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XERJ Query-1, new in rc.101
Most AI search finds text in milliseconds and then asks a language model to write the answer. The model is what
makes a RAG answer good, and it is also where the seconds and the per-question cost come from.
XERJ Query-1 works the other way. It reads your question and writes a short program (look this up, compare these,
count those), and the engine runs that program over facts you stored. Lookup and comparison steps point at a stored
fact and its quoted span; counts return a program hash, not the records. If the data cannot support an answer you get
UNKNOWN; if two facts disagree you get CONFLICT. It runs on four CPU cores, offline, with no embeddings to build
and no GPU.
| XERJ Query-1 | the usual way | measured on |
|---|
| Question to answer | 220 ms (p50) | 1.5 to 2.3 s for Opus 5.5 to write a RAG answer (API time per call, our command-line client's start-up not counted), after a search of 0.8 to 106 ms | same 160 questions, rc.101 release, 4 model threads, laptop CPU |
| Wrong answers | 1 of 160 (124 right, 35 declined) | 9 to 15 (RAG with Opus 5.5), 6 (Opus 5.5 reading the whole store), 5 (Opus 5.5 given each question's records) | our 160-question test, drawn from the model's development set; XERJ Query-1 was given each question's records |
| Cost per million questions | $42 of CPU | $13,400 (RAG) to $694,000 (Opus re-reading the whole store per question, uncached) | same test, Opus 5.5 list prices; CPU at AWS c7g list price, 4.1 CPU-s per question on a shared ARM server (0.9 on a laptop) |
| Build a search index of 10 GB of text | 17 min (BM25, measured) | an estimated 62 to 97 hours to embed it with bge-small on CPU | English Wikipedia (wikimedia/wikipedia 20231101.en, first 22 of 41 files, 3,299,578 articles); XERJ Query-1 does not use this index |
| Correct a fact | 2.3 ms (one write; the next answer uses it) | re-embed the changed chunk (about 30 ms with bge-small), and the whole store (19.6 s for our 5,000-record test store) when the embedding model changes | rc.101 release, laptop |
Retrieval quality (R@k, nDCG@k) on BEIR, on SearchTome and on our 160-question test, the latency split and every data
set: benchmarks/query-1-2026-11.
The model is off by default; without the flag there is no /_facts route and the MCP tool list is unchanged. rc.101
upgrades the data directory to format 4 on first write, and rc.100 cannot open it again. What it does not do yet,
measured: sums and totals (a sum question can come back as a member count), and large fact sets (on a 95,000-fact
namespace asks took 0.82 s at the median and all 20 test questions were declined). It declines more often than an LLM
would guess. The numbers and their caveats: blog post,
capabilities.
Try it with the model
curl -fsSL https://xerj.org/get | sh -s -- --with-model
Verify: the installer ends by printing the start line with the model directory it unpacked.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
