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kyleandClaude Opus 5.5 64d08a358d P5b review: background embedder re-queues a failed document and waits for a busy gate; no empty vectors cached
Owner review of p5b (tasks 01-03 by Ornith):
- background.go: `seen` was never cleared, so a document whose background embedding failed
  (Dixie hiccup) or was abandoned could never be queued again: every later question on it stayed
  BM25 for the life of the process. Dedupe now covers only queued and in-progress documents.
- background.go: acquireGate gave up after one retry and dropped the document; the task said to
  keep retrying once a second. It now waits (background work has no deadline).
- retrieval.go/background.go: a vector the server left out is not cached as an empty vector.
- bm25.go: Scores on a nil *BM25 dereferenced it; returns nil.
Regression tests in review_test.go; both fail on the old code.

End-to-end against Dixie (local serve): Karpathy's thesis, 95 pages / 51 chunks: bm25 in 19 s,
hybrid (51 embedded) after the background; RP2040 datasheet, 642 pages / 284 chunks (a 422
before P5b): bm25 in 13.5 s, all 284 embedded within 90 s, hybrid answer correct with its quote.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-09-26 19:02:51 -07:00

150 lines
4.4 KiB
Go

package digest
import (
"context"
"strings"
"git.wntrmute.dev/kyle/sift/internal/dcache"
)
// hybridOrBM25 scores a chunked document either hybrid (cosine + lexical overlap) or, when there
// are too many uncached chunks to embed inline, lexical-only BM25. mode is "hybrid" or "bm25".
func (e *Engine) hybridOrBM25(ctx context.Context, chunks []string, question, contentHash, callerClass string) (string, int, int, string, int, error) {
key := func(i int) string {
return dcache.EmbedKey(contentHash+"/sp1200", i, e.embedModel(), callerClass)
}
if e.Cache == nil {
if len(chunks) <= EmbedSyncMax {
vecs, err := e.embed(ctx, append([]string{question}, capEmbed(chunks)...))
if err != nil {
return joinBudget(chunks, SmallDoc), min(len(chunks), countBudget(chunks, SmallDoc)), len(chunks), "hybrid", 0, err
}
chunkVecs := make([][]float64, len(chunks))
for i := range chunks {
chunkVecs[i] = vecs[i+1]
}
body, used := greedyBudget(chunks, hybridScores(chunks, question, vecs[0], chunkVecs), SmallDoc)
return body, used, len(chunks), "hybrid", len(chunks), nil
}
body, used := greedyBudget(chunks, bm25Scores(chunks, question), SmallDoc)
return body, used, len(chunks), "bm25", 0, nil
}
// Cache present: look up every chunk's vector.
var cached []int
var cachedVecs [][]float64
var uncached []int
for i := range chunks {
if v, ok := e.Cache.GetEmbed(key(i)); ok {
cached = append(cached, i)
cachedVecs = append(cachedVecs, v)
continue
}
uncached = append(uncached, i)
}
if len(uncached) <= EmbedSyncMax {
in := append([]string{question}, capEmbed(uncachedChunks(chunks, uncached))...)
vecs, err := e.embed(ctx, in)
if err != nil {
return joinBudget(chunks, SmallDoc), min(len(chunks), countBudget(chunks, SmallDoc)), len(chunks), "hybrid", len(cached), err
}
for j, i := range uncached {
if len(vecs[j+1]) > 0 { // a vector the server left out is not cached as empty
e.Cache.PutEmbed(key(i), vecs[j+1])
}
}
chunkVecs := make([][]float64, len(chunks))
for k, i := range cached {
chunkVecs[i] = cachedVecs[k]
}
for j, i := range uncached {
chunkVecs[i] = vecs[j+1]
}
body, used := greedyBudget(chunks, hybridScores(chunks, question, vecs[0], chunkVecs), SmallDoc)
return body, used, len(chunks), "hybrid", len(chunks), nil
}
body, used := greedyBudget(chunks, bm25Scores(chunks, question), SmallDoc)
e.queueForEmbed(chunks, contentHash, callerClass)
return body, used, len(chunks), "bm25", len(cached), nil
}
func uncachedChunks(chunks []string, uncached []int) []string {
out := make([]string, len(uncached))
for j, i := range uncached {
out[j] = chunks[i]
}
return out
}
// hybridScores builds the hybrid scorer 0.7*cosine(q, v_i) + 0.3*lexicalOverlap(question, chunk),
// where chunkVecs[i] is chunk i's vector.
func hybridScores(chunks []string, question string, q []float64, chunkVecs [][]float64) func(int) float64 {
return func(i int) float64 {
return 0.7*cosine(q, chunkVecs[i]) + 0.3*lexicalOverlap(question, chunks[i])
}
}
// bm25Scores builds the BM25 scorer, normalised by its maximum so scores land in [0, 1].
func bm25Scores(chunks []string, question string) func(int) float64 {
scores := NewBM25(chunks).Scores(question)
var max float64
for _, s := range scores {
if s > max {
max = s
}
}
if max == 0 {
return func(int) float64 { return 0 }
}
return func(i int) float64 { return scores[i] / max }
}
// greedyBudget picks chunk 0 first, then the highest-scoring remaining chunks while the total
// stays within budget tokens, and returns them in document order with the count used.
func greedyBudget(chunks []string, score func(int) float64, budget int) (string, int) {
type scored struct {
i int
s float64
}
var ss []scored
for i := range chunks {
ss = append(ss, scored{i: i, s: score(i)})
}
picked := map[int]struct{}{}
used := 0
if len(chunks) > 0 {
picked[0] = struct{}{}
used = EstTokens(chunks[0])
}
for len(picked) < len(ss) {
best, bi := -1.0, -1
for _, x := range ss {
if _, ok := picked[x.i]; ok {
continue
}
if x.s > best {
best, bi = x.s, x.i
}
}
if bi < 0 {
break
}
need := EstTokens(chunks[bi])
if used+need > budget && used > 0 {
break
}
picked[bi] = struct{}{}
used += need
}
var ordered []string
for i := range chunks {
if _, ok := picked[i]; ok {
ordered = append(ordered, chunks[i])
}
}
return strings.Join(ordered, "\n\n"), len(ordered)
}