search chunks — hybrid retrieval (no LLM)
Raw vector + keyword retrieval against one knowledge base. Returns ranked chunks
for you to reason over; it does NOT synthesize an answer (use chat for that).
Command & flags
weknora search chunks "<query>" --kb <name-or-id> [flags]| Flag | Default | Meaning |
|---|---|---|
--kb |
(required) | KB name or UUID |
--limit, -L |
8 | max chunks returned (1..1000); 8 is tuned for an LLM context window |
--vector-threshold |
0 (off) | min vector similarity, per-channel pre-fusion |
--keyword-threshold |
0 (off) | min keyword score, per-channel pre-fusion |
--no-vector |
false | disable the vector channel (keyword-only) |
--no-keyword |
false | disable the keyword channel (vector-only) |
You cannot disable both channels. --limit is a hard cap on returned chunks
applied client-side (the server may internally retrieve a larger pool for recall,
then the CLI trims).
Output (--format json)
data is an array of chunk objects; meta.count is the number returned.
{"ok":true,"meta":{"count":3},"data":[
{"id":"chunk_…","content":"…","knowledge_id":"doc_…","knowledge_title":"…",
"chunk_index":4,"score":0.82,"match_type":"hybrid","chunk_type":"text"}
]}scoreis the fused rank;match_typeindicates which channel(s) hit.knowledge_id/knowledge_titleattribute the chunk to its source document.- Project just what you need with
--jq, e.g.weknora search chunks "q" --kb eng --jq '.data[] | {score,content}'.
When to use vs alternatives
- Need an answer →
chat(it does retrieval + synthesis internally). - Building your own prompt/context from sources →
search chunks(this). - Finding which documents exist by keyword →
search docs --kb <kb>. - Inspecting/debugging a specific document's chunks →
chunk list --doc <id>.