Embeddings & Vector Search implementation checklist

Ensure the vector database is configured with the exact distance metric (Cosine, Euclidean, or Dot Product) recommended by the embedding model provider.

Measure the round-trip time for a single vector generation; if it exceeds 200ms, implement a client-side timeout and fallback mechanism.

Programmatically truncate input text to the model's maximum context window (e.g., 8192 tokens for text-embedding-3-small) before calling the API to avoid 400 errors.

Group multiple documents into a single API request up to the provider's batch limit to minimize network overhead during bulk indexing.

Deploy a lightweight local model (e.g., BGE-micro) as a fallback for critical path queries if the primary embedding API experiences an outage.

Set ef_construction and M parameters based on the dataset size; verify that recall rates meet the 90%+ threshold in a staging environment.

Configure automated snapshots of the vector index to a persistent storage layer like S3 or GCS to prevent total data loss on node failure.

Calculate the RAM required to hold the index in memory (Dimensions * 4 bytes * Number of Vectors) and ensure the instance has 20% overhead.

Explicitly define which metadata fields require filtering (e.g., user_id, timestamp) and enable indexing on those fields to avoid full-table scans.

Test Scalar Quantization (SQ) or Product Quantization (PQ) to reduce memory footprint and compare the resulting drop in recall against storage savings.

Implement a reciprocal rank fusion (RRF) or weighted score algorithm to combine BM25 keyword results with vector similarity scores.

Apply the same text normalization (lowercasing, punctuation removal) to the search query that was applied to the document chunks during indexing.

Determine the optimal 'k' value by measuring the tradeoff between retrieval recall and the latency added by processing more candidates.

Integrate a Cross-Encoder reranker to re-score the top 20-50 results retrieved from the vector database for higher precision.

Define a minimum similarity score cutoff to prevent the system from returning irrelevant results when no high-quality matches exist.

Use a unique deterministic ID (e.g., a hash of the source URL) for each vector to prevent duplicate entries during pipeline retries.

Implement a listener on the source database to trigger vector deletions when records are removed from the primary system of record.

Include a model_version field in the vector metadata to facilitate zero-downtime migrations when switching to a newer embedding model.

Monitor and alert if the lag between source data updates and vector index updates exceeds a defined threshold (e.g., 5 minutes).

Validate that text chunking includes a 10-15% overlap to ensure semantic context is preserved across chunk boundaries.

Log the number of tokens processed per request to monitor costs and detect anomalous spikes in API consumption.

Schedule a weekly job to compare the average distance of new embeddings against a baseline to detect shifts in data distribution.

Verify that queries are strictly scoped using metadata filters to prevent cross-tenant data leakage in multi-user applications.

Route text snippets that fail the embedding process (e.g., due to content filtering or API errors) to a DLQ for manual inspection.

Implement a leaky-bucket rate limiter on the search endpoint to protect the vector database and embedding API from exhaustion.