fix(llm): strip non-generation keys from gen_conf for LiteLLM providers (#15427) (#15432)

### What problem does this PR solve?

Fixes #15427.

All LiteLLM-routed chats fail with:

- Anthropic: `litellm.BadRequestError: AnthropicException -
{"type":"invalid_request_error","message":"model_type: Extra inputs are
not permitted"}`
- OpenAI: `litellm.BadRequestError: OpenAIException - Unknown parameter:
'model_type'`

This is a regression from v0.25.4.

#### Root cause

A chat assistant's `llm_setting` is forwarded to the model as
`gen_conf`. `llm_setting` can legitimately carry RAGFlow-internal
metadata such as `model_type` (the chat REST APIs in
`api/apps/restful_apis/` read it back out of `llm_setting`), so that key
ends up inside `gen_conf`.

`Base._clean_conf` (OpenAI-compatible providers) already **whitelists**
the keys it forwards, so direct-OpenAI providers were unaffected.
`LiteLLMBase._clean_conf` only dropped `max_tokens` and passed
everything else straight through to `litellm.acompletion`, which
forwarded `model_type` to the upstream provider — and Anthropic / OpenAI
reject it. Because both Claude and GPT route through LiteLLM, every chat
broke.

#### Fix

- Extract the allowed-key set into a shared `ALLOWED_GEN_CONF_KEYS`
constant and reuse it in `Base._clean_conf`.
- Apply the same whitelist in `LiteLLMBase._clean_conf`, plus the
LiteLLM-specific reasoning params (`thinking`, `reasoning_effort`,
`extra_body`) that the model-family policies inject for reasoning
models.

This covers all four LiteLLM completion paths (`async_chat`,
`async_chat_streamly`, `async_chat_with_tools`,
`async_chat_streamly_with_tools`), since they all route through
`_clean_conf`.

#### Tests

Adds `test/unit_test/rag/llm/test_clean_conf_whitelist.py` covering both
backends: `model_type` (and other stray keys) are dropped, genuine
generation params and `thinking` survive, `max_tokens` is removed, and
the whitelist invariants hold.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Added test cases

test/unit_test/rag/graphrag/conftest.py

@@ -47,4 +47,12 @@ sys.modules["common.connection_utils"].timeout = lambda *a, **kw: (lambda fn: fn
 sys.modules["api.db.services.task_service"].has_canceled = lambda *_a, **_kw: False
 sys.modules["rag.graphrag.general.leiden"].run = lambda *_a, **_kw: {}
 sys.modules["rag.graphrag.general.leiden"].add_community_info2graph = lambda *_a, **_kw: None
-sys.modules["rag.llm.chat_model"].Base = object
+# Only stub ``Base`` when we actually mocked chat_model. This conftest mutates
+# the global sys.modules at import time, and rag/graphrag/ is collected before
+# rag/llm/. If an earlier test package already imported the real chat_model,
+# unconditionally assigning ``Base = object`` clobbered the genuine class and
+# leaked into the rag/llm unit tests that import it (AttributeError: no
+# attribute '_clean_conf'). graphrag only uses ``Base`` as a type alias, so the
+# real class works just as well when it is already loaded.
+if isinstance(sys.modules["rag.llm.chat_model"], MagicMock):
+    sys.modules["rag.llm.chat_model"].Base = object

test/unit_test/rag/llm/test_clean_conf_whitelist.py

@@ -0,0 +1,123 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+"""
+Regression guard for issue #15427: LiteLLM-routed chats failed with
+``model_type: Extra inputs are not permitted`` (Anthropic) /
+``Unknown parameter: 'model_type'`` (OpenAI).
+
+A chat assistant's ``llm_setting`` is forwarded to the provider as
+``gen_conf``. ``llm_setting`` can legitimately carry RAGFlow-internal
+metadata such as ``model_type`` (the new chat REST APIs read it back out).
+``Base._clean_conf`` already whitelisted the keys it forwards, so OpenAI-
+compatible providers were unaffected, but ``LiteLLMBase._clean_conf`` only
+dropped ``max_tokens`` and passed everything else straight through to
+``litellm.acompletion`` — which forwarded ``model_type`` to the upstream
+provider and got rejected.
+
+These tests pin the whitelisting behaviour for both backends so the leak
+cannot reappear.
+"""
+
+import pytest
+
+from rag.llm.chat_model import (
+    ALLOWED_GEN_CONF_KEYS,
+    LITELLM_ALLOWED_GEN_CONF_KEYS,
+    Base,
+    LiteLLMBase,
+)
+
+
+class _ConcreteBase(Base):
+    """Concrete subclass so we can build an instance without touching the
+    real OpenAI client constructor."""
+
+
+class _ConcreteLiteLLM(LiteLLMBase):
+    """Concrete subclass for the same reason on the LiteLLM path."""
+
+
+def _make_base(model_name="gpt-4o"):
+    inst = _ConcreteBase.__new__(_ConcreteBase)
+    inst.model_name = model_name
+    return inst
+
+
+def _make_litellm(model_name="gpt-4o", provider=""):
+    inst = _ConcreteLiteLLM.__new__(_ConcreteLiteLLM)
+    inst.model_name = model_name
+    inst.provider = provider
+    return inst
+
+
+# --------------------------------------------------------------------------- #
+# The actual bug: model_type must never reach the provider.
+# --------------------------------------------------------------------------- #
+def test_litellm_drops_model_type():
+    cleaned = _make_litellm()._clean_conf({"temperature": 0.5, "model_type": "chat"})
+    assert "model_type" not in cleaned
+    assert cleaned["temperature"] == 0.5
+
+
+def test_base_drops_model_type():
+    cleaned = _make_base()._clean_conf({"temperature": 0.5, "model_type": "chat"})
+    assert "model_type" not in cleaned
+    assert cleaned["temperature"] == 0.5
+
+
+@pytest.mark.parametrize("stray_key", ["model_type", "llm_id", "parameter", "icon", "foo"])
+def test_litellm_drops_arbitrary_internal_keys(stray_key):
+    cleaned = _make_litellm()._clean_conf({stray_key: "x", "top_p": 0.9})
+    assert stray_key not in cleaned
+    assert cleaned["top_p"] == 0.9
+
+
+# --------------------------------------------------------------------------- #
+# The fix must not over-filter: genuine generation params still pass through.
+# --------------------------------------------------------------------------- #
+def test_litellm_preserves_known_generation_params():
+    gen_conf = {
+        "temperature": 0.7,
+        "top_p": 0.95,
+        "presence_penalty": 0.1,
+        "frequency_penalty": 0.2,
+    }
+    cleaned = _make_litellm()._clean_conf(dict(gen_conf))
+    assert cleaned == gen_conf
+
+
+def test_litellm_preserves_thinking_param():
+    """``thinking`` is injected by the model-family policy for reasoning
+    models and must survive the whitelist (it is a valid LiteLLM param)."""
+    cleaned = _make_litellm()._clean_conf({"thinking": {"type": "enabled"}, "temperature": 1.0})
+    assert cleaned["thinking"] == {"type": "enabled"}
+
+
+def test_max_tokens_is_dropped_on_both_backends():
+    assert "max_tokens" not in _make_litellm()._clean_conf({"max_tokens": 100, "temperature": 0.3})
+    assert "max_tokens" not in _make_base()._clean_conf({"max_tokens": 100, "temperature": 0.3})
+
+
+# --------------------------------------------------------------------------- #
+# Whitelist invariants.
+# --------------------------------------------------------------------------- #
+def test_litellm_whitelist_is_superset_of_base():
+    assert ALLOWED_GEN_CONF_KEYS <= LITELLM_ALLOWED_GEN_CONF_KEYS
+
+
+def test_model_type_not_whitelisted_anywhere():
+    assert "model_type" not in ALLOWED_GEN_CONF_KEYS
+    assert "model_type" not in LITELLM_ALLOWED_GEN_CONF_KEYS