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517eeaf4b9
- Local servers (LM Studio, vLLM) reject the object-form tool_choice langchain sends for function calling. The generic openai_compatible provider now binds the schema as a tool without forcing tool_choice. - A structured call can return no parsed result (a thinking model answering in plain text); fall back to free text with a clear reason instead of an opaque render error.
101 lines
4.3 KiB
Python
101 lines
4.3 KiB
Python
"""Generic OpenAI-compatible provider (vLLM / LM Studio / llama.cpp / relays).
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Verifies the user-supplied base_url is required and honored, the key is optional
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(keyless local default), Chat Completions (not the Responses API) is used, any
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model name is accepted, and the env backend URL precedence (#978).
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"""
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import pytest
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from tradingagents.llm_clients.api_key_env import get_api_key_env
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from tradingagents.llm_clients.factory import create_llm_client
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from tradingagents.llm_clients.validators import validate_model
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# Note: assert by class NAME, not isinstance — other tests reload the
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# openai_client module, which would otherwise create a second class identity.
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@pytest.mark.unit
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def test_factory_routes_to_openai_client():
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client = create_llm_client(
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provider="openai_compatible", model="my-model", base_url="http://localhost:8000/v1"
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)
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assert type(client).__name__ == "OpenAIClient"
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@pytest.mark.unit
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def test_base_url_required(monkeypatch):
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monkeypatch.delenv("OPENAI_COMPATIBLE_API_KEY", raising=False)
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with pytest.raises(ValueError, match="requires a base_url"):
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create_llm_client(provider="openai_compatible", model="m").get_llm()
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@pytest.mark.unit
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def test_keyless_local_uses_placeholder_and_chat_completions(monkeypatch):
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monkeypatch.delenv("OPENAI_COMPATIBLE_API_KEY", raising=False)
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llm = create_llm_client(
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provider="openai_compatible", model="qwen2.5", base_url="http://localhost:8000/v1"
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).get_llm()
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assert type(llm).__name__ == "LocalCompatibleChatOpenAI"
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assert str(llm.openai_api_base) == "http://localhost:8000/v1"
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# keyless local servers: a placeholder key is sent
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key = llm.openai_api_key.get_secret_value() if hasattr(llm.openai_api_key, "get_secret_value") else llm.openai_api_key
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assert key == "EMPTY"
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# must use Chat Completions, not OpenAI's Responses API
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assert getattr(llm, "use_responses_api", False) in (False, None)
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@pytest.mark.unit
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def test_optional_key_from_env(monkeypatch):
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monkeypatch.setenv("OPENAI_COMPATIBLE_API_KEY", "sk-relay-123")
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llm = create_llm_client(
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provider="openai_compatible", model="m", base_url="https://relay.example/v1"
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).get_llm()
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key = llm.openai_api_key.get_secret_value() if hasattr(llm.openai_api_key, "get_secret_value") else llm.openai_api_key
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assert key == "sk-relay-123"
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@pytest.mark.unit
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def test_any_model_accepted_no_forced_key():
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assert validate_model("openai_compatible", "literally-anything") is True
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# The key env exists (read for keyed relays) but the provider is marked
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# key-optional, so the CLI never forces a prompt and keyless servers work.
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assert get_api_key_env("openai_compatible") == "OPENAI_COMPATIBLE_API_KEY"
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from tradingagents.llm_clients.openai_client import OPENAI_COMPATIBLE_PROVIDERS
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assert OPENAI_COMPATIBLE_PROVIDERS["openai_compatible"].key_optional is True
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@pytest.mark.unit
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def test_env_backend_url_precedence():
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# #978: explicit env URL wins over the menu/default regardless of provider source.
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from cli.utils import resolve_backend_url
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assert resolve_backend_url("openai", "https://api.openai.com/v1", env_url="http://proxy/v1") == "http://proxy/v1"
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assert resolve_backend_url("openai", "https://api.openai.com/v1", env_url=None) == "https://api.openai.com/v1"
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assert resolve_backend_url("deepseek", None, None) == "https://api.deepseek.com"
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@pytest.mark.unit
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def test_structured_output_suppresses_object_tool_choice(monkeypatch):
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# LM Studio / vLLM reject the object-form tool_choice langchain sends for
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# function-calling structured output (#1057). The generic provider binds the
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# schema as a tool but must not force tool_choice.
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from langchain_openai import ChatOpenAI
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from pydantic import BaseModel
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class Schema(BaseModel):
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x: int
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captured = {}
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monkeypatch.setattr(
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ChatOpenAI,
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"with_structured_output",
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lambda self, schema, method=None, **kw: captured.update({"method": method, **kw}) or "BOUND",
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)
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llm = create_llm_client(
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provider="openai_compatible", model="local-llm-30b", base_url="http://localhost:1234/v1"
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).get_llm()
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out = llm.with_structured_output(Schema)
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assert out == "BOUND"
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assert captured["method"] == "function_calling"
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assert captured["tool_choice"] is None # not the object form
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