feat: add large tool call with string example

examples/agent/large_toolcall_with_string.ts

@@ -0,0 +1,77 @@
+import { FunctionTool, OpenAI, ToolCallOptions } from "llamaindex";
+
+(async () => {
+  // The tool call will generate a partial JSON for `gpt-4-turbo`
+  // See thread: https://community.openai.com/t/gpt-4o-doesnt-consistently-respect-json-schema-on-tool-use/751125/7
+
+  const models = ["gpt-4o", "gpt-4-turbo"];
+  for (const model of models) {
+    const validJSON = await callLLM({ model });
+    console.log(
+      `LLM call resulting in large tool input with '${model}': LLM generates ${validJSON ? "valid" : "invalid"} JSON.`,
+    );
+  }
+})();
+
+async function callLLM(init: Partial<OpenAI>) {
+  const csvData = `Country,Average Height (cm)\n${"Netherlands,156\n".repeat(
+    50,
+  )}`;
+
+  const userQuestion = "Describe data in this csv";
+
+  // fake code interpreter tool
+  const interpreterTool = FunctionTool.from(
+    ({ data }: { data: string }) => data,
+    {
+      name: "interpreter",
+      description:
+        "Analyze csv raw data from a CSV file and return detail information.",
+      parameters: {
+        type: "object",
+        properties: {
+          data: {
+            type: "string",
+            description: "The data to analyze",
+          },
+        },
+        required: ["data"],
+      },
+    },
+  );
+
+  const systemPrompt =
+    "You are a Python interpreter.\n- You are given tasks to complete and you run python code to solve them.\n- The python code runs in a Jupyter notebook. Every time you call $(interpreter) tool, the python code is executed in a separate cell. It's okay to make multiple calls to $(interpreter).\n- Display visualizations using matplotlib or any other visualization library directly in the notebook. Shouldn't save the visualizations to a file, just return the base64 encoded data.\n- You can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n- You can run any python code you want in a secure environment.";
+
+  const llm = new OpenAI(init);
+  const response = await llm.chat({
+    tools: [interpreterTool],
+    messages: [
+      { role: "system", content: systemPrompt },
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: userQuestion,
+          },
+          {
+            type: "text",
+            text: `Use data from following CSV raw contents:\n${csvData}`,
+          },
+        ],
+      },
+    ],
+  });
+
+  const options = response.message?.options as ToolCallOptions;
+  const input = options.toolCall[0].input as string;
+
+  console.log({ input });
+  try {
+    JSON.parse(input);
+    return true;
+  } catch {
+    return false;
+  }
+}