ANSWERS · FOLDER INDEXING

Answer questions about a docs folder, with a file to cite

AUTHOR · XERJ documentation team · REVIEWED BY · XERJ engineering team · PUBLISHED · 2026-10-20 · UPDATED · 2026-10-20

SHORT ANSWER · UPDATED 2026-10-20

Answer questions about a docs folder by indexing it with xerj autoindex, then running a phrase query. Each hit carries ax_path, so the agent can cite the file. On 2026-10-20 a phrase query for "what is in here" returned WHY_XERJ.md and took 1 ms. xerj search on the same words did not.

Key number: A phrase query for "what is in here" returned WHY_XERJ.md and took 1 ms. (as of 2026-10-20 · source: benchmarks/agent-queries-2026-10-20/08-docs-phrase.json)

TL;DR — Answer questions about a docs folder by indexing it with xerj autoindex, then running a phrase query. Each hit carries ax_path, so the agent can cite the file. On 2026-10-20 a phrase query for "what is in here" returned WHY_XERJ.md and took 1 ms. xerj search on the same words did not.

PASTE THIS TO YOUR AI AGENT — IT DOES THE REST

Read https://xerj.org/llms.txt. Start one local node, run xerj autoindex on the docs folder, then run xerj autoindex map and query the prose field it names. Cite ax_path for every passage you use. If xerj search returns code identifiers, say so and run a match_phrase on the prose field.

RUN THIS XERJ COMMAND

xerj autoindex --url http://127.0.0.1:9217 --yes --no-graph --progress plain --disable-feedback /workspace/docs --prefix docs --state-dir /tmp/xerj-aq-2026-10-20/state-docs

Index the docs folder. The capture exited 3, which is completed-with-junk.

RUN THIS XERJ COMMAND xerj search --url http://127.0.0.1:9217 --prefix docs -k 3 what is in here Definition-first search. On this question it returned code lines, not the prose sentence.

RUN THIS XERJ COMMAND

curl -s -XPOST http://127.0.0.1:9217/docs-docs/_search -H 'content-type: application/json' -d '{"size":3,"_source":["ax_path","title"],"query":{"match_phrase":{"body":"what is in here"}}}'

Phrase query on the prose field. One hit, WHY_XERJ.md, took 1 ms.

XERJ is a new way for AI to search for data.

Index the folder

The capture pointed xerj autoindex at this repo's docs/ directory. It walked 294 files, skipped 10 by ignore rules, and committed 11 datasets and 9,940 records. Wall time was 14.3 s. Exit 3, reason=completed-with-junk: 3 files were junk, and the rest is searchable. Exit 3 is success.

xerj-done ok=true exit=3 reason=completed-with-junk wall=14.3s files=290 records=9940 generation=1

xerj autoindex map names the real fields. On the markdown dataset the prose field was body.

The code command missed the sentence

xerj search --url http://127.0.0.1:9217 --prefix docs -k 3 "what is in here"

The command printed three passages, all code, all a variable named here. It said to cite ax_path:line. The capture file is 689 bytes.

rg -n on docs/**/*.md printed one line, in WHY_XERJ.md, and that capture file is 270 bytes. For this question rg was shorter and it was the right line.

Ask the prose field

curl -s -XPOST http://127.0.0.1:9217/docs-docs/_search -H 'content-type: application/json' -d '{"size":3,"_source":["ax_path","title"],"query":{"match_phrase":{"body":"what is in here"}}}'

One hit. ax_path was WHY_XERJ.md. took was 1. The title on the hit was the file's first heading.

The same response said the query scored body with BM25 and did not read the embedding. Ranking is lexical unless the node was started with --embed-mode neural.

XERJ does not write the answer. It returns the passage and the path. The agent quotes the path.

Measured on 2026-10-20

Check	Result
`xerj autoindex` on `docs/`	Exit 3, 9,940 records, wall 14.3 s, 3 junk files
`xerj search` "what is in here"	3 code passages. Capture file 689 bytes
`rg` the same words	1 line in `WHY_XERJ.md`. Capture file 270 bytes
`match_phrase` on `body`	1 hit, `WHY_XERJ.md`, `took` 1 ms

One node, binary v1.0.0-rc.97. rg won this comparison on both bytes and the right line.

When not to use XERJ

If the question is one exact string in a handful of files, rg already returned it. Use the index when the folder is large enough that you want a ranked passage and a path, or when the next step is an agent call rather than a scroll.

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