How can I efficiently chunk large PDFs to extract budgets, scope, and key dates?

Below is an outline of an approach that many have found effective when summarizing large documents to extract targeted facts, along with a simple code example for handling chunking:

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1. Use Hierarchical Summarization

Instead of summarizing paragraph-by-paragraph, consider a two-step (or hierarchical) summarization process:

• Step 1: Chunk-Level Extraction

  Split the document into moderately sized, possibly overlapping, chunks. Then run a specialized prompt on each chunk to extract the key metadata fields (budgets, scope, and key dates). Using overlapping windows helps reduce the chances of missing information that lies on the boundary between chunks.

• Step 2: Global Aggregation

  Combine the outputs of all chunks and use another summarization or extraction step on this aggregated text. This step aggregates the partial extractions to produce a final, consolidated set of facts.

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2. Use Extraction Prompts

Instead of generic summarization prompts, design prompts that explicitly ask for:

• Budget details
• Project scope
• Key dates (submission deadline, project length, completion date)

This helps focus the model on what’s relevant and minimizes the risk of missing out some details.

Example prompt for each chunk:

"Extract all budget details, the defined scope, and the key dates (submission deadline, project length, and project completion date) from the following text: <insert chunk text>"

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3. Code Example for Chunking (Python)

Below is a simple example in Python that demonstrates how you might implement a basic overlapping chunking function:

PYTHONCopied
def chunk_text(text, chunk_size=1024, overlap=100):
    """
    Splits text into overlapping chunks.
    
    Args:
        text (str): The full text of the document.
        chunk_size (int): The maximum size (in tokens or words) of each chunk.
        overlap (int): The number of tokens/words to overlap between chunks.
    
    Returns:
        List[str]: A list of text chunks.
    """
    # For simplicity, using space-based tokenization; you could use a proper tokenizer for your model.
    tokens = text.split()
    chunks = []
    start = 0
    while start < len(tokens):
        end = start + chunk_size
        chunk = " ".join(tokens[start:end])
        chunks.append(chunk)
        # Advance start by chunk_size-overlap to create the desired overlap
        start += (chunk_size - overlap)
    return chunks

# Example usage
with open("document.txt", "r") as f:
    full_text = f.read()

chunks = chunk_text(full_text, chunk_size=256, overlap=50)
print(f"Generated {len(chunks)} chunks.")

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4. Integrating with Completions

After chunking, for each chunk you would typically call your model’s API with an extraction prompt. Then accumulate all outputs into a combined text that gets processed by a final extraction stage:

PYTHONCopied
import openai  # Assuming using OpenAI's API

def extract_key_facts(chunk):
    prompt = (
        "Extract the following details from the text: "
        "Budget information, project scope, and key dates (submission deadline, project length, project completion date).\n\n"
        f"Text: {chunk}"
    )
    response = openai.Completion.create(
        engine="text-davinci-003",  # Or another engine of your choice
        prompt=prompt,
        max_tokens=150,
        temperature=0.0
    )
    return response.choices[0].text.strip()

# Extract from each chunk
chunk_summaries = [extract_key_facts(chunk) for chunk in chunks]

# Aggregate the summaries and extract a final consolidated summary
combined_summary = "\n".join(chunk_summaries)
final_prompt = (
    "Based on the following extracted information, provide a final consolidated extraction "
    "of the budget details, project scope, and key dates (submission deadline, project length, project completion date).\n\n"
    f"{combined_summary}"
)
final_response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=final_prompt,
    max_tokens=200,
    temperature=0.0
)

final_extraction = final_response.choices[0].text.strip()
print("Final Key Facts:\n", final_extraction)

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Final Advice

• Experiment with Chunk Size: Depending on your PDF content, you might need to adjust chunk sizes and the amount of overlap.
• Post-Processing and Deduplication: After extracting from multiple chunks, you might see duplicate data. Consider additional parsing or deduplication logic.
• Edge Cases: Some PDFs (or extracted texts) may have formatting quirks. You might need additional preprocessing to clean the text.

This hierarchical approach should be more robust as it allows for redundancy in fact extraction, minimizing the chance of missing details that might only appear in a single location within the document.

Hope this helps!