Files
CyberStrikeAI/internal/knowledge/sqlite_indexer.go
T
Sycun cfaa95d7bd refactor(runtime): remove Eino and make Pi the sole agent engine
Delete the Eino model adapters, callbacks, middleware, orchestration,
summarization and single-agent runner together with their dependencies.
Chat, batch tasks, workflow Agent nodes and sub-agents now share the Pi
runtime, and dispatch is a single code path instead of an engine switch.

Configuration migrates eino_middleware/eino_skills to runtime/skills and
drops the retired callback settings while old files still load. Settings,
i18n and docs describe Pi only. Also carry the Pi follow-up fixes: empty
required lists are omitted from tool schemas, historical tool results stay
with their call batch, model proxies work over HTTP/1.1, single-query
retrieval skips query expansion, and finalization docs match Pi lifecycle
events.
2026-11-12 17:55:36 +08:00

140 lines
4.2 KiB
Go

package knowledge
import (
"context"
"database/sql"
"encoding/json"
"fmt"
"strings"
"github.com/google/uuid"
)
// SQLiteIndexer implements [indexer.Indexer] against knowledge_embeddings + existing schema.
type SQLiteIndexer struct {
db *sql.DB
batchSize int
embeddingModel string
}
// NewSQLiteIndexer returns an indexer that writes chunk rows for one knowledge item per Store call.
// batchSize is the embedding batch size; if <= 0, default 64 is used.
// embeddingModel is persisted per row for retrieval-time consistency checks (may be empty).
func NewSQLiteIndexer(db *sql.DB, batchSize int, embeddingModel string) *SQLiteIndexer {
return &SQLiteIndexer{db: db, batchSize: batchSize, embeddingModel: strings.TrimSpace(embeddingModel)}
}
// GetType identifies this native indexer in diagnostics.
func (s *SQLiteIndexer) GetType() string {
return "SQLiteKnowledgeIndexer"
}
// Store embeds documents and inserts rows. Each doc must carry MetaData:
// kb_item_id, kb_category, kb_title, kb_chunk_index (int). Content is chunk text only.
func (s *SQLiteIndexer) Store(ctx context.Context, docs []*Document, opts ...IndexOption) (ids []string, err error) {
options := indexOptions(opts...)
if options.Embedding == nil {
return nil, fmt.Errorf("sqlite indexer: embedding is required")
}
if len(docs) == 0 {
return nil, nil
}
subIdxStr := strings.Join(options.SubIndexes, ",")
texts := make([]string, len(docs))
for i, d := range docs {
if d == nil {
return nil, fmt.Errorf("sqlite indexer: nil document at %d", i)
}
cat := MetaLookupString(d.MetaData, metaKBCategory)
title := MetaLookupString(d.MetaData, metaKBTitle)
texts[i] = FormatEmbeddingInput(cat, title, d.Content)
}
bs := s.batchSize
if bs <= 0 {
bs = 64
}
var allVecs [][]float64
for start := 0; start < len(texts); start += bs {
end := start + bs
if end > len(texts) {
end = len(texts)
}
batch := texts[start:end]
vecs, embedErr := options.Embedding.EmbedStrings(ctx, batch)
if embedErr != nil {
return nil, fmt.Errorf("sqlite indexer: embed batch %d-%d: %w", start, end, embedErr)
}
if len(vecs) != len(batch) {
return nil, fmt.Errorf("sqlite indexer: embed count mismatch: got %d want %d", len(vecs), len(batch))
}
allVecs = append(allVecs, vecs...)
}
embedDim := 0
if len(allVecs) > 0 {
embedDim = len(allVecs[0])
}
tx, err := s.db.BeginTx(ctx, nil)
if err != nil {
return nil, fmt.Errorf("sqlite indexer: begin tx: %w", err)
}
defer tx.Rollback()
items := map[string]bool{}
for _, d := range docs {
id, err := RequireMetaString(d.MetaData, metaKBItemID)
if err != nil {
return nil, err
}
items[id] = true
}
for id := range items {
if _, err := tx.ExecContext(ctx, "DELETE FROM knowledge_embeddings WHERE item_id = ?", id); err != nil {
return nil, err
}
}
ids = make([]string, 0, len(docs))
for i, d := range docs {
chunkID := uuid.New().String()
itemID, metaErr := RequireMetaString(d.MetaData, metaKBItemID)
if metaErr != nil {
return nil, fmt.Errorf("sqlite indexer: doc %d: %w", i, metaErr)
}
chunkIdx, metaErr := RequireMetaInt(d.MetaData, metaKBChunkIndex)
if metaErr != nil {
return nil, fmt.Errorf("sqlite indexer: doc %d: %w", i, metaErr)
}
vec := allVecs[i]
if embedDim > 0 && len(vec) != embedDim {
return nil, fmt.Errorf("sqlite indexer: inconsistent embedding dim at doc %d: got %d want %d", i, len(vec), embedDim)
}
vec32 := make([]float32, len(vec))
for j, v := range vec {
vec32[j] = float32(v)
}
embeddingJSON, jsonErr := json.Marshal(vec32)
if jsonErr != nil {
return nil, fmt.Errorf("sqlite indexer: marshal embedding: %w", jsonErr)
}
_, err = tx.ExecContext(ctx,
`INSERT INTO knowledge_embeddings (id, item_id, chunk_index, chunk_text, embedding, sub_indexes, embedding_model, embedding_dim, created_at)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, datetime('now'))`,
chunkID, itemID, chunkIdx, d.Content, string(embeddingJSON), subIdxStr, s.embeddingModel, embedDim,
)
if err != nil {
return nil, fmt.Errorf("sqlite indexer: insert chunk %d: %w", i, err)
}
ids = append(ids, chunkID)
}
if err := tx.Commit(); err != nil {
return nil, fmt.Errorf("sqlite indexer: commit: %w", err)
}
return ids, nil
}