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https://gitee.com/wanwujie/deer-flow
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4a78cfe12a
* fix(llm): filter unexpected config keys to prevent LangChain warnings (#411) Add allowlist validation for LLM configuration keys to prevent unexpected parameters like SEARCH_ENGINE from being passed to LLM constructors. Changes: - Add ALLOWED_LLM_CONFIG_KEYS set with valid LLM configuration parameters - Filter out unexpected keys before creating LLM instances - Log clear warning messages when unexpected keys are removed - Add unit test for configuration key filtering This fixes the confusing LangChain warning "WARNING! SEARCH_ENGINE is not default parameter. SEARCH_ENGINE was transferred to model_kwargs" that occurred when users accidentally placed configuration keys in wrong sections of conf.yaml. * Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
128 lines
4.4 KiB
Python
128 lines
4.4 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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import pytest
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from src.llms import llm
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class DummyChatOpenAI:
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def __init__(self, **kwargs):
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self.kwargs = kwargs
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def invoke(self, msg):
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return f"Echo: {msg}"
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@pytest.fixture(autouse=True)
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def patch_chat_openai(monkeypatch):
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monkeypatch.setattr(llm, "ChatOpenAI", DummyChatOpenAI)
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@pytest.fixture
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def dummy_conf():
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return {
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"BASIC_MODEL": {"api_key": "test_key", "base_url": "http://test"},
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"REASONING_MODEL": {"api_key": "reason_key"},
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"VISION_MODEL": {"api_key": "vision_key"},
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}
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def test_get_env_llm_conf(monkeypatch):
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# Clear any existing environment variables that might interfere
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monkeypatch.delenv("BASIC_MODEL__API_KEY", raising=False)
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monkeypatch.delenv("BASIC_MODEL__BASE_URL", raising=False)
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monkeypatch.delenv("BASIC_MODEL__MODEL", raising=False)
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monkeypatch.setenv("BASIC_MODEL__API_KEY", "env_key")
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monkeypatch.setenv("BASIC_MODEL__BASE_URL", "http://env")
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conf = llm._get_env_llm_conf("basic")
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assert conf["api_key"] == "env_key"
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assert conf["base_url"] == "http://env"
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def test_create_llm_use_conf_merges_env(monkeypatch, dummy_conf):
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# Clear any existing environment variables that might interfere
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monkeypatch.delenv("BASIC_MODEL__BASE_URL", raising=False)
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monkeypatch.delenv("BASIC_MODEL__MODEL", raising=False)
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monkeypatch.setenv("BASIC_MODEL__API_KEY", "env_key")
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result = llm._create_llm_use_conf("basic", dummy_conf)
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assert isinstance(result, DummyChatOpenAI)
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assert result.kwargs["api_key"] == "env_key"
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assert result.kwargs["base_url"] == "http://test"
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def test_create_llm_use_conf_invalid_type(monkeypatch, dummy_conf):
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# Clear any existing environment variables that might interfere
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monkeypatch.delenv("BASIC_MODEL__API_KEY", raising=False)
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monkeypatch.delenv("BASIC_MODEL__BASE_URL", raising=False)
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monkeypatch.delenv("BASIC_MODEL__MODEL", raising=False)
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with pytest.raises(ValueError):
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llm._create_llm_use_conf("unknown", dummy_conf)
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def test_create_llm_use_conf_empty_conf(monkeypatch):
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# Clear any existing environment variables that might interfere
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monkeypatch.delenv("BASIC_MODEL__API_KEY", raising=False)
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monkeypatch.delenv("BASIC_MODEL__BASE_URL", raising=False)
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monkeypatch.delenv("BASIC_MODEL__MODEL", raising=False)
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with pytest.raises(ValueError):
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llm._create_llm_use_conf("basic", {})
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def test_get_llm_by_type_caches(monkeypatch, dummy_conf):
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called = {}
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def fake_load_yaml_config(path):
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called["called"] = True
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return dummy_conf
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monkeypatch.setattr(llm, "load_yaml_config", fake_load_yaml_config)
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llm._llm_cache.clear()
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inst1 = llm.get_llm_by_type("basic")
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inst2 = llm.get_llm_by_type("basic")
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assert inst1 is inst2
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assert called["called"]
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def test_create_llm_filters_unexpected_keys(monkeypatch, caplog):
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"""Test that unexpected configuration keys like SEARCH_ENGINE are filtered out (Issue #411)."""
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import logging
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# Clear any existing environment variables that might interfere
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monkeypatch.delenv("BASIC_MODEL__API_KEY", raising=False)
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monkeypatch.delenv("BASIC_MODEL__BASE_URL", raising=False)
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monkeypatch.delenv("BASIC_MODEL__MODEL", raising=False)
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# Config with unexpected keys that should be filtered
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conf_with_unexpected_keys = {
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"BASIC_MODEL": {
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"api_key": "test_key",
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"base_url": "http://test",
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"model": "gpt-4",
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"SEARCH_ENGINE": {"include_domains": ["example.com"]}, # Should be filtered
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"engine": "tavily", # Should be filtered
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}
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}
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with caplog.at_level(logging.WARNING):
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result = llm._create_llm_use_conf("basic", conf_with_unexpected_keys)
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# Verify the LLM was created
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assert isinstance(result, DummyChatOpenAI)
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# Verify unexpected keys were not passed to the LLM
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assert "SEARCH_ENGINE" not in result.kwargs
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assert "engine" not in result.kwargs
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# Verify valid keys were passed
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assert result.kwargs["api_key"] == "test_key"
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assert result.kwargs["base_url"] == "http://test"
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assert result.kwargs["model"] == "gpt-4"
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# Verify warnings were logged
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assert any("SEARCH_ENGINE" in record.message for record in caplog.records)
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assert any("engine" in record.message for record in caplog.records)
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