Add test point for function calls

This test point requires an OpenAI key. Thus, also move the “disable key from
env” from the class setup into the “test error if no key is given” test
point, where it is needed.

Author: ccreutzi

tests/topenAIChat.m

@@ -3,18 +3,6 @@
 
 %   Copyright 2023-2024 The MathWorks, Inc.
 
-    methods (TestClassSetup)
-        function saveEnvVar(testCase)
-            % Ensures key is not in environment variable for tests
-            openAIEnvVar = "OPENAI_API_KEY";
-            if isenv(openAIEnvVar)
-                key = getenv(openAIEnvVar);
-                unsetenv(openAIEnvVar);
-                testCase.addTeardown(@(x) setenv(openAIEnvVar, x), key);
-            end
-        end
-    end
-
     properties(TestParameter)
         ValidConstructorInput = iGetValidConstructorInput();
         InvalidConstructorInput = iGetInvalidConstructorInput();
@@ -39,10 +27,6 @@ function generateAcceptsMessagesAsInput(testCase)
             testCase.verifyWarningFree(@()generate(chat,messages));
         end
 
-        function keyNotFound(testCase)
-            testCase.verifyError(@()openAIChat, "llms:keyMustBeSpecified");
-        end
-
         function constructChatWithAllNVP(testCase)
             functions = openAIFunction("funName");
             modelName = "gpt-3.5-turbo";
@@ -104,6 +88,80 @@ function invalidInputsConstructor(testCase, InvalidConstructorInput)
             testCase.verifyError(@()openAIChat(InvalidConstructorInput.Input{:}), InvalidConstructorInput.Error);
         end
 
+        function generateWithTools(testCase)
+            import matlab.unittest.constraints.HasField
+
+            f = openAIFunction("writePaperDetails", "Function to write paper details to a table.");
+            f = addParameter(f, "name", type="string", description="Name of the paper.");
+            f = addParameter(f, "url", type="string", description="URL containing the paper.");
+            f = addParameter(f, "explanation", type="string", description="Explanation on why the paper is related to the given topic.");
+
+            paperExtractor = openAIChat("You are an expert in extracting information from a paper.", Tools=f);
+
+            input = join([
+            "    <id>http://arxiv.org/abs/2406.04344v1</id>"
+            "    <updated>2024-06-06T17:59:56Z</updated>"
+            "    <published>2024-06-06T17:59:56Z</published>"
+            "    <title>Verbalized Machine Learning: Revisiting Machine Learning with Language"
+            "  Models</title>"
+            "    <summary>  Motivated by the large progress made by large language models (LLMs), we"
+            "introduce the framework of verbalized machine learning (VML). In contrast to"
+            "conventional machine learning models that are typically optimized over a"
+            "continuous parameter space, VML constrains the parameter space to be"
+            "human-interpretable natural language. Such a constraint leads to a new"
+            "perspective of function approximation, where an LLM with a text prompt can be"
+            "viewed as a function parameterized by the text prompt. Guided by this"
+            "perspective, we revisit classical machine learning problems, such as regression"
+            "and classification, and find that these problems can be solved by an"
+            "LLM-parameterized learner and optimizer. The major advantages of VML include"
+            "(1) easy encoding of inductive bias: prior knowledge about the problem and"
+            "hypothesis class can be encoded in natural language and fed into the"
+            "LLM-parameterized learner; (2) automatic model class selection: the optimizer"
+            "can automatically select a concrete model class based on data and verbalized"
+            "prior knowledge, and it can update the model class during training; and (3)"
+            "interpretable learner updates: the LLM-parameterized optimizer can provide"
+            "explanations for why each learner update is performed. We conduct several"
+            "studies to empirically evaluate the effectiveness of VML, and hope that VML can"
+            "serve as a stepping stone to stronger interpretability and trustworthiness in"
+            "ML."
+            "</summary>"
+            "    <author>"
+            "      <name>Tim Z. Xiao</name>"
+            "    </author>"
+            "    <author>"
+            "      <name>Robert Bamler</name>"
+            "    </author>"
+            "    <author>"
+            "      <name>Bernhard Schölkopf</name>"
+            "    </author>"
+            "    <author>"
+            "      <name>Weiyang Liu</name>"
+            "    </author>"
+            "    <arxiv:comment xmlns:arxiv='http://arxiv.org/schemas/atom'>Technical Report v1 (92 pages, 15 figures)</arxiv:comment>"
+            "    <link href='http://arxiv.org/abs/2406.04344v1' rel='alternate' type='text/html'/>"
+            "    <link title='pdf' href='http://arxiv.org/pdf/2406.04344v1' rel='related' type='application/pdf'/>"
+            "    <arxiv:primary_category xmlns:arxiv='http://arxiv.org/schemas/atom' term='cs.LG' scheme='http://arxiv.org/schemas/atom'/>"
+            "    <category term='cs.LG' scheme='http://arxiv.org/schemas/atom'/>"
+            "    <category term='cs.CL' scheme='http://arxiv.org/schemas/atom'/>"
+            "    <category term='cs.CV' scheme='http://arxiv.org/schemas/atom'/>"
+            ], newline);
+
+            topic = "Large Language Models";
+
+            prompt =  "Given the following paper:" + newline + string(input)+ newline +...
+                "Given the topic: "+ topic + newline + "Write the details to a table.";
+            [~, response] = generate(paperExtractor, prompt);
+
+            testCase.assertThat(response, HasField("tool_calls"));
+            testCase.verifyEqual(response.tool_calls.type,'function');
+            testCase.verifyEqual(response.tool_calls.function.name,'writePaperDetails');
+            data = testCase.verifyWarningFree( ...
+                @() jsondecode(response.tool_calls.function.arguments));
+            testCase.verifyThat(data,HasField("name"));
+            testCase.verifyThat(data,HasField("url"));
+            testCase.verifyThat(data,HasField("explanation"));
+        end
+
         function invalidInputsGenerate(testCase, InvalidGenerateInput)
             f = openAIFunction("validfunction");
             chat = openAIChat(Tools=f, APIKey="this-is-not-a-real-key");
@@ -177,6 +235,15 @@ function createOpenAIChatWithOpenAIKeyLatestModel(testCase)
             testCase.verifyWarningFree(@()generate(chat, "Hello world."));
         end
 
+        function keyNotFound(testCase)
+            % to verify the error, we need to unset the environment variable
+            % OPENAI_API_KEY, if given. Use a fixture to restore the
+            % value on leaving the test point:
+            import matlab.unittest.fixtures.EnvironmentVariableFixture
+            testCase.applyFixture(EnvironmentVariableFixture("OPENAI_API_KEY","dummy"));
+            unsetenv("OPENAI_API_KEY");
+            testCase.verifyError(@()openAIChat, "llms:keyMustBeSpecified");
+        end
     end    
 end