Feat v0.8.3 vector column (#70)
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Co-authored-by: Jeffrey Smith <jasafpro@gmail.com> Co-committed-by: Jeffrey Smith <jasafpro@gmail.com>
This commit was merged in pull request #70.
This commit is contained in:
@@ -30,7 +30,10 @@ import (
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"crypto/rand"
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"database/sql"
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"encoding/hex"
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"encoding/json"
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"fmt"
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"math"
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"sort"
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"strings"
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"go.starlark.net/starlark"
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@@ -39,10 +42,11 @@ import (
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// DBModuleConfig holds configuration for a db module instance.
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type DBModuleConfig struct {
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PackageID string
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CanWrite bool // true when db.write permission is granted
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DB *sql.DB
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IsPostgres bool
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PackageID string
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CanWrite bool // true when db.write permission is granted
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DB *sql.DB
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IsPostgres bool
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HasPgvector bool // true when pgvector extension is available on Postgres
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}
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// allowedViews is the set of platform views extensions may query via db.view().
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@@ -55,12 +59,13 @@ var allowedViews = map[string]bool{
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// prefixed with ext_{packageID}_. Write operations are guarded by CanWrite.
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func BuildDBModule(ctx context.Context, cfg DBModuleConfig) *starlarkstruct.Module {
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fns := starlark.StringDict{
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"query": starlark.NewBuiltin("db.query", dbQuery(ctx, cfg)),
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"count": starlark.NewBuiltin("db.count", dbCount(ctx, cfg)),
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"aggregate": starlark.NewBuiltin("db.aggregate", dbAggregate(ctx, cfg)),
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"query_batch": starlark.NewBuiltin("db.query_batch", dbQueryBatch(ctx, cfg)),
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"view": starlark.NewBuiltin("db.view", dbView(ctx, cfg)),
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"list_tables": starlark.NewBuiltin("db.list_tables", dbListTables(ctx, cfg)),
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"query": starlark.NewBuiltin("db.query", dbQuery(ctx, cfg)),
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"count": starlark.NewBuiltin("db.count", dbCount(ctx, cfg)),
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"aggregate": starlark.NewBuiltin("db.aggregate", dbAggregate(ctx, cfg)),
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"query_batch": starlark.NewBuiltin("db.query_batch", dbQueryBatch(ctx, cfg)),
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"query_similar": starlark.NewBuiltin("db.query_similar", dbQuerySimilar(ctx, cfg)),
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"view": starlark.NewBuiltin("db.view", dbView(ctx, cfg)),
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"list_tables": starlark.NewBuiltin("db.list_tables", dbListTables(ctx, cfg)),
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}
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if cfg.CanWrite {
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@@ -160,6 +165,20 @@ func starlarkToGoValue(v starlark.Value) (any, error) {
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return bool(val), nil
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case starlark.NoneType:
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return nil, nil
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case *starlark.List:
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goSlice := make([]any, val.Len())
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for i := 0; i < val.Len(); i++ {
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elem, err := starlarkToGoValue(val.Index(i))
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if err != nil {
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return nil, fmt.Errorf("list[%d]: %w", i, err)
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}
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goSlice[i] = elem
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}
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b, err := json.Marshal(goSlice)
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if err != nil {
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return nil, fmt.Errorf("list marshal: %w", err)
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}
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return string(b), nil
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default:
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return nil, fmt.Errorf("unsupported type %s", v.Type())
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}
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@@ -926,3 +945,271 @@ func dbDelete(ctx context.Context, cfg DBModuleConfig) func(*starlark.Thread, *s
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return starlark.True, nil
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}
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}
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// ── Vector Similarity ─────────────────────────
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// starlarkListToFloats converts a Starlark list to a Go float64 slice.
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func starlarkListToFloats(list *starlark.List) ([]float64, error) {
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n := list.Len()
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if n == 0 {
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return nil, fmt.Errorf("vector must not be empty")
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}
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out := make([]float64, n)
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for i := 0; i < n; i++ {
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switch v := list.Index(i).(type) {
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case starlark.Float:
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out[i] = float64(v)
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case starlark.Int:
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i64, _ := v.Int64()
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out[i] = float64(i64)
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default:
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return nil, fmt.Errorf("vector[%d]: expected number, got %s", i, list.Index(i).Type())
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}
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}
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return out, nil
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}
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// floatsToVectorString serializes a float64 slice as a JSON array string.
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// e.g. [0.1, 0.2, 0.3] → "[0.1,0.2,0.3]"
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func floatsToVectorString(v []float64) string {
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b, _ := json.Marshal(v)
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return string(b)
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}
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// parseVectorJSON parses a JSON array string (or pgvector text) into float64 slice.
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func parseVectorJSON(s string) ([]float64, error) {
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var out []float64
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if err := json.Unmarshal([]byte(s), &out); err != nil {
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return nil, fmt.Errorf("parse vector: %w", err)
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}
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return out, nil
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}
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// cosineDistance computes cosine distance between two vectors.
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// Returns 0.0 for identical vectors, 1.0 for orthogonal, 2.0 for opposite.
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func cosineDistance(a, b []float64) float64 {
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if len(a) != len(b) || len(a) == 0 {
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return 1.0
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}
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var dot, normA, normB float64
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for i := range a {
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dot += a[i] * b[i]
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normA += a[i] * a[i]
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normB += b[i] * b[i]
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}
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if normA == 0 || normB == 0 {
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return 1.0
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}
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return 1.0 - (dot / (math.Sqrt(normA) * math.Sqrt(normB)))
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}
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// dbQuerySimilar implements db.query_similar(table, column, vector=[], limit=10, filters=None, metric="cosine").
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// Returns rows ordered by distance with an injected _distance float.
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//
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// Three dispatch paths:
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// - pgvector: native <=> operator with HNSW index
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// - fallback: fetch rows, compute cosine distance in Go, sort, return top N
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func dbQuerySimilar(ctx context.Context, cfg DBModuleConfig) func(*starlark.Thread, *starlark.Builtin, starlark.Tuple, []starlark.Tuple) (starlark.Value, error) {
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return func(thread *starlark.Thread, b *starlark.Builtin, args starlark.Tuple, kwargs []starlark.Tuple) (starlark.Value, error) {
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var table, column string
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var vectorVal *starlark.List
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var limit starlark.Int = starlark.MakeInt(10)
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var filters starlark.Value = starlark.None
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var metric starlark.String = "cosine"
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if err := starlark.UnpackArgs(b.Name(), args, kwargs,
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"table", &table,
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"column", &column,
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"vector", &vectorVal,
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"limit?", &limit,
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"filters?", &filters,
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"metric?", &metric,
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); err != nil {
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return nil, err
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}
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if string(metric) != "cosine" {
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return nil, fmt.Errorf("db.query_similar: unsupported metric %q (only \"cosine\" is supported)", string(metric))
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}
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queryVec, err := starlarkListToFloats(vectorVal)
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if err != nil {
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return nil, fmt.Errorf("db.query_similar: %w", err)
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}
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lim, ok := limit.Int64()
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if !ok || lim < 1 {
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lim = 10
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}
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if lim > 100 {
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lim = 100
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}
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if strings.ContainsAny(column, " \t\n\"';-") {
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return nil, fmt.Errorf("db.query_similar: invalid column name %q", column)
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}
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physTable, err := cfg.physicalTable(table)
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if err != nil {
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return nil, err
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}
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if cfg.HasPgvector {
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return querySimilarPgvector(ctx, cfg, physTable, column, queryVec, int(lim), filters)
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}
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return querySimilarFallback(ctx, cfg, physTable, column, queryVec, int(lim), filters)
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}
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}
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// querySimilarPgvector uses the native pgvector <=> operator.
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func querySimilarPgvector(ctx context.Context, cfg DBModuleConfig, physTable, column string, queryVec []float64, limit int, filters starlark.Value) (starlark.Value, error) {
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vecStr := floatsToVectorString(queryVec)
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whereClause, whereArgs, err := cfg.starlarkFiltersToSQL(filters, 1)
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if err != nil {
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return nil, err
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}
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nextIdx := len(whereArgs) + 1
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vecPH := cfg.ph(nextIdx)
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limitPH := cfg.ph(nextIdx + 1)
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query := fmt.Sprintf("SELECT *, (%s <=> %s::vector) AS _distance FROM %s",
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column, vecPH, physTable)
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if whereClause != "" {
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query += " " + whereClause
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}
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query += fmt.Sprintf(" ORDER BY %s <=> %s::vector LIMIT %s", column, vecPH, limitPH)
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allArgs := append(whereArgs, vecStr, limit)
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rows, err := cfg.DB.QueryContext(ctx, query, allArgs...)
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if err != nil {
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return nil, fmt.Errorf("db.query_similar: %w", err)
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}
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defer rows.Close()
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return rowsToStarlark(rows)
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}
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// querySimilarFallback fetches rows and computes cosine distance in Go.
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// Used for SQLite (TEXT) and Postgres without pgvector (JSONB).
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func querySimilarFallback(ctx context.Context, cfg DBModuleConfig, physTable, column string, queryVec []float64, limit int, filters starlark.Value) (starlark.Value, error) {
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whereClause, whereArgs, err := cfg.starlarkFiltersToSQL(filters, 1)
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if err != nil {
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return nil, err
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}
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// Fetch up to 1000 rows for distance computation.
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fetchLimit := limit * 10
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if fetchLimit > 1000 {
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fetchLimit = 1000
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}
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if fetchLimit < limit {
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fetchLimit = limit
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}
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limitPH := cfg.ph(len(whereArgs) + 1)
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query := fmt.Sprintf("SELECT * FROM %s", physTable)
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if whereClause != "" {
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query += " " + whereClause
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}
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query += " LIMIT " + limitPH
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allArgs := append(whereArgs, fetchLimit)
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rows, err := cfg.DB.QueryContext(ctx, query, allArgs...)
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if err != nil {
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return nil, fmt.Errorf("db.query_similar: %w", err)
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}
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defer rows.Close()
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cols, err := rows.Columns()
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if err != nil {
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return nil, err
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}
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// Find the vector column index.
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vecColIdx := -1
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for i, c := range cols {
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if c == column {
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vecColIdx = i
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break
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}
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}
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if vecColIdx == -1 {
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return nil, fmt.Errorf("db.query_similar: column %q not found in table", column)
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}
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type scoredRow struct {
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vals []any
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distance float64
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}
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var scored []scoredRow
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for rows.Next() {
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vals := make([]any, len(cols))
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ptrs := make([]any, len(cols))
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for i := range vals {
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ptrs[i] = &vals[i]
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}
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if err := rows.Scan(ptrs...); err != nil {
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return nil, err
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}
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// Parse the vector column value.
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var vecStr string
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switch v := vals[vecColIdx].(type) {
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case string:
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vecStr = v
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case []byte:
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vecStr = string(v)
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default:
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continue // skip rows with unparseable vectors
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}
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rowVec, err := parseVectorJSON(vecStr)
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if err != nil {
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continue // skip malformed vectors
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}
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dist := cosineDistance(queryVec, rowVec)
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scored = append(scored, scoredRow{vals: vals, distance: dist})
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}
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if err := rows.Err(); err != nil {
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return nil, err
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}
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// Sort by distance ascending.
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sort.Slice(scored, func(i, j int) bool {
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return scored[i].distance < scored[j].distance
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})
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// Take top N and build Starlark dicts.
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if len(scored) > limit {
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scored = scored[:limit]
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}
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var result []starlark.Value
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for _, sr := range scored {
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d := starlark.NewDict(len(cols) + 1)
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for i, col := range cols {
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sv, err := goToStarlark(sr.vals[i])
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if err != nil {
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return nil, fmt.Errorf("column %q: %w", col, err)
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}
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if err := d.SetKey(starlark.String(col), sv); err != nil {
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return nil, err
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}
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}
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// Inject _distance.
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if err := d.SetKey(starlark.String("_distance"), starlark.Float(sr.distance)); err != nil {
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return nil, err
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}
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result = append(result, d)
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}
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if result == nil {
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result = []starlark.Value{}
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}
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return starlark.NewList(result), nil
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}
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