4 Commits

Author	SHA1	Message	Date
gitea	c91ec03b83	AI: personal assistants without the dead API, and keep Ollama warm ... Two things the field report asked for. 1) PERSONAL ASSISTANTS (replacing the sunset OpenAI Assistants API) The old implementation gave three capabilities. Two are reimplemented here, the third was confirmed unused and is deliberately not replaced: * per-user persona - it already lived in system_gpt_settings.json; it was only ever being shipped to OpenAI. It is now the system prompt. * per-user conversation thread - OpenAI held this server-side. It now lives in assistant_memory.json, keyed by discord user id, trimmed to the most recent turns (CONJURER_ASSISTANT_MEMORY_TURNS) and written atomically so a torn write cannot lose someone's history. Deliberately a plain trim, not the AI summarisation used for the bar's shared memory: these are private DMs and must not end up in a public "legend". * file_search - not replaced. Confirmed not in use. The conversation goes through handle_response with request_type="NONE" and an explicit message list, which keeps it out of the bar's shared memory. The big win: create_chat_assistant hardcoded model="gpt-4o", so assistants were locked to OpenAI. They now run on whatever $gadaj_teraz selects - Claude and Ollama included. create_chat_assistant / chat_with_assistant are gone, and with them the last call to beta.threads in the startup path - so the cog cannot be killed by that API again. (add_files_to_vector_store / delete_files_from_vector_store still reference beta.assistants but are dead code - nothing calls them - so they cannot crash anything; left alone rather than widening this change.) 2) KEEPING A SELF-HOSTED MODEL WARM Loading is the slow part - the GPU is shared with other users - so we preload via Ollama's documented mechanism: /api/generate with a model, a keep_alive and NO prompt. It loads the model and generates nothing. * on switching to ollama, $gadaj_teraz fires a preload in the BACKGROUND (not awaited: loading can take minutes and the command must answer at once), so the wait lands on the operator rather than the first user; * a warm loop re-asserts keep_alive every CONJURER_OLLAMA_WARM_MINUTES. Both are hard-guarded on the ACTIVE provider being ollama. Warming a metered API would burn tokens and money for nothing, so that guard is pinned by a test asserting the preload is never called for gpt/claude, and another asserting the preload body carries no prompt (a prompt would make every warm-up generate). Tests: 82 unit + 71 integration green. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-27 16:25:01 +02:00
gitea	cfd19e2b34	AI: persist only the pinned field, not the whole config block ... Adversarial review of the previous commit found a real regression it introduced, reproduced against the actual code rather than inferred. Changing _persist_active_ai_config from setdefault("configs", ...) to a direct assignment made every backend switch write the whole in-memory AI_CONFIGS over the settings file. Because AI_CONFIGS is now the built-in defaults merged UNDER the file, that meant: * an operator's hand edits were destroyed - and hand editing is the only way to change cheap_model / temperature / max_tokens, since set_active_model writes latest_model and there is no command for the rest, * a config deliberately deleted from the file was re-seeded from the defaults and written back, permanently, * pinning a model for one provider silently reverted another provider's entry, * CONJURER_OLLAMA_MODEL stopped having any effect once the env-derived block had been persisted once. The original motivation was still valid (plain setdefault would drop a pinned model), so the fix is narrower rather than a revert: persist ONLY the field this process actually changed. _persist_active_ai_config takes model_for and writes back just that config's latest_model; everything else in the on-disk block is left exactly as found. The constants.py merge stays - it is what keeps a newly added provider visible after an upgrade - and is now in-memory only, so it cannot reach the file. Tests: the disk-write path had ZERO coverage, which is precisely how this got in. Added four tests that drive the real _persist_active_ai_config against a temp settings file: the pin lands while operator edits survive and a deleted config is not resurrected; a plain switch leaves the configs block byte-identical; a pin survives a re-read; a corrupt file does not raise. Verified they have teeth - reintroducing the regression fails two of them. Also hardened two weak tests the review caught: the pin test asserted on the object set_active_model returns, which IS the mutated dict (so it passed regardless), and the unconfigured-endpoint test monkeypatched OLLAMACLIENT to None when it was already None, passing vacuously. Suite: 72 unit + 70 integration green. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-24 15:41:28 +00:00
gitea	6a6b821a0d	AI: add a self-hosted Ollama backend, and let the picker choose the model ... The bot could talk to OpenAI or Anthropic; this adds Ollama as a third provider so it can run against models hosted on our own box, and extends the switch command to pick WHICH model - not just which backend. Provider: Ollama exposes an OpenAI-compatible /v1 surface, so the client is just openai.AsyncOpenAI(base_url=OLLAMA_URL + "/v1"). That reuses the existing message format and the whole _map_openai_error mapping instead of forking a second error taxonomy. There is no API key - the endpoint IS the configuration, so the backend stays dormant (and refuses to be selected, with a message naming the variable) until CONJURER_OLLAMA_URL is set, the same way the Conan bridge behaves. Model selection: * list_provider_models() asks the SERVER for Ollama (/v1/models), so the picker shows what is actually pulled on the box rather than a hardcoded list. Hosted providers just report what they are wired to. * set_active_model() pins the config's latest_model and persists it; cheap_model is left alone so the MUSIC path keeps its cheaper backend. * $gadaj_teraz now takes "<config> [model]", and a new read-only $modele_ai lists what is available. Pinning an id Ollama does not have is rejected up front with the real list - otherwise the typo only surfaces later as a failed reply. Two fixes this exposed: * AI_CONFIGS now merges built-in defaults with the settings-file block instead of letting the file win outright. Every provider switch persists a "configs" block, so a file written by an older build would have permanently hidden ollama from the picker after an upgrade. * _persist_active_ai_config assigns "configs" instead of setdefault, so a pinned model actually survives a restart. * the hardcoded 120s response timeout is now CONJURER_AI_TIMEOUT_SECONDS - a self-hosted model on a modest GPU can legitimately need longer. Tests cover: ollama appears in the picker, select_model maps the legacy gpt-4o default instead of leaking it, model listing (server-queried, sorted, de-duplicated, failure -> AIError, unconfigured -> auth), pinning (latest only, blank/unknown rejected), and that provider_generate routes to the new path. Suite: 68 unit + 70 integration green. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>	2026-08-24 15:41:28 +00:00
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