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()
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"""API tools for interacting with external services - Adapted for DDD architecture."""
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from typing import Dict, Any
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"""API tools for interacting with external services."""
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import logging
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from typing import Any
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# Import use cases instead of direct API clients
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from application.movies import SearchMovieUseCase
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from application.torrents import SearchTorrentsUseCase, AddTorrentUseCase
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# Import infrastructure clients
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from infrastructure.api.tmdb import tmdb_client
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from application.torrents import AddTorrentUseCase, SearchTorrentsUseCase
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from infrastructure.api.knaben import knaben_client
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from infrastructure.api.qbittorrent import qbittorrent_client
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from infrastructure.api.tmdb import tmdb_client
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from infrastructure.persistence import get_memory
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logger = logging.getLogger(__name__)
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def find_media_imdb_id(media_title: str) -> Dict[str, Any]:
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def find_media_imdb_id(media_title: str) -> dict[str, Any]:
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"""
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Find the IMDb ID for a given media title using TMDB API.
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This is a wrapper that uses the SearchMovieUseCase.
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Args:
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media_title: Title of the media to search for
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media_title: Title of the media to search for.
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Returns:
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Dict with IMDb ID or error information
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Example:
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>>> result = find_media_imdb_id("Inception")
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>>> print(result)
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{'status': 'ok', 'imdb_id': 'tt1375666', 'title': 'Inception', ...}
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Dict with IMDb ID and media info, or error details.
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"""
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# Create use case with TMDB client
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use_case = SearchMovieUseCase(tmdb_client)
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# Execute use case
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response = use_case.execute(media_title)
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# Return as dict
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return response.to_dict()
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result = response.to_dict()
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if result.get("status") == "ok":
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memory = get_memory()
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memory.stm.set_entity(
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"last_media_search",
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{
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"title": result.get("title"),
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"imdb_id": result.get("imdb_id"),
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"media_type": result.get("media_type"),
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"tmdb_id": result.get("tmdb_id"),
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},
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)
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memory.stm.set_topic("searching_media")
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logger.debug(f"Stored media search result in STM: {result.get('title')}")
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return result
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def find_torrent(media_title: str) -> Dict[str, Any]:
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def find_torrent(media_title: str) -> dict[str, Any]:
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"""
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Find torrents for a given media title using Knaben API.
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This is a wrapper that uses the SearchTorrentsUseCase.
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Results are stored in episodic memory so the user can reference them
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by index (e.g., "download the 3rd one").
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Args:
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media_title: Title of the media to search for
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media_title: Title of the media to search for.
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Returns:
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Dict with torrent information or error details
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Dict with torrent list or error details.
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"""
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# Create use case with Knaben client
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logger.info(f"Searching torrents for: {media_title}")
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use_case = SearchTorrentsUseCase(knaben_client)
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# Execute use case
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response = use_case.execute(media_title, limit=10)
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# Return as dict
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return response.to_dict()
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result = response.to_dict()
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if result.get("status") == "ok":
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memory = get_memory()
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torrents = result.get("torrents", [])
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memory.episodic.store_search_results(
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query=media_title, results=torrents, search_type="torrent"
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)
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memory.stm.set_topic("selecting_torrent")
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logger.info(f"Stored {len(torrents)} torrent results in episodic memory")
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return result
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def add_torrent_to_qbittorrent(magnet_link: str) -> Dict[str, Any]:
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def get_torrent_by_index(index: int) -> dict[str, Any]:
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"""
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Get a torrent from the last search results by its index.
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Allows the user to reference results by number after a search.
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Args:
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index: 1-based index of the torrent in the search results.
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Returns:
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Dict with torrent data or error if not found.
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"""
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logger.info(f"Getting torrent at index: {index}")
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memory = get_memory()
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if memory.episodic.last_search_results:
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results_count = len(memory.episodic.last_search_results.get("results", []))
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query = memory.episodic.last_search_results.get("query", "unknown")
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logger.debug(f"Episodic memory has {results_count} results from: {query}")
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else:
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logger.warning("No search results in episodic memory")
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result = memory.episodic.get_result_by_index(index)
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if result:
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logger.info(f"Found torrent at index {index}: {result.get('name', 'unknown')}")
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return {"status": "ok", "torrent": result}
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logger.warning(f"No torrent found at index {index}")
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return {
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"status": "error",
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"error": "not_found",
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"message": f"No torrent found at index {index}. Search for torrents first.",
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}
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def add_torrent_to_qbittorrent(magnet_link: str) -> dict[str, Any]:
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"""
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Add a torrent to qBittorrent using a magnet link.
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This is a wrapper that uses the AddTorrentUseCase.
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Args:
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magnet_link: Magnet link of the torrent to add
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magnet_link: Magnet link of the torrent to add.
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Returns:
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Dict with success or error information
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Example:
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>>> result = add_torrent_to_qbittorrent("magnet:?xt=urn:btih:...")
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>>> print(result)
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{'status': 'ok', 'message': 'Torrent added successfully'}
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Dict with success status or error details.
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"""
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# Create use case with qBittorrent client
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logger.info("Adding torrent to qBittorrent")
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use_case = AddTorrentUseCase(qbittorrent_client)
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# Execute use case
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response = use_case.execute(magnet_link)
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# Return as dict
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return response.to_dict()
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result = response.to_dict()
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if result.get("status") == "ok":
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memory = get_memory()
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last_search = memory.episodic.get_search_results()
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torrent_name = "Unknown"
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if last_search:
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for t in last_search.get("results", []):
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if t.get("magnet") == magnet_link:
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torrent_name = t.get("name", "Unknown")
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break
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memory.episodic.add_active_download(
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{
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"task_id": magnet_link[:20],
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"name": torrent_name,
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"magnet": magnet_link,
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"progress": 0,
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"status": "queued",
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}
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)
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memory.stm.set_topic("downloading")
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memory.stm.end_workflow()
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logger.info(f"Added download to episodic memory: {torrent_name}")
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return result
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def add_torrent_by_index(index: int) -> dict[str, Any]:
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"""
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Add a torrent from the last search results by its index.
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Combines get_torrent_by_index and add_torrent_to_qbittorrent.
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Args:
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index: 1-based index of the torrent in the search results.
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Returns:
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Dict with success status or error details.
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"""
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logger.info(f"Adding torrent by index: {index}")
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torrent_result = get_torrent_by_index(index)
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if torrent_result.get("status") != "ok":
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return torrent_result
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torrent = torrent_result.get("torrent", {})
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magnet = torrent.get("magnet")
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if not magnet:
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logger.error("Torrent has no magnet link")
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return {
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"status": "error",
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"error": "no_magnet",
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"message": "The selected torrent has no magnet link",
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}
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logger.info(f"Adding torrent: {torrent.get('name', 'unknown')}")
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result = add_torrent_to_qbittorrent(magnet)
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if result.get("status") == "ok":
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result["torrent_name"] = torrent.get("name", "Unknown")
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return result
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