@maximai/maxim-js/src/lib/tests/vercel/weather_prediction_test.js

Maxim AI JS SDK. Visit https://getmaxim.ai for more info.

"use strict";
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    Object.defineProperty(o, "default", { enumerable: true, value: v });
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        __setModuleDefault(result, mod);
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Object.defineProperty(exports, "__esModule", { value: true });
const uuid_1 = require("uuid");
const ai_1 = require("ai");
const openai_1 = require("@ai-sdk/openai");
const v3_1 = require("zod/v3");
const dotenv = __importStar(require("dotenv"));
const vercel_1 = require("src/lib/logger/vercel");
const maxim_1 = require("src/lib/maxim");
// Load environment variables
dotenv.config();
// Initialize Maxim SDK
async function initializeMaxim() {
    const apiKey = process.env["MAXIM_API_KEY"];
    const repoId = process.env["MAXIM_LOG_REPO_ID"];
    const baseUrl = process.env["MAXIM_BASE_URL"];
    if (!apiKey || !repoId) {
        throw new Error("MAXIM_API_KEY or MAXIM_LOG_REPO_ID is not defined in the environment variables");
    }
    const maxim = new maxim_1.Maxim({ baseUrl, apiKey, debug: true });
    const logger = await maxim.logger({
        id: repoId,
    });
    if (!logger) {
        throw new Error("Logger is not available");
    }
    return { maxim, logger };
}
// Define weather tool that will be called
const getWeatherInfo = (0, ai_1.tool)({
    description: "Get current weather information for a city. Use this tool when the user asks about weather conditions.",
    inputSchema: v3_1.z.object({
        city: v3_1.z.string().describe("Name of the city"),
        unit: v3_1.z.enum(["celsius", "fahrenheit"]).describe("Temperature unit preferred by the user"),
    }),
    execute: async ({ city, unit }) => {
        console.log(`[WEATHER TOOL] 🔧 Executing getWeatherInfo for ${city} in ${unit}`);
        // Simulate weather data
        const weatherData = {
            city,
            temperature: unit === "celsius" ? 22 : 72,
            humidity: 65,
            windSpeed: 12,
            conditions: "Partly Cloudy",
            forecast: "Clear skies expected for the next few days",
            lastUpdated: new Date().toISOString(),
            unit,
        };
        console.log(`[WEATHER TOOL] ✅ Tool result:`, JSON.stringify(weatherData, null, 2));
        return weatherData;
    },
});
// Main test function
async function main() {
    var _a;
    const { logger } = await initializeMaxim();
    try {
        // Create a session for testing
        const sessionId = (0, uuid_1.v4)();
        const session = logger.session({
            id: sessionId,
            name: "Weather Prediction Test Session",
        });
        // User message asking for weather prediction
        const userMessage = {
            id: (0, uuid_1.v4)(),
            role: "user",
            parts: [{ type: "text", text: "What's the weather like in San Francisco today? Please explain the conditions." }],
        };
        const messages = [userMessage];
        // Validate UI messages
        const validatedMessages = await (0, ai_1.validateUIMessages)({ messages });
        console.log("\n🌤️  Weather Prediction Test");
        console.log("=".repeat(50));
        console.log("User question:", (_a = userMessage.parts[0]) === null || _a === void 0 ? void 0 : _a.text);
        console.log("\nExpected flow:");
        console.log("1. Model calls getWeatherInfo tool");
        console.log("2. Tool executes and returns weather data");
        console.log("3. Model receives tool result");
        console.log("4. Model responds with explanation of weather conditions");
        console.log("=".repeat(50) + "\n");
        // System prompt that encourages tool usage and explanation
        const systemPrompt = `You are a helpful weather assistant. 
When users ask about weather:
1. Always use the getWeatherInfo tool to get current weather data
2. After receiving the tool result, provide a clear, friendly explanation of the weather conditions
3. Include details like temperature, conditions, and any relevant information from the tool response
4. Be conversational and helpful`;
        // Create UI message stream
        const stream = (0, ai_1.createUIMessageStream)({
            execute: async ({ writer: dataStream }) => {
                console.log("[DEBUG] Starting execute function");
                // Convert validated messages to model messages
                const modelMessages = await (0, ai_1.convertToModelMessages)(validatedMessages, {
                    tools: {
                        getWeatherInfo,
                    },
                });
                console.log("[DEBUG] Converted to model messages:", modelMessages.length, "messages");
                const wrappedModel = (0, vercel_1.wrapMaximAISDKModel)((0, openai_1.openai)("gpt-4o-mini"), logger);
                // Stream text with Maxim integration
                const result = (0, ai_1.streamText)({
                    messages: modelMessages,
                    tools: {
                        getWeatherInfo,
                    },
                    system: systemPrompt,
                    model: wrappedModel,
                    providerOptions: {
                        maxim: {
                            sessionId: sessionId,
                            traceTags: {
                                environment: process.env["NODE_ENV"] || "test",
                                test_type: "weather_prediction",
                            },
                        },
                        openai: {},
                    },
                });
                console.log("[DEBUG] streamText called, getting UI message stream");
                // toUIMessageStream() automatically handles tool execution and stream consumption
                const uiStream = result.toUIMessageStream();
                console.log("[DEBUG] Got UI message stream, merging...");
                dataStream.merge(uiStream);
                console.log("[DEBUG] Stream merged");
            },
            generateId: () => (0, uuid_1.v4)(),
            originalMessages: validatedMessages,
        });
        // Convert stream to SSE format
        const sseStream = stream.pipeThrough(new ai_1.JsonToSseTransformStream());
        // Read the stream to verify it works
        const reader = sseStream.getReader();
        let streamContent = "";
        let chunkCount = 0;
        let toolCallDetected = false;
        let toolResultDetected = false;
        let assistantResponseDetected = false;
        try {
            while (true) {
                const { done, value } = await reader.read();
                if (done)
                    break;
                // JsonToSseTransformStream outputs strings directly
                const chunk = typeof value === "string" ? value : new TextDecoder().decode(value);
                streamContent += chunk;
                chunkCount++;
                // Detect tool call
                if (chunk.includes("tool-call") || chunk.includes("getWeatherInfo")) {
                    if (!toolCallDetected) {
                        console.log("\n✅ [TEST] Tool call detected in stream");
                        toolCallDetected = true;
                    }
                }
                // Detect tool result
                if (chunk.includes("tool-result") || chunk.includes("temperature") || chunk.includes("conditions")) {
                    if (!toolResultDetected && toolCallDetected) {
                        console.log("✅ [TEST] Tool result detected in stream");
                        toolResultDetected = true;
                    }
                }
                // Detect assistant response (text content after tool result)
                if (chunk.includes("text-delta") && toolResultDetected && !assistantResponseDetected) {
                    console.log("✅ [TEST] Assistant response detected");
                    assistantResponseDetected = true;
                }
                // Log first few chunks and tool-related chunks
                if (chunkCount <= 10) {
                    console.log(`[DEBUG] Chunk ${chunkCount}:`, chunk.substring(0, 150));
                }
                else if (chunk.includes("tool") || chunk.includes("text-delta")) {
                    console.log(`[DEBUG] Important chunk ${chunkCount}:`, chunk.substring(0, 200));
                }
            }
        }
        catch (error) {
            console.error("Error reading stream:", error);
            throw error;
        }
        finally {
            reader.releaseLock();
        }
        console.log("\n" + "=".repeat(50));
        console.log("📊 Test Results:");
        console.log("=".repeat(50));
        console.log(`Total chunks received: ${chunkCount}`);
        console.log(`Total stream length: ${streamContent.length} characters`);
        console.log(`Tool call detected: ${toolCallDetected ? "✅ YES" : "❌ NO"}`);
        console.log(`Tool result detected: ${toolResultDetected ? "✅ YES" : "❌ NO"}`);
        console.log(`Assistant response detected: ${assistantResponseDetected ? "✅ YES" : "❌ NO"}`);
        // Extract and display assistant response
        try {
            const responseMatch = streamContent.match(/"text":"([^"]+)"/g);
            if (responseMatch && responseMatch.length > 0) {
                console.log("\n💬 Assistant Response Preview:");
                console.log("-".repeat(50));
                responseMatch.slice(-3).forEach((match, idx) => {
                    const text = JSON.parse(`{${match}}`).text;
                    if (text && text.length > 20) {
                        console.log(text.substring(0, 200) + "...");
                    }
                });
            }
        }
        catch (e) {
            console.log("\n(Unable to parse response from stream)");
        }
        // Verify test passed
        const testPassed = toolCallDetected && toolResultDetected && assistantResponseDetected;
        console.log("\n" + "=".repeat(50));
        if (testPassed) {
            console.log("✅ TEST PASSED: Tool was called, executed, and assistant responded!");
        }
        else {
            console.log("❌ TEST FAILED: Missing required steps");
            if (!toolCallDetected)
                console.log("   - Tool call was not detected");
            if (!toolResultDetected)
                console.log("   - Tool result was not detected");
            if (!assistantResponseDetected)
                console.log("   - Assistant response was not detected");
        }
        console.log("=".repeat(50) + "\n");
        // End session
        session.end();
        console.log("✅ Test completed!");
        console.log("Note: Check Maxim dashboard to verify the trace contains tool call, tool result, and assistant response.");
    }
    catch (error) {
        console.error("Error in weather prediction test:", error);
        throw error;
    }
    finally {
        await logger.cleanup();
    }
}
// Run the test
main().catch((error) => {
    console.error("Fatal error:", error);
    process.exit(1);
});
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