitleStreamInfo",
]
@@ -0,0 +1,59 @@
"""FilesystemScanner port — abstracts filesystem inspection.
The domain never calls ``Path.iterdir``, ``Path.is_file``, ``Path.stat`` or
``open()`` directly. It asks the scanner for a ``FileEntry`` snapshot and
reasons from there. One scan = one I/O round-trip; no callbacks back to disk.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Protocol
@dataclass(frozen=True)
class FileEntry:
"""Frozen snapshot of one filesystem entry, taken at scan time.
The entry carries enough metadata for the domain to classify and order
files without re-querying the OS. ``size_kb`` is ``None`` for directories
and for files whose size could not be read.
"""
path: Path
is_file: bool
is_dir: bool
size_kb: float | None
@property
def name(self) -> str:
return self.path.name
@property
def stem(self) -> str:
return self.path.stem
@property
def suffix(self) -> str:
return self.path.suffix
class FilesystemScanner(Protocol):
"""Read-only filesystem inspection."""
def scan_dir(self, path: Path) -> list[FileEntry]:
"""Return sorted entries directly inside ``path``.
Returns an empty list when ``path`` is not a directory or is
unreadable. Adapters must not raise.
"""
...
def stat(self, path: Path) -> FileEntry | None:
"""Stat a single path; ``None`` when it doesn't exist or is unreadable."""
...
def read_text(self, path: Path, encoding: str = "utf-8") -> str | None:
"""Read a text file in one go; ``None`` on any error."""
...
@@ -0,0 +1,39 @@
"""MediaProber port — abstracts media stream inspection (e.g. ffprobe).
The adapter (typically wrapping ffprobe) maps low-level container metadata
into the small set of stream attributes the domain reasons about. Replacing
ffprobe with another tool only requires a new adapter — domain stays put.
"""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import Protocol
@dataclass(frozen=True)
class SubtitleStreamInfo:
"""A single embedded subtitle stream, as seen by the prober.
``language`` is the raw language tag emitted by the container (typically
ISO 639-2 like ``"fre"``, ``"eng"``); may be empty/None when the stream
has no language tag. The domain resolves it to a canonical ``Language``
via the knowledge base.
"""
language: str | None
is_hearing_impaired: bool
is_forced: bool
class MediaProber(Protocol):
"""Inspect a media file's stream metadata."""
def list_subtitle_streams(self, video: Path) -> list[SubtitleStreamInfo]:
"""Return all subtitle streams in ``video``.
Returns an empty list when the file is missing, unreadable, or has
no subtitle streams. Adapters must not raise.
"""
...
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@@ -0,0 +1,215 @@
"""Shared value objects used across multiple domains."""
import re
from dataclasses import dataclass
from pathlib import Path
from .exceptions import ValidationError
@dataclass(frozen=True)
class ImdbId:
"""
Value object representing an IMDb ID.
IMDb IDs follow the format: tt followed by 7-8 digits (e.g., tt1375666)
"""
value: str
def __post_init__(self):
"""Validate IMDb ID format."""
if not self.value:
raise ValidationError("IMDb ID cannot be empty")
if not isinstance(self.value, str):
raise ValidationError(f"IMDb ID must be a string, got {type(self.value)}")
# IMDb ID format: tt + 7-8 digits
pattern = r"^tt\d{7,8}$"
if not re.match(pattern, self.value):
raise ValidationError(
f"Invalid IMDb ID format: {self.value}. "
"Expected format: tt followed by 7-8 digits (e.g., tt1375666)"
)
def __str__(self) -> str:
return self.value
def __repr__(self) -> str:
return f"ImdbId('{self.value}')"
@dataclass(frozen=True)
class FilePath:
"""
Value object representing a file path with validation.
Ensures the path is valid and optionally checks existence.
"""
value: Path
def __init__(self, path: str | Path):
"""
Initialize FilePath.
Args:
path: String or Path object representing the file path
"""
if isinstance(path, str):
path_obj = Path(path)
elif isinstance(path, Path):
path_obj = path
else:
raise ValidationError(f"Path must be str or Path, got {type(path)}")
# Use object.__setattr__ because dataclass is frozen
object.__setattr__(self, "value", path_obj)
def __str__(self) -> str:
return str(self.value)
def __repr__(self) -> str:
return f"FilePath('{self.value}')"
@dataclass(frozen=True)
class FileSize:
"""
Value object representing a file size in bytes.
Provides human-readable formatting.
"""
bytes: int
def __post_init__(self):
"""Validate file size."""
if not isinstance(self.bytes, int):
raise ValidationError(
f"File size must be an integer, got {type(self.bytes)}"
)
if self.bytes < 0:
raise ValidationError(f"File size cannot be negative: {self.bytes}")
def to_human_readable(self) -> str:
"""
Convert bytes to human-readable format.
Returns:
String like "1.5 GB", "500 MB", etc.
"""
units = ["B", "KB", "MB", "GB", "TB"]
size = float(self.bytes)
unit_index = 0
while size >= 1024 and unit_index < len(units) - 1:
size /= 1024
unit_index += 1
if unit_index == 0:
return f"{int(size)} {units[unit_index]}"
else:
return f"{size:.2f} {units[unit_index]}"
def __str__(self) -> str:
return self.to_human_readable()
def __repr__(self) -> str:
return f"FileSize({self.bytes})"
@dataclass(frozen=True)
class Language:
"""
Canonical language value object.
The primary identifier is the ISO 639-2/B code (3 letters, bibliographic form,
e.g. "fre", "eng", "ger"). This is what ffprobe emits and the project-wide
canonical form. All other representations (ISO 639-1 code, ISO 639-2/T
variant, english/native names, common spellings) live in ``aliases`` and are
used by ``matches()`` for case-insensitive lookup.
Equality and hashing are based solely on ``iso`` so two Language objects with
the same canonical code are interchangeable regardless of aliases.
"""
iso: str
english_name: str
native_name: str
aliases: tuple[str, ...] = ()
def __post_init__(self):
if not isinstance(self.iso, str) or not self.iso:
raise ValidationError(
f"Language.iso must be a non-empty string, got {self.iso!r}"
)
if len(self.iso) != 3:
raise ValidationError(
f"Language.iso must be a 3-letter ISO 639-2/B code, got {self.iso!r}"
)
# Normalize iso to lowercase
object.__setattr__(self, "iso", self.iso.lower())
# Normalize aliases to a tuple of lowercase strings (dedup, preserve order)
seen: set[str] = set()
normalized: list[str] = []
for alias in self.aliases:
if not isinstance(alias, str):
continue
a = alias.lower().strip()
if a and a not in seen:
seen.add(a)
normalized.append(a)
object.__setattr__(self, "aliases", tuple(normalized))
def matches(self, raw: str) -> bool:
"""
True if ``raw`` is any known representation of this language.
Comparison is case-insensitive and whitespace-trimmed. The match space is
the union of the canonical ``iso`` code, the english/native names, and
every alias.
"""
if not isinstance(raw, str):
return False
needle = raw.lower().strip()
if not needle:
return False
if needle == self.iso:
return True
if needle == self.english_name.lower():
return True
if needle == self.native_name.lower():
return True
return needle in self.aliases
def __eq__(self, other: object) -> bool:
if not isinstance(other, Language):
return NotImplemented
return self.iso == other.iso
def __hash__(self) -> int:
return hash(self.iso)
def __str__(self) -> str:
return self.iso
def __repr__(self) -> str:
return f"Language({self.iso!r}, {self.english_name!r})"
# Characters allowed in dot-separated folder/filename forms:
# alphanumerics, underscores, spaces (about to be replaced with dots),
# literal dots, and hyphens. Everything else is stripped.
_FS_SAFE_CHARS = re.compile(r"[^\w\s\.\-]")
def to_dot_folder_name(title: str) -> str:
"""Sanitize ``title`` for filesystem use and convert spaces to dots.
Produces e.g. ``Breaking.Bad`` from ``"Breaking Bad"`` or
``Spider.Man`` from ``"Spider-Man: No Way Home"``.
"""
return _FS_SAFE_CHARS.sub("", title).replace(" ", ".")

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