project
Weaviate is an open-source vector database for AI search and retrieval-augmented generation, offering hybrid BM25 plus dense-vector queries.
No evidence-backed relationships are recorded.
A dev.to post argues that moving embeddings out of Postgres buys a second source of truth, a four-leg query path and 50 to 150 ms of internet latency. Its 8 ms pgvector counter-figure has no benchmark behind it.
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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.
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DiskANN keeps product-quantized vectors for every node resident in RAM, so its footprint tracks the corpus. A paper on arxiv puts those codes on storage instead and reports millisecond-order latency at 95 percent 1-recall@1.
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pgvector ships hnsw.ef_search at 40, the size of the candidate list its graph walk keeps in flight, and a top-20 query with a tenant filter can come back with two rows and no error to explain it.
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An independent rebuild of the SQLite side says the gap is real, but the published tables measure two different queries on two different graphs, so the depth at which SQLite's plan collapses on your data is a number you have to take yourself.
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Jordy Zomer built a Datalog engine so an agent maintains what it currently knows instead of searching its own transcript, and his own benchmark runs put the weak link in the model that writes the facts.
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A comparison of Mem0, Zep and LangChain's memory classes puts the split at adjudication rather than storage. Mem0 pays two model calls per message to get it.
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A consultant's two client failures, a supplier filter and a 10kg shipping quote, both broke on a numeric comparison that top-k similarity does not perform.
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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.
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