TypeError: Cannot read properties of undefined (reading 'callKeys')

[Elindo586]

Why am I getting this error and how can I fix it?

"hello"
[chain/start] [1:chain:SimpleSequentialChain] Entering Chain run with input: {
  "input": {
    "input": "\"hello\""
  }
}
[chain/start] [1:chain:SimpleSequentialChain > 2:chain:LLMChain] Entering Chain run with input: {
  "input": {
    "input": "\"hello\""
  }
}
[chain/error] [1:chain:SimpleSequentialChain > 2:chain:LLMChain] [0ms] Chain run errored with error: "Cannot read properties of undefined (reading 'callKeys')"
[chain/error] [1:chain:SimpleSequentialChain] [2ms] Chain run errored with error: "Cannot read properties of undefined (reading 'callKeys')"
 ⨯ TypeError: Cannot read properties of undefined (reading 'callKeys')
    at LLMChain._call (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/llm_chain.js:114:36)
    at LLMChain.call (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/base.js:110:24)
    at async SimpleSequentialChain._call (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/sequential_chain.js:240:22)
    at async SimpleSequentialChain.call (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/base.js:101:28)
    at async SimpleSequentialChain.run (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/base.js:66:30)
    at async useStore (webpack-internal:///(api)/./pages/api/usestore.js:93:24) {
  page: '/api/usestore'

This is the code:

import { TextLoader } from "langchain/document_loaders/fs/text";
import { CharacterTextSplitter } from "langchain/text_splitter";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { FaissStore } from "langchain/vectorstores/faiss";

import { OpenAI } from "langchain/llms/openai";
import { PromptTemplate } from "langchain/prompts";
import {
  SimpleSequentialChain,
  LLMChain,
  RetrievalQAChain,
  loadQAStuffChain,
} from "langchain/chains";

let loader;
let docs;
let splitter;
let documents;
let embeddings1;
let vectorstore;

let template1;
let reviewPromptTemplate1;
let reviewChain1 = null;

let model1;
let model2;
let embeddings;
let vectoreStore;
let vectorChain = null;
let overallChain;

const useStore = async (req, res) => {
  if (req.method === "POST") {
    loader = new TextLoader("./documents/restaurant.txt");
    docs = await loader.load();
    splitter = new CharacterTextSplitter({
      chunkSize: 200,
      chunkOverlap: 50,
    });
    documents = await splitter.splitDocuments(docs);
    embeddings1 = new OpenAIEmbeddings();
    vectorstore = await FaissStore.fromDocuments(documents, embeddings1);
    await vectorstore.save("./");

    const message = req.body.newText;
    const firstOne = req.body.firstMsg;

    console.log(message);
    if (firstOne) {
      model1 = new OpenAI({
        openAIApiKey: process.env.OPENAI_API_KEY,
        temperature: 0,
      });
      template1 = `
    Be very funny when answering questions. You are a helpful bot that creates a 'thank you' response text.
    If customers are unsatisfied, offer them a real world assistant to talk to.
    You will get a sentiment and subject as input and evaluate.
    
    text: {input}
    `;
      reviewPromptTemplate1 = new PromptTemplate({
        template: template1,
        inputVariables: ["input"],
      });

      reviewChain1 = new LLMChain({
        model1,
        prompt: reviewPromptTemplate1,
      });

      model2 = new OpenAI({
        openAIApiKey: process.env.OPENAI_API_KEY,
        temperature: 0,
      });

      embeddings = new OpenAIEmbeddings();
      vectoreStore = await FaissStore.load("./", embeddings);

      vectorChain = new RetrievalQAChain({
        combineDocumentsChain: loadQAStuffChain(model2),
        retriever: vectoreStore.asRetriever(),
        returnSourceDocuments: false,
      });
    }
    overallChain = new SimpleSequentialChain({
      chains: [reviewChain1, vectorChain].filter((chain) => chain !== null),
      verbose: true,
    });

    const result = await overallChain.run({
      input: message,
    });

    console.log(result.text);

    return res.status(200).json({ output: result });
  } else {
    res.status(405).json({ say: "Only POST is allowed" });
  }
};
export default useStore;

[dosubot[bot]]

🤖

Hello,

The error message "TypeError: Cannot read properties of undefined (reading 'callKeys')" typically indicates that you're trying to access a property or call a method on an object that is undefined. In this case, it seems like the error is originating from the LLMChain class in the langchainjs library.

Looking at the stack trace you provided, the error seems to be thrown at this line:

at LLMChain._call (file:///C:/Users/Edgar/Desktop/Stuff/Acode/nextprojectd/node_modules/langchain/dist/chains/llm_chain.js:114:36)

This suggests that the error is happening inside the _call method of the LLMChain class. However, without the exact code of the LLMChain class, it's hard to pinpoint the exact cause of the error.

From the code you provided, it seems like you're initializing the LLMChain class correctly:

reviewChain1 = new LLMChain({
  model1,
  prompt: reviewPromptTemplate1,
});

However, the error might be caused by the model1 or reviewPromptTemplate1 objects being undefined at the time of the LLMChain initialization.

To debug this issue, I would recommend adding some console.log statements before the LLMChain initialization to check if model1 and reviewPromptTemplate1 are defined:

console.log(model1);
console.log(reviewPromptTemplate1);

reviewChain1 = new LLMChain({
  model1,
  prompt: reviewPromptTemplate1,
});

If either model1 or reviewPromptTemplate1 is undefined, you'll need to debug why that's the case.

If both model1 and reviewPromptTemplate1 are defined, the issue might be with the LLMChain class itself. In that case, you might want to check the version of langchainjs you're using and see if there are any known issues with that version.

I hope this helps! Let me know if you have any other questions.

Sources

• langchain/src/load/map_keys.ts
• langchain/src/chains/llm_chain.ts

---

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