feat!: migrate to OpenAI native tool calls and fix circular deps (#fuck-gemini)

- Fix circular dependencies in agent/tools
- Migrate from custom JSON to OpenAI tool calls format
- Add async streaming (step_stream, complete_stream)
- Simplify prompt system and remove token counting
- Add 5 new API endpoints (/health, /v1/models, /api/memory/*)
- Add 3 new tools (get_torrent_by_index, add_torrent_by_index, set_language)
- Fix all 500 tests and add coverage config (80% threshold)
- Add comprehensive docs (README, pytest guide)

BREAKING: LLM interface changed, memory injection via get_memory()
This commit is contained in:
2025-12-06 19:11:05 +01:00
parent 2c8cdd3ab1
commit 9ca31e45e0
92 changed files with 7897 additions and 1786 deletions
+24
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@@ -1 +1,25 @@
"""Persistence layer - Data storage implementations."""
from .context import (
get_memory,
has_memory,
init_memory,
set_memory,
)
from .memory import (
EpisodicMemory,
LongTermMemory,
Memory,
ShortTermMemory,
)
__all__ = [
"Memory",
"LongTermMemory",
"ShortTermMemory",
"EpisodicMemory",
"init_memory",
"set_memory",
"get_memory",
"has_memory",
]
+79
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@@ -0,0 +1,79 @@
"""
Memory context using contextvars.
Provides thread-safe and async-safe access to the Memory instance
without passing it explicitly through all function calls.
Usage:
# At application startup
from infrastructure.persistence import init_memory, get_memory
init_memory("memory_data")
# Anywhere in the code
memory = get_memory()
memory.ltm.set_config("key", "value")
"""
from contextvars import ContextVar
from .memory import Memory
_memory_ctx: ContextVar[Memory | None] = ContextVar("memory", default=None)
def init_memory(storage_dir: str = "memory_data") -> Memory:
"""
Initialize the memory and set it in the context.
Call this once at application startup.
Args:
storage_dir: Directory for persistent storage.
Returns:
The initialized Memory instance.
"""
memory = Memory(storage_dir=storage_dir)
_memory_ctx.set(memory)
return memory
def set_memory(memory: Memory) -> None:
"""
Set an existing Memory instance in the context.
Useful for testing or when injecting a specific instance.
Args:
memory: Memory instance to set.
"""
_memory_ctx.set(memory)
def get_memory() -> Memory:
"""
Get the Memory instance from the context.
Returns:
The Memory instance.
Raises:
RuntimeError: If memory has not been initialized.
"""
memory = _memory_ctx.get()
if memory is None:
raise RuntimeError(
"Memory not initialized. Call init_memory() at application startup."
)
return memory
def has_memory() -> bool:
"""
Check if memory has been initialized.
Returns:
True if memory is available, False otherwise.
"""
return _memory_ctx.get() is not None
+2 -1
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@@ -1,7 +1,8 @@
"""JSON-based repository implementations."""
from .movie_repository import JsonMovieRepository
from .tvshow_repository import JsonTVShowRepository
from .subtitle_repository import JsonSubtitleRepository
from .tvshow_repository import JsonTVShowRepository
__all__ = [
"JsonMovieRepository",
@@ -1,11 +1,14 @@
"""JSON-based movie repository implementation."""
from typing import List, Optional, Dict, Any
import logging
from domain.movies.repositories import MovieRepository
import logging
from datetime import datetime
from typing import Any
from domain.movies.entities import Movie
from domain.shared.value_objects import ImdbId
from ..memory import Memory
from domain.movies.repositories import MovieRepository
from domain.movies.value_objects import MovieTitle, Quality, ReleaseYear
from domain.shared.value_objects import FilePath, FileSize, ImdbId
from infrastructure.persistence import get_memory
logger = logging.getLogger(__name__)
@@ -13,103 +16,129 @@ logger = logging.getLogger(__name__)
class JsonMovieRepository(MovieRepository):
"""
JSON-based implementation of MovieRepository.
Stores movies in the memory.json file.
Stores movies in the LTM library using the memory context.
"""
def __init__(self, memory: Memory):
"""
Initialize repository.
Args:
memory: Memory instance for persistence
"""
self.memory = memory
def save(self, movie: Movie) -> None:
"""Save a movie to the repository."""
movies = self._load_all()
"""
Save a movie to the repository.
Updates existing movie if IMDb ID matches.
Args:
movie: Movie entity to save.
"""
memory = get_memory()
movies = memory.ltm.library.get("movies", [])
# Remove existing movie with same IMDb ID
movies = [m for m in movies if m.get('imdb_id') != str(movie.imdb_id)]
# Add new movie
movies = [m for m in movies if m.get("imdb_id") != str(movie.imdb_id)]
movies.append(self._to_dict(movie))
# Save to memory
self.memory.set('movies', movies)
memory.ltm.library["movies"] = movies
memory.save()
logger.debug(f"Saved movie: {movie.imdb_id}")
def find_by_imdb_id(self, imdb_id: ImdbId) -> Optional[Movie]:
"""Find a movie by its IMDb ID."""
movies = self._load_all()
def find_by_imdb_id(self, imdb_id: ImdbId) -> Movie | None:
"""
Find a movie by its IMDb ID.
Args:
imdb_id: IMDb ID to search for.
Returns:
Movie if found, None otherwise.
"""
memory = get_memory()
movies = memory.ltm.library.get("movies", [])
for movie_dict in movies:
if movie_dict.get('imdb_id') == str(imdb_id):
if movie_dict.get("imdb_id") == str(imdb_id):
return self._from_dict(movie_dict)
return None
def find_all(self) -> List[Movie]:
"""Get all movies in the repository."""
movies_dict = self._load_all()
def find_all(self) -> list[Movie]:
"""
Get all movies in the repository.
Returns:
List of all Movie entities.
"""
memory = get_memory()
movies_dict = memory.ltm.library.get("movies", [])
return [self._from_dict(m) for m in movies_dict]
def delete(self, imdb_id: ImdbId) -> bool:
"""Delete a movie from the repository."""
movies = self._load_all()
"""
Delete a movie from the repository.
Args:
imdb_id: IMDb ID of movie to delete.
Returns:
True if deleted, False if not found.
"""
memory = get_memory()
movies = memory.ltm.library.get("movies", [])
initial_count = len(movies)
# Filter out the movie
movies = [m for m in movies if m.get('imdb_id') != str(imdb_id)]
movies = [m for m in movies if m.get("imdb_id") != str(imdb_id)]
if len(movies) < initial_count:
self.memory.set('movies', movies)
memory.ltm.library["movies"] = movies
memory.save()
logger.debug(f"Deleted movie: {imdb_id}")
return True
return False
def exists(self, imdb_id: ImdbId) -> bool:
"""Check if a movie exists in the repository."""
"""
Check if a movie exists in the repository.
Args:
imdb_id: IMDb ID to check.
Returns:
True if exists, False otherwise.
"""
return self.find_by_imdb_id(imdb_id) is not None
def _load_all(self) -> List[Dict[str, Any]]:
"""Load all movies from memory."""
return self.memory.get('movies', [])
def _to_dict(self, movie: Movie) -> Dict[str, Any]:
def _to_dict(self, movie: Movie) -> dict[str, Any]:
"""Convert Movie entity to dict for storage."""
return {
'imdb_id': str(movie.imdb_id),
'title': movie.title.value,
'release_year': movie.release_year.value if movie.release_year else None,
'quality': movie.quality.value,
'file_path': str(movie.file_path) if movie.file_path else None,
'file_size': movie.file_size.bytes if movie.file_size else None,
'tmdb_id': movie.tmdb_id,
'overview': movie.overview,
'poster_path': movie.poster_path,
'vote_average': movie.vote_average,
'added_at': movie.added_at.isoformat(),
"imdb_id": str(movie.imdb_id),
"title": movie.title.value,
"release_year": movie.release_year.value if movie.release_year else None,
"quality": movie.quality.value,
"file_path": str(movie.file_path) if movie.file_path else None,
"file_size": movie.file_size.bytes if movie.file_size else None,
"tmdb_id": movie.tmdb_id,
"added_at": movie.added_at.isoformat(),
}
def _from_dict(self, data: Dict[str, Any]) -> Movie:
def _from_dict(self, data: dict[str, Any]) -> Movie:
"""Convert dict from storage to Movie entity."""
from domain.movies.value_objects import MovieTitle, ReleaseYear, Quality
from domain.shared.value_objects import FilePath, FileSize
from datetime import datetime
# Parse quality string to enum
quality_str = data.get("quality", "unknown")
quality = Quality.from_string(quality_str)
return Movie(
imdb_id=ImdbId(data['imdb_id']),
title=MovieTitle(data['title']),
release_year=ReleaseYear(data['release_year']) if data.get('release_year') else None,
quality=Quality(data.get('quality', 'unknown')),
file_path=FilePath(data['file_path']) if data.get('file_path') else None,
file_size=FileSize(data['file_size']) if data.get('file_size') else None,
tmdb_id=data.get('tmdb_id'),
overview=data.get('overview'),
poster_path=data.get('poster_path'),
vote_average=data.get('vote_average'),
added_at=datetime.fromisoformat(data['added_at']) if data.get('added_at') else datetime.now(),
imdb_id=ImdbId(data["imdb_id"]),
title=MovieTitle(data["title"]),
release_year=(
ReleaseYear(data["release_year"]) if data.get("release_year") else None
),
quality=quality,
file_path=FilePath(data["file_path"]) if data.get("file_path") else None,
file_size=FileSize(data["file_size"]) if data.get("file_size") else None,
tmdb_id=data.get("tmdb_id"),
added_at=(
datetime.fromisoformat(data["added_at"])
if data.get("added_at")
else datetime.now()
),
)
@@ -1,12 +1,13 @@
"""JSON-based subtitle repository implementation."""
from typing import List, Optional, Dict, Any
import logging
from domain.subtitles.repositories import SubtitleRepository
import logging
from typing import Any
from domain.shared.value_objects import FilePath, ImdbId
from domain.subtitles.entities import Subtitle
from domain.subtitles.repositories import SubtitleRepository
from domain.subtitles.value_objects import Language, SubtitleFormat, TimingOffset
from domain.shared.value_objects import ImdbId, FilePath
from ..memory import Memory
from infrastructure.persistence import get_memory
logger = logging.getLogger(__name__)
@@ -14,114 +15,130 @@ logger = logging.getLogger(__name__)
class JsonSubtitleRepository(SubtitleRepository):
"""
JSON-based implementation of SubtitleRepository.
Stores subtitles in the memory.json file.
Stores subtitles in the LTM library using the memory context.
"""
def __init__(self, memory: Memory):
"""
Initialize repository.
Args:
memory: Memory instance for persistence
"""
self.memory = memory
def save(self, subtitle: Subtitle) -> None:
"""Save a subtitle to the repository."""
subtitles = self._load_all()
# Add new subtitle (we allow multiple subtitles for same media)
"""
Save a subtitle to the repository.
Multiple subtitles can exist for the same media.
Args:
subtitle: Subtitle entity to save.
"""
memory = get_memory()
subtitles = memory.ltm.library.get("subtitles", [])
subtitles.append(self._to_dict(subtitle))
# Save to memory
self.memory.set('subtitles', subtitles)
if "subtitles" not in memory.ltm.library:
memory.ltm.library["subtitles"] = []
memory.ltm.library["subtitles"] = subtitles
memory.save()
logger.debug(f"Saved subtitle for: {subtitle.media_imdb_id}")
def find_by_media(
self,
media_imdb_id: ImdbId,
language: Optional[Language] = None,
season: Optional[int] = None,
episode: Optional[int] = None
) -> List[Subtitle]:
"""Find subtitles for a media item."""
subtitles = self._load_all()
language: Language | None = None,
season: int | None = None,
episode: int | None = None,
) -> list[Subtitle]:
"""
Find subtitles for a media item.
Args:
media_imdb_id: IMDb ID of the media.
language: Optional language filter.
season: Optional season number filter.
episode: Optional episode number filter.
Returns:
List of matching Subtitle entities.
"""
memory = get_memory()
subtitles = memory.ltm.library.get("subtitles", [])
results = []
for sub_dict in subtitles:
# Filter by IMDb ID
if sub_dict.get('media_imdb_id') != str(media_imdb_id):
if sub_dict.get("media_imdb_id") != str(media_imdb_id):
continue
# Filter by language if specified
if language and sub_dict.get('language') != language.value:
if language and sub_dict.get("language") != language.value:
continue
# Filter by season/episode if specified
if season is not None and sub_dict.get('season_number') != season:
if season is not None and sub_dict.get("season_number") != season:
continue
if episode is not None and sub_dict.get('episode_number') != episode:
if episode is not None and sub_dict.get("episode_number") != episode:
continue
results.append(self._from_dict(sub_dict))
return results
def delete(self, subtitle: Subtitle) -> bool:
"""Delete a subtitle from the repository."""
subtitles = self._load_all()
"""
Delete a subtitle from the repository.
Matches by file path.
Args:
subtitle: Subtitle entity to delete.
Returns:
True if deleted, False if not found.
"""
memory = get_memory()
subtitles = memory.ltm.library.get("subtitles", [])
initial_count = len(subtitles)
# Filter out the subtitle (match by file path)
subtitles = [
s for s in subtitles
if s.get('file_path') != str(subtitle.file_path)
s for s in subtitles if s.get("file_path") != str(subtitle.file_path)
]
if len(subtitles) < initial_count:
self.memory.set('subtitles', subtitles)
memory.ltm.library["subtitles"] = subtitles
memory.save()
logger.debug(f"Deleted subtitle: {subtitle.file_path}")
return True
return False
def _load_all(self) -> List[Dict[str, Any]]:
"""Load all subtitles from memory."""
return self.memory.get('subtitles', [])
def _to_dict(self, subtitle: Subtitle) -> Dict[str, Any]:
def _to_dict(self, subtitle: Subtitle) -> dict[str, Any]:
"""Convert Subtitle entity to dict for storage."""
return {
'media_imdb_id': str(subtitle.media_imdb_id),
'language': subtitle.language.value,
'format': subtitle.format.value,
'file_path': str(subtitle.file_path),
'season_number': subtitle.season_number,
'episode_number': subtitle.episode_number,
'timing_offset': subtitle.timing_offset.milliseconds,
'hearing_impaired': subtitle.hearing_impaired,
'forced': subtitle.forced,
'source': subtitle.source,
'uploader': subtitle.uploader,
'download_count': subtitle.download_count,
'rating': subtitle.rating,
"media_imdb_id": str(subtitle.media_imdb_id),
"language": subtitle.language.value,
"format": subtitle.format.value,
"file_path": str(subtitle.file_path),
"season_number": subtitle.season_number,
"episode_number": subtitle.episode_number,
"timing_offset": subtitle.timing_offset.milliseconds,
"hearing_impaired": subtitle.hearing_impaired,
"forced": subtitle.forced,
"source": subtitle.source,
"uploader": subtitle.uploader,
"download_count": subtitle.download_count,
"rating": subtitle.rating,
}
def _from_dict(self, data: Dict[str, Any]) -> Subtitle:
def _from_dict(self, data: dict[str, Any]) -> Subtitle:
"""Convert dict from storage to Subtitle entity."""
return Subtitle(
media_imdb_id=ImdbId(data['media_imdb_id']),
language=Language.from_code(data['language']),
format=SubtitleFormat.from_extension(data['format']),
file_path=FilePath(data['file_path']),
season_number=data.get('season_number'),
episode_number=data.get('episode_number'),
timing_offset=TimingOffset(data.get('timing_offset', 0)),
hearing_impaired=data.get('hearing_impaired', False),
forced=data.get('forced', False),
source=data.get('source'),
uploader=data.get('uploader'),
download_count=data.get('download_count'),
rating=data.get('rating'),
media_imdb_id=ImdbId(data["media_imdb_id"]),
language=Language.from_code(data["language"]),
format=SubtitleFormat.from_extension(data["format"]),
file_path=FilePath(data["file_path"]),
season_number=data.get("season_number"),
episode_number=data.get("episode_number"),
timing_offset=TimingOffset(data.get("timing_offset", 0)),
hearing_impaired=data.get("hearing_impaired", False),
forced=data.get("forced", False),
source=data.get("source"),
uploader=data.get("uploader"),
download_count=data.get("download_count"),
rating=data.get("rating"),
)
@@ -1,12 +1,14 @@
"""JSON-based TV show repository implementation."""
from typing import List, Optional, Dict, Any
import logging
from domain.tv_shows.repositories import TVShowRepository
from domain.tv_shows.entities import TVShow
from domain.tv_shows.value_objects import ShowStatus
import logging
from datetime import datetime
from typing import Any
from domain.shared.value_objects import ImdbId
from ..memory import Memory
from domain.tv_shows.entities import TVShow
from domain.tv_shows.repositories import TVShowRepository
from domain.tv_shows.value_objects import ShowStatus
from infrastructure.persistence import get_memory
logger = logging.getLogger(__name__)
@@ -14,99 +16,121 @@ logger = logging.getLogger(__name__)
class JsonTVShowRepository(TVShowRepository):
"""
JSON-based implementation of TVShowRepository.
Stores TV shows in the memory.json file (compatible with existing tv_shows structure).
Stores TV shows in the LTM library using the memory context.
"""
def __init__(self, memory: Memory):
"""
Initialize repository.
Args:
memory: Memory instance for persistence
"""
self.memory = memory
def save(self, show: TVShow) -> None:
"""Save a TV show to the repository."""
shows = self._load_all()
"""
Save a TV show to the repository.
Updates existing show if IMDb ID matches.
Args:
show: TVShow entity to save.
"""
memory = get_memory()
shows = memory.ltm.library.get("tv_shows", [])
# Remove existing show with same IMDb ID
shows = [s for s in shows if s.get('imdb_id') != str(show.imdb_id)]
# Add new show
shows = [s for s in shows if s.get("imdb_id") != str(show.imdb_id)]
shows.append(self._to_dict(show))
# Save to memory
self.memory.set('tv_shows', shows)
memory.ltm.library["tv_shows"] = shows
memory.save()
logger.debug(f"Saved TV show: {show.imdb_id}")
def find_by_imdb_id(self, imdb_id: ImdbId) -> Optional[TVShow]:
"""Find a TV show by its IMDb ID."""
shows = self._load_all()
def find_by_imdb_id(self, imdb_id: ImdbId) -> TVShow | None:
"""
Find a TV show by its IMDb ID.
Args:
imdb_id: IMDb ID to search for.
Returns:
TVShow if found, None otherwise.
"""
memory = get_memory()
shows = memory.ltm.library.get("tv_shows", [])
for show_dict in shows:
if show_dict.get('imdb_id') == str(imdb_id):
if show_dict.get("imdb_id") == str(imdb_id):
return self._from_dict(show_dict)
return None
def find_all(self) -> List[TVShow]:
"""Get all TV shows in the repository."""
shows_dict = self._load_all()
def find_all(self) -> list[TVShow]:
"""
Get all TV shows in the repository.
Returns:
List of all TVShow entities.
"""
memory = get_memory()
shows_dict = memory.ltm.library.get("tv_shows", [])
return [self._from_dict(s) for s in shows_dict]
def delete(self, imdb_id: ImdbId) -> bool:
"""Delete a TV show from the repository."""
shows = self._load_all()
"""
Delete a TV show from the repository.
Args:
imdb_id: IMDb ID of show to delete.
Returns:
True if deleted, False if not found.
"""
memory = get_memory()
shows = memory.ltm.library.get("tv_shows", [])
initial_count = len(shows)
# Filter out the show
shows = [s for s in shows if s.get('imdb_id') != str(imdb_id)]
shows = [s for s in shows if s.get("imdb_id") != str(imdb_id)]
if len(shows) < initial_count:
self.memory.set('tv_shows', shows)
memory.ltm.library["tv_shows"] = shows
memory.save()
logger.debug(f"Deleted TV show: {imdb_id}")
return True
return False
def exists(self, imdb_id: ImdbId) -> bool:
"""Check if a TV show exists in the repository."""
"""
Check if a TV show exists in the repository.
Args:
imdb_id: IMDb ID to check.
Returns:
True if exists, False otherwise.
"""
return self.find_by_imdb_id(imdb_id) is not None
def _load_all(self) -> List[Dict[str, Any]]:
"""Load all TV shows from memory."""
return self.memory.get('tv_shows', [])
def _to_dict(self, show: TVShow) -> Dict[str, Any]:
def _to_dict(self, show: TVShow) -> dict[str, Any]:
"""Convert TVShow entity to dict for storage."""
return {
'imdb_id': str(show.imdb_id),
'title': show.title,
'seasons_count': show.seasons_count,
'status': show.status.value,
'tmdb_id': show.tmdb_id,
'overview': show.overview,
'poster_path': show.poster_path,
'first_air_date': show.first_air_date,
'vote_average': show.vote_average,
'added_at': show.added_at.isoformat(),
"imdb_id": str(show.imdb_id),
"title": show.title,
"seasons_count": show.seasons_count,
"status": show.status.value,
"tmdb_id": show.tmdb_id,
"first_air_date": show.first_air_date,
"added_at": show.added_at.isoformat(),
}
def _from_dict(self, data: Dict[str, Any]) -> TVShow:
def _from_dict(self, data: dict[str, Any]) -> TVShow:
"""Convert dict from storage to TVShow entity."""
from datetime import datetime
return TVShow(
imdb_id=ImdbId(data['imdb_id']),
title=data['title'],
seasons_count=data['seasons_count'],
status=ShowStatus.from_string(data['status']),
tmdb_id=data.get('tmdb_id'),
overview=data.get('overview'),
poster_path=data.get('poster_path'),
first_air_date=data.get('first_air_date'),
vote_average=data.get('vote_average'),
added_at=datetime.fromisoformat(data['added_at']) if data.get('added_at') else datetime.now(),
imdb_id=ImdbId(data["imdb_id"]),
title=data["title"],
seasons_count=data["seasons_count"],
status=ShowStatus.from_string(data["status"]),
tmdb_id=data.get("tmdb_id"),
first_air_date=data.get("first_air_date"),
added_at=(
datetime.fromisoformat(data["added_at"])
if data.get("added_at")
else datetime.now()
),
)
+555 -70
View File
@@ -1,86 +1,571 @@
"""Memory storage - Migrated from agent/memory.py"""
from pathlib import Path
from typing import Any, Dict
import json
"""
Memory - Unified management of 3 memory types.
from agent.config import settings
from agent.parameters import validate_parameter, get_parameter_schema
Architecture:
- LTM (Long-Term Memory): Configuration, library, preferences - Persistent
- STM (Short-Term Memory): Conversation, current workflow - Volatile
- Episodic Memory: Search results, transient states - Very volatile
"""
import json
import logging
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
# =============================================================================
# LONG-TERM MEMORY (LTM) - Persistent
# =============================================================================
@dataclass
class LongTermMemory:
"""
Long-term memory - Persistent and static.
Stores:
- User configuration (folders, URLs)
- Preferences (quality, languages)
- Library (owned movies/TV shows)
- Followed shows (watchlist)
"""
# Folder and service configuration
config: dict[str, str] = field(default_factory=dict)
# User preferences
preferences: dict[str, Any] = field(
default_factory=lambda: {
"preferred_quality": "1080p",
"preferred_languages": ["en", "fr"],
"auto_organize": False,
"naming_format": "{title}.{year}.{quality}",
}
)
# Library of owned media
library: dict[str, list[dict]] = field(
default_factory=lambda: {"movies": [], "tv_shows": []}
)
# Followed shows (watchlist)
following: list[dict] = field(default_factory=list)
def get_config(self, key: str, default: Any = None) -> Any:
"""Get a configuration value."""
return self.config.get(key, default)
def set_config(self, key: str, value: Any) -> None:
"""Set a configuration value."""
self.config[key] = value
logger.debug(f"LTM: Set config {key}")
def has_config(self, key: str) -> bool:
"""Check if a configuration exists."""
return key in self.config and self.config[key] is not None
def add_to_library(self, media_type: str, media: dict) -> None:
"""Add a media item to the library."""
if media_type not in self.library:
self.library[media_type] = []
# Avoid duplicates by imdb_id
existing_ids = [m.get("imdb_id") for m in self.library[media_type]]
if media.get("imdb_id") not in existing_ids:
media["added_at"] = datetime.now().isoformat()
self.library[media_type].append(media)
logger.info(f"LTM: Added {media.get('title')} to {media_type}")
def get_library(self, media_type: str) -> list[dict]:
"""Get the library for a media type."""
return self.library.get(media_type, [])
def follow_show(self, show: dict) -> None:
"""Add a show to the watchlist."""
existing_ids = [s.get("imdb_id") for s in self.following]
if show.get("imdb_id") not in existing_ids:
show["followed_at"] = datetime.now().isoformat()
self.following.append(show)
logger.info(f"LTM: Now following {show.get('title')}")
def to_dict(self) -> dict:
"""Convert to dictionary for serialization."""
return {
"config": self.config,
"preferences": self.preferences,
"library": self.library,
"following": self.following,
}
@classmethod
def from_dict(cls, data: dict) -> "LongTermMemory":
"""Create an instance from a dictionary."""
return cls(
config=data.get("config", {}),
preferences=data.get(
"preferences",
{
"preferred_quality": "1080p",
"preferred_languages": ["en", "fr"],
"auto_organize": False,
"naming_format": "{title}.{year}.{quality}",
},
),
library=data.get("library", {"movies": [], "tv_shows": []}),
following=data.get("following", []),
)
# =============================================================================
# SHORT-TERM MEMORY (STM) - Conversation
# =============================================================================
@dataclass
class ShortTermMemory:
"""
Short-term memory - Volatile and conversational.
Stores:
- Current conversation history
- Current workflow (what we're doing)
- Extracted entities from conversation
- Current discussion topic
"""
# Conversation message history
conversation_history: list[dict[str, str]] = field(default_factory=list)
# Current workflow
current_workflow: dict | None = None
# Extracted entities (title, year, requested quality, etc.)
extracted_entities: dict[str, Any] = field(default_factory=dict)
# Current conversation topic
current_topic: str | None = None
# History message limit
max_history: int = 20
def add_message(self, role: str, content: str) -> None:
"""Add a message to history."""
self.conversation_history.append(
{"role": role, "content": content, "timestamp": datetime.now().isoformat()}
)
# Keep only the last N messages
if len(self.conversation_history) > self.max_history:
self.conversation_history = self.conversation_history[-self.max_history :]
logger.debug(f"STM: Added {role} message")
def get_recent_history(self, n: int = 10) -> list[dict]:
"""Get the last N messages."""
return self.conversation_history[-n:]
def start_workflow(self, workflow_type: str, target: dict) -> None:
"""Start a new workflow."""
self.current_workflow = {
"type": workflow_type,
"target": target,
"stage": "started",
"started_at": datetime.now().isoformat(),
}
logger.info(f"STM: Started workflow '{workflow_type}'")
def update_workflow_stage(self, stage: str) -> None:
"""Update the workflow stage."""
if self.current_workflow:
self.current_workflow["stage"] = stage
logger.debug(f"STM: Workflow stage -> {stage}")
def end_workflow(self) -> None:
"""End the current workflow."""
if self.current_workflow:
logger.info(f"STM: Ended workflow '{self.current_workflow.get('type')}'")
self.current_workflow = None
def set_entity(self, key: str, value: Any) -> None:
"""Store an extracted entity."""
self.extracted_entities[key] = value
logger.debug(f"STM: Set entity {key}={value}")
def get_entity(self, key: str, default: Any = None) -> Any:
"""Get an extracted entity."""
return self.extracted_entities.get(key, default)
def clear_entities(self) -> None:
"""Clear extracted entities."""
self.extracted_entities = {}
def set_topic(self, topic: str) -> None:
"""Set the current topic."""
self.current_topic = topic
logger.debug(f"STM: Topic -> {topic}")
def clear(self) -> None:
"""Reset short-term memory."""
self.conversation_history = []
self.current_workflow = None
self.extracted_entities = {}
self.current_topic = None
logger.info("STM: Cleared")
def to_dict(self) -> dict:
"""Convert to dictionary."""
return {
"conversation_history": self.conversation_history,
"current_workflow": self.current_workflow,
"extracted_entities": self.extracted_entities,
"current_topic": self.current_topic,
}
# =============================================================================
# EPISODIC MEMORY - Transient states
# =============================================================================
@dataclass
class EpisodicMemory:
"""
Episodic/sensory memory - Temporary and event-driven.
Stores:
- Last search results
- Active downloads
- Recent errors
- Pending questions awaiting user response
- Background events
"""
# Last search results
last_search_results: dict | None = None
# Active downloads
active_downloads: list[dict] = field(default_factory=list)
# Recent errors
recent_errors: list[dict] = field(default_factory=list)
# Pending question awaiting user response
pending_question: dict | None = None
# Background events (download complete, new files, etc.)
background_events: list[dict] = field(default_factory=list)
# Limits for errors/events kept
max_errors: int = 5
max_events: int = 10
def store_search_results(
self, query: str, results: list[dict], search_type: str = "torrent"
) -> None:
"""
Store search results with index.
Args:
query: The search query
results: List of results
search_type: Type of search (torrent, movie, tvshow)
"""
self.last_search_results = {
"query": query,
"type": search_type,
"timestamp": datetime.now().isoformat(),
"results": [{"index": i + 1, **r} for i, r in enumerate(results)],
}
logger.info(f"Episodic: Stored {len(results)} search results for '{query}'")
def get_result_by_index(self, index: int) -> dict | None:
"""
Get a result by its number (1-indexed).
Args:
index: Result number (1, 2, 3, ...)
Returns:
The result or None if not found
"""
if not self.last_search_results:
logger.warning("Episodic: No search results stored")
return None
for result in self.last_search_results.get("results", []):
if result.get("index") == index:
return result
logger.warning(f"Episodic: Result #{index} not found")
return None
def get_search_results(self) -> dict | None:
"""Get the last search results."""
return self.last_search_results
def clear_search_results(self) -> None:
"""Clear search results."""
self.last_search_results = None
def add_active_download(self, download: dict) -> None:
"""Add an active download."""
download["started_at"] = datetime.now().isoformat()
self.active_downloads.append(download)
logger.info(f"Episodic: Added download '{download.get('name')}'")
def update_download_progress(
self, task_id: str, progress: int, status: str = "downloading"
) -> None:
"""Update download progress."""
for dl in self.active_downloads:
if dl.get("task_id") == task_id:
dl["progress"] = progress
dl["status"] = status
dl["updated_at"] = datetime.now().isoformat()
break
def complete_download(self, task_id: str, file_path: str) -> dict | None:
"""Mark a download as complete and remove it."""
for i, dl in enumerate(self.active_downloads):
if dl.get("task_id") == task_id:
completed = self.active_downloads.pop(i)
completed["status"] = "completed"
completed["file_path"] = file_path
completed["completed_at"] = datetime.now().isoformat()
# Add a background event
self.add_background_event(
"download_complete",
{"name": completed.get("name"), "file_path": file_path},
)
logger.info(f"Episodic: Download completed '{completed.get('name')}'")
return completed
return None
def get_active_downloads(self) -> list[dict]:
"""Get active downloads."""
return self.active_downloads
def add_error(
self, action: str, error: str, context: dict | None = None
) -> None:
"""Record a recent error."""
self.recent_errors.append(
{
"timestamp": datetime.now().isoformat(),
"action": action,
"error": error,
"context": context or {},
}
)
# Keep only the last N errors
self.recent_errors = self.recent_errors[-self.max_errors :]
logger.warning(f"Episodic: Error in '{action}': {error}")
def get_recent_errors(self) -> list[dict]:
"""Get recent errors."""
return self.recent_errors
def set_pending_question(
self,
question: str,
options: list[dict],
context: dict,
question_type: str = "choice",
) -> None:
"""
Record a question awaiting user response.
Args:
question: The question asked
options: List of possible options
context: Question context
question_type: Type of question (choice, confirmation, input)
"""
self.pending_question = {
"type": question_type,
"question": question,
"options": options,
"context": context,
"timestamp": datetime.now().isoformat(),
}
logger.info(f"Episodic: Pending question set ({question_type})")
def get_pending_question(self) -> dict | None:
"""Get the pending question."""
return self.pending_question
def resolve_pending_question(
self, answer_index: int | None = None
) -> dict | None:
"""
Resolve the pending question and return the chosen option.
Args:
answer_index: Answer index (1-indexed) or None to cancel
Returns:
The chosen option or None
"""
if not self.pending_question:
return None
result = None
if answer_index is not None and self.pending_question.get("options"):
for opt in self.pending_question["options"]:
if opt.get("index") == answer_index:
result = opt
break
self.pending_question = None
logger.info("Episodic: Pending question resolved")
return result
def add_background_event(self, event_type: str, data: dict) -> None:
"""Add a background event."""
self.background_events.append(
{
"type": event_type,
"timestamp": datetime.now().isoformat(),
"data": data,
"read": False,
}
)
# Keep only the last N events
self.background_events = self.background_events[-self.max_events :]
logger.info(f"Episodic: Background event '{event_type}'")
def get_unread_events(self) -> list[dict]:
"""Get unread events and mark them as read."""
unread = [e for e in self.background_events if not e.get("read")]
for e in self.background_events:
e["read"] = True
return unread
def clear(self) -> None:
"""Reset episodic memory."""
self.last_search_results = None
self.active_downloads = []
self.recent_errors = []
self.pending_question = None
self.background_events = []
logger.info("Episodic: Cleared")
def to_dict(self) -> dict:
"""Convert to dictionary."""
return {
"last_search_results": self.last_search_results,
"active_downloads": self.active_downloads,
"recent_errors": self.recent_errors,
"pending_question": self.pending_question,
"background_events": self.background_events,
}
# =============================================================================
# MEMORY MANAGER - Unified manager
# =============================================================================
class Memory:
"""
Generic memory storage for agent state.
Unified manager for the 3 memory types.
Provides a simple key-value store that persists to JSON.
Usage:
memory = Memory("memory_data")
memory.ltm.set_config("download_folder", "/path")
memory.stm.add_message("user", "Hello")
memory.episodic.store_search_results("query", results)
memory.save()
"""
def __init__(self, path: str = "memory.json"):
self.file = Path(path)
self.data: Dict[str, Any] = {}
self.load()
def __init__(self, storage_dir: str = "memory_data"):
"""
Initialize the memory.
def load(self) -> None:
"""Load memory from file or initialize with defaults."""
if self.file.exists():
Args:
storage_dir: Directory for persistent storage
"""
self.storage_dir = Path(storage_dir)
self.storage_dir.mkdir(exist_ok=True)
self.ltm_file = self.storage_dir / "ltm.json"
# Initialize the 3 memory types
self.ltm = self._load_ltm()
self.stm = ShortTermMemory()
self.episodic = EpisodicMemory()
logger.info(f"Memory initialized (storage: {storage_dir})")
def _load_ltm(self) -> LongTermMemory:
"""Load LTM from file."""
if self.ltm_file.exists():
try:
self.data = json.loads(self.file.read_text(encoding="utf-8"))
except (json.JSONDecodeError, IOError) as e:
print(f"Warning: Could not load memory file: {e}")
self.data = {
"config": {},
"tv_shows": [],
"history": [],
}
else:
self.data = {
"config": {},
"tv_shows": [],
"history": [],
}
data = json.loads(self.ltm_file.read_text(encoding="utf-8"))
logger.info("LTM loaded from file")
return LongTermMemory.from_dict(data)
except (OSError, json.JSONDecodeError) as e:
logger.warning(f"Could not load LTM: {e}")
return LongTermMemory()
def save(self) -> None:
self.file.write_text(
json.dumps(self.data, indent=2, ensure_ascii=False),
encoding="utf-8",
)
"""Save LTM (the only persistent memory)."""
try:
self.ltm_file.write_text(
json.dumps(self.ltm.to_dict(), indent=2, ensure_ascii=False),
encoding="utf-8",
)
logger.debug("LTM saved to file")
except OSError as e:
logger.error(f"Failed to save LTM: {e}")
raise
def get(self, key: str, default: Any = None) -> Any:
"""Get a value from memory by key."""
return self.data.get(key, default)
def get_context_for_prompt(self) -> dict:
"""
Generate context to include in the system prompt.
def set(self, key: str, value: Any) -> None:
Returns:
Dictionary with relevant context from all 3 memories
"""
Set a value in memory and save.
Validates the value against the parameter schema if one exists.
"""
# Validate if schema exists
is_valid, error_msg = validate_parameter(key, value)
if not is_valid:
print(f'Validation failed for {key}: {error_msg}')
raise ValueError(f"Invalid value for {key}: {error_msg}")
print(f'Setting {key} in memory to: {value}')
self.data[key] = value
self.save()
return {
"config": self.ltm.config,
"preferences": self.ltm.preferences,
"current_workflow": self.stm.current_workflow,
"current_topic": self.stm.current_topic,
"extracted_entities": self.stm.extracted_entities,
"last_search": {
"query": (
self.episodic.last_search_results.get("query")
if self.episodic.last_search_results
else None
),
"result_count": (
len(self.episodic.last_search_results.get("results", []))
if self.episodic.last_search_results
else 0
),
},
"active_downloads_count": len(self.episodic.active_downloads),
"pending_question": self.episodic.pending_question is not None,
"unread_events": len(
[e for e in self.episodic.background_events if not e.get("read")]
),
}
def has(self, key: str) -> bool:
"""Check if a key exists and has a non-None value."""
return key in self.data and self.data[key] is not None
def append_history(self, role: str, content: str) -> None:
"""
Append a message to conversation history.
Args:
role: Message role ('user' or 'assistant')
content: Message content
"""
if "history" not in self.data:
self.data["history"] = []
self.data["history"].append({
"role": role,
"content": content
})
self.save()
def get_full_state(self) -> dict:
"""Return the full state of all 3 memories (for debug)."""
return {
"ltm": self.ltm.to_dict(),
"stm": self.stm.to_dict(),
"episodic": self.episodic.to_dict(),
}
def clear_session(self) -> None:
"""Clear session memories (STM + Episodic)."""
self.stm.clear()
self.episodic.clear()
logger.info("Session memories cleared")