content-hash-cache-pattern

Use SHA-256 content hashes as cache keys instead of file paths to cache file processing results. This content-hash approach decouples caching from file locations, ensuring results survive file moves or renames while automatically invalidating when file content changes.

The best way to structure caching for text extraction pipelines is to separate the service layer from pure processing functions. Wrap your pure processing function with a caching layer to satisfy the Single Responsibility Principle, using deterministic {hash}.json storage for O(1) lookups without needing an index file.

Content-hash caching handles invalidation automatically by computing a SHA-256 hash of the file content. If the file content changes, the generated hash changes, creating a new cache key and ensuring stale results are never returned during Python file processing operations.

Yes, content-hash cache patterns are designed for pipelines that repeatedly process the same files across runs. By storing entries as deterministic {hash}.json files, the system achieves O(1) lookups to instantly retrieve prior PDF parsing, text extraction, or image analysis results without recomputation.

SHA-256 based cache keys are used instead of file paths because path-based caching breaks when files move or are renamed. SHA-256 content hashes ensure the cache remains valid regardless of file location and auto-invalidates when the actual file content changes.

No, you do not need a separate index file when using deterministic {hash}.json cache storage. The file naming convention itself acts as the index, allowing O(1) cache lookups by directly checking for the existence of a file matching the computed SHA-256 hash.