k (4) is greater than the number of elements in the index (1), setting k to 1

[Elindo586]

1 How can I have the AI know information about the outside world? such as the current day for example?

2 What does this means? and if it is a problem, how can I fix it?

This is attached to the response in the console:
k (4) is greater than the number of elements in the index (1), setting k to 1

"what day is today?"
k (4) is greater than the number of elements in the index (1), setting k to 1
k (4) is greater than the number of elements in the index (1), setting k to 1
{ text: "I'm sorry, but I don't have that information." }

Code is here:

import { ChatOpenAI } from "langchain/chat_models/openai";
import { ConversationalRetrievalQAChain } from "langchain/chains";
import { BufferMemory } from "langchain/memory";

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 chain;
let result;

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

let model1;

let embeddings;
let vectoreStore;
let vectorStoreRetriever;
let vectorChain;
// 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 ChatOpenAI({
        modelName: "gpt-4",
        openAIApiKey: process.env.OPENAI_API_KEY,
        temperature: 0,
      });

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

      chain = ConversationalRetrievalQAChain.fromLLM(
        model1,
        vectoreStore.asRetriever(),
        {
          memory: new BufferMemory({
            memoryKey: "chat_history", // Must be set to "chat_history"
            inputKey: "question", // The key for the input to the chain
            outputKey: "text", // The key for the final conversational output of the chain
          }),
        }
      );
    }

    result = await chain.call({
      question: message,
      chat_history: "",
      // Add the new user message here
      // { role: 'user', content: message },
    });
    const chatHistory = `${message}\n${result.text}`;
    const followUpRes = await chain.call({
      question: "Was that nice?",
      chat_history: chatHistory,
    });

    console.log(result);

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

[dosubot[bot]]

🤖

Hello,

To allow the AI to know real-world information such as the current date, you can use JavaScript's built-in Date object and add this information to the context that is saved in the memory. Here's how you can modify the code:

// Save the current date to the memory
await memory.saveContext(
  { input: "Today's date" },
  { output: new Date().toLocaleDateString() }
);

Now, the AI can retrieve the current date from the memory when needed.

As for the issue of "k (4) is greater than the number of elements in the index (1), setting k to 1" appearing in the console, it seems like you're trying to retrieve more documents from the memory than what's available. The parameter passed to vectorStore.asRetriever() determines how many documents to return. If you're trying to retrieve more documents than what's available in the memory, you'll get this warning.

However, in the context shared, it seems you're using BufferMemory and not VectorStoreRetrieverMemory. The warning message you're seeing might be due to a different part of your code not shown in the shared context. Please check other parts of your code where you might be using VectorStoreRetrieverMemory and adjust the number of documents to retrieve accordingly.

The k parameter in the similaritySearchVectorWithScore method of the FaissStore class in the LangChainJS framework represents the number of top results to return after performing a similarity search in the vector store using a query vector. In this method, the k parameter is used in the this.index.search(query, k) call to limit the number of results returned by the search. The method then returns an array of tuples, each containing a Document and its corresponding score, for the top k results.

Please note that the above modifications are based on the provided context and may need to be adjusted based on your specific use case.

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

Sources

• examples/src/memory/vector_store.ts
• examples/src/memory/buffer.ts
• langchain/src/vectorstores/faiss.ts

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