ai_commands.py

@@ -174,19 +174,57 @@ class Events(commands.Cog):
                 await ctx.reply("Nope. Nie wiesz jak użyć")
 
     @commands.hybrid_command(
-        name="gadaj_teraz",
-        description="Pokaż/przełącz backend AI (bez argumentu = status). Przełączanie: Vykidailo.",
+        name="modele_ai",
+        description="Pokaż modele dostępne dla danego backendu (Ollamę pyta na żywo).",
     )
-    async def gadaj_teraz(self, ctx, nazwa_konfigu: Optional[str] = None):
+    async def modele_ai(self, ctx, nazwa_konfigu: Optional[str] = None):
+        """Read-only model listing. For Ollama this queries the server, so it
+        shows exactly what is pulled on the box right now."""
+        async with ctx.channel.typing():
+            target = nazwa_konfigu or ai_functions.get_active_ai_config()
+            if target not in ai_functions.list_ai_configs():
+                await discord_friendly_reply(
+                    ctx,
+                    f"Nie znam configu '{target}'. Dostępne: "
+                    f"{', '.join(ai_functions.list_ai_configs())}",
+                )
+                return
+            try:
+                models = await ai_functions.list_provider_models(target)
+            except ai_functions.AIError as exc:
+                await discord_friendly_reply(
+                    ctx, f"Nie mogę pobrać modeli dla '{target}': {exc}"
+                )
+                return
+            if not models:
+                await discord_friendly_reply(ctx, f"Brak modeli dla '{target}'.")
+                return
+            await discord_friendly_reply(
+                ctx,
+                f"Modele dla **{target}**: {', '.join(models)}\n"
+                f"Wepniesz przez `$gadaj_teraz {target} <model>` (tylko Vykidailo).",
+            )
+
+    @commands.hybrid_command(
+        name="gadaj_teraz",
+        description="Pokaż/przełącz backend AI i model (bez argumentu = status). Przełączanie: Vykidailo.",
+    )
+    async def gadaj_teraz(
+        self, ctx, nazwa_konfigu: Optional[str] = None, model: Optional[str] = None
+    ):
         async with ctx.channel.typing():
             available = ai_functions.list_ai_configs()
             # No argument -> report the active backend (read-only, open to all).
             if not nazwa_konfigu:
                 active = ai_functions.get_active_ai_config()
+                active_cfg = ai_functions.AI_CONFIGS.get(active, {})
                 await discord_friendly_reply(
                     ctx,
-                    f"Teraz gadam przez **{active}**. Dostępne: {', '.join(available)}. "
-                    "Przełączysz przez `$gadaj_teraz <config>` (tylko Vykidailo).",
+                    f"Teraz gadam przez **{active}** "
+                    f"({active_cfg.get('provider')} / {active_cfg.get('latest_model')}). "
+                    f"Dostępne: {', '.join(available)}. "
+                    "Przełączysz przez `$gadaj_teraz <config> [model]` (tylko Vykidailo), "
+                    "modele zobaczysz przez `$modele_ai`.",
                 )
                 return
             is_admin = isinstance(ctx.author, discord.Member) and any(
@@ -208,13 +246,60 @@ class Events(commands.Cog):
                     ctx, f"Nie mogę przełączyć na '{nazwa_konfigu}': {exc}"
                 )
                 return
+
+            # Optional second argument pins the model. For a backend we can
+            # enumerate (Ollama), reject an unknown id up front with the list -
+            # otherwise the typo only surfaces later as a failed reply.
+            if model:
+                try:
+                    known = await ai_functions.list_provider_models(nazwa_konfigu)
+                except ai_functions.AIError:
+                    known = []  # cannot enumerate -> accept verbatim
+                if known and cfg.get("provider") == "ollama" and model not in known:
+                    await discord_friendly_reply(
+                        ctx,
+                        f"Model '{model}' nie jest wgrany na Ollamie. "
+                        f"Dostępne: {', '.join(known)}",
+                    )
+                    return
+                try:
+                    cfg = ai_functions.set_active_model(model, nazwa_konfigu)
+                except (KeyError, ValueError) as exc:
+                    await discord_friendly_reply(
+                        ctx, f"Nie mogę wpiąć modelu '{model}': {exc}"
+                    )
+                    return
+
             self.logger.info(
-                "Przełączono AI na config %s (%s)", nazwa_konfigu, cfg.get("provider")
+                "Przełączono AI na config %s (%s / %s)",
+                nazwa_konfigu, cfg.get("provider"), cfg.get("latest_model"),
             )
-            await discord_friendly_reply(
-                ctx,
-                f"Teraz gadam przez **{nazwa_konfigu}** — {cfg.get('provider')} / {cfg.get('latest_model')}.",
+            message = (
+                f"Teraz gadam przez **{nazwa_konfigu}** — "
+                f"{cfg.get('provider')} / {cfg.get('latest_model')}."
             )
+            # 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"(`$gadaj_teraz {nazwa_konfigu} <model>`)."
+                    )
+                elif len(others) > 1:
+                    message += (
+                        f"\nDostępne modele: {', '.join(others)} "
+                        f"(`$gadaj_teraz {nazwa_konfigu} <model>`)."
+                    )
+            await discord_friendly_reply(ctx, message)
 
     @commands.hybrid_command(
         name="armia_hammera",

ai_functions.py

@@ -9,6 +9,7 @@ import time
 from other_functions import discord_friendly_send
 from constants import (
     AI_CONFIGS,
+    AI_TIMEOUT_SECONDS,
     ASSISTANTS,
     CLAUDECLIENT,
     CYCLIC_WORDS,
@@ -19,6 +20,7 @@ from constants import (
     MEMORY_FIVE_SIARA,
     MESSAGE_TABLE,
     MESSAGE_TABLE_MUZYKA,
+    OLLAMACLIENT,
     OPENAICLIENT,
     SYSTEM_GPT_SETTINGS,
     WORD_REACTIONS,
@@ -92,12 +94,54 @@ def set_active_ai_config(name: str) -> dict:
         raise RuntimeError("klient Anthropic nie jest skonfigurowany (brak ANTHROPIC_API_KEY)")
     if provider == "openai" and OPENAICLIENT is None:
         raise RuntimeError("klient OpenAI nie jest skonfigurowany (brak OPENAI_API_KEY)")
+    if provider == "ollama" and OLLAMACLIENT is None:
+        raise RuntimeError("Ollama nie jest skonfigurowana (ustaw CONJURER_OLLAMA_URL)")
     _ACTIVE_CONFIG_NAME = name
     _persist_active_ai_config(name)
     return cfg
 
 
-def _persist_active_ai_config(name: str) -> None:
+async def list_provider_models(name: str = None):
+    """Model ids selectable for a config.
+
+    For Ollama this ASKS THE SERVER (its OpenAI-compatible /v1/models), so the
+    picker always reflects what is actually pulled on the box rather than a
+    hardcoded list. Hosted providers are not enumerated - we only report what
+    the config is wired to.
+    """
+    cfg = AI_CONFIGS.get(name or _ACTIVE_CONFIG_NAME) or _active_config()
+    if cfg.get("provider") == "ollama":
+        if OLLAMACLIENT is None:
+            raise AIError(
+                "auth",
+                RuntimeError("Ollama nie jest skonfigurowana (ustaw CONJURER_OLLAMA_URL)"),
+            )
+        try:
+            resp = await OLLAMACLIENT.models.list()
+        except Exception as exc:  # pylint: disable=broad-except
+            raise _map_openai_error(exc)
+        return sorted({item.id for item in resp.data})
+    return [m for m in (cfg.get("latest_model"), cfg.get("cheap_model")) if m]
+
+
+def set_active_model(model: str, name: str = None) -> dict:
+    """Pin the model a config uses for normal replies, and persist it.
+
+    Only ``latest_model`` is changed; ``cheap_model`` stays as configured so the
+    MUSIC path keeps its cheaper backend.
+    """
+    cfg_name = name or _ACTIVE_CONFIG_NAME
+    if cfg_name not in AI_CONFIGS:
+        raise KeyError(cfg_name)
+    if not model or not model.strip():
+        raise ValueError("pusta nazwa modelu")
+    cfg = AI_CONFIGS[cfg_name]
+    cfg["latest_model"] = model.strip()
+    _persist_active_ai_config(_ACTIVE_CONFIG_NAME, model_for=cfg_name)
+    return cfg
+
+
+def _persist_active_ai_config(name: str, model_for: str = None) -> None:
     """Best-effort write of the active-config choice into system_gpt_settings.json.
 
     Keeps the historical two-element structure intact: updates index 2 if it
@@ -117,7 +161,16 @@ def _persist_active_ai_config(name: str) -> None:
         return
     if len(data) > 2 and isinstance(data[2], dict):
         data[2]["active"] = name
-        data[2].setdefault("configs", AI_CONFIGS)
+        # Write back ONLY what this process actually changed. Assigning the whole
+        # in-memory AI_CONFIGS here would clobber operator hand-edits (the only
+        # way to change cheap_model/temperature/max_tokens) and re-seed configs
+        # deliberately deleted from the file, because AI_CONFIGS is the built-in
+        # defaults merged under the file. setdefault alone is not enough either:
+        # it would drop a model pinned via set_active_model, hence model_for.
+        block = data[2].setdefault("configs", AI_CONFIGS)
+        if model_for and model_for in AI_CONFIGS:
+            entry = block.setdefault(model_for, dict(AI_CONFIGS[model_for]))
+            entry["latest_model"] = AI_CONFIGS[model_for]["latest_model"]
     else:
         data = data[:2] + [{"active": name, "configs": AI_CONFIGS}]
     try:
@@ -214,12 +267,37 @@ async def _anthropic_call(messages, model, cfg):
     return text.strip()
 
 
+async def _ollama_call(messages, model, cfg):
+    """Self-hosted counterpart of openai_call. Returns a plain string.
+
+    Ollama exposes an OpenAI-compatible /v1 surface, so the same message format
+    and the same error mapping apply - only the base_url and the model ids
+    differ. Chat Completions (not the Responses API) is what Ollama implements.
+    """
+    if OLLAMACLIENT is None:
+        raise AIError(
+            "auth",
+            RuntimeError("Ollama nie jest skonfigurowana (ustaw CONJURER_OLLAMA_URL)"),
+        )
+    try:
+        resp = await OLLAMACLIENT.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=float(cfg.get("temperature", 0.2)),
+        )
+    except Exception as exc:  # pylint: disable=broad-except
+        raise _map_openai_error(exc)
+    return (resp.choices[0].message.content or "").strip()
+
+
 async def provider_generate(messages, model, temperature=0.2):
     """Dispatch a chat completion to the active backend, normalising errors."""
     cfg = _active_config()
     try:
         if cfg.get("provider") == "anthropic":
             return await _anthropic_call(messages, model, cfg)
+        if cfg.get("provider") == "ollama":
+            return await _ollama_call(messages, model, cfg)
         return await openai_call(messages, model, temperature)
     except AIError:
         raise
@@ -459,7 +537,7 @@ async def handle_response(
     try:
         # ...przygotowanie messages/system prompt/itp. jak masz...
         # retry/backoff + deadline (zachowuje Twoją semantykę logowania)
-        timeout_sec = 120
+        timeout_sec = AI_TIMEOUT_SECONDS
         deadline = time.time() + timeout_sec
         response = await asyncio.wait_for(
             provider_generate(messages=history_msgs, model=model_to_use),

constants.py

@@ -400,6 +400,25 @@ if anthropic and ANTHROPIC_API_KEY:
 else:
     CLAUDECLIENT = None
 
+# Ollama (self-hosted models). There is no API key - the endpoint IS the whole
+# configuration, so the feature stays dormant until CONJURER_OLLAMA_URL is set
+# (same pattern as the Conan bridge). We talk to Ollama's OpenAI-COMPATIBLE
+# surface (/v1) with the openai SDK we already depend on, which means the
+# existing message format and _map_openai_error handling work unchanged.
+# How long handle_response waits for ANY backend before giving up. 120s was
+# hardcoded and is fine for hosted APIs, but a self-hosted model on a modest GPU
+# can legitimately take longer, so it is now tunable.
+AI_TIMEOUT_SECONDS = int(os.getenv("CONJURER_AI_TIMEOUT_SECONDS", "120"))
+
+OLLAMA_URL = os.getenv("CONJURER_OLLAMA_URL", "").rstrip("/")
+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:
+    # api_key is required by the SDK but ignored by Ollama.
+    OLLAMACLIENT = openai.AsyncOpenAI(base_url=f"{OLLAMA_URL}/v1", api_key="ollama")
+else:
+    OLLAMACLIENT = None
+
 TOKEN = _resolve_token("discord", "DISCORD_TOKEN")
 
 # Voice recognition (AssemblyAI). None = the voice cog reports and disables.
@@ -489,6 +508,12 @@ def _default_ai_configs():
             # sent for Claude (Opus 4.8 / Sonnet 5 reject sampling params).
             "max_tokens": 2048,
         },
+        "ollama": {
+            "provider": "ollama",
+            "latest_model": OLLAMA_LATEST_MODEL,
+            "cheap_model": OLLAMA_CHEAP_MODEL,
+            "temperature": 0.2,
+        },
         # Template for wiring further providers. Copy it, rename the key, point
         # "provider" at a backend ai_functions.provider_generate implements, and
         # fill in the model ids. Keys starting with "_" are treated as inert
@@ -508,7 +533,12 @@ _ai_block = (
     if isinstance(GPT_SETTINGS, list) and len(GPT_SETTINGS) > 2 and isinstance(GPT_SETTINGS[2], dict)
     else {}
 )
-AI_CONFIGS = _ai_block.get("configs") or _default_ai_configs()
+# Built-in defaults FIRST, then whatever the settings file defines on top. The
+# file cannot simply win outright: every provider switch persists a "configs"
+# block, so a file written by an older build would permanently hide providers
+# added later (ollama) from the picker.
+AI_CONFIGS = _default_ai_configs()
+AI_CONFIGS.update(_ai_block.get("configs") or {})
 # Single switch: env var wins, then the settings-file "active" key, then "gpt".
 DEFAULT_AI_CONFIG = (
     os.getenv("CONJURER_AI_CONFIG")

docker/env/bot.env.example

@@ -13,10 +13,29 @@ CONJURER_NETRC_FILE=/secrets/.netrc
 
 # --- AI backend switch --------------------------------------------------
 # Which AI config from system_gpt_settings.json is active at startup
-# (e.g. "gpt" or "claude"). Runtime switch: $gadaj_teraz <config>. Unset =
-# whatever the settings file's "active" key says, falling back to "gpt".
+# (e.g. "gpt", "claude" or "ollama"). Runtime switch:
+# $gadaj_teraz <config> [model]. Unset = whatever the settings file's "active"
+# key says, falling back to "gpt".
 # CONJURER_AI_CONFIG=gpt
 
+# --- Ollama (self-hosted models) ----------------------------------------
+# The endpoint IS the whole configuration - no API key. Leave unset and the
+# "ollama" backend simply refuses to be selected. In-cluster, use the Service
+# DNS name; from outside, host:port. Port 11434 is Ollama's default.
+#   CONJURER_OLLAMA_URL=http://ollama.ollama.svc.cluster.local:11434
+# CONJURER_OLLAMA_URL=
+# DEFAULT model for normal replies. $modele_ai lists what the server actually
+# has pulled. Pinning one at runtime with `$gadaj_teraz ollama <model>` is
+# persisted into system_gpt_settings.json and from then on WINS over this
+# variable - the pin is the more recent, more explicit choice. Clear the
+# "latest_model" of the ollama entry in that file to fall back to this default.
+# CONJURER_OLLAMA_MODEL=llama3.1:8b
+# Model used for the cheaper MUSIC path; defaults to CONJURER_OLLAMA_MODEL.
+# CONJURER_OLLAMA_CHEAP_MODEL=
+# How long to wait for ANY backend to answer. 120s suits hosted APIs; a
+# self-hosted model on a modest GPU may need more.
+# CONJURER_AI_TIMEOUT_SECONDS=120
+
 # --- Data ---------------------------------------------------------------
 # Single mounted volume; all writable state is rooted here.
 CONJURER_DATA_DIR=/data

tests/unit/test_ai_provider_switch.py

@@ -136,3 +136,261 @@ def test_map_openai_error_categories():
     assert ai_functions._map_openai_error(_bare(openai.RateLimitError)).category == "rate_limit"
     assert ai_functions._map_openai_error(_bare(openai.APITimeoutError)).category == "timeout"
     assert ai_functions._map_openai_error(ValueError("x")).category == "api"
+
+
+# ---------------------------------------------------------------- Ollama
+# The self-hosted backend is wired through Ollama's OpenAI-compatible surface,
+# so it reuses the message format and the error mapping above. What is new and
+# worth pinning: it must appear in the picker even on an upgraded settings file,
+# models come from the SERVER, and pinning one must stick.
+import asyncio  # noqa: E402
+
+
+class _FakeModel:
+    def __init__(self, ident):
+        self.id = ident
+
+
+class _FakeModels:
+    def __init__(self, ids, raises=None):
+        self._ids = ids
+        self._raises = raises
+
+    async def list(self):
+        if self._raises:
+            raise self._raises
+        return types.SimpleNamespace(data=[_FakeModel(i) for i in self._ids])
+
+
+class _FakeOllamaClient:
+    def __init__(self, ids=(), raises=None):
+        self.models = _FakeModels(list(ids), raises)
+
+
+def test_ollama_config_is_offered_in_the_picker():
+    assert "ollama" in ai_functions.list_ai_configs()
+    cfg = ai_functions.AI_CONFIGS["ollama"]
+    assert cfg["provider"] == "ollama"
+
+
+def test_select_model_uses_ollama_models_when_active(monkeypatch):
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "llama3.1:8b", "cheap_model": "qwen2.5:3b"},
+    )
+    _reset_active("ollama")
+    try:
+        # the legacy gpt-4o default must auto-map, not leak to Ollama
+        assert ai_functions.select_model("GENERAL", "gpt-4o") == "llama3.1:8b"
+        assert ai_functions.select_model("MUSIC", "gpt-4o") == "qwen2.5:3b"
+        # an explicit id is still honoured verbatim
+        assert ai_functions.select_model("GENERAL", "mistral:7b") == "mistral:7b"
+    finally:
+        _reset_active("gpt")
+
+
+def test_list_provider_models_queries_the_ollama_server(monkeypatch):
+    monkeypatch.setattr(
+        ai_functions, "OLLAMACLIENT", _FakeOllamaClient(["b:2", "a:1", "a:1"])
+    )
+    models = asyncio.run(ai_functions.list_provider_models("ollama"))
+    assert models == ["a:1", "b:2"]  # sorted + de-duplicated
+
+
+def test_list_provider_models_for_hosted_provider_reports_configured_ids():
+    models = asyncio.run(ai_functions.list_provider_models("gpt"))
+    assert models == [
+        ai_functions.AI_CONFIGS["gpt"]["latest_model"],
+        ai_functions.AI_CONFIGS["gpt"]["cheap_model"],
+    ]
+
+
+def test_list_provider_models_wraps_server_failure(monkeypatch):
+    monkeypatch.setattr(
+        ai_functions, "OLLAMACLIENT", _FakeOllamaClient(raises=ValueError("boom"))
+    )
+    try:
+        asyncio.run(ai_functions.list_provider_models("ollama"))
+    except ai_functions.AIError as exc:
+        assert exc.category == "api"
+    else:
+        raise AssertionError("a server failure must surface as AIError")
+
+
+def test_list_provider_models_without_endpoint_is_an_auth_error(monkeypatch):
+    # Contrast, so the assertion cannot pass vacuously: with a client present the
+    # call succeeds, and ONLY setting it to None turns it into an auth error.
+    monkeypatch.setattr(ai_functions, "OLLAMACLIENT", _FakeOllamaClient(["a:1"]))
+    assert asyncio.run(ai_functions.list_provider_models("ollama")) == ["a:1"]
+
+    monkeypatch.setattr(ai_functions, "OLLAMACLIENT", None)
+    try:
+        asyncio.run(ai_functions.list_provider_models("ollama"))
+    except ai_functions.AIError as exc:
+        assert exc.category == "auth"
+    else:
+        raise AssertionError("an unconfigured Ollama must surface as AIError")
+
+
+def test_set_active_model_pins_latest_and_keeps_cheap(monkeypatch):
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "old:1", "cheap_model": "cheap:1"},
+    )
+    written = {}
+    monkeypatch.setattr(
+        ai_functions,
+        "_persist_active_ai_config",
+        lambda name, model_for=None: written.update(name=name, model_for=model_for),
+    
+    )
+    ai_functions.set_active_model("mistral:7b", "ollama")
+    # Assert on the shared registry, not on the returned object - that object IS
+    # the mutated dict, so asserting on it would pass even if nothing was stored.
+    stored = ai_functions.AI_CONFIGS["ollama"]
+    assert stored["latest_model"] == "mistral:7b"
+    assert stored["cheap_model"] == "cheap:1"   # MUSIC path untouched
+    assert written["name"]                      # the choice was persisted...
+    assert written["model_for"] == "ollama"     # ...scoped to the config we changed
+
+
+def test_set_active_model_rejects_blank_and_unknown_config(monkeypatch):
+    monkeypatch.setattr(
+        ai_functions, "_persist_active_ai_config", lambda _n, model_for=None: None
+    )
+    for bad in ("", "   "):
+        try:
+            ai_functions.set_active_model(bad, "gpt")
+        except ValueError:
+            pass
+        else:
+            raise AssertionError("a blank model id must be rejected")
+    try:
+        ai_functions.set_active_model("x", "nie-ma-takiego")
+    except KeyError:
+        pass
+    else:
+        raise AssertionError("an unknown config must be rejected")
+
+
+def test_switching_to_ollama_without_endpoint_explains_itself(monkeypatch):
+    monkeypatch.setattr(ai_functions, "OLLAMACLIENT", None)
+    try:
+        ai_functions.set_active_ai_config("ollama")
+    except RuntimeError as exc:
+        assert "CONJURER_OLLAMA_URL" in str(exc)
+    else:
+        raise AssertionError("switching to an unconfigured Ollama must raise")
+    finally:
+        _reset_active("gpt")
+
+
+def test_provider_generate_routes_to_ollama(monkeypatch):
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "m:1", "cheap_model": "m:1"},
+    )
+    _reset_active("ollama")
+    seen = {}
+
+    async def _fake_ollama_call(messages, model, cfg):
+        seen.update(model=model, messages=messages)
+        return "odpowiedź z domu"
+
+    monkeypatch.setattr(ai_functions, "_ollama_call", _fake_ollama_call)
+    try:
+        out = asyncio.run(
+            ai_functions.provider_generate([{"role": "user", "content": "hej"}], "m:1")
+        )
+    finally:
+        _reset_active("gpt")
+    assert out == "odpowiedź z domu"
+    assert seen["model"] == "m:1"
+
+
+# ------------------------------------------------- persistence (real disk path)
+# This path had NO coverage, which is exactly how a config-clobbering regression
+# got in: persisting the whole in-memory AI_CONFIGS (built-in defaults merged
+# under the file) overwrote operator hand-edits and resurrected deleted configs.
+import json  # noqa: E402
+
+
+def _settings_file(tmp_path, configs, active="gpt"):
+    path = tmp_path / "system_gpt_settings.json"
+    path.write_text(
+        json.dumps(
+            [
+                {"role": "system", "content": "sys"},
+                {"someuser": [1, "a", "b", "c", "asst_x"]},
+                {"active": active, "configs": configs},
+            ]
+        ),
+        encoding="utf-8",
+    )
+    return path
+
+
+def test_persist_writes_the_pin_without_clobbering_operator_edits(tmp_path, monkeypatch):
+    # The file is authoritative for everything the bot does not itself change:
+    # a hand-tuned cheap_model, and a config deliberately deleted from it.
+    settings = _settings_file(
+        tmp_path,
+        {"gpt": {"provider": "openai", "latest_model": "gpt-4.1", "cheap_model": "hand-tuned"}},
+    )
+    monkeypatch.setattr(ai_functions, "SYSTEM_GPT_SETTINGS", str(settings))
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "mistral:7b", "cheap_model": "c:1"},
+    )
+
+    ai_functions._persist_active_ai_config("ollama", model_for="ollama")
+
+    data = json.loads(settings.read_text(encoding="utf-8"))
+    configs = data[2]["configs"]
+    assert data[2]["active"] == "ollama"
+    assert configs["ollama"]["latest_model"] == "mistral:7b"   # the pin landed
+    assert configs["gpt"]["latest_model"] == "gpt-4.1"         # edit survived
+    assert configs["gpt"]["cheap_model"] == "hand-tuned"       # edit survived
+    assert "claude" not in configs   # a deleted config is NOT resurrected
+    assert data[0]["content"] == "sys" and "someuser" in data[1]  # rest intact
+
+
+def test_plain_switch_leaves_the_configs_block_untouched(tmp_path, monkeypatch):
+    original = {"gpt": {"provider": "openai", "latest_model": "gpt-4.1", "cheap_model": "hand-tuned"}}
+    settings = _settings_file(tmp_path, original, active="claude")
+    monkeypatch.setattr(ai_functions, "SYSTEM_GPT_SETTINGS", str(settings))
+
+    # Switching backend without pinning a model must only move "active".
+    ai_functions._persist_active_ai_config("gpt")
+
+    data = json.loads(settings.read_text(encoding="utf-8"))
+    assert data[2]["active"] == "gpt"
+    assert data[2]["configs"] == original
+
+
+def test_pinned_model_survives_a_restart(tmp_path, monkeypatch):
+    # The whole point of persisting: re-reading the file must yield the pin.
+    settings = _settings_file(tmp_path, {"ollama": {"provider": "ollama", "latest_model": "old:1"}})
+    monkeypatch.setattr(ai_functions, "SYSTEM_GPT_SETTINGS", str(settings))
+    monkeypatch.setitem(
+        ai_functions.AI_CONFIGS,
+        "ollama",
+        {"provider": "ollama", "latest_model": "new:2", "cheap_model": "c:1"},
+    )
+
+    ai_functions._persist_active_ai_config("ollama", model_for="ollama")
+
+    reread = json.loads(settings.read_text(encoding="utf-8"))[2]
+    assert reread["configs"]["ollama"]["latest_model"] == "new:2"
+
+
+def test_persist_survives_an_unreadable_settings_file(tmp_path, monkeypatch):
+    # Best-effort by contract: a broken file must not raise into the command.
+    broken = tmp_path / "broken.json"
+    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