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>

ai_commands.py

@@ -1,5 +1,4 @@
 # ai command cogs
-import asyncio
 import logging
 import re
 import sys
@@ -18,7 +17,7 @@ from communication_subroutine import AI_QUERY_Q
 
 import ai_functions
 from constants import (
-    OLLAMA_WARM_MINUTES,
+    ASSISTANTS,
     DATA,
     GRAPHICS_PATH,
     INITIAL_TIME_WAIT,
@@ -102,45 +101,55 @@ class Events(commands.Cog):
             text = text[1900:]
 
     async def cog_load(self):
-        # The AI query worker answers via handle_response, so it works on every
-        # backend. Start it first.
+        # The AI query worker must run regardless of the OpenAI guard below - it
+        # answers via handle_response, which works on Claude too. Start it first.
         if not self.ai_query_worker.is_running():
             self.ai_query_worker.start()
-        # Keeps a self-hosted model resident; it no-ops on any other provider.
-        if not self.ollama_warm_loop.is_running():
-            self.ollama_warm_loop.start()
-        # NOTE: there is no OpenAI-Assistants bootstrap any more. It called a
-        # sunset API (beta threads), 404'd, and failed the WHOLE extension -
-        # taking every AI command with it. Personal assistants now ride
-        # handle_response with per-user memory (ai_functions), so they work on
-        # Claude and Ollama too and nothing has to be created at startup.
-        self.logger.info("Osobiści asystenci: pamięć per-user, aktywny backend AI")
-
-    @tasks.loop(minutes=OLLAMA_WARM_MINUTES)
-    async def ollama_warm_loop(self):
-        """Keep a self-hosted model resident so users don't pay the load wait.
-
-        Loading is the slow part on a GPU shared with other users, so we
-        re-assert Ollama's keep_alive well inside its window. This preloads
-        WITHOUT generating - no tokens, no cost.
-
-        Hard guard: it does nothing unless the ACTIVE backend is Ollama. Firing
-        warm-ups at a metered API would burn tokens and money for nothing.
-        """
-        try:
-            if ai_functions.active_provider() != "ollama":
+        self.logger.info("Starting personal assistants")
+        # Personal assistants use the OpenAI Assistants API (threads/runs), which
+        # has no Anthropic equivalent - skip cleanly when OpenAI isn't wired up
+        # (e.g. a Claude-only deployment) instead of crashing the cog load.
+        if OPENAICLIENT is None:
+            self.logger.warning(
+                "OPENAICLIENT niedostępny - osobiści asystenci (OpenAI Assistants API) wyłączeni"
+            )
             return
-            await ai_functions.warm_active_model()
-        except Exception as exc:  # pylint: disable=broad-exception-caught
-            self.logger.info("Rozgrzewanie Ollamy nieudane (nieszkodliwe): %s", exc)
+        for superfryta_id, superfryta in SPECJALNE_ZIEMNIACZKI.items():
 
-    @ollama_warm_loop.before_loop
-    async def before_ollama_warm_loop(self):
-        await self.bot.wait_until_ready()
+            if superfryta[4] != "":
+                self.logger.info(
+                    "Personal assistant for user: %s, exists id: %s,name: %s, owner: %s, special instructions: %s assistant id: %s ",
+                    superfryta_id,
+                    superfryta[0],
+                    superfryta[1],
+                    superfryta[2],
+                    superfryta[3],
+                    superfryta[4],
+                )
+                thread = await OPENAICLIENT.beta.threads.create()
+                self.logger.info("Thread id: %s", thread.id)
+                ASSISTANTS[superfryta[1]] = (
+                    superfryta[2],
+                    superfryta[4],
+                    superfryta[0],
+                    thread,
+                )
+            else:
+                self.logger.info(
+                    "Creating personal assistant for user: %s, id: %s,name: %s, owner: %s, special instructions: %s",
+                    superfryta_id,
+                    superfryta[0],
+                    superfryta[1],
+                    superfryta[2],
+                    superfryta[3],
+                )
+                await ai_functions.create_chat_assistant(
+                    superfryta_id, superfryta[0], superfryta[1], superfryta[2], superfryta[3]
+                )
+        self.logger.info("Started personal assistants")
 
     async def cog_unload(self):
         self.ai_query_worker.cancel()
-        self.ollama_warm_loop.cancel()
 
     @commands.hybrid_command(
         name="switch_dm_mode",
@@ -269,32 +278,13 @@ class Events(commands.Cog):
                 f"Teraz gadam przez **{nazwa_konfigu}** — "
                 f"{cfg.get('provider')} / {cfg.get('latest_model')}."
             )
-            if cfg.get("provider") == "ollama":
-                # Pay the (slow, shared-GPU) load cost NOW, in the background,
-                # so it lands on the operator switching backends rather than on
-                # whoever sends the first message. Not awaited: loading can take
-                # minutes and the command must answer immediately.
-                asyncio.create_task(ai_functions.warm_active_model())
-                message += (
-                    "\nRozgrzewam model w tle — pierwsza odpowiedź może chwilę potrwać."
-                )
             # Switched without pinning a model: show what else is on offer.
             if not model:
                 try:
                     others = await ai_functions.list_provider_models(nazwa_konfigu)
                 except ai_functions.AIError:
                     others = []
-                current = cfg.get("latest_model")
-                if others and current not in others:
-                    # The configured/pinned model is not on the server: every
-                    # reply would fail with "model not found" and nothing would
-                    # say why. Flag it here, where the list is already in hand.
-                    message += (
-                        f"\n⚠ Uwaga: '{current}' nie jest wgrany na serwerze. "
-                        f"Dostępne: {', '.join(others)} "
-                        f"(`$gadaj_teraz {nazwa_konfigu} <model>`)."
-                    )
-                elif len(others) > 1:
+                if len(others) > 1:
                     message += (
                         f"\nDostępne modele: {', '.join(others)} "
                         f"(`$gadaj_teraz {nazwa_konfigu} <model>`)."
@@ -411,10 +401,8 @@ class Events(commands.Cog):
                 if message.author.id == superfryta[0]:
                     self.logger.info("Specjalny ziemniak")
                     if self.armia[message.author.id] == Dm_Mode.SPECJALNY_ZIEMNIACZEK:
-                        # superfryta = [discord_id, assistant_name, owner, instructions, legacy_assistant_id]
-                        await ai_functions.chat_with_personal_assistant(
-                            message, superfryta[2], superfryta[3]
-                        )
+                        #await self.bot.process_commands(message)
+                        await ai_functions.chat_with_assistant(message, superfryta[1])
                         return
                     elif self.armia[message.author.id] == Dm_Mode.ECHO_ECHO:
                         await ai_functions.echo(message)

ai_functions.py

@@ -1,21 +1,16 @@
 import asyncio
 import json
 import logging
-import os
 import random
-import tempfile
 
 import openai
 import tiktoken
 import time
 from other_functions import discord_friendly_send
-import requests
-
 from constants import (
     AI_CONFIGS,
     AI_TIMEOUT_SECONDS,
-    ASSISTANT_MEMORY_FILE,
-    ASSISTANT_MEMORY_TURNS,
+    ASSISTANTS,
     CLAUDECLIENT,
     CYCLIC_WORDS,
     DEFAULT_AI_CONFIG,
@@ -26,9 +21,6 @@ from constants import (
     MESSAGE_TABLE,
     MESSAGE_TABLE_MUZYKA,
     OLLAMACLIENT,
-    OLLAMA_KEEP_ALIVE,
-    OLLAMA_PRELOAD_TIMEOUT,
-    OLLAMA_URL,
     OPENAICLIENT,
     SYSTEM_GPT_SETTINGS,
     WORD_REACTIONS,
@@ -41,6 +33,8 @@ try:
 except ImportError:  # pragma: no cover - optional at runtime
     anthropic = None
 
+# this do per user
+VECTOR_STORE_ID = -1
 
 
 # *=========================================== AI provider abstraction
@@ -296,52 +290,6 @@ async def _ollama_call(messages, model, cfg):
     return (resp.choices[0].message.content or "").strip()
 
 
-def _ollama_preload(model, keep_alive=None) -> bool:
-    """Load ``model`` into Ollama and keep it resident, generating NOTHING.
-
-    Ollama's /api/generate with a model and no prompt is the documented preload:
-    it pays the (slow, GPU-shared) load cost once and returns, producing no
-    tokens. Used to warm up on switch and to re-assert keep_alive periodically.
-
-    Blocking on purpose - callers wrap it in asyncio.to_thread.
-    """
-    if not OLLAMA_URL:
-        return False
-    logger = logging.getLogger("discord")
-    try:
-        resp = requests.post(
-            f"{OLLAMA_URL}/api/generate",
-            json={"model": model, "keep_alive": keep_alive or OLLAMA_KEEP_ALIVE},
-            timeout=OLLAMA_PRELOAD_TIMEOUT,
-        )
-        ok = resp.status_code == 200
-        logger.info("Ollama preload %s -> HTTP %s", model, resp.status_code)
-        return ok
-    except requests.exceptions.RequestException as exc:
-        logger.info("Ollama preload %s failed: %s", model, exc)
-        return False
-
-
-def active_provider() -> str:
-    """Provider of the active config - the guard every warm-up must check.
-
-    Preloading only makes sense for a self-hosted model; firing it at a metered
-    API would burn tokens (and money) for nothing.
-    """
-    return (_active_config() or {}).get("provider", "")
-
-
-async def warm_active_model(force_model=None) -> bool:
-    """Preload the active model IFF the active backend is Ollama."""
-    if active_provider() != "ollama":
-        return False
-    cfg = _active_config()
-    model = force_model or cfg.get("latest_model")
-    if not model:
-        return False
-    return await asyncio.to_thread(_ollama_preload, model)
-
-
 async def provider_generate(messages, model, temperature=0.2):
     """Dispatch a chat completion to the active backend, normalising errors."""
     cfg = _active_config()
@@ -408,6 +356,58 @@ async def openai_call(messages, model, temperature=0.2):
         )
 
 
+def create_vector_store():
+    # Create a vector store caled "Financial Statements"
+    return OPENAICLIENT.beta.vector_stores.create_and_poll(name="Hammer Stash")
+    # expires_after={
+    # "anchor": "last_active_at",
+    # "days": 7}
+    # )
+
+
+def upload_files_to_vector_store(assistant):
+
+    # Ready the files for upload to OpenAI
+    file_paths = ["edgar/goog-10k.pdf", "edgar/brka-10k.txt"]
+    file_streams = [open(path, "rb") for path in file_paths]
+
+    # file = client.beta.vector_stores.files.create_and_poll(
+    # vector_store_id="vs_abc123",
+    # file_id="file-abc123"
+    # )
+    # batch = client.beta.vector_stores.file_batches.create_and_poll(
+    # vector_store_id="vs_abc123",
+    # file_ids=['file_1', 'file_2', 'file_3', 'file_4', 'file_5']
+    # )
+
+    # Use the upload and poll SDK helper to upload the files, add them to the vector store,
+    # and poll the status of the file batch for completion.
+    file_batch = OPENAICLIENT.beta.vector_stores.file_batches.upload_and_poll(
+        vector_store_id=VECTOR_STORE_ID, files=file_streams
+    )
+
+    # You can print the status and the file counts of the batch to see the result of this operation.
+    print(file_batch.status)
+    print(file_batch.file_counts)
+    assistant = OPENAICLIENT.beta.assistants.update(
+        assistant_id=assistant.id,
+        tool_resources={"file_search": {"vector_store_ids": [VECTOR_STORE_ID]}},
+    )
+
+
+def delete_files_from_vector_store(assistant, file_id):
+    result = OPENAICLIENT.beta.vector_stores.file_batches.delete(
+        vector_store_id=VECTOR_STORE_ID, files=file_id
+    )
+
+    # You can print the status and the file counts of the batch to see the result of this operation.
+    print(result)
+    assistant = OPENAICLIENT.beta.assistants.update(
+        assistant_id=assistant.id,
+        tool_resources={"file_search": {"vector_store_ids": [VECTOR_STORE_ID]}},
+    )
+
+
 def num_tokens_from_string(message, model):
     """
     The function takes a string message and a model as input and returns the number of tokens in the
@@ -664,104 +664,60 @@ async def get_random_cyclic_message(client):
     return result
 
 
-# ----------------------------------------------------------------- assistants
-# The OpenAI Assistants API (beta threads/runs) that used to back these was
-# sunset and now answers 404, taking the whole AI cog down with it. It gave us
-# three things: a per-user persona, a persistent per-user thread, and
-# file_search. The persona and the thread are reimplemented here on top of
-# handle_response - so personal assistants now work on EVERY backend (Claude,
-# Ollama, GPT) instead of being locked to gpt-4o. file_search is deliberately
-# not replaced: it was not in use.
-_ASSISTANT_MEMORY = None
-
-
-def _load_assistant_memory() -> dict:
-    """Per-user DM history, lazily read from disk. Corruption is not fatal."""
-    global _ASSISTANT_MEMORY  # pylint: disable=global-statement
-    if _ASSISTANT_MEMORY is not None:
-        return _ASSISTANT_MEMORY
+async def create_chat_assistant(owner_id, id, name, owner, special_instructions):
     logger = logging.getLogger("discord")
-    try:
-        with open(ASSISTANT_MEMORY_FILE, "r", encoding=ENCODING) as handle:
-            data = json.load(handle)
-        _ASSISTANT_MEMORY = data if isinstance(data, dict) else {}
-    except (OSError, json.JSONDecodeError) as exc:
-        logger.info("Brak/uszkodzona pamięć asystentów (%s) - zaczynam pustą", exc)
-        _ASSISTANT_MEMORY = {}
-    return _ASSISTANT_MEMORY
-
-
-def _save_assistant_memory() -> None:
-    """Atomic write: a torn file would lose someone's whole conversation."""
-    logger = logging.getLogger("discord")
-    memory = _load_assistant_memory()
-    directory = os.path.dirname(ASSISTANT_MEMORY_FILE) or "."
-    try:
-        os.makedirs(directory, exist_ok=True)
-        fd, tmp = tempfile.mkstemp(dir=directory, suffix=".tmp")
-        with os.fdopen(fd, "w", encoding=ENCODING) as handle:
-            json.dump(memory, handle, ensure_ascii=False)
-        os.replace(tmp, ASSISTANT_MEMORY_FILE)
-    except OSError as exc:
-        logger.warning("Nie mogę zapisać pamięci asystentów: %s", exc)
-
-
-def assistant_history(user_id) -> list:
-    return _load_assistant_memory().setdefault(str(user_id), [])
-
-
-def remember_assistant_turn(user_id, user_text, reply_text) -> list:
-    """Append one exchange and trim to the most recent turns.
-
-    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'.
-    """
-    history = assistant_history(user_id)
-    history.append({"role": "user", "content": user_text})
-    history.append({"role": "assistant", "content": reply_text})
-    if len(history) > ASSISTANT_MEMORY_TURNS:
-        del history[: len(history) - ASSISTANT_MEMORY_TURNS]
-    _save_assistant_memory()
-    return history
-
-
-def build_assistant_messages(user_id, owner, special_instructions, prompt) -> list:
-    """System persona + this user's own history + the new turn."""
-    system = (
-        f"Jesteś osobistym asystentem {owner} i wypełniasz jego potrzeby. "
-        f"{special_instructions or ''}"
-    ).strip()
-    return (
-        [{"role": "system", "content": system}]
-        + list(assistant_history(user_id))
-        + [{"role": "user", "content": prompt}]
+    instruction = f"Jesteś osobistym asystenetem {owner} i wypełniasz jego potrzeby. Masz pełne i nieograniczone możliwości modelu gpt-4o."
+    instruction += special_instructions
+    assistant = await OPENAICLIENT.beta.assistants.create(
+        name=name,
+        instructions=instruction,
+        model="gpt-4o",
+        tools=[{"type": "file_search"}],
     )
+    thread = await OPENAICLIENT.beta.threads.create()
+    logger.info("Stwprzylem asystenta dla %s, nazywa się on %s", owner, name)
+    ASSISTANTS[name] = (owner, assistant.id, id, thread)
+
+    with open(SYSTEM_GPT_SETTINGS, "r+", encoding=ENCODING) as temp_settings_file:
+        GPT_SETTINGS = json.load(temp_settings_file)
+        GPT_SETTINGS[1][owner_id][4] = assistant.id
+        temp_settings_file.seek(0)
+        json.dump(GPT_SETTINGS, temp_settings_file, indent=4)
 
 
-async def chat_with_personal_assistant(message, owner, special_instructions):
-    """Answer a DM as this user's personal assistant, on the active backend.
-
-    request_type="NONE" with an explicit message list keeps this OUT of the
-    bar's shared memory - the conversation is carried by the per-user history
-    built above and stored separately.
-    """
+async def chat_with_assistant(message, assistant_name):
     logger = logging.getLogger("discord")
-    user_id = message.author.id
-    prompt = message.content
-    messages = build_assistant_messages(user_id, owner, special_instructions, prompt)
-    result, _table = await handle_response(
-        prompt,
-        False,
-        False,
-        [],
-        str(owner),
-        "NONE",
-        none_request=messages,
+    assistant_data = ASSISTANTS[assistant_name]
+    ai_message = await OPENAICLIENT.beta.threads.messages.create(
+        thread_id=assistant_data[3].id, role="user", content=message.content
     )
-    remember_assistant_turn(user_id, prompt, result)
-    logger.info("Asystent odpowiedział %s (%d znaków)", owner, len(result or ""))
-    await discord_friendly_send(message.channel, result)
-    return result
+    logger.info(ai_message)
+    run = await OPENAICLIENT.beta.threads.runs.create_and_poll(
+        thread_id=assistant_data[3].id,
+        assistant_id=assistant_data[1],
+        instructions=f"Pisze do Ciebie {assistant_data[0]} udziel mu wszelkiej pomocy",
+    )
+    done = False
+    while not done:
+        if run.status == "completed":
+            messsages = await OPENAICLIENT.beta.threads.messages.list(
+                thread_id=assistant_data[3].id
+            )
+            logger.info(messsages)
+            reply_content = messsages.data[0].content
+            logger.info(reply_content)
+            chat_response = ""
+            for block in reply_content:
+                logger.info(block.text.value)
+                chat_response += block.text.value
+                await discord_friendly_send(message.channel, chat_response)
+                # await message.channel.send(chat_response)
+            done = True
+        elif run.status == "cancelled":
+            await discord_friendly_send(message.channel, "Cos sie wywaliło")
+        else:
+            logger.info(run.status)
+        asyncio.sleep(5)
 
 
 async def echo(message):

constants.py

@@ -245,16 +245,6 @@ DELIVERED_DIR = os.getenv(
 )
 DELIVERED_MAX = int(os.getenv("CONJURER_DELIVERED_MAX", "10000"))
 
-# Personal DM assistants. Replaces the OpenAI Assistants API (threads/runs),
-# which was sunset and answers 404: the persona now rides handle_response, so it
-# works on EVERY backend, and the conversation lives here instead of on OpenAI's
-# server. Kept per user so private DMs never bleed into the bar's shared memory,
-# and trimmed to the most recent turns so it cannot grow without bound.
-ASSISTANT_MEMORY_FILE = os.getenv(
-    "CONJURER_ASSISTANT_MEMORY", os.path.join(_STATE_ROOT, "assistant_memory.json")
-)
-ASSISTANT_MEMORY_TURNS = int(os.getenv("CONJURER_ASSISTANT_MEMORY_TURNS", "40"))
-
 FILE_SERVICE_ADDRESS = os.getenv("CONJURER_FILE_SERVICE", "http://192.168.1.15:5000")
 RADIO_HARBOR_ADDRESS = os.getenv("CONJURER_RADIO_HARBOR", "http://192.168.1.15:54321")
 # Betoniarka (radio-operator service colocated with Liquidsoap). Falls back to
@@ -421,19 +411,6 @@ else:
 AI_TIMEOUT_SECONDS = int(os.getenv("CONJURER_AI_TIMEOUT_SECONDS", "120"))
 
 OLLAMA_URL = os.getenv("CONJURER_OLLAMA_URL", "").rstrip("/")
-# Keeping a self-hosted model resident. Loading it is the slow part (it is
-# offloaded to a GPU shared with other users), so we preload it - Ollama's
-# /api/generate with a model and NO prompt loads it and generates nothing, which
-# costs no tokens and no money. KEEP_ALIVE is how long Ollama should then hold
-# it; the warm loop re-asserts that well inside the window.
-# STRICTLY Ollama-only: doing this against a paid API would burn tokens for
-# nothing, so every caller checks the active provider first.
-OLLAMA_KEEP_ALIVE = os.getenv("CONJURER_OLLAMA_KEEP_ALIVE", "30m")
-OLLAMA_WARM_MINUTES = float(os.getenv("CONJURER_OLLAMA_WARM_MINUTES", "10"))
-# A preload waits for the model to finish loading, which on a shared GPU is the
-# slow path we are trying to move off the user's first message.
-OLLAMA_PRELOAD_TIMEOUT = int(os.getenv("CONJURER_OLLAMA_PRELOAD_TIMEOUT", "600"))
-
 OLLAMA_LATEST_MODEL = os.getenv("CONJURER_OLLAMA_MODEL", "llama3.1:8b")
 OLLAMA_CHEAP_MODEL = os.getenv("CONJURER_OLLAMA_CHEAP_MODEL", OLLAMA_LATEST_MODEL)
 if openai and OLLAMA_URL:

tests/integration/test_librarian_heartbeat.py

@@ -60,49 +60,25 @@ def test_current_search_registration_round_trip():
         assert not lib._current_search
 
 
-def _write_two_chunks(tmp_path):
+def test_search_fills_progress_with_live_positions_and_total(tmp_path, monkeypatch):
+    # End to end against the real scan: total_bytes matches the chunk files on
+    # disk, and once finished the recorded offsets cover them.
+    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
     (tmp_path / "0_chunk.txt").write_text("10.1/a\n10.1/b\n", encoding="utf-8")
     (tmp_path / "1_chunk.txt").write_text("10.1/c\n", encoding="utf-8")
-    return sum(
+    expected_total = sum(
         (tmp_path / name).stat().st_size for name in ("0_chunk.txt", "1_chunk.txt")
     )
 
-
-def test_search_fills_progress_and_reaches_full_coverage(tmp_path, monkeypatch):
-    # Coverage must be measured on a search that CANNOT stop early. Once every
-    # queried DOI is found the consumer signals TERM and the producers stop
-    # mid-file, so a search for a DOI that exists reaches an arbitrary offset -
-    # asserting 100% there is a race (it failed roughly one run in two).
-    # An absent DOI forces the whole database to be read.
-    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
-    expected_total = _write_two_chunks(tmp_path)
-
-    progress = {}
-    search_bot.search_for_doi([("10.9/absent", "DATA")], [], _LOG, progress=progress)
-
-    assert progress["total_bytes"] == expected_total
-    assert progress["chunk_files"] == 2
-    done, total, percent = lib._progress_summary(progress)
-    assert total == expected_total
-    assert done == expected_total          # nothing stopped it: whole DB scanned
-    assert percent == pytest.approx(100.0)
-
-
-def test_progress_is_populated_for_a_search_that_finds_its_target(tmp_path, monkeypatch):
-    # The early-termination case: the target is found, so coverage is whatever
-    # the producers reached. Assert what IS deterministic - the total is known,
-    # progress is bounded and sane, and the hit is reported.
-    monkeypatch.setattr(search_bot, "DATABASE_PATH", str(tmp_path) + "/")
-    expected_total = _write_two_chunks(tmp_path)
-
     progress = {}
     result, _positions, _interrupted = search_bot.search_for_doi(
         [("10.1/c", "DATA")], [], _LOG, progress=progress
     )
 
     assert progress["total_bytes"] == expected_total
+    assert progress["chunk_files"] == 2
     done, total, percent = lib._progress_summary(progress)
     assert total == expected_total
-    assert 0 <= done <= total              # bounded, never nonsense
-    assert 0.0 <= percent <= 100.0
+    assert done == expected_total          # whole DB scanned
+    assert percent == pytest.approx(100.0)
     assert [r for r in result if r["DOI"] == "10.1/c" and r["exists"]]

tests/unit/test_ai_provider_switch.py

@@ -394,143 +394,3 @@ def test_persist_survives_an_unreadable_settings_file(tmp_path, monkeypatch):
     broken.write_text("{ not json", encoding="utf-8")
     monkeypatch.setattr(ai_functions, "SYSTEM_GPT_SETTINGS", str(broken))
     ai_functions._persist_active_ai_config("gpt", model_for="gpt")  # must not raise
-
-
-# ----------------------------------------------- keep-warm (Ollama ONLY) ----
-# The money guard: preloading a self-hosted model is free, but firing the same
-# thing at a metered API would burn tokens for nothing. These pin that it can
-# only ever happen for Ollama.
-
-
-def test_warm_active_model_is_a_noop_for_paid_providers(monkeypatch):
-    called = []
-    monkeypatch.setattr(
-        ai_functions, "_ollama_preload", lambda *a, **k: called.append(a) or True
-    )
-    for paid in ("gpt", "claude"):
-        _reset_active(paid)
-        try:
-            assert asyncio.run(ai_functions.warm_active_model()) is False
-        finally:
-            _reset_active("gpt")
-    assert called == [], "a paid backend must never be preloaded"
-
-
-def test_warm_active_model_preloads_when_ollama_is_active(monkeypatch):
-    monkeypatch.setitem(
-        ai_functions.AI_CONFIGS,
-        "ollama",
-        {"provider": "ollama", "latest_model": "qwen2.5:7b", "cheap_model": "c"},
-    )
-    seen = {}
-    monkeypatch.setattr(
-        ai_functions, "_ollama_preload", lambda model, *a, **k: seen.update(model=model) or True
-    )
-    _reset_active("ollama")
-    try:
-        assert asyncio.run(ai_functions.warm_active_model()) is True
-    finally:
-        _reset_active("gpt")
-    assert seen["model"] == "qwen2.5:7b"
-
-
-def test_active_provider_reports_the_switch():
-    _reset_active("gpt")
-    assert ai_functions.active_provider() == "openai"
-    _reset_active("claude")
-    try:
-        assert ai_functions.active_provider() == "anthropic"
-    finally:
-        _reset_active("gpt")
-
-
-def test_preload_sends_no_prompt_so_it_generates_nothing(monkeypatch):
-    # Ollama's documented preload: a model and keep_alive, and NO prompt. If a
-    # prompt ever crept in, every warm-up would silently generate tokens.
-    sent = {}
-
-    class _Resp:
-        status_code = 200
-
-    monkeypatch.setattr(ai_functions, "OLLAMA_URL", "http://ollama:11434")
-    monkeypatch.setattr(
-        ai_functions.requests, "post",
-        lambda url, json=None, timeout=None: sent.update(url=url, body=json) or _Resp(),
-    )
-    assert ai_functions._ollama_preload("qwen2.5:7b") is True
-    assert sent["url"].endswith("/api/generate")
-    assert sent["body"]["model"] == "qwen2.5:7b"
-    assert "keep_alive" in sent["body"]
-    assert "prompt" not in sent["body"], "a preload must not generate"
-
-
-def test_preload_without_endpoint_is_a_noop(monkeypatch):
-    monkeypatch.setattr(ai_functions, "OLLAMA_URL", "")
-    assert ai_functions._ollama_preload("x") is False
-
-
-# ------------------------------------------- personal assistants (per user) --
-# Replaces the sunset OpenAI Assistants API. The two properties that matter:
-# each user's DM history is ISOLATED (private DMs must not leak into another
-# user's context or the bar's shared memory), and it stays BOUNDED.
-
-
-def _fresh_assistant_memory(tmp_path, monkeypatch, turns=40):
-    monkeypatch.setattr(
-        ai_functions, "ASSISTANT_MEMORY_FILE", str(tmp_path / "assistant_memory.json")
-    )
-    monkeypatch.setattr(ai_functions, "ASSISTANT_MEMORY_TURNS", turns)
-    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
-
-
-def test_assistant_history_is_isolated_per_user(tmp_path, monkeypatch):
-    _fresh_assistant_memory(tmp_path, monkeypatch)
-    ai_functions.remember_assistant_turn(111, "sekret Anny", "ok Anna")
-    ai_functions.remember_assistant_turn(222, "sekret Bartka", "ok Bartek")
-
-    anna = ai_functions.assistant_history(111)
-    bartek = ai_functions.assistant_history(222)
-    assert [m["content"] for m in anna] == ["sekret Anny", "ok Anna"]
-    assert [m["content"] for m in bartek] == ["sekret Bartka", "ok Bartek"]
-    assert "sekret Anny" not in str(bartek)   # no cross-user bleed
-
-
-def test_assistant_history_is_trimmed_to_the_bound(tmp_path, monkeypatch):
-    _fresh_assistant_memory(tmp_path, monkeypatch, turns=4)
-    for i in range(10):
-        ai_functions.remember_assistant_turn(1, f"u{i}", f"a{i}")
-    history = ai_functions.assistant_history(1)
-    assert len(history) == 4                          # bounded
-    assert history[-1]["content"] == "a9"             # newest kept
-    assert all("u0" != m["content"] for m in history)  # oldest dropped
-
-
-def test_assistant_history_survives_a_restart(tmp_path, monkeypatch):
-    _fresh_assistant_memory(tmp_path, monkeypatch)
-    ai_functions.remember_assistant_turn(7, "pamietaj", "pamietam")
-    # Simulate a restart: drop the in-memory cache, re-read from disk.
-    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
-    assert [m["content"] for m in ai_functions.assistant_history(7)] == [
-        "pamietaj",
-        "pamietam",
-    ]
-
-
-def test_assistant_messages_carry_persona_history_and_new_turn(tmp_path, monkeypatch):
-    _fresh_assistant_memory(tmp_path, monkeypatch)
-    ai_functions.remember_assistant_turn(5, "wczoraj", "odpowiedz")
-    msgs = ai_functions.build_assistant_messages(
-        5, "Towarzysz Młotek", "Mówisz po polsku.", "dzisiaj"
-    )
-    assert msgs[0]["role"] == "system"
-    assert "Towarzysz Młotek" in msgs[0]["content"]
-    assert "Mówisz po polsku." in msgs[0]["content"]
-    assert [m["content"] for m in msgs[1:]] == ["wczoraj", "odpowiedz", "dzisiaj"]
-
-
-def test_corrupt_assistant_memory_starts_empty_instead_of_crashing(tmp_path, monkeypatch):
-    path = tmp_path / "assistant_memory.json"
-    path.write_text("{ not json", encoding="utf-8")
-    monkeypatch.setattr(ai_functions, "ASSISTANT_MEMORY_FILE", str(path))
-    monkeypatch.setattr(ai_functions, "_ASSISTANT_MEMORY", None)
-    assert ai_functions.assistant_history(1) == []