8 Commits

Author	SHA1	Message	Date
gitea	3f4a1d5083	lore: bound pamiec.json by summarising old memory into "Legendy Baru" ... The conversation memory file grows forever (every chat appends a user+assistant pair), so startup load gets slower and the disk fills. New always-loaded cog lore_commands turns that growth into content: a background task summarises the oldest slice into one in-character "legend" via the ACTIVE AI backend, replaces those old messages with the summary (bounding the file, keeping continuity for the next startup's context), archives it to legendy.json, and announces it on Safety: the compaction transforms (build_transcript, apply_compaction) are pure and unit-tested. The file rewrite is re-read -> back up -> atomic write with no await in between, so a handle_response append that lands while the summary is being generated can neither be lost (it's in the preserved tail) nor corrupt the file (single-threaded, no interleave). A .bak is kept. Scope note: this bounds the on-disk file (startup/disk); the in-RAM MESSAGE_TABLE is a separate concern left untouched to avoid yanking context from a live conversation. Commands: $zapisz_legende (Vykidailo) forces a compaction now; $legendy recalls a random past legend. All thresholds env-overridable (CONJURER_MEMORY_COMPACT_*, CONJURER_LEGENDS_CHANNEL). constants gains LEGENDS_FILE + config + seed; bot.py registers the cog. Verified: tests/unit/test_lore_commands.py covers prefix-replace/tail-keep, preservation of appends made during summarisation, and transcript formatting + head/tail truncation. Unit job 32 passed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-31 20:12:59 +02:00
gitea	e9e731e2bd	bar: $nalej invents cocktails, $menu keeps the bar's growing lore ... The most in-character capability the bot has: the persona is literally a 200kg bartender who mixes strong drinks with intriguing names. New always-loaded cog bar_commands: * $nalej [motyw] asks the ACTIVE AI backend (whatever $gadaj_teraz selects) to invent one themed cocktail in Conjurer's voice - persona reused from GPT_SETTINGS[0] as a system message, instructions as the user turn, via handle_response request_type NONE so it never pollutes the bar's conversation memory. Empty motyw = a surprise; "radio"/"pod muzykę" themes the drink on the track currently playing (PREPPED_TRACKS["now_playing"]). * every drink is appended to menu.json (new seeded state file, CONJURER_MENU_FILE overridable) with name/theme/author/timestamp/full text - emergent bar lore. * $menu lists the invented drinks and pours one at random from the archive. Text-only for now; a DALL-E drink image is an easy follow-up (the render path already exists in ai_commands, OpenAI-only). constants gains MENU_FILE (next to pamiec.json by default) + its seed; bot.py registers bar_commands as a core cog. Verified: tests/unit/test_bar_commands.py covers name extraction (markers/markdown/fallback) and the menu round-trip incl. corrupt-file tolerance; unit job 32 passed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-31 16:55:20 +02:00
gitea	031c1f8aea	librarian: tolerate bad bytes in chunks; log what the result-send does ... Two field-reported robustness gaps on top of the hang fix. 1) A stray non-UTF-8 byte in a chunk (0x96 in the report) raised UnicodeDecodeError from readline() - which is a ValueError, so the earlier `except OSError` did NOT catch it. The finally-sentinel meant no hang, but the producer died mid-file with a loud traceback and every DOI after the bad byte went unsearched. Now chunks are opened with errors="replace" (bad bytes become U+FFFD; DOIs are ASCII so a match is never affected) so the read runs to EOF, and the producer's except is broadened from OSError to Exception so no per-file error can ever crash the thread - it's logged and the sentinel still fires. 2) The result-send back to the bot (BackgroundTaskSearch._run) now logs exactly what goes out - target URL, uuid, DOI count and the DOI list - so the librarian log plainly shows a result was sent and what was in it. And a failed POST is no longer fatal: a RequestException used to propagate out of the worker loop and kill the thread, stalling every future query until restart; it's now caught and logged, and a non-200 from the bot is logged as a warning. Verified: tests/unit/test_search_bot.py gains a case writing a chunk with a 0x96 byte before a valid DOI and asserting that DOI is still found (file read to completion, not aborted). All 5 search_bot unit tests pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-30 15:59:07 +02:00
gitea	491f957315	tests: retarget /clear_pr_pls auth tests after the musician/radio split ... test_musician_auth.py still probed /clear_pr_pls on the musician, but that endpoint moved to betoniarka during the split - the musician now 404s it, so all three tests failed 404 != 401/200. This was pre-existing debt, unrelated to the AI/share/bridge work; it just kept the integration job red. Split the coverage to match the current architecture: * test_musician_auth.py exercises the same auth contract (no key -> 401, key -> 200, key unset -> open) against /get_share_list, an authenticated endpoint the musician still serves, with a valid body so the permitted case is a clean 200 rather than a 400; * new test_betoniarka_auth.py covers /clear_pr_pls where it now lives, pointing PRIORITY_PLAYLIST_PATH at a tmp file so the authorised case can truncate it, and checks /ping stays open; * conftest.py adds conjurer_betoniarka to sys.path so the service imports. Verified in a clean venv (pytest flask waitress requests), matching the CI integration job: 13 passed, up from 3 failed / 6 passed. Unit suite unaffected (23 passed). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-30 11:41:39 +02:00
gitea	b13a8afa01	bot: queued AI query interface + librarian AI review of results ... Two connected features. 1) AI query interface (via the comm layer). communication_subroutine gains an AI_QUERY_Q, a submit_ai_query() in-process entry point, and an authed POST /ai_query endpoint ({prompt, channel_id, request_type?, username?}). The prompt is queued and answered asynchronously by a new tasks.loop worker in the always-loaded AI cog (Events), which calls handle_response - so it runs on whichever backend $gadaj_teraz currently selects (GPT or Claude) - and posts the answer to the requested channel, chunked to Discord's limit. request_type "NONE" (default) is a clean one-shot: no persona system prompt, no memory write. The worker starts before the OpenAI guard in cog_load, so it also runs on a Claude-only box; cog_unload cancels it. 2) Librarian AI review. New command $wyszukaj_z_recenzja mirrors $wyszukaj_linki_do_dokumentow but sets ai_review=True on the QueryControl, which rides the round-trip and is matched back by UUID. When the hits return, check_data_q sends the raw list as before, then - if flagged - hands the same list (already in Crossref-relevance order) plus the search phrase to the AI queue for a weighted re-rank and per-source review, delivered to the same channel. QueryControl gains an ai_review flag (default False, so the orphan path and all existing callers are unaffected). Confirmed separately (and noted in the docs): the DOI list the AI receives is pre-sorted by Crossref relevance - the librarian pipeline only filters (drops title-less items) and splits (in-db / not-in-db), never re-sorts, and relies on insertion-ordered dicts (Py 3.7+). Verified: /ai_query auth (401/200/400/open), submit_ai_query and the queued dict shape, and the QueryControl flag - via a Flask test client and tests/integration/test_ai_query_endpoint.py (5 tests, all pass; integration suite 11 passed, the 3 failures are the pre-existing /clear_pr_pls musician tests fixed on a separate branch). Full first-party compile clean. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-30 09:27:11 +00:00
gitea	1a59c9f6c5	librarian: stop the DOI search from hanging on a chunk-count mismatch ... search_bot conflated MAXTHREADS into two jobs at once - how many chunk files to read (files 0..MAXTHREADS-1) AND how many producer sentinels to wait for - so the two had to match exactly. Set too low it silently skipped trailing chunks; set too high (or with any chunk missing/unreadable) a producer crashed before emitting its sentinel, the consumers' count never reached the threshold, and search_for_doi hung on join() forever. The idle-timeout failsafe that was meant to break a starved consumer was dead code: `if empty_counter > 5: ... elif empty_counter > 10: break` - >10 implies >5, so the elif never ran. Fix, three layers: * auto-discover the chunk files present (discover_chunk_files: <n>_chunk.txt in numeric order) instead of range(0, MAXTHREADS). All files are read regardless of count, and no producer is ever pointed at a missing file; * the sentinel threshold is now the number of producers actually started, so it can't drift from what's emitted; * producers emit their sentinel in a finally, so even a crash (missing/unreadable chunk) can't starve the count; and the idle backstop is reordered so it can actually fire (>EMPTY_LIMIT seconds) as a last resort. MAXTHREADS is deprecated and unused (kept only so old env files don't break); docs/env updated to say chunk files are auto-discovered. For the reported case (MAXTHREADS=40, files 0..43): before, files 40-43 were silently never searched, and any run that referenced a missing chunk hung forever. After, all 44 are searched and it always terminates. Verified in a pytest-only venv (tests/unit/test_search_bot.py): DOI in a trailing chunk is found; an unreadable chunk still terminates; empty dir returns at once; discovery is numeric-sorted. Full unit job 27 passed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-30 00:15:03 +02:00
Michal Tuszowski	3d9d47aa90	ai: single-switch GPT/Claude backend for the chat cog ... Wire the bot's AI chat pipeline (ai_functions.handle_response) to talk to either OpenAI or the Anthropic Messages API, chosen by one active-config switch. Behaviour on the default "gpt" config is unchanged. constants.py: * guarded `import anthropic` + CLAUDECLIENT (mirrors OPENAICLIENT), netrc machine 'anthropic' / ANTHROPIC_API_KEY; * CLAUDE_LATEST_MODEL / CLAUDE_CHEAP_MODEL (opus-4-8 / haiku-4-5); * AI_CONFIGS + DEFAULT_AI_CONFIG loaded from an optional 3rd element of system_gpt_settings.json (backward compatible - a 2-element file falls back to built-in defaults, active "gpt"). Single switch: CONJURER_AI_CONFIG env > settings "active" > "gpt". ai_functions.py: * provider_generate() dispatches to OpenAI (unchanged openai_call) or the new _anthropic_call() (splits system out, alternating messages, max_tokens, temperature omitted - Opus 4.8 rejects sampling params); * AIError normalises both SDKs' exceptions into one category set so handle_response keeps its single set of in-character error replies; * select_model() reads the active config; legacy "gpt-4o" default auto-maps to the active provider's model so the switch actually changes the backend; * set_active_ai_config()/list_ai_configs() with best-effort persistence back into system_gpt_settings.json index 2. ai_commands.py: * $gadaj_teraz <config> hybrid command (Vykidailo-gated) switches backend at runtime; * graceful guards when OPENAICLIENT is None: personal assistants (OpenAI Assistants API) and DALL-E image gen degrade instead of crashing, so a Claude-only deployment boots. system_gpt_settings.json: add the configs block (gpt/claude/_template) as the collection point for future backends. requirements_bot.txt: add anthropic. bot.env.example: ANTHROPIC_API_KEY + CONJURER_AI_CONFIG. Unit tests cover the message splitter, model selection, config listing, and error mapping. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-07-09 21:34:59 +02:00
Michal Tuszowski	a6c20a0054	ci: replace broken default workflows with compile/unit/integration CI ... The two scaffold workflows (Python application / Python package) failed on every PR: they installed deps from a non-existent requirements.txt, ran flake8/pytest over the vendored yt_dlp fork (new syntax under the 3.8/3.9 matrix), and collected ad-hoc root scripts — notably test_ai.py, which is an invalid pasted object dump (not Python). - Remove python-app.yml / python-package.yml and the junk root scripts (test.py, test_ai.py, test_time.py) - Add .github/workflows/ci.yml with three PR-check jobs: * compile — py_compile every first-party .py (no deps) * unit — pytest on pure logic (conanjurer_functions, constants) * integration — boot the Flask services and assert the X-Conjurer-Api-Key auth contract (communication_subroutine + conjurer_musician) - Add tests/ suite, pytest.ini (testpaths=tests) and conftest.py (sys.path) Fixes surfaced by the compile gate / needed for the integration job: - conjurer_librarian/search_bot.py + search_bot2.py: f-string reused the same quote ({item["exists"]}) -> SyntaxError on Python < 3.12 - conjurer_musician/media_search_functions.py: made import-safe (env-overridable paths, lazy DB load / mkdir) so the service can be imported and tested off the Pi Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-06-29 12:24:30 +02:00