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_functions.py

@@ -137,11 +137,11 @@ def set_active_model(model: str, name: str = None) -> dict:
         raise ValueError("pusta nazwa modelu")
     cfg = AI_CONFIGS[cfg_name]
     cfg["latest_model"] = model.strip()
-    _persist_active_ai_config(_ACTIVE_CONFIG_NAME)
+    _persist_active_ai_config(_ACTIVE_CONFIG_NAME, model_for=cfg_name)
     return cfg
 
 
-def _persist_active_ai_config(name: str) -> None:
+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
@@ -161,10 +161,16 @@ def _persist_active_ai_config(name: str) -> None:
         return
     if len(data) > 2 and isinstance(data[2], dict):
         data[2]["active"] = name
-        # Assign (not setdefault): AI_CONFIGS is the in-memory truth and may
-        # carry a model pinned via set_active_model, which setdefault would
-        # silently drop on restart.
-        data[2]["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:

docker/env/bot.env.example

@@ -24,8 +24,11 @@ CONJURER_NETRC_FILE=/secrets/.netrc
 # 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=
-# Model used for normal replies. $modele_ai lists what the server actually has
-# pulled, and $gadaj_teraz ollama <model> pins one at runtime (persisted).
+# 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=

tests/unit/test_ai_provider_switch.py

@@ -219,6 +219,11 @@ def test_list_provider_models_wraps_server_failure(monkeypatch):
 
 
 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"))
@@ -236,16 +241,25 @@ def test_set_active_model_pins_latest_and_keeps_cheap(monkeypatch):
     )
     written = {}
     monkeypatch.setattr(
-        ai_functions, "_persist_active_ai_config", lambda name: written.update(name=name)
+        ai_functions,
+        "_persist_active_ai_config",
+        lambda name, model_for=None: written.update(name=name, model_for=model_for),
+    
     )
-    cfg = ai_functions.set_active_model("mistral:7b", "ollama")
-    assert cfg["latest_model"] == "mistral:7b"
-    assert cfg["cheap_model"] == "cheap:1"   # MUSIC path untouched
-    assert written  # the choice was persisted
+    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: None)
+    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")
@@ -295,3 +309,88 @@ def test_provider_generate_routes_to_ollama(monkeypatch):
         _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