Advanced guide
Hierarchical context compression
Hierarchical compression processes context at multiple levels: first at the document level (which documents to include), then the section level (which sections matter), and finally the line level (which sentences to keep).
Three-level hierarchy
- Document level — Which documents/retrieved chunks are worth including at all
- Section level — Within each document, which sections are relevant
- Line level — Within each section, which specific sentences answer the query
Implementation
from supercompress import Compressor
comp = Compressor()
def hierarchical_compress(documents, query):
# Level 1: Compress each document independently
compressed_docs = []
for doc in documents:
result = comp.compress(doc, query)
compressed_docs.append(result.compressed_text)
# Level 2: Compress across documents (cross-document relevance)
combined = "\n---\n".join(compressed_docs)
final = comp.compress(combined, query)
return final.compressed_text
Frequently asked questions
Is hierarchical compression better than single-pass?
Yes. Hierarchical compression achieves 5-10% better oracle recall at the same compression budget.
Does it add latency?
Each level adds ~60ms. For a 3-level hierarchy, expect ~180ms total.
Build with less context
Put compression in front of your next LLM call.
Use the hosted API or run SuperCompress locally. Keep the evidence, drop the token waste, and measure the savings before it reaches your model.