What is the best method for handling large context files (e.g., 30k tokens) in API prompts?

Below is a concise explanation outlining the recommended approaches when you need to pass large context files (e.g., 30k tokens) into a call to an API:

1. Chunking the Input

When the entire file exceeds the context window, split the document into overlapping or sequential chunks that fit into the token limit. For example:

• Sequential Chunks: Simply split the text into non-overlapping pieces and process them one by one.
• Sliding Window: Use overlapping chunks so the context isn't abruptly chopped off at boundaries.

2. Summarization and Hierarchical Methods

If you can’t pass the entire document at once, summarize sections of the text separately, then combine those summaries to create an overall summary that fits within the allowed token count. This approach can also be applied hierarchically:

• Section Summaries: Generate summaries for each section or chapter.
• Aggregate Summary: Combine section summaries into a final, more concise summary.

3. Retrieval Augmented Generation (RAG)

Implement a two-step process:

• Index and Search: Pre-index your large document (e.g., using embeddings) so you can retrieve the most relevant parts on a per-request basis.
• Combine with Generation: Use the retrieved snippets as additional context for your prompt. This helps balance the need for long context without overloading the model’s prompt.

4. Efficiency in Prompt Design

Make sure your prompt is token-efficient:

• Minimize Redundancy: Reduce unnecessary words and focus on the essential details.
• Iterative Processing: Instead of feeding everything at once, use multi-step interactions where you incrementally refine the response.

Example Workflow

PYTHONCopied
# Example: Chunking and summarization
def chunk_text(text, max_tokens):
    # Split text into chunks that fit within the max token limit.
    # This is a simplified illustration; you'll need a tokenizer for precise splitting.
    words = text.split()
    chunks = []
    current_chunk = []
    
    for word in words:
        # Check if adding the next word exceeds our tokens limit (approximated by word count)
        if len(current_chunk) + 1 > max_tokens:
            chunks.append(" ".join(current_chunk))
            current_chunk = []
        current_chunk.append(word)
    
    if current_chunk:
        chunks.append(" ".join(current_chunk))
    
    return chunks

text = "Your large context text here..."
max_tokens_per_chunk = 3000  # Approximate value; adjust per model requirements.
chunks = chunk_text(text, max_tokens_per_chunk)

# Process each chunk (e.g., summarization pass)
summaries = []
for chunk in chunks:
    # Pass chunk to a summarization model
    summary = summarize_chunk(chunk)  # hypothetical function call
    summaries.append(summary)

# Optionally, create an aggregated summary
final_summary = summarize_chunk(" ".join(summaries))

Final Thoughts

By breaking down the large context into manageable parts and using summarization or retrieval techniques, you can ensure that your API calls remain within token limits while still capturing as much of the context as possible. Depending on your exact requirements, you might need to experiment with the chunking and summarization strategies.

This approach should work well when interfacing with APIs that have strict token limits.