How to embed the CSV file using Langchain in Node.js

[GoldenDragon0710]

const { CSVLoader } = require("langchain/document_loaders/fs/csv");
let loader;
let pageContent = null;
let vectorStore = null;

loader = new CSVLoader("uploads/" + file.filename);
  const docs = await loader.load();
  if (docs) {
    docs.map((item) => {
      pageContent += item.pageContent + ", ";
    });
}
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 300,
  chunkOverlap: 20,
});
const output = await splitter.createDocuments([pageContent]);

output.map((item) => {
  vectorStore.push(
    new Document({
      metadata: {
        ...item.metadata,
        id: id.toString(),
      },
      pageContent: item.pageContent,
    })
  );
});

Now I am using CSVLoader in LangChain for embedding CSV file, with this code. The content of the CSV file is stored in Pinecone. But the bot can't response accurately about the CSV file. For example, the file content are:

Number,  Name of Event, Attending Parties
123423-01,  Stand Up Meeting,  CEO and Team Members
123423-02,  Team A Meeting,  CEO and Team A
123423-03,  Team B Meeting,  CEO and Team B
123423-04,  Team A Meeting,  CEO and Team A
123423-05,  Team B Meeting,  CEO and Team B
123423-06,  Team B Meeting,  CEO and Team B
123423-07,  Team B Meeting,  CEO and Team B

The bot does not respond accurately for question like: How many ameetings does Team A have with CEO?

Note: The format of CSV file content can change randomly rather than fixed column names.

[dosubot[bot]]

🤖

Hello @GoldenDragon0710! I'm Dosu, a bot here to lend a hand while you're waiting for a human maintainer. I'm proficient in squashing bugs, answering some of your questions, and guiding you to become a better contributor. I'm here to help, so don't hesitate to reach out!

It seems like you're trying to use LangChain to process and query data from a CSV file. However, the bot is not responding accurately to your queries. This could be due to several reasons, including how the data is processed and the nature of the queries.

Firstly, it's important to note that LangChain processes text data and uses language models to generate responses. If the CSV file content is not properly processed into a format that the language model can understand, it may not be able to generate accurate responses.

In your code, you're using the CSVLoader to load the CSV file and then creating documents from the loaded data. However, it's not clear how the data is being processed before it's passed to the RecursiveCharacterTextSplitter. If the CSV file content is simply being concatenated into a single string, the language model may not be able to accurately understand and process the data.

To improve the accuracy of the bot's responses, you could consider the following steps:

1. Data Preprocessing: Ensure that your CSV file is properly cleaned and preprocessed. This includes removing any irrelevant data, handling missing values, and ensuring that the data is in a format that can be easily understood by the language model.

2. Use a More Specific Query: The accuracy of the bot's responses can be improved by using a more specific query. For example, if you're looking for specific meeting details, you might want to include the date, time, or other specific details in your query.

3. Increase the Chunk Size: In your code, the chunk size is set to 300. You might want to experiment with increasing this value to see if it improves the accuracy of the bot's responses. However, keep in mind that a larger chunk size will also increase the amount of memory required.

4. Use a Different Language Model: The provided code uses the OpenAI language model. You might want to experiment with using a different language model to see if it improves the accuracy of the bot's responses.

Here's how you might modify your code to implement these suggestions:

const { CSVLoader } = require("langchain/document_loaders/fs/csv");
let loader;
let pageContent = null;
let vectorStore = null;

loader = new CSVLoader("uploads/" + file.filename);
const docs = await loader.load();
if (docs) {
  docs.map((item) => {
    pageContent += item.pageContent + ", ";
  });
}
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500, // Increase the chunk size
  chunkOverlap: 20,
});
const output = await splitter.createDocuments([pageContent]);

output.map((item) => {
  vectorStore.push(
    new Document({
      metadata: {
        ...item.metadata,
        id: id.toString(),
      },
      pageContent: item.pageContent,
    })
  );
});

Please note that the effectiveness of these suggestions may vary depending on the specific details of your CSV file and the nature of the queries you're making. If you're still having trouble, please provide more details about the nature of the queries and the structure of the CSV file, and I'll be happy to help further.

Sources

• examples/src/chains/retrieval_qa_custom.ts
• langchain/src/document_loaders/tests/csv-blob.test.ts

---

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[MJDeligan]

There's a couple of problems here. First off, joining the pageContent of the documents from the csv loader, then splitting them again, has the disadvantage of not preserving your row structure. The documents your store in pinecone will have information from different row and rows are unlikely to be stored as a whole. Additionally, you are not garanteed that the data in a column is not split across documents. For example consider this csv:

Name, Description
Bob, Bob is a hard worker. He likes trains.
Alice, Alice is a senior software engineer. She dislikes Bob.

Loading the csv will yield the rows as documents such as

[Document(...,pageContent="Name:'Bob', Description: 'Bob is a hard ...'"), Document(...,pageContent="Name: 'Alice', ...)]

You then join the pageContent of these documents, yielding:

pageContent = "Name: 'Bob', Description: ..., Name: 'Alice', ..."

The splitting then depends on what the splitter splits. Let's assume it simply splits by string length:

splitter.createDocuments([pageContent]) 
// yields [Document(...pageContent="Name: 'Bob', Description: 'Bob is a ha'"), Document(...,pageContent="rd worker, He likes trains', Nam"), Document(...pageContent: "e: 'Alice', Description: "Alice is a seni"), Document(...,pageContent="or software engineering. She dislikes Bob.'")]

When performing the vector search you will therefore get results that do not accurately represent your data points.

Secondly, aggregation requests with embeddings generally perform poorly. In this case, it would have to look at a ton of documents, since it would have to find all the documents, where Team A and CEO occur (which does not guarantee that team A had a meeting with CEO). This is exacerbated by the fact, that, in your case, documents do not represent rows and the information of "Team A and CEO" may be split across documents. LLMs aren't good at these types of tasks, especially counting. Such queries are better handled by traditional querying. You could attempt, having the LLM create those queries, although this bears security risks.

I would suggest, not joining the document pageContent loaded from the csv loader (to then split them again) to preserve rows. As for that specific type of query; those are tricky. LLMs are good with textual information, but aren't fully capable of reasoning about meta information (such as how many times does x occur in this text). There are chains to query data sources such as SQL, which can query such meta information well, but with unstructured data as you've described this is of course much more difficult.

[qthwng01]

I'm also experiencing the situations you mentioned, is there an alternative?