Cookbook: Vercel AI SDK (JS/TS)

import { Langfuse } from "npm:langfuse"
import { Configuration, OpenAIApi } from "npm:openai-edge";
 
const langfuse = new Langfuse({
  publicKey: "",
  secretKey: "",
  baseUrl: "https://cloud.langfuse.com",
});
 
const openAIconfig = new Configuration({
  apiKey: "",
});
const openai = new OpenAIApi(openAIconfig);

await langfuse.createPrompt({
    name: "qa-prompt",
    prompt: "You are an extremely helpful assistant. Please assume that the person asking you questions needs your support and is totally honest with you.",
    isActive: true // immediately promote to production
})

import { OpenAIStream, StreamingTextResponse } from "npm:ai";

// select edge rutime for low latency
export const runtime = 'edge';

/**
 * Example application simulating a QA chat bot using Langfuse and Vercel AI framework. 
 * 
 * Creates trace, retrieves a pre-saved prompt template, generates answer, and scores generation.
 * Utilizes OpenAI API for chat completion.
 *
 * @param {Request} req - The request object, containing session information, user ID, and messages.
 * @param {Response} res - The response object used to send back the processed data.
 * @returns {StreamingTextResponse} - A streamed response containing the output of the OpenAI model.
 */
export default async function handler(req: Request, res: Response) {
    // initialize Langfuse 
    const trace = langfuse.trace({
        name: "QA",
        // Langfuse session tracking: https://langfuse.com/docs/tracing-features/sessions
        sessionId: req.sessionId, 
        // Langfuse user tracking: https://langfuse.com/docs/tracing-features/users
        userId: req.userId,
        // Make public, so everyone can view it via its URL (for this demo)
        public: true
    });
    
    // Format incoming messages for OpenAI API
    const messages = req.messages
    const openAiMessages = messages.map(({ content, role }) => ({
        content,
        role: role,
    }));
    
    // get last message
    const sanitizedQuery = messages[messages.length - 1].content.trim();
 
    trace.update({
        input: sanitizedQuery,
    });
    
    const promptName = req.promptName
    
    const promptSpan = trace.span({
        name: "fetch-prompt-from-langfuse",
        input: {
            promptName,
        },
    });
    
    // retrieve Langfuse prompt template with promptName
    const prompt = await langfuse.getPrompt(promptName);
    
    const promptTemplate = prompt.prompt
  
    promptSpan.end({
        output: { 
            promptTemplate,
        },
    });
    
    // merge prompt template and user input
    const assembledMessages = [
        {
            role: "system",
            content: promptTemplate,
        },
        ...openAiMessages,
    ];
      
    const generation = trace.generation({
        name: "generation",
        input: assembledMessages,
        model: "gpt-3.5-turbo",
        prompt, // link to prompt version from Langfuse prompt management
    });
 
    const response = await openai.createChatCompletion({
        model: "gpt-3.5-turbo",
        stream: true,
        messages: assembledMessages,
    });
    
    // Stream the response from OpenAI
    const stream = OpenAIStream(response, {
        onStart: () => {
            // Add completionStartTime timestamp to be able to break down latency
            // into delay until first token and the streaming duration
            generation.update({
                completionStartTime: new Date(),
            });
        },
        onCompletion: async (completion) => {
            generation.end({
                output: completion,
                // Conditionally log a warning state
                level: completion.includes("I don't know how to help with that")
                    ? "WARNING"
                    : "DEFAULT",
                statusMessage: completion.includes("I don't know how to help with that")
                    ? "Refused to answer"
                    : undefined,
            });
            // Score generation, assume these answers are all correct
            if (!completion.includes("I don't know how to help with that")) {
                generation.score({
                    name: "quality",
                    value: 1,
                    comment: "Factually correct",
                });
            }
            trace.update({
                output: completion,
            });
 
            // Make sure all events are successifully send to Langfuse
            // before the stream terminates.
            await langfuse.shutdownAsync();
        },
    });
 
    // The AI package makes it super easy to add metadata as headers
    // It is a bit hacky, but we can e.g. pass the TraceURL to the frontend
    return new StreamingTextResponse(stream, {
        headers: {
            "X-Langfuse-Trace-Url": trace.getTraceUrl()
        },
    });
}

// sample request to test handler function
const mockRequest = {
    "sessionId": "testSession",
    "userId": "testUser",
    "promptName": "qa-prompt",
    "messages": [
        {
            "role": "user",
            "content": "What is love?"
        },
    ]
}

const response = await handler(mockRequest);
const data = await response.text();
console.log(data);

Love is a complex and deep emotion that can manifest in various forms such as romantic love, platonic love, familial love, and love for oneself. It often involves feelings of care, affection, empathy, and a strong bond with another person. Love can bring joy, happiness, and fulfillment to our lives, but it can also be challenging and require effort, communication, and understanding to maintain healthy relationships. Overall, love is a fundamental aspect of human experience that can bring meaning and purpose to our lives.

console.log(response.headers.get("X-Langfuse-Trace-Url"))

https://cloud.langfuse.com/trace/14cd44b6-1a56-46af-ba85-3fd91bbf9739