PineconeError: Error, message length too large: found 5453452 bytes, the limit is: 4194304 bytes

[YIN-Renlong]

Hi everyone, for some pdf files, I get the following error during ingest process npm run ingest, is there any way to fix it? Thanks

creating vector store... error [PineconeError: PineconeClient: Error calling upsert: PineconeError: Error, message length too large: found 5453452 bytes, the limit is: 4194304 bytes] /Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:46 throw new Error('Failed to ingest your data'); ^

Error: Failed to ingest your data at run (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:46:11) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at <anonymous> (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:51:3)

[dosubot]

🤖

Hello @YIN-Renlong! Good to see you here again. I hope you're having a great day.

The error you're encountering is due to the size of the data being ingested exceeding the limit set by Pinecone. This is likely happening because the chunkSize parameter in the RecursiveCharacterTextSplitter function is set too high for the size of your PDF files.

The chunkSize parameter determines the size of the text chunks that are being processed. If the message length is too large, reducing the chunkSize should help. Currently, the chunkSize is set to 1000 in the ingest-data.ts script:

const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

You can try reducing the chunkSize to a smaller value, for example, 500:

const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500, // reduce chunkSize from 1000 to 500
  chunkOverlap: 200,
});

Please note that reducing the chunkSize will increase the number of chunks and may affect the performance of the ingestion process. It's important to find a balance between the chunk size and the performance that suits your specific use case.

You can find the ingest-data.ts script in the repository here.

Let me know if this helps or if you need further assistance.

Sources

• npm run ingest / Pinecone error
• Error: Failed to ingest your data
• npm run ingest error
• scripts/ingest-data.ts

[YIN-Renlong]

🤖

Hello @YIN-Renlong! Good to see you here again. I hope you're having a great day.

The error you're encountering is due to the size of the data being ingested exceeding the limit set by Pinecone. This is likely happening because the chunkSize parameter in the RecursiveCharacterTextSplitter function is set too high for the size of your PDF files.

The chunkSize parameter determines the size of the text chunks that are being processed. If the message length is too large, reducing the chunkSize should help. Currently, the chunkSize is set to 1000 in the ingest-data.ts script:

const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

You can try reducing the chunkSize to a smaller value, for example, 500:

const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500, // reduce chunkSize from 1000 to 500
  chunkOverlap: 200,
});

Please note that reducing the chunkSize will increase the number of chunks and may affect the performance of the ingestion process. It's important to find a balance between the chunk size and the performance that suits your specific use case.

You can find the ingest-data.ts script in the repository here.

Let me know if this helps or if you need further assistance.

Sources

• npm run ingest / Pinecone error
• Error: Failed to ingest your data
• npm run ingest error
• scripts/ingest-data.ts

@dosubot thanks. According to your plan, I changed the value from 1000 to 500 (chunkSize: 1000 to chunkSize: 500). However, the problem still arises and I got this error:

creating vector store... error [PineconeError: PineconeClient: Error calling upsert: PineconeError: Error, message length too large: found 5432637 bytes, the limit is: 4194304 bytes] /Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:46 throw new Error('Failed to ingest your data'); ^ Error: Failed to ingest your data at run (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:46:11) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at <anonymous> (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:51:3)

This means that your solution may not be successful in solving the problem. So, do you have a better opinion?

[dosubot]

🤖

Based on the error message you're encountering, it seems that the size of the data you're trying to upsert into Pinecone is exceeding its limit. Even though you've reduced the chunkSize parameter in the RecursiveCharacterTextSplitter function, the data size might still be too large due to the size of the embeddings created by the OpenAIEmbeddings class.

One solution could be to further reduce the chunkSize parameter. However, this might not be the most efficient solution as it could lead to a large number of very small chunks.

Another solution could be to modify the OpenAIEmbeddings class to produce smaller embeddings. This could involve using a different model or reducing the dimensionality of the embeddings. However, this might affect the quality of the embeddings and therefore the performance of your chatbot.

A third solution could be to implement a check in the PineconeStore.fromDocuments method to ensure that the size of the data being upserted does not exceed Pinecone's limit. If the data size is too large, you could split the data into smaller batches and upsert them separately. Here's a rough example of how you could implement this:

const BATCH_SIZE = 100; // Adjust this value as needed

// ...

const docs = await textSplitter.splitDocuments(rawDocs);

// ...

const embeddings = new OpenAIEmbeddings();
const index = pinecone.Index(PINECONE_INDEX_NAME);

// Split docs into batches
for (let i = 0; i < docs.length; i += BATCH_SIZE) {
  const batch = docs.slice(i, i + BATCH_SIZE);

  // Embed and upsert each batch separately
  await PineconeStore.fromDocuments(batch, embeddings, {
    pineconeIndex: index,
    namespace: PINECONE_NAME_SPACE,
    textKey: 'text',
  });
}

This code splits the documents into batches and upserts each batch separately, which should help to avoid exceeding Pinecone's data size limit. Please adjust the BATCH_SIZE as needed to fit your specific use case.

In addition to the above, you might want to check the following points based on similar issues in the repository:

1. Ensure that the index name in the .env file matches the one in the pinecone.ts file.
2. Consider changing the OpenAI API key from a free trial account to a paid one, or rotating the Pinecone API key.
3. Set the dimensions correctly in the Pinecone dashboard to 1536. (These are OpenAI embeddings dimensions).
4. Switch your Environment in pinecone to us-east1-gcp if the other environment is causing issues.
5. Ensure you have a .env file in the root that contains valid API keys from the pinecone dashboard.
6. Pinecone indexes of users on the Starter(free) plan are deleted after 7 days of inactivity. To prevent this, send an API request to Pinecone to reset the counter.

You can find more details about these points in the following issues: npm run ingest / Pinecone error, Pinecone Ingest Error, and Error: Failed to ingest your data.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Sources

• npm run ingest / Pinecone error
• Pinecone Ingest Error
• Error: Failed to ingest your data
• scripts/ingest-data.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.

[YIN-Renlong]

A third solution could be to implement a check in the PineconeStore.fromDocuments method to ensure that the size of the data being upserted does not exceed Pinecone's limit. If the data size is too large, you could split the data into smaller batches and upsert them separately. Here's a rough example of how you could implement this:

thanks, I followed your third solution, however, error still happens:
creating vector store... error [PineconeError: PineconeClient: Error calling upsert: PineconeError: Error, message length too large: found 5451652 bytes, the limit is: 4194304 bytes] /Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54 throw new Error('Failed to ingest your data'); ^ Error: Failed to ingest your data at run (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54:11) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at <anonymous> (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:59:3)

the following is the full code of 'ingest-data.ts' after modify:

`
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { PineconeStore } from 'langchain/vectorstores/pinecone';
import { pinecone } from '@/utils/pinecone-client';
import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
import { PINECONE_INDEX_NAME } from '@/config/pinecone';
import { DirectoryLoader } from 'langchain/document_loaders/fs/directory';

/* Name of directory to retrieve your files from
Make sure to add your PDF files inside the 'docs' folder
*/
const filePath = 'docs';

export const run = async () => {
try {
/*load raw docs from the all files in the directory */
const directoryLoader = new DirectoryLoader(filePath, {
'.pdf': (path) => new PDFLoader(path),
});

// const loader = new PDFLoader(filePath);
const rawDocs = await directoryLoader.load();

/* Split text into chunks */
const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

const BATCH_SIZE = 100; // Adjust this value as needed


const docs = await textSplitter.splitDocuments(rawDocs);
console.log('split docs', docs);

console.log('creating vector store...');
/*create and store the embeddings in the vectorStore*/
const embeddings = new OpenAIEmbeddings();
const index = pinecone.Index(PINECONE_INDEX_NAME); //change to your own index name

// Split docs into batches
for (let i = 0; i < docs.length; i += BATCH_SIZE) {
  const batch = docs.slice(i, i + BATCH_SIZE);

  // Embed and upsert each batch separately
  await PineconeStore.fromDocuments(batch, embeddings, {
    pineconeIndex: index,
    //namespace: PINECONE_NAME_SPACE,
    textKey: 'text',
  });
}

} catch (error) {
console.log('error', error);
throw new Error('Failed to ingest your data');
}
};

(async () => {
await run();
console.log('ingestion complete');
})();
`

Let me know please what should I do now, thanks.

[YIN-Renlong]

thanks, I followed your third solution, however, error still happens: creating vector store... error [PineconeError: PineconeClient: Error calling upsert: PineconeError: Error, message length too large: found 5451652 bytes, the limit is: 4194304 bytes] /Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54 throw new Error('Failed to ingest your data'); ^ Error: Failed to ingest your data at run (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54:11) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at <anonymous> (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:59:3)

the following is the full code of 'ingest-data.ts' after modify:

` import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter'; import { OpenAIEmbeddings } from 'langchain/embeddings/openai'; import { PineconeStore } from 'langchain/vectorstores/pinecone'; import { pinecone } from '@/utils/pinecone-client'; import { PDFLoader } from 'langchain/document_loaders/fs/pdf'; import { PINECONE_INDEX_NAME } from '@/config/pinecone'; import { DirectoryLoader } from 'langchain/document_loaders/fs/directory';

/* Name of directory to retrieve your files from Make sure to add your PDF files inside the 'docs' folder */ const filePath = 'docs';

export const run = async () => { try { /*load raw docs from the all files in the directory */ const directoryLoader = new DirectoryLoader(filePath, { '.pdf': (path) => new PDFLoader(path), });

// const loader = new PDFLoader(filePath);
const rawDocs = await directoryLoader.load();

/* Split text into chunks */
const textSplitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

const BATCH_SIZE = 100; // Adjust this value as needed


const docs = await textSplitter.splitDocuments(rawDocs);
console.log('split docs', docs);

console.log('creating vector store...');
/*create and store the embeddings in the vectorStore*/
const embeddings = new OpenAIEmbeddings();
const index = pinecone.Index(PINECONE_INDEX_NAME); //change to your own index name

// Split docs into batches
for (let i = 0; i < docs.length; i += BATCH_SIZE) {
  const batch = docs.slice(i, i + BATCH_SIZE);

  // Embed and upsert each batch separately
  await PineconeStore.fromDocuments(batch, embeddings, {
    pineconeIndex: index,
    //namespace: PINECONE_NAME_SPACE,
    textKey: 'text',
  });
}

} catch (error) { console.log('error', error); throw new Error('Failed to ingest your data'); } };

(async () => { await run(); console.log('ingestion complete'); })(); `

Let me know please what should I do now, thanks.

thanks, I followed your third solution, however, error still happens:
creating vector store... error [PineconeError: PineconeClient: Error calling upsert: PineconeError: Error, message length too large: found 5451652 bytes, the limit is: 4194304 bytes] /Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54 throw new Error('Failed to ingest your data'); ^ Error: Failed to ingest your data at run (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:54:11) at process.processTicksAndRejections (node:internal/process/task_queues:95:5) at (/Applications/gpt4chatbot/gpt4-pdf-chatbot-langchain-cnr/scripts/ingest-data.ts:59:3)

the following is the full code of 'ingest-data.ts' after modify:

`
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
import { PineconeStore } from 'langchain/vectorstores/pinecone';
import { pinecone } from '@/utils/pinecone-client';
import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
import { PINECONE_INDEX_NAME } from '@/config/pinecone';
import { DirectoryLoader } from 'langchain/document_loaders/fs/directory';

/* Name of directory to retrieve your files from
Make sure to add your PDF files inside the 'docs' folder
*/
const filePath = 'docs';

export const run = async () => {
try {
/*load raw docs from the all files in the directory */
const directoryLoader = new DirectoryLoader(filePath, {
'.pdf': (path) => new PDFLoader(path),
});

// const loader = new PDFLoader(filePath);
const rawDocs = await directoryLoader.load();

/* Split text into chunks */
const textSplitter = new RecursiveCharacterTextSplitter({
chunkSize: 1000,
chunkOverlap: 200,
});

const BATCH_SIZE = 100; // Adjust this value as needed

const docs = await textSplitter.splitDocuments(rawDocs);
console.log('split docs', docs);

console.log('creating vector store...');
/create and store the embeddings in the vectorStore/
const embeddings = new OpenAIEmbeddings();
const index = pinecone.Index(PINECONE_INDEX_NAME); //change to your own index name

// Split docs into batches
for (let i = 0; i < docs.length; i += BATCH_SIZE) {
const batch = docs.slice(i, i + BATCH_SIZE);

// Embed and upsert each batch separately
await PineconeStore.fromDocuments(batch, embeddings, {
pineconeIndex: index,
//namespace: PINECONE_NAME_SPACE,
textKey: 'text',
});
}
} catch (error) {
console.log('error', error);
throw new Error('Failed to ingest your data');
}
};

(async () => {
await run();
console.log('ingestion complete');
})();
`

Please let me know please what should I debug now, thanks.

[dosubot]

Hi, @YIN-Renlong

I'm helping the gpt4-pdf-chatbot-langchain team manage their backlog and am marking this issue as stale. It looks like you encountered the "PineconeError: Error, message length too large" issue during the ingest process for certain PDF files, and you were seeking a solution to fix this issue when running npm run ingest. Despite attempting to reduce the chunkSize parameter in the RecursiveCharacterTextSplitter function as a suggested solution, the error persisted, and you requested further debugging steps.

Could you please confirm if this issue is still relevant to the latest version of the gpt4-pdf-chatbot-langchain repository? If it is, please let the gpt4-pdf-chatbot-langchain team know by commenting on this issue. Otherwise, feel free to close the issue yourself, or it will be automatically closed in 7 days. Thank you!