Topic
Combining BM25 or full-text keyword retrieval with dense vector retrieval and merging the ranked results.
One developer's test on 484 SEPA rulebook passages found that plain-English questions push several answers out of a top-5 vector search. Because the test measures each answer's rank directly, the failure shows up in retrieval, before the language model writes anything.
Reality
Databricks made Lakebase Search generally available, pairing BM25 with a Postgres vector index it says runs 4 times cheaper than pgvector at 100M vectors. The vector index lives in object storage behind a cache, so large agent corpora no longer need RAM sized to the whole index.
Reality
LiveReview's Maneshwar says Gemini File Search cost over 50 cents a review run because a reasoning model performed each search. A local index and a cheaper model brought runs down to 4 cents.
Reality
An embedding swap to bge-large made two long-failing negative tests pass because the retriever stopped surfacing the trap chunks at all. A chunk-id check in CI gives the suite a third verdict, test did not run.
Reality
A September 2026 comparison pins the workload at 10 million 1536-dimension vectors and reports Qdrant between $250 and $947 a month, while Pinecone's serverless bill is set by the gigabytes each query scans.
Reality
The paper reports 2 to 1,000 times the throughput of prior filtered-search methods at fixed recall, and says an existing HNSW library is enough to implement it. Both claims turn on how the graph is built.
Reality
Name-only BM25 matched nothing for "money we gave back to shoppers". The description channel put the right table second and embeddings put it twenty-second, and reciprocal rank fusion let the blind channel cost the answer nothing.
Reality
Open Walnut patched QMD's compiled output 15 times from the outside, then found that the one stage it needed to change, tokenization, lived in the engine core. Writing its own engine again took eleven days.
Reality
Vector search ranks by meaning, so literal tokens like part numbers and ticket IDs fall just outside the top results. The fix is lexical plus dense retrieval, not a model upgrade.
Reality