How can I overcome the OpenAI API's max token limit error when summarizing PDFs?

Below is some guidance to help address the issue:

Reduce Token Usage in Requests

• Trim Down max_tokens:
  Adjust your max_tokens parameter to be as close to your expected output size as possible. This ensures that your requests don't allocate more tokens than needed.

• Be Aware of Input Tokens:
  The token limit applies to both the input text and the generated output. Ensure your pdf text extraction is optimized and only includes the necessary content.

Splitting the PDF into Chunks

Splitting the PDF text into smaller chunks is a viable solution. Here’s how you can approach it:

• Chunking the Text:
  Break your text into segments that ensure each chunk plus the expected output stays within the maximum token limit. This prevents hitting the token ceiling.

• Sequential Summarization:
  Summarize each chunk individually. Then, if needed, you can combine the summaries and further summarize the aggregated result for an overall summary.

• Batching (if applicable):
  If your use case doesn’t require immediate responses, consider batching these requests. This approach allows you to optimize throughput without heavy synchronous rate limits.

Example Approach

Below is a simplified pseudocode (in Python) example to demonstrate chunking and summarizing sequentially:

PYTHONCopied
import openai
import tiktoken  # Optional package to count tokens

def count_tokens(text, model="gpt-3.5-turbo"):
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))

def split_text(text, max_input_tokens, model="gpt-3.5-turbo"):
    paragraphs = text.split("\n\n")
    chunks = []
    current_chunk = ""
    
    for para in paragraphs:
        if count_tokens(current_chunk + para, model) < max_input_tokens:
            current_chunk += "\n\n" + para
        else:
            chunks.append(current_chunk)
            current_chunk = para
    if current_chunk:
        chunks.append(current_chunk)
    return chunks

def summarize_text(chunk):
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "user", "content": f"Summarize the following text:\n{chunk}"}
        ],
        max_tokens=150  # Adjust based on expected summary length
    )
    return response.choices[0].message.content

# Main routine
pdf_text = "Your extracted PDF text here"

# Let's assume a safe max input token size of 2500 tokens for the selected model:
chunks = split_text(pdf_text, max_input_tokens=2500)

summaries = [summarize_text(chunk) for chunk in chunks]

# Optionally, summarize the summaries for an overall summarization:
final_summary = summarize_text("\n\n".join(summaries))
print(final_summary)

Final Recommendations

• Experiment with Chunk Size:
  Adjust the maximum input tokens per chunk according to your model’s total context size (e.g., 4096 tokens for gpt-3.5-turbo).

• Optimize Your Prompt:
  Tailor your summarization prompt and parameters to help reduce token usage while preserving critical information.

Using this approach should help you manage the token limits effectively and avoid the "max token limit reached" error when summarizing larger PDF documents.