Cache expensive file processing results using SHA-256 content hashes — path-independent, auto-invalidating, with service layer separation. Use when repeated file processing is slow and results should be cached and invalidated by content rather than path.

Content Hash File Cache Pattern Cache expensive file processing results (PDF parsing, text extraction, image analysis) using SHA 256 content hashes as cache keys. Unlike path based caching, this approach survives file moves/renames and auto invalidates when content changes. When to Activate Building file processing pipelines (PDF, images, text extraction) Processing cost is high and same files are processed repeatedly Need a cache/ no cache CLI option Want to add caching to existing pure functions without modifying them Core Pattern 1. Content Hash Based Cache Key Use file content (not path) as the cache key: Why content hash? File rename/move = cache hit. Content change = automatic invalidation. No index file needed. 2. Frozen Dataclass for Cache Entry 3. File Based Cache Storage Each cache entry is stored as {hash}.json — O(1) lookup by hash, no index file required. 4. Service Layer Wrapper (SRP) Keep the processing function pure. Add caching as a separate service layer. Key Design Decisions Decision Rationale SHA 256 content hash Path independent, auto invalidates on content change {hash}.json file naming O(1) lookup, no index file needed Service layer wrapper SRP: extraction stays pure, cache is a separate concern Manual JSON serialization Full control over frozen dataclass serialization Corruption returns None Graceful degradation, re processes on next run cache dir.mkdir(parents=True) Lazy directory creation on first write Best Practices Hash content, not paths — paths change, content identity doesn't Chunk large files when hashing — avoid loading entire files into memory Keep processing functions pure — they should know nothing about caching Log cache hit/miss with truncated hashes for debugging Handle corruption gracefully — treat invalid cache entries as misses, never crash Anti Patterns to Avoid When to Use File processing pipelines (PDF parsing, OCR, text extraction, image analysis) CLI tools that benefit from cache/ no cache options Batch processing where the same files appear across runs Adding caching to existing pure functions without modifying them When NOT to Use Data that must always be fresh (real time feeds) Cache entries that would be extremely large (consider streaming instead) Results that depend on parameters beyond file content (e.g., different extraction configs)

content-hash-cache-pattern