No spans for LangChain #466

// Load .env file, if any.
require('dotenv').config()

const traceloop = require('@traceloop/node-server-sdk');

// Reuse the OTEL_EXPORTER_OTLP_ENDPOINT variable instead of TRACELOOP_BASE_URL
const baseUrl = process.env.OTEL_EXPORTER_OTLP_ENDPOINT;
// Reuse the OTEL_SERVICE_NAME variable instead of TRACELOOP_APP_NAME
const appName = process.env.OTEL_SERVICE_NAME;

traceloop.initialize({baseUrl, appName, disableBatch: true});


const {ChatOpenAI, OpenAIEmbeddings} = require("@langchain/openai");
const {MemoryVectorStore} = require("langchain/vectorstores/memory");
const {ChatPromptTemplate} = require("@langchain/core/prompts");
const {StringOutputParser} = require("@langchain/core/output_parsers");
const {RunnablePassthrough, RunnableSequence} = require("@langchain/core/runnables");

async function main() {
    const llm = new ChatOpenAI({model: 'qwen2.5:0.5b'});
    const embeddings = new OpenAIEmbeddings({model: 'all-minilm:33m'});

    const monsters = [
        "Goblin: Weak but numerous, attacks in groups.",
        "Orc: Strong and aggressive, fights head-on.",
        "Skeleton: Undead warrior, immune to poison but fragile.",
        "Giant Spider: Webs players, poisonous bite.",
        "Dragon: Powerful and magical, breathes fire.",
        "Keegorg: Senior Solution Architect at Docker",
    ].map((pageContent) => ({ pageContent, metadata: {} }));

    const vectorStore = new MemoryVectorStore(embeddings);
    // Create embeddings for the monsters
    await vectorStore.addDocuments(monsters);

    // Retrieve only one monster
    const retriever = vectorStore.asRetriever(1);

    // Create prompt template
    const ANSWER_PROMPT = ChatPromptTemplate.fromTemplate(
        `You are a monster expert, and the context includes relevant monsters. Answer the user concisely only using the provided context. If you don't know the answer, just say that you don't know.

        context: {context}
        Question: "{question}"
        Answer:`
    );

    function onlyContent(docs) {
        return docs.map(doc => doc.page_content).join('\n\n');
    }

    const chain = RunnableSequence.from([
        {
            context: retriever.pipe(onlyContent),
            question: new RunnablePassthrough(),
        },
        ANSWER_PROMPT,
        llm,
        new StringOutputParser(),
    ]);


    // Pass the user's question to the sequence
    const response = await chain.invoke("Who is Keegorg?");
    console.log(response);
}

main();