Replaces the per-item Add loop in the HTTP handler with one call to Index.BatchAdd, which acquires the write-lock once and pushes the whole batch through coder/hnsw's variadic Graph.Add. Pre-validation stays in the handler so per-item error messages keep their item-index precision. Microbench (internal/vectord/batch_bench_test.go) at d=768 cosine: N=16 SingleAdd 283µs/op → BatchAdd 170µs/op 1.66× N=128 SingleAdd 7.9ms/op → BatchAdd 7.5ms/op 1.05× N=1024 SingleAdd 87.5ms/op → BatchAdd 83.4ms/op 1.05× Win is biggest at staffing-driver batch sizes (N=16) where per-call lock + validation overhead is a meaningful fraction. At larger N the inner HNSW neighborhood search per insert dominates, which is the load-bearing finding for Option B (sharded indexes): the throughput ceiling lives inside the library, not at the lock, so sharding to N parallel Graphs is the only path to true concurrent-Add throughput. g1, g1p, g2 smokes all PASS post-change. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
415 lines
13 KiB
Go
415 lines
13 KiB
Go
// Package vectord owns the vector-search surface — HNSW indexes
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// keyed by string IDs with optional opaque JSON metadata. The
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// underlying library is github.com/coder/hnsw (pure Go, no cgo).
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//
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// G1 scope: in-memory only. Persistence to storaged + rehydrate
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// across restart is the next piece — keeping it out of this layer
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// makes the index API easier to test and keeps the storaged
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// dependency optional for downstream tooling.
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package vectord
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import (
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"encoding/json"
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"errors"
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"fmt"
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"io"
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"math"
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"sync"
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"github.com/coder/hnsw"
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)
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// Distance names accepted by IndexParams.Distance.
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const (
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DistanceCosine = "cosine"
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DistanceEuclidean = "euclidean"
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)
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// Default HNSW parameters — match coder/hnsw's NewGraph defaults
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// which are tuned for OpenAI-shaped embeddings (1536-d, but the
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// hyperparameters generalize).
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const (
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DefaultM = 16
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DefaultEfSearch = 20
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)
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// IndexParams describes one vector index. Once an Index is built,
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// these are fixed — changing M / dimension / distance requires a
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// rebuild.
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type IndexParams struct {
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Name string `json:"name"`
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Dimension int `json:"dimension"`
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M int `json:"m"`
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EfSearch int `json:"ef_search"`
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Distance string `json:"distance"`
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}
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// Result is one search hit. Distance semantics depend on the
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// configured distance function — for cosine it's `1 - cos(a,b)`
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// where smaller = closer; for euclidean it's the L2 norm of
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// `a - b`. Either way, smaller = closer and the result list is
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// sorted ascending.
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type Result struct {
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ID string `json:"id"`
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Distance float32 `json:"distance"`
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Metadata json.RawMessage `json:"metadata,omitempty"`
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}
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// Index wraps a coder/hnsw graph plus a side map of opaque JSON
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// metadata per ID. Concurrency: read-heavy via Search (read-lock);
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// Add and Delete take the write lock.
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type Index struct {
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params IndexParams
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g *hnsw.Graph[string]
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meta map[string]json.RawMessage
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mu sync.RWMutex
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}
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// Errors surfaced to HTTP handlers. Sentinel-based so the wire
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// layer can map to status codes via errors.Is.
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var (
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ErrDimensionMismatch = errors.New("vectord: vector dimension mismatch")
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ErrUnknownDistance = errors.New("vectord: unknown distance function")
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ErrInvalidParams = errors.New("vectord: invalid index params")
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)
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// NewIndex builds a fresh index from validated params.
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func NewIndex(p IndexParams) (*Index, error) {
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if p.Name == "" {
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return nil, fmt.Errorf("%w: empty name", ErrInvalidParams)
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}
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if p.Dimension <= 0 {
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return nil, fmt.Errorf("%w: dimension must be > 0 (got %d)", ErrInvalidParams, p.Dimension)
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}
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if p.M <= 0 {
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p.M = DefaultM
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}
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if p.EfSearch <= 0 {
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p.EfSearch = DefaultEfSearch
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}
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if p.Distance == "" {
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p.Distance = DistanceCosine
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}
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dist, err := distanceFn(p.Distance)
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if err != nil {
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return nil, err
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}
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g := hnsw.NewGraph[string]()
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g.M = p.M
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g.EfSearch = p.EfSearch
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g.Distance = dist
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// Ml stays at the library default (0.25); exposing it as a knob
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// is a G2 concern when we have real tuning data.
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return &Index{
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params: p,
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g: g,
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meta: make(map[string]json.RawMessage),
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}, nil
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}
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// distanceFn maps the string name to the underlying function.
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// Easier to unit-test than calling out to coder/hnsw's registry.
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func distanceFn(name string) (hnsw.DistanceFunc, error) {
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switch name {
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case DistanceCosine, "":
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return hnsw.CosineDistance, nil
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case DistanceEuclidean:
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return hnsw.EuclideanDistance, nil
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}
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return nil, fmt.Errorf("%w: %q (want cosine or euclidean)", ErrUnknownDistance, name)
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}
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// Params returns a copy of the immutable index params.
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func (i *Index) Params() IndexParams { return i.params }
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// Len returns the number of vectors currently in the index.
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func (i *Index) Len() int {
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i.mu.RLock()
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defer i.mu.RUnlock()
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return i.g.Len()
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}
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// Add inserts a vector with optional metadata, with replace
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// semantics for the vector: if id already exists, the prior
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// vector is removed first. Dim must match the index dim or
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// ErrDimensionMismatch is returned.
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//
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// Metadata semantics (post-scrum K-B1): nil meta is "leave
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// existing alone" (upsert-style); to clear metadata, pass an
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// empty `{}` or Delete+Add. This avoids silent metadata loss
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// when the JSON `metadata` field is omitted on re-add.
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//
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// Validates that all vector components are finite (post-scrum
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// O-W3). NaN/Inf in any component poisons HNSW: distance
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// comparisons return false for both `<` and `>`, breaking the
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// search heap invariants. Zero-norm vectors are also rejected
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// under cosine distance — cos(0,x) = NaN.
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//
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// Note: coder/hnsw's Graph.Add panics on re-adding an existing
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// key (internal "node not added" length-invariant check). We
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// pre-Delete to make Add idempotent on re-insert.
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func (i *Index) Add(id string, vec []float32, meta json.RawMessage) error {
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if id == "" {
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return errors.New("vectord: empty id")
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}
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if len(vec) != i.params.Dimension {
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return fmt.Errorf("%w: index dim=%d, got=%d", ErrDimensionMismatch, i.params.Dimension, len(vec))
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}
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if err := validateVector(vec, i.params.Distance); err != nil {
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return err
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}
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i.mu.Lock()
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defer i.mu.Unlock()
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// coder/hnsw has two sharp edges on re-add:
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// 1. Add of an existing key panics with "node not added"
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// (length-invariant fires because internal delete+re-add
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// doesn't change Len). Pre-Delete fixes this for n>1.
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// 2. Delete of the LAST node leaves layers[0] non-empty but
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// entryless; the next Add SIGSEGVs in Dims() because
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// entry().Value is nil. We rebuild the graph in that case.
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_, exists := i.g.Lookup(id)
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if exists {
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if i.g.Len() == 1 {
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i.resetGraphLocked()
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} else {
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i.g.Delete(id)
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}
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}
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i.g.Add(hnsw.MakeNode(id, vec))
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if meta != nil {
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// Per scrum K-B1 (Kimi): only OVERWRITE on explicit non-nil.
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// nil = "leave existing meta alone" (upsert). To clear, the
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// caller should send an empty `{}` body or Delete the id.
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i.meta[id] = meta
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}
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return nil
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}
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// resetGraphLocked recreates the underlying coder/hnsw Graph with
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// the same params. Caller MUST hold i.mu (write-lock). Used to
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// dodge the library's "delete the last node, then segfault on
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// next Add" bug — see Add for details. Metadata map is preserved
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// because the only entry it could affect is the one being
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// re-added, which Add overwrites.
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func (i *Index) resetGraphLocked() {
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g := hnsw.NewGraph[string]()
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g.M = i.params.M
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g.EfSearch = i.params.EfSearch
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g.Distance = i.g.Distance
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i.g = g
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}
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// ValidateVector is the exported form of validateVector — the HTTP
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// handler pre-validates batches before committing, so it needs the
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// same predicate Add uses internally. Per scrum O-I3 (G1P).
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func ValidateVector(vec []float32, distance string) error {
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return validateVector(vec, distance)
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}
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// validateVector rejects vectors that would poison the HNSW
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// graph or produce NaN distances. Per scrum O-W3 (Opus, G1).
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func validateVector(vec []float32, distance string) error {
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var sumSq float64
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for j, v := range vec {
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f := float64(v)
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if math.IsNaN(f) || math.IsInf(f, 0) {
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return fmt.Errorf("vectord: vec[%d] is non-finite (got %v)", j, v)
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}
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sumSq += f * f
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}
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if distance == DistanceCosine && sumSq == 0 {
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return errors.New("vectord: zero-norm vector under cosine distance")
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}
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return nil
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}
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// BatchItem is one entry in a BatchAdd call. Same per-field
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// contract as Add: ID + Vector required, Metadata follows
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// upsert-style semantics (nil = leave existing alone).
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type BatchItem struct {
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ID string
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Vector []float32
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Metadata json.RawMessage
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}
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// BatchAdd inserts a slice of items under a single write-lock, with
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// one variadic call into coder/hnsw's Graph.Add. Net win vs. a loop
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// of single Add calls: N→1 lock acquisitions per HTTP batch and one
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// variadic library call instead of N.
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//
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// Contract: items MUST be pre-validated by the caller (id non-empty,
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// vector dimension matches, vector finite + non-zero-norm under
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// cosine). Pre-validation lives in the HTTP handler so per-item
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// error messages stay precise; reproducing it here would force
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// position-encoded errors on every consumer.
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//
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// Intra-batch duplicate IDs are undefined behavior — coder/hnsw's
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// internal "node not added" length-invariant fires on the second
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// occurrence. Callers must de-dup before calling. The HTTP smoke
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// uses unique IDs so this isn't an exercised path; documented for
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// future callers.
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func (i *Index) BatchAdd(items []BatchItem) error {
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if len(items) == 0 {
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return nil
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}
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i.mu.Lock()
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defer i.mu.Unlock()
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// Pre-pass: drop any existing IDs so coder/hnsw's variadic Add
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// never sees a re-add. Same library-quirk handling as single
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// Add — Len()==1 needs a full graph reset because Delete of the
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// last node leaves layers[0] entryless.
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for _, it := range items {
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if _, exists := i.g.Lookup(it.ID); exists {
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if i.g.Len() == 1 {
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i.resetGraphLocked()
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} else {
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i.g.Delete(it.ID)
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}
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}
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}
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nodes := make([]hnsw.Node[string], len(items))
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for j, it := range items {
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nodes[j] = hnsw.MakeNode(it.ID, it.Vector)
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}
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i.g.Add(nodes...)
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for _, it := range items {
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if it.Metadata != nil {
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i.meta[it.ID] = it.Metadata
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}
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}
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return nil
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}
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// Delete removes id from the index. Returns true if present.
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func (i *Index) Delete(id string) bool {
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i.mu.Lock()
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defer i.mu.Unlock()
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delete(i.meta, id)
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return i.g.Delete(id)
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}
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// Search returns the k nearest neighbors of query, sorted
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// ascending by distance.
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//
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// Note: coder/hnsw's Search returns `[]Node[K]` without distances —
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// they're computed internally in the search candidate heap but
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// dropped from the public API. We recompute distance from the
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// returned vectors. O(k·dim) per search, negligible at typical
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// k=10 / dim<2048.
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func (i *Index) Search(query []float32, k int) ([]Result, error) {
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if len(query) != i.params.Dimension {
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return nil, fmt.Errorf("%w: index dim=%d, got=%d", ErrDimensionMismatch, i.params.Dimension, len(query))
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}
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if k <= 0 {
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return nil, errors.New("vectord: k must be > 0")
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}
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i.mu.RLock()
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defer i.mu.RUnlock()
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// Per scrum O-I2 (Opus): use the stored distance function
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// directly rather than re-resolving the string name on every
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// search. The graph's Distance is set once at NewIndex.
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dist := i.g.Distance
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hits := i.g.Search(query, k)
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out := make([]Result, len(hits))
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for j, n := range hits {
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out[j] = Result{
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ID: n.Key,
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Distance: dist(query, n.Value),
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Metadata: i.meta[n.Key],
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}
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}
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return out, nil
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}
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// IndexEnvelope is the JSON shape persisted alongside the binary
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// HNSW graph bytes. params + metadata + format version travel
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// together; the graph itself is opaque binary that round-trips
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// through hnsw.Graph.Export / Import.
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type IndexEnvelope struct {
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Version int `json:"version"`
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Params IndexParams `json:"params"`
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Metadata map[string]json.RawMessage `json:"metadata"`
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}
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// envelopeVersion bumps when the on-disk JSON shape changes
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// incompatibly. Reading a future version returns ErrVersionMismatch
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// rather than producing a half-decoded index.
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const envelopeVersion = 1
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// ErrVersionMismatch is returned by DecodeIndex when the envelope
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// claims a version this build doesn't understand.
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var ErrVersionMismatch = errors.New("vectord: unknown envelope version")
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// Encode writes the index's JSON envelope (params + metadata) and
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// the binary HNSW graph bytes through two writers. Two-stream
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// shape lets the persistor write each to a distinct storaged key
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// without reframing.
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//
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// envelopeW receives params+metadata as JSON; graphW receives the
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// raw output of hnsw.Graph.Export.
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func (i *Index) Encode(envelopeW, graphW io.Writer) error {
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i.mu.RLock()
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defer i.mu.RUnlock()
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env := IndexEnvelope{
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Version: envelopeVersion,
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Params: i.params,
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Metadata: i.meta,
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}
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if err := json.NewEncoder(envelopeW).Encode(env); err != nil {
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return fmt.Errorf("encode envelope: %w", err)
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}
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if err := i.g.Export(graphW); err != nil {
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return fmt.Errorf("export graph: %w", err)
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}
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return nil
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}
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// DecodeIndex reconstructs an Index from a previously-Encoded pair
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// of streams. The returned Index is independent — closing either
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// reader after this call doesn't affect the result.
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func DecodeIndex(envelopeR, graphR io.Reader) (*Index, error) {
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var env IndexEnvelope
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if err := json.NewDecoder(envelopeR).Decode(&env); err != nil {
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return nil, fmt.Errorf("decode envelope: %w", err)
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}
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if env.Version != envelopeVersion {
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return nil, fmt.Errorf("%w: have %d, got %d",
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ErrVersionMismatch, envelopeVersion, env.Version)
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}
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idx, err := NewIndex(env.Params)
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if err != nil {
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return nil, err
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}
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if err := idx.g.Import(graphR); err != nil {
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return nil, fmt.Errorf("import graph: %w", err)
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}
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if env.Metadata != nil {
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idx.meta = env.Metadata
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}
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return idx, nil
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}
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// Lookup returns the stored vector + metadata for an id.
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//
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// Per scrum O-W1 (Opus): the vector is COPIED before return.
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// coder/hnsw's Lookup hands back the underlying graph slice;
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// caller mutation would corrupt the index without locking.
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func (i *Index) Lookup(id string) (vec []float32, meta json.RawMessage, ok bool) {
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i.mu.RLock()
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defer i.mu.RUnlock()
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v, found := i.g.Lookup(id)
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if !found {
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return nil, nil, false
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}
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out := make([]float32, len(v))
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copy(out, v)
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return out, i.meta[id], true
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}
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