embedding caching

**Embedding caching** is the **technique of reusing previously computed vector embeddings for repeated texts, queries, or chunks** - it reduces model inference load and speeds up both ingestion and query pipelines. **What Is Embedding caching?** - **Definition**: Storage and reuse of embedding outputs keyed by content hash and model version. - **Cache Scope**: Applies to document embeddings, query embeddings, and reranker feature vectors. - **Consistency Requirement**: Entries are valid only for the exact embedding model and tokenizer settings. - **System Role**: Acts as a compute-saving layer in vector-heavy retrieval stacks. **Why Embedding caching Matters** - **Compute Reduction**: Avoids repeated embedding inference for identical or near-identical inputs. - **Latency Improvement**: Query embedding hits cut end-to-end retrieval time. - **Cost Control**: Lower model-inference volume reduces GPU or API spend. - **Pipeline Stability**: Cached vectors reduce load spikes during bulk reindex operations. - **Operational Predictability**: Improves throughput under repeated query patterns. **How It Is Used in Practice** - **Hash Keys**: Use deterministic content hashes plus model ID and preprocessing signature. - **TTL and Invalidation**: Expire or purge entries when model upgrades occur. - **Quality Safeguards**: Monitor cache hit quality to detect unintended semantic drift. Embedding caching is **a practical efficiency layer for vector retrieval infrastructure** - model-version-aware caching delivers speed and cost benefits with controlled risk.