P5b and P5c plans: long-document retrieval (BM25 first, background embedding) and /v1/extract + sift extract; given tests
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,89 @@
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package digest
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// P5b task 01: BM25, the lexical ranking used while a long document's embeddings are still
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// being built (and inside the hybrid score after).
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import (
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"math"
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"strings"
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"testing"
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)
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func TestBM25RanksTheRareTerm(t *testing.T) {
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docs := []string{
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"the ship sailed from helsinki to tallinn in the morning",
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"the ship stayed in port all day",
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"a zanzibarite sample was found near the harbour",
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"the harbour master logged the ship",
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}
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s := NewBM25(docs).Scores("where was the zanzibarite found")
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if len(s) != len(docs) {
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t.Fatalf("%d scores for %d docs", len(s), len(docs))
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}
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best := 0
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for i := range s {
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if s[i] > s[best] {
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best = i
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}
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}
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if best != 2 {
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t.Errorf("best = doc %d (%v), want doc 2, the only one with the rare term", best, s)
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}
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}
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func TestBM25Properties(t *testing.T) {
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filler := strings.Repeat("word ", 50)
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docs := []string{
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"alpha " + filler, // 0: tf 1
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strings.Repeat("alpha ", 10) + filler, // 1: tf 10, same filler
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"alpha common", // 2: tf 1, short
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"common " + filler, // 3: no alpha
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}
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idx := NewBM25(docs)
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s := idx.Scores("alpha")
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if !(s[1] > s[0]) {
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t.Errorf("more occurrences must score higher: tf10 %v vs tf1 %v", s[1], s[0])
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}
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if !(s[1] < 10*s[0]) {
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t.Errorf("term frequency must saturate: tf10 %v, 10 × tf1 %v", s[1], 10*s[0])
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}
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if !(s[2] > s[0]) {
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t.Errorf("the same term in a shorter document must score higher: short %v, long %v", s[2], s[0])
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}
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if s[3] != 0 {
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t.Errorf("a document without the term scores %v, want 0", s[3])
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}
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// IDF: a term in every document is worth less than a rare one.
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all := NewBM25([]string{"xx yy", "xx zz", "xx ww"}).Scores("xx")
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rare := NewBM25([]string{"xx yy", "qq zz", "qq ww"}).Scores("xx")
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if !(rare[0] > all[0]) {
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t.Errorf("idf: a term in 1 of 3 docs must outscore one in 3 of 3: %v vs %v", rare[0], all[0])
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}
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}
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func TestBM25Degenerate(t *testing.T) {
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for _, docs := range [][]string{nil, {}, {""}, {"", "a b"}} {
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s := NewBM25(docs).Scores("a")
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if len(s) != len(docs) {
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t.Errorf("%q: %d scores", docs, len(s))
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}
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for _, v := range s {
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if math.IsNaN(v) || math.IsInf(v, 0) || v < 0 {
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t.Errorf("%q: score %v", docs, v)
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}
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}
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}
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for _, v := range NewBM25([]string{"a b", "c d"}).Scores("") {
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if v != 0 {
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t.Errorf("empty query scored %v", v)
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}
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}
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// Deterministic.
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a := NewBM25([]string{"alpha beta", "beta gamma", "gamma alpha"}).Scores("alpha gamma")
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b := NewBM25([]string{"alpha beta", "beta gamma", "gamma alpha"}).Scores("alpha gamma")
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for i := range a {
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if a[i] != b[i] {
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t.Fatal("scores differ between identical runs")
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}
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}
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}
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@@ -0,0 +1,244 @@
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package digest
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// P5b task 02: retrieval for long documents. Embeddings are cached per chunk; a document with
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// more uncached chunks than EmbedSyncMax is answered at once from BM25 while its chunks are
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// embedded in the background, and later questions use the hybrid score.
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import (
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"context"
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"encoding/json"
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"fmt"
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"hash/fnv"
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"net/http"
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"net/http/httptest"
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"path/filepath"
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"strings"
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"sync"
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"sync/atomic"
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"testing"
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"time"
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"git.wntrmute.dev/kyle/sift/internal/dcache"
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)
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// fakeDixie serves /embed (a bag-of-words hash into 64 dims, so cosine similarity means
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// something) and /chat (the SSE stub). It counts embedded inputs and can hold /embed shut.
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type fakeDixie struct {
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srv *httptest.Server
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embedded atomic.Int64 // inputs embedded, questions included
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hold chan struct{}
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holdOnce sync.Once
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lastChat atomic.Pointer[chatReq]
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}
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func newFakeDixie(t *testing.T, hold bool) *fakeDixie {
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f := &fakeDixie{}
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if hold {
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f.hold = make(chan struct{})
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}
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var disc atomic.Bool
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mux := http.NewServeMux()
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mux.HandleFunc("/embed", func(w http.ResponseWriter, r *http.Request) {
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if f.hold != nil {
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select {
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case <-f.hold:
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case <-r.Context().Done():
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return
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}
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}
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var body struct {
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Input []string `json:"input"`
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}
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_ = json.NewDecoder(r.Body).Decode(&body)
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var data []map[string]any
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for i, s := range body.Input {
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data = append(data, map[string]any{"index": i, "embedding": bow(s)})
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}
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f.embedded.Add(int64(len(body.Input)))
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_ = json.NewEncoder(w).Encode(map[string]any{"data": data})
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})
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mux.Handle("/chat", sseHandler(t, "ok", &f.lastChat, &disc))
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f.srv = httptest.NewServer(mux)
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t.Cleanup(func() { f.release(); f.srv.Close() })
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return f
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}
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func (f *fakeDixie) release() {
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if f.hold != nil {
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f.holdOnce.Do(func() { close(f.hold) })
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}
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}
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func bow(s string) []float64 {
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v := make([]float64, 64)
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for t := range terms(s) {
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h := fnv.New32a()
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_, _ = h.Write([]byte(t))
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v[h.Sum32()%64]++
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}
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return v
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}
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func (f *fakeDixie) engine(t *testing.T, gate *Gate, withCache bool) *Engine {
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e := &Engine{ChatURL: f.srv.URL + "/chat", EmbedURL: f.srv.URL + "/embed", Gate: gate, Client: f.srv.Client()}
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if withCache {
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db, err := dcache.Open(filepath.Join(t.TempDir(), "c.db"), 0, nil)
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if err != nil {
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t.Fatal(err)
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}
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t.Cleanup(func() { _ = db.Close() })
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e.Cache = db
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}
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return e
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}
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// longDoc is n pages of distinct filler; the page named rare carries a rare term.
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func longDoc(n, rare int) string {
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var b strings.Builder
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words := []string{"harbour", "vessel", "cargo", "anchor", "pilot", "tug", "berth", "quay", "hull", "keel", "mast", "deck"}
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for p := 1; p <= n; p++ {
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if p > 1 {
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b.WriteString("\n\n")
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}
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fmt.Fprintf(&b, "--- page %d ---\n\n", p)
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for l := 0; l < 40; l++ {
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fmt.Fprintf(&b, "page %d line %d %s %s %s %s\n", p, l, words[(p+l)%12], words[(p*3+l)%12], words[(p*5+l)%12], words[(p*7+l)%12])
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}
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if p == rare {
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b.WriteString("the zanzibarite cargo manifest was signed by the second officer\n")
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}
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}
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return b.String()
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}
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func chatDoc(f *fakeDixie) string {
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req := f.lastChat.Load()
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if req == nil {
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return ""
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}
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var s strings.Builder
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for _, m := range req.Messages {
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s.WriteString(m.Content)
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}
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return s.String()
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}
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func TestEmbeddingsAreCachedAcrossQuestions(t *testing.T) {
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f := newFakeDixie(t, false)
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e := f.engine(t, NewGate(2, 8, 5*time.Second), true)
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doc := longDoc(12, 0) // ~12 pages, a few chunks: under EmbedSyncMax
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r := e.Run(context.Background(), doc, "which berth did the tug use?", "https://ex.test/a.pdf", dcache.ClassFleet)
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if r.Fallback != "" || r.Retrieval != "hybrid" {
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t.Fatalf("first question: fallback %q retrieval %q", r.Fallback, r.Retrieval)
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}
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n := len(SplitPages(doc, ChunkTokens))
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if got := f.embedded.Load(); got != int64(n+1) {
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t.Fatalf("first question embedded %d inputs, want %d chunks + the question", got, n)
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}
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r = e.Run(context.Background(), doc, "where was the pilot?", "https://ex.test/a.pdf", dcache.ClassFleet)
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if r.Fallback != "" || r.Retrieval != "hybrid" || r.Embedded != n {
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t.Fatalf("second question: %+v", r)
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}
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if got := f.embedded.Load(); got != int64(n+2) {
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t.Errorf("second question embedded %d inputs in total, want %d: only its own question is new", got, n+2)
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}
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}
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// A long document is answered from BM25 without waiting for a single embedding, and becomes
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// hybrid once the background has embedded every chunk.
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func TestLongDocumentBM25ThenHybrid(t *testing.T) {
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f := newFakeDixie(t, true) // /embed held shut until release
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e := f.engine(t, NewGate(2, 8, 5*time.Second), true)
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doc := longDoc(300, 250)
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n := len(SplitPages(doc, ChunkTokens))
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if n <= EmbedSyncMax {
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t.Fatalf("test document has %d chunks; it must exceed EmbedSyncMax (%d)", n, EmbedSyncMax)
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}
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start := time.Now()
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r := e.Run(context.Background(), doc, "who signed the zanzibarite manifest?", "https://ex.test/big.pdf", dcache.ClassFleet)
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if d := time.Since(start); d > 10*time.Second {
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t.Fatalf("first answer took %v: it waited on embeddings", d)
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}
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if r.Fallback != "" || r.Retrieval != "bm25" || r.Embedded != 0 {
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t.Fatalf("first answer: %+v, want retrieval bm25 with 0 embedded", r)
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}
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if doc := chatDoc(f); !strings.Contains(doc, "zanzibarite") || !strings.Contains(doc, "page 250") {
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t.Errorf("BM25 did not bring page 250 (the only one with the term) into the prompt")
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}
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f.release()
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ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
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defer cancel()
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if err := e.WaitBackground(ctx); err != nil {
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t.Fatalf("background embedding did not finish: %v", err)
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}
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r = e.Run(context.Background(), doc, "who signed the zanzibarite manifest?", "https://ex.test/big.pdf", dcache.ClassFleet)
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if r.Fallback != "" || r.Retrieval != "hybrid" || r.Embedded != n {
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t.Fatalf("after background: %+v, want hybrid with all %d embedded", r, n)
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}
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if doc := chatDoc(f); !strings.Contains(doc, "zanzibarite") {
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t.Errorf("hybrid lost the page with the term")
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}
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}
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// Two questions on the same long document queue it once: every chunk is embedded once.
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func TestBackgroundDedupes(t *testing.T) {
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f := newFakeDixie(t, true)
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e := f.engine(t, NewGate(2, 8, 5*time.Second), true)
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doc := longDoc(200, 0)
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n := len(SplitPages(doc, ChunkTokens))
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e.Run(context.Background(), doc, "first question about the quay", "https://ex.test/b.pdf", dcache.ClassFleet)
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e.Run(context.Background(), doc, "second question about the keel", "https://ex.test/b.pdf", dcache.ClassFleet)
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f.release()
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ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
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defer cancel()
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if err := e.WaitBackground(ctx); err != nil {
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t.Fatal(err)
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}
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if got := f.embedded.Load(); got > int64(n) {
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t.Errorf("embedded %d inputs for a %d-chunk document: queued twice", got, n)
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}
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}
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// Background work takes the gate one batch at a time, so an interactive digest is not starved.
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func TestBackgroundYieldsTheGate(t *testing.T) {
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f := newFakeDixie(t, false)
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e := f.engine(t, NewGate(1, 8, 10*time.Second), true)
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e.Run(context.Background(), longDoc(400, 0), "start the background", "https://ex.test/c.pdf", dcache.ClassFleet)
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start := time.Now()
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r := e.Run(context.Background(), "a short page about a tug and a quay", "what is here?", "https://ex.test/small", dcache.ClassFleet)
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if r.Fallback != "" {
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t.Fatalf("interactive digest fell back (%q) while background embedding ran", r.Fallback)
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}
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if d := time.Since(start); d > 5*time.Second {
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t.Errorf("interactive digest waited %v behind background embedding", d)
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}
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ctx, cancel := context.WithTimeout(context.Background(), 60*time.Second)
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defer cancel()
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_ = e.WaitBackground(ctx)
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}
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// Without a cache there is nowhere to keep background work: a long document is BM25 and
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// nothing is embedded.
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func TestNoCacheLongDocumentIsBM25Only(t *testing.T) {
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f := newFakeDixie(t, false)
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e := f.engine(t, NewGate(2, 8, 5*time.Second), false)
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r := e.Run(context.Background(), longDoc(300, 250), "who signed the zanzibarite manifest?", "https://ex.test/d.pdf", dcache.ClassFleet)
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if r.Fallback != "" || r.Retrieval != "bm25" {
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t.Fatalf("%+v, want bm25", r)
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}
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time.Sleep(200 * time.Millisecond)
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if got := f.embedded.Load(); got != 0 {
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t.Errorf("embedded %d inputs with no cache to keep them in", got)
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}
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}
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// A document that fits the digest budget whole is sent whole, as today.
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func TestSmallDocumentIsWhole(t *testing.T) {
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f := newFakeDixie(t, false)
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e := f.engine(t, NewGate(2, 8, 5*time.Second), true)
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r := e.Run(context.Background(), "--- page 1 ---\n\nOne short page.", "what is it?", "https://ex.test/e.pdf", dcache.ClassFleet)
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if r.Fallback != "" || r.Retrieval != "whole" {
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t.Fatalf("%+v, want whole", r)
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}
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}
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@@ -0,0 +1,90 @@
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package digest
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// P5b task 01: SplitPages keeps every chunk of a PDF's text attached to its page, so a digest
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// built from a chunk that starts mid-page can still cite the page.
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import (
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"fmt"
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"regexp"
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"strings"
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"testing"
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)
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// pagesText builds n pages of about perPage tokens each, every line naming its page.
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func pagesText(n, perPage int) string {
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var b strings.Builder
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for p := 1; p <= n; p++ {
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if p > 1 {
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b.WriteString("\n\n")
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}
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fmt.Fprintf(&b, "--- page %d ---\n\n", p)
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for l := 0; l*12 < perPage; l++ {
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fmt.Fprintf(&b, "page %d line %d alpha bravo charlie delta echo foxtrot golf hotel\n", p, l)
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}
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}
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return b.String()
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}
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var markerRE = regexp.MustCompile(`^--- page (\d+)( \(cont\.\))? ---`)
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func TestSplitPagesEveryChunkNamesItsPage(t *testing.T) {
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text := pagesText(4, 3000) // each page ~3000 tokens: several chunks per page at 1200
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chunks := SplitPages(text, ChunkTokens)
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if len(chunks) < 8 {
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t.Fatalf("%d chunks, want at least 8 for 4 × 3000 tokens at %d", len(chunks), ChunkTokens)
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}
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cont := 0
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for i, c := range chunks {
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m := markerRE.FindStringSubmatch(c)
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if m == nil {
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t.Fatalf("chunk %d does not start with a page marker: %.80q", i, c)
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}
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if m[2] != "" {
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cont++
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// A continuation marker names the page the chunk's first line belongs to.
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first := strings.SplitN(strings.TrimPrefix(c, m[0]), "\n", 3)
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line := strings.TrimSpace(strings.Join(first, " "))
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if !strings.Contains(line, "page "+m[1]+" line ") {
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t.Errorf("chunk %d marked page %s but starts with %.60q", i, m[1], line)
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}
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}
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if EstTokens(c) > ChunkTokens+20 {
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t.Errorf("chunk %d is %d tokens, over the %d budget (plus a marker line)", i, EstTokens(c), ChunkTokens)
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}
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}
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if cont == 0 {
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t.Error("no continuation markers: pages longer than a chunk must produce some")
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}
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}
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// Nothing is lost or duplicated: removing the continuation markers gives back the original lines.
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func TestSplitPagesPreservesContent(t *testing.T) {
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text := pagesText(3, 2500)
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var got []string
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for _, c := range SplitPages(text, ChunkTokens) {
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for _, line := range strings.Split(c, "\n") {
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if strings.HasSuffix(line, " (cont.) ---") || strings.TrimSpace(line) == "" {
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continue
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}
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got = append(got, line)
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}
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}
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var want []string
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for _, line := range strings.Split(text, "\n") {
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if strings.TrimSpace(line) != "" {
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want = append(want, line)
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}
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}
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if strings.Join(got, "\n") != strings.Join(want, "\n") {
|
||||
t.Errorf("content changed: %d lines back, %d in", len(got), len(want))
|
||||
}
|
||||
}
|
||||
|
||||
// Text with no markers splits exactly as Split does.
|
||||
func TestSplitPagesWithoutMarkersIsSplit(t *testing.T) {
|
||||
text := strings.Repeat("plain words without any page markers at all\n", 2000)
|
||||
a, b := SplitPages(text, ChunkTokens), Split(text, ChunkTokens)
|
||||
if strings.Join(a, "\x00") != strings.Join(b, "\x00") {
|
||||
t.Errorf("SplitPages differs from Split on unmarked text: %d vs %d chunks", len(a), len(b))
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user