Can anyone suggest a way to fetch page number from file_search tool along with chunks
#Page number ,Chat with pdf solution with response api, using file_search tool
shy loom
white laurelBOT
Hi @shy loom. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:
The knowledge sources do not explicitly state that the OpenAI file_search tool in the Responses API will return the page number of a chunk when searching PDFs. The typical response from a file_search call includes fields like file_id, filename, score, attributes, and content (the relevant chunk of text), but there is no mention of a page number field in the returned data structure Search vector store.
When you upload a PDF to a vector store, OpenAI automatically chunks the document, but the metadata about which page a chunk came from is not included by default in the search results. If you require page numbers, you would need to preprocess your PDFs before uploading—such as adding page number information into the chunk text or as custom attributes/metadata during chunking. However, the official documentation and examples do not show a built-in way to do this with the file_search tool alone Doing RAG on PDFs using File Search in the Responses API.
Summary:
- By default, the file_search tool does not return page numbers with chunks.
- To include page numbers, you must add them as part of the chunk text or as custom metadata during preprocessing and chunking, but this is not natively supported or documented in the current OpenAI API examples.
If you need page numbers, consider preprocessing your PDFs to embed page information into each chunk before uploading to the vector store.
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shy loom
@white laurel Can you please provide example?
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Certainly! Here’s an example approach, based on the available knowledge sources, for including page numbers in your chunks when preparing PDFs for use with OpenAI’s file_search tool:
1. Preprocess the PDF and Add Page Numbers to Chunks
You can extract text from each page of the PDF and prepend or append the page number to each chunk. Here’s a Python snippet inspired by the OpenAI Cookbook:
from PyPDF2 import PdfReader
def read_pdf_with_page_numbers(filepath):
reader = PdfReader(filepath)
pdf_text_chunks = []
for page_number, page in enumerate(reader.pages, start=1):
text = page.extract_text()
# Add page number to the chunk
chunk = f"Page Number: {page_number}\n{text}"
pdf_text_chunks.append(chunk)
return pdf_text_chunks
You would then upload these chunks (with page numbers included in the text) to your vector store. When you later use the file_search tool, the returned content will include the page number as part of the chunk text, allowing you to identify the source page.
2. Limitations
- The knowledge sources do not show a way to natively store or retrieve page numbers as a separate metadata field using the file_search tool. The only reliable way, as shown above, is to embed the page number directly in the chunk text How to use functions with a knowledge base.
- If you need more structured metadata, you would need to manage this during your own chunking and uploading process, but the file_search tool does not guarantee returning custom metadata fields in its results.
In summary:
Add the page number to each chunk’s text before uploading. When you retrieve chunks with file_search, the page number will be present in the text, allowing you to reference it in your responses.
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shy loom
I am using this syntax: await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
@white laurel i am using this syntax: await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Based on the knowledge sources, when you use the following syntax:
await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
the file(s) you upload will be automatically chunked and embedded by OpenAI, and added to the vector store. However, the file_search tool does not natively return the page number of each chunk when you later search the vector store. The chunks returned will contain the relevant text, but there is no built-in field for page numbers in the response Customizing File Search settings.
If you need page numbers in your search results, you must preprocess your PDF before uploading:
- Extract text from each page.
- Add the page number to the text of each chunk (e.g., prepend "Page Number: X" to each chunk).
- Upload the modified chunks as a new file to the vector store.
This way, when you use file_search, the returned chunk text will include the page number, allowing you to identify the source page How to use functions with a knowledge base.
Summary:
- The API call you provided uploads and processes files, but does not automatically track or return page numbers.
- To get page numbers, include them in the chunk text during preprocessing before uploading the file.
If you need a code example for preprocessing, let me know!
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white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Certainly! Here’s a sample code snippet, directly based on the knowledge sources, that demonstrates how to extract text from each page of a PDF, prepend the page number to each chunk, and prepare the file for upload to a vector store. This approach ensures that when you later use the file_search tool, the returned chunks will include the page number in the text.
from PyPDF2 import PdfReader
def extract_text_with_page_numbers(pdf_path):
reader = PdfReader(pdf_path)
chunks = []
for page_number, page in enumerate(reader.pages, start=1):
text = page.extract_text()
if text:
# Prepend page number to the chunk
chunk = f"Page Number: {page_number}\n{text}"
chunks.append(chunk)
return chunks
# Example usage:
pdf_chunks = extract_text_with_page_numbers("your_document.pdf")
# Now, save these chunks to a .txt file or process as needed before uploading to OpenAI
with open("processed_pdf.txt", "w", encoding="utf-8") as f:
for chunk in pdf_chunks:
f.write(chunk + "\n\n")
How this helps:
- When you upload the processed file to your vector store using
openai.vectorStores.fileBatches.uploadAndPoll, each chunk will contain the page number. - When you search with file_search, the returned content will include the page number in the text, allowing you to reference it in your application.
This method is directly supported by the approach described in the knowledge sources, which recommend embedding page information in the chunk text since the file_search tool does not natively return page numbers as metadata How to use functions with a knowledge base.
If you need to process the file in JavaScript or another language, let me know!
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shy loom
provide js example @white laurel
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Certainly! While the knowledge sources do not provide a full JavaScript example for extracting page numbers from a PDF and including them in chunks before uploading to a vector store, they do show how to upload files to a vector store using the OpenAI SDK File search guide.
To achieve your goal, you would need to:
- Extract text from each page of the PDF and prepend the page number.
- Save the processed text to a file.
- Upload the processed file to the vector store.
The knowledge sources do not provide a JavaScript PDF text extraction example, but here is a pattern you can follow using pdf-parse (for local processing), and then upload as shown in the official docs:
const fs = require("fs");
const pdf = require("pdf-parse");
const OpenAI = require("openai");
const openai = new OpenAI();
async function extractTextWithPageNumbers(pdfPath, outputPath) {
const dataBuffer = fs.readFileSync(pdfPath);
const data = await pdf(dataBuffer);
// pdf-parse returns the full text, but not page-by-page.
// For true page-by-page extraction, consider using a library like pdfjs-dist or pdf-lib.
// Here is a simplified placeholder:
const pages = data.text.split('\f'); // '\f' is a common page break in extracted text
const chunks = pages.map((text, idx) => `Page Number: ${idx + 1}\n${text.trim()}`);
fs.writeFileSync(outputPath, chunks.join('\n\n'));
}
// Usage
extractTextWithPageNumbers("your_document.pdf", "processed_pdf.txt").then(async () => {
// Upload the processed file to OpenAI
const fileStream = fs.createReadStream("processed_pdf.txt");
const file = await openai.files.create({
file: fileStream,
purpose: "assistants",
});// Add the file to your vector store (replace with your vector store ID)
await openai.vectorStores.fileBatches.uploadAndPoll("your_vector_store_id", {
files: [fs.createReadStream("processed_pdf.txt")],
});
});
**Note:**
- The above code uses `pdf-parse` for simplicity, but for more accurate page-by-page extraction, you may need a more advanced library (such as `pdfjs-dist` or `pdf-lib`).
- The upload and vector store steps are directly from the official OpenAI documentation [File search guide](<https://platform.openai.com/docs/guides/tools-file-search#page-top>).
This approach ensures that when you search your vector store, the returned chunks will include the page number in the text, as recommended in the knowledge sources.
----------------------
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----------------------I have this function:
export async function generateEmbeddingForFile(uploadedFile: UploadedFile, vector_store_id: string): Promise<string> {
// Determine the file path: use tempFilePath if available; otherwise, create one in the OS temp directory.
const filePath = uploadedFile.tempFilePath && uploadedFile.tempFilePath.length > 0
? uploadedFile.tempFilePath
: path.join(os.tmpdir(), ${Date.now()}-${uploadedFile.name});
// If there is no tempFilePath, write the file buffer to the temporary file.
if (!uploadedFile.tempFilePath) {
if (!uploadedFile.data || uploadedFile.data.length === 0) {
throw new Error("Uploaded file buffer is empty.");
}
// @ts-ignore
await fs.writeFile(filePath, uploadedFile.data);
}
try {
// Ensure the file exists and is non-empty.
if (!(await fs.pathExists(filePath))) {
throw new Error("Temporary file not found at: " + filePath);
}
const stats = await fs.stat(filePath);
if (stats.size === 0) {
throw new Error("File is empty at path: " + filePath);
}
// Create a vector store with a unique name.
// const vectorStore = await openai.vectorStores.create({
// name: `${uploadedFile.name}-store`,
// });
// Create a readable stream from the file.
const fileStream = fs.createReadStream(filePath);
// Upload the file to the vector store and poll for completion.
await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
// Optionally, cleanup the temporary file.
await fs.unlink(filePath);
// Return the generated vector store ID.
return vector_store_id;
} catch (error: any) {
// Cleanup the temporary file if it exists.
if (await fs.pathExists(filePath)) {
await fs.unlink(filePath);
}
throw error;
}
}
@white laurel i have this function can you modify to attach pagenumber and provide full code? export async function generateEmbeddingForFile(uploadedFile: UploadedFile, vector_store_id: string): Promise<string> {
// Determine the file path: use tempFilePath if available; otherwise, create one in the OS temp directory.
const filePath = uploadedFile.tempFilePath && uploadedFile.tempFilePath.length > 0
? uploadedFile.tempFilePath
: path.join(os.tmpdir(), ${Date.now()}-${uploadedFile.name});
// If there is no tempFilePath, write the file buffer to the temporary file.
if (!uploadedFile.tempFilePath) {
if (!uploadedFile.data || uploadedFile.data.length === 0) {
throw new Error("Uploaded file buffer is empty.");
}
// @ts-ignore
await fs.writeFile(filePath, uploadedFile.data);
}
try {
// Ensure the file exists and is non-empty.
if (!(await fs.pathExists(filePath))) {
throw new Error("Temporary file not found at: " + filePath);
}
const stats = await fs.stat(filePath);
if (stats.size === 0) {
throw new Error("File is empty at path: " + filePath);
}
// Create a vector store with a unique name.
// const vectorStore = await openai.vectorStores.create({
// name: `${uploadedFile.name}-store`,
// });
// Create a readable stream from the file.
const fileStream = fs.createReadStream(filePath);
// Upload the file to the vector store and poll for completion.
await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
// Optionally, cleanup the temporary file.
await fs.unlink(filePath);
// Return the generated vector store ID.
return vector_store_id;
} catch (error: any) {
// Cleanup the temporary file if it exists.
if (await fs.pathExists(filePath)) {
await fs.unlink(filePath);
}
throw error;
}
}
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Certainly! Based on the knowledge sources, OpenAI’s file_search tool and vector store APIs do not natively extract or attach page numbers as metadata when uploading a PDF. To include page numbers, you must preprocess the PDF: extract text page by page, prepend the page number to each chunk, and then upload the processed file to the vector store. This ensures that when you search, the returned chunk text will include the page number How to use functions with a knowledge base.
Below is a modified version of your function. It uses the pdf-parse library to extract text, splits the text by page, prepends the page number, writes the processed text to a temporary file, and uploads that file to the vector store. This approach is consistent with the recommendations in the knowledge sources.
import fs from "fs-extra";
import path from "path";
import os from "os";
import pdf from "pdf-parse";
import { UploadedFile } from "./your-types"; // Adjust import as needed
export async function generateEmbeddingForFile(
uploadedFile: UploadedFile,
vector_store_id: string
): Promise<string> {
// Determine the file path
const filePath =
uploadedFile.tempFilePath && uploadedFile.tempFilePath.length > 0
? uploadedFile.tempFilePath
: path.join(os.tmpdir(), `${Date.now()}-${uploadedFile.name}`);
// If there is no tempFilePath, write the file buffer to the temporary file.
if (!uploadedFile.tempFilePath) {
if (!uploadedFile.data || uploadedFile.data.length === 0) {
throw new Error("Uploaded file buffer is empty.");
}
await fs.writeFile(filePath, uploadedFile.data);
}// Prepare a processed file path
const processedFilePath = path.join(
os.tmpdir(),
processed-${Date.now()}-${uploadedFile.name}.txt
);
try {
// Ensure the file exists and is non-empty.
if (!(await fs.pathExists(filePath))) {
throw new Error("Temporary file not found at: " + filePath);
}
const stats = await fs.stat(filePath);
if (stats.size === 0) {
throw new Error("File is empty at path: " + filePath);
}
// Only process PDFs for page numbers
if (uploadedFile.name.toLowerCase().endsWith(".pdf")) {
const dataBuffer = await fs.readFile(filePath);
const data = await pdf(dataBuffer);
// pdf-parse returns the full text, but not page-by-page.
// For true page-by-page extraction, you may need a more advanced library.
// Here, we split by form feed (\f), which is a common page break.
const pages = data.text.split('\f');
const chunks = pages.map(
(text, idx) => `Page Number: ${idx + 1}\n${text.trim()}`
);
await fs.writeFile(processedFilePath, chunks.join('\n\n'));
} else {
// For non-PDFs, just copy the file as-is
await fs.copyFile(filePath, processedFilePath);
}
// Create a readable stream from the processed file.
const fileStream = fs.createReadStream(processedFilePath);
// Upload the file to the vector store and poll for completion.
await openai.vectorStores.fileBatches.uploadAndPoll(vector_store_id, {
files: [fileStream],
});
// Cleanup temporary files.
await fs.unlink(filePath);
await fs.unlink(processedFilePath);
return vector_store_id;
} catch (error: any) {
// Cleanup temporary files if they exist.
if (await fs.pathExists(filePath)) {
await fs.unlink(filePath);
}
if (await fs.pathExists(processedFilePath)) {
await fs.unlink(processedFilePath);
}
throw error;
}
}
**Key points:**
- This code ensures each chunk of PDF text is prepended with its page number before uploading.
- When you later search the vector store, the returned chunk text will include the page number, as recommended in the knowledge sources.
- The approach is based on the best practices described in the OpenAI Cookbook for knowledge retrieval and file search [How to use functions with a knowledge base](<https://cookbook.openai.com/examples/how_to_call_functions_for_knowledge_retrieval#search-utilities>).
If you need more accurate page-by-page extraction, consider using a more advanced PDF parsing library, as `pdf-parse` may not always split perfectly by page.
----------------------
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----------------------@white laurel this is my prompt when i ask question, is there any specific change when it should comulsoty includes pagenumber?
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
If you want your prompt to compulsorily include the page number in the model’s answer, you should explicitly instruct the model to do so in your prompt. The knowledge sources recommend being very clear and explicit with your instructions, especially for GPT-4.1 and GPT-4o, as these models follow instructions more literally than previous versions Instruction Following.
Here’s how you can modify your prompt to require the page number in every answer:
Example Prompt:
You are a helpful assistant.
Use the provided document context to answer the user's question.
You must always include the page number(s) where you found the answer, formatted as "Page Number: X".
If you cannot find the answer, reply: "No information found."Question: {user question}
Context:
{retrieved chunks, each including page numbers in the text}
Why this works:
- The instruction is explicit and leaves no ambiguity.
- The model is told exactly how to format the page number.
- This approach is recommended in the knowledge sources for reliable instruction following Instruction Following.
Tip:
If you want to make it even more robust, you can provide an example in your prompt, as shown in the OpenAI Cookbook:
Question: What is the capital expenditure limit?
Answer: The capital expenditure limit is USD 30,000,000. (Page Number: 32)
Summary:
Yes, you should explicitly require the page number in your prompt if it is compulsory for your use case. This is the best practice for reliable, consistent results with OpenAI models.
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
shy loom
his is my prompt when i ask question, is there any specific change when it should comulsoty includes pagenumber?
await openai.beta.threads.messages.create(chat?.thread_id, {
role: 'user',
content: ${question}. Please Give answer in ${language} language. Be accurate, helpful, concise, and clear. Use bullet points when there is a list in response. ${ans_type != 'Q/A' ?Please generate a ${ans_type}. : ". "}. Make sure the generate your answer in proper markdown format. Use bullet points, lists, tables, headings etc wherever required.
// Do NOT add default citations or annotations in your response.
// For every fact or quote you include, also make sure to provide the document’s absolute page number—i.e. count page 1 as the very first page of the PDF (cover, front matter, table of contents, introduction, etc.), then page 2, page 3, and so on, regardless of what (if any) page marks appear in headers or footers.
// Format all citations exactly like this: [Page 1, Page 2, …].
// Do not use any printed page numbers on the page—you must compute the position from the start of the file.
});
@white laurel This is my prompt when i ask question, is there any specific change when it should comulsoty includes pagenumber?
await openai.beta.threads.messages.create(chat?.thread_id, {
role: 'user',
content:
${question}. Please Give answer in ${language} language.
Be accurate, helpful, concise, and clear. Use bullet points when there is a list in response.
${ans_type != 'Q/A' ? Please generate a ${ans_type}. : ". "}.
Make sure the generate your answer in proper markdown format. Use bullet points, lists, tables, headings etc wherever required.
// Do NOT add default citations or annotations in your response.
// For every fact or quote you include, also make sure to provide the document’s absolute page number—i.e. count page 1 as the very first page of the PDF (cover, front matter, table of contents, introduction, etc.), then page 2, page 3, and so on, regardless of what (if any) page marks appear in headers or footers.
// Format all citations exactly like this: [[Page 1](#page=1), [Page 2](#page=2), …].
// Do not use any printed page numbers on the page—you must compute the position from the start of the file.
});
Let me check the knowledge base for you again. One moment! :books:
Your current prompt is already well-structured and explicit in requiring the model to include page numbers in its answers. According to OpenAI’s best practices, if you want the model to compulsorily include page numbers, you should:
- Clearly state that every fact or quote must be accompanied by the absolute page number.
- Specify the format for citations (e.g.,
[[Page 1](#page=1)]). - Instruct the model not to use printed page numbers, but to count from the start of the file.
- Place these instructions prominently in your prompt, ideally at the beginning or in a dedicated “Instructions” section, as models like GPT-4.1 follow explicit instructions more reliably Instruction Following Prompt Engineering Best Practices.
Your prompt already does this.
However, to further increase reliability, you can:
- Move the page number instructions to the very top of your prompt.
- Add a short example showing the required citation format.
- Use clear section headers (Markdown or all-caps) to separate instructions from the question/context Message formatting with Markdown.
Example (improved for clarity and compliance):
## Instructions- For every fact or quote, you must provide the document’s absolute page number, counting from the very first page of the PDF (cover, front matter, etc.).
- Format all citations exactly like this: [Page 1], [Page 2], etc.
- Do not use any printed page numbers—always compute from the start of the file.
- Do not add default citations or annotations in your response.
Example:
The capital expenditure limit is USD 30,000,000. [Page 32]
Question
${question}
Please answer in ${language}. Be accurate, helpful, concise, and clear. Use bullet points for lists.
${ans_type != 'Q/A' ? Please generate a ${ans_type}. : ""}
Format your answer in proper markdown, using bullet points, lists, tables, and headings as needed.
---
**Summary:**
Your prompt is already correct, but moving the page number requirement to the top and providing an explicit example will further improve compliance, as recommended in the knowledge sources [Prompt Engineering Best Practices](<https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api#h_ad50145be0>) [Instruction Following](<https://cookbook.openai.com/examples/gpt4-1_prompting_guide#4-instruction-following>).
----------------------
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----------------------@white laurel is there any way i can set page number for each chunk?
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
You cannot directly set a page number as a native metadata field for each chunk when uploading files to an OpenAI vector store using the file_search tool. The OpenAI API automatically chunks and embeds your files, but it does not expose a built-in way to assign or retrieve page numbers for each chunk in the search results How it works.
Workaround:
The recommended approach, as described in the knowledge sources, is to embed the page number directly into the text of each chunk during preprocessing. For example, when extracting text from a PDF, prepend or append the page number to each chunk’s text before uploading. This way, when you retrieve a chunk via file_search, the page number is present in the chunk’s content Doing RAG on PDFs using File Search in the Responses API.
There is currently no documented support for attaching custom metadata (like page numbers) to each chunk in a way that is returned by the file_search tool. The only metadata you can attach is at the file level, not at the chunk level Customizing File Search settings.
Summary:
- You cannot set a page number as a separate field for each chunk.
- The best practice is to include the page number in the chunk text itself before uploading the file to the vector store.
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shy loom
but sometime it can lose page number, for example , when if one page hase 800 token of content , and it automatically chunk 400 token so what happen whenyou appent file number , for second chunk there won't we a page number in content how to handle that?>
@white laurel but sometime it can lose page number, for example , when if one page hase 800 token of content , and it automatically chunk 400 token so what happen whenyou appent file number , for second chunk there won't we a page number in content how to handle that?>
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
You are correct—if you simply prepend the page number to the start of each page’s text, but the automatic chunking splits a single page into multiple chunks (e.g., every 800 tokens with 400 overlap), only the first chunk from that page will have the page number at the top. The subsequent chunks may not include the page number, making it difficult to reliably identify the source page for every chunk.
The knowledge sources do not describe a built-in way to solve this in the OpenAI API. The chunking process is automatic, and there is no way to attach custom metadata (like page number) to each chunk that will be returned by file_search Customizing File Search settings.
Best practice from the knowledge sources:
- The only reliable method described is to embed the page number within the text of every chunk. This means, during your own preprocessing (before uploading), you would need to split the PDF into chunks yourself (using the same chunk size and overlap as OpenAI, e.g., 800 tokens with 400 overlap), and prepend or append the page number to each chunk. Then, upload this processed file as plain text to the vector store. This way, every chunk will have the correct page number, regardless of how the content is split How to use functions with a knowledge base.
Summary:
- If you rely on OpenAI’s automatic chunking, you cannot guarantee every chunk will contain the page number.
- To ensure every chunk has the correct page number, you must chunk the document yourself (matching OpenAI’s chunking parameters) and add the page number to each chunk before uploading.
- There is no built-in support for chunk-level metadata in the current OpenAI file_search/vector store APIs.
If you need code examples for manual chunking with page numbers, let me know!
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
shy loom
@white laurel Can we store chunks in vectore store? after creating chunk how do we handle after flow?
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
Yes, you can store chunks in a vector store with OpenAI’s API, but the process is typically automated: when you upload a file to a vector store, OpenAI automatically chunks, embeds, and indexes the content for you. Each chunk becomes a searchable unit in the vector store Vector stores.
If you want to control chunking yourself (for example, to add page numbers or custom metadata to each chunk), you would need to:
- Manually split your document into chunks (using your own logic for chunk size and overlap).
- Add any desired metadata (like page numbers) to each chunk’s text.
- Save the chunks as a new file (e.g., a plain text or JSON file).
- Upload this processed file to the vector store using the Files API and then attach it to the vector store File search guide.
After uploading, the vector store will treat each line or entry as a chunk, and these will be searchable via the file_search tool. However, the knowledge sources do not describe a way to upload individual chunks directly as separate objects; you upload a file containing your pre-chunked content, and OpenAI indexes it as-is.
After flow:
- Once the file is uploaded and attached to the vector store, you can use the file_search tool (via the Responses API or Assistants API) to search for relevant chunks based on user queries. The search will return the most relevant chunks, including any metadata you embedded in the chunk text Doing RAG on PDFs using File Search in the Responses API.
Summary:
- You can store pre-chunked content in a vector store by uploading a file containing your chunks.
- After uploading, use file_search to retrieve relevant chunks for answering questions.
- There is no built-in support for chunk-level metadata fields; include metadata (like page numbers) in the chunk text itself.
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shy loom
appending will increase chunk size automatically for next chunk, so how we can handle for each chunk size and how do we handle when page changes and having two page number? etcs @white laurel
white laurelBOT
Let me check the knowledge base for you again. One moment! :books:
You are correct that appending page numbers or metadata to each chunk will increase the chunk’s token count, which can affect chunk size and overlap, especially when a chunk spans two pages. The knowledge sources provide guidance on chunking strategies and best practices, but do not describe a built-in way to handle page transitions or dual page numbers in OpenAI’s automatic chunking.
Key points from the knowledge sources:
-
Manual Chunking:
If you need precise control (such as including page numbers and handling page transitions), you should manually chunk your document before uploading. This allows you to:- Set the chunk size (e.g., 800 tokens).
- Add page numbers to each chunk.
- Handle cases where a chunk spans two pages by including both page numbers in the chunk’s metadata or text How to use functions with a knowledge base Chunking the input text.
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Chunk Size Management:
When you append metadata (like page numbers), you must account for the extra tokens in your chunking logic. For example, if your chunk size is 800 tokens and you add a page number string, ensure the total (content + metadata) does not exceed 800 tokens per chunk Prepare data.
- Handling Page Transitions:
If a chunk contains text from two pages (e.g., the end of one page and the start of the next), you can:- Include both page numbers in the chunk’s metadata or at the start of the chunk text (e.g., “Page Number: 5-6”).
- Alternatively, split chunks at page boundaries to avoid spanning pages, though this may result in smaller or uneven chunk sizes.
Example approach:
# Pseudocode for manual chunking with page numbers
for each page in PDF:
extract text
tokenize text
for each chunk in page:
if chunk spans into next page:
add both page numbers to chunk metadata/text
else:
add single page number
ensure chunk size (content + metadata) <= max_chunk_size
Summary:
- Manually chunk your document to control chunk size and page number inclusion.
- Always account for metadata in your chunk size calculation.
- If a chunk spans two pages, include both page numbers in the chunk’s text or metadata.
- There is no built-in OpenAI API feature to automate this; it must be handled in your preprocessing step Chunking.