Test for llm models --options, refs #169

llm/cli.py

@@ -599,10 +599,13 @@ def models_list(options):
             for name, field in model_with_aliases.model.Options.schema()[
                 "properties"
             ].items():
+                any_of = field.get("anyOf")
+                if any_of is None:
+                    any_of = [{"type": field["type"]}]
                 types = ", ".join(
                     [
                         _type_lookup.get(item["type"], item["type"])
-                        for item in field["anyOf"]
+                        for item in any_of
                         if item["type"] != "null"
                     ]
                 )

tests/test_llm.py

@@ -298,3 +298,40 @@ def test_openai_localai_configuration(mocked_localai, user_path):
         "messages": [{"role": "user", "content": "three names\nfor a pet pelican"}],
         "stream": False,
     }
+
+
+EXPECTED_OPTIONS = """
+OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
+  temperature: float
+    What sampling temperature to use, between 0 and 2. Higher values like
+    0.8 will make the output more random, while lower values like 0.2 will
+    make it more focused and deterministic.
+  max_tokens: int
+    Maximum number of tokens to generate.
+  top_p: float
+    An alternative to sampling with temperature, called nucleus sampling,
+    where the model considers the results of the tokens with top_p
+    probability mass. So 0.1 means only the tokens comprising the top 10%
+    probability mass are considered. Recommended to use top_p or
+    temperature but not both.
+  frequency_penalty: float
+    Number between -2.0 and 2.0. Positive values penalize new tokens based
+    on their existing frequency in the text so far, decreasing the model's
+    likelihood to repeat the same line verbatim.
+  presence_penalty: float
+    Number between -2.0 and 2.0. Positive values penalize new tokens based
+    on whether they appear in the text so far, increasing the model's
+    likelihood to talk about new topics.
+  stop: str
+    A string where the API will stop generating further tokens.
+  logit_bias: dict, str
+    Modify the likelihood of specified tokens appearing in the completion.
+    Pass a JSON string like '{"1712":-100, "892":-100, "1489":-100}'
+"""
+
+
+def test_llm_models_options(user_path):
+    runner = CliRunner()
+    result = runner.invoke(cli, ["models", "--options"], catch_exceptions=False)
+    assert result.exit_code == 0
+    assert EXPECTED_OPTIONS.strip() in result.output