feat(rag): pgvector knowledge base with API and LLM tools

- services/rag: knowledge_docs store — vector search via the shared
  embedder (HNSW index, graceful ILIKE fallback), list/delete scoped to
  the user, search intentionally global for the single-user deployment
- gateway: GET/POST /knowledge/docs, DELETE /knowledge/docs/:id,
  POST /knowledge/search
- plugins/knowledge: knowledge_search and knowledge_ingest tools so the
  persona can save and recall information mid-conversation
- main: migrations, store wiring, plugin registration

backend/services/rag/rag.go

@@ -0,0 +1,181 @@
+// Package rag provides a minimal pgvector-backed knowledge base.
+package rag
+
+import (
+	"context"
+	"database/sql"
+	"fmt"
+	"strings"
+	"time"
+
+	"git.yeij.top/AskaEth/Cyrene_v2/services/memory"
+)
+
+// Document is a knowledge base entry.
+type Document struct {
+	ID        int64     `json:"id"`
+	UserID    string    `json:"user_id"`
+	Title     string    `json:"title"`
+	Content   string    `json:"content"`
+	CreatedAt time.Time `json:"created_at"`
+}
+
+// Embedder is the subset of memory.Embedder used here.
+type Embedder interface {
+	Embed(ctx context.Context, text string) ([]float64, error)
+	Dims() int
+}
+
+// Store persists and searches documents.
+type Store struct {
+	db            *sql.DB
+	embedder      Embedder
+	vectorEnabled bool
+}
+
+// NewStore creates a knowledge store. Embedder may be nil (ILIKE-only search).
+func NewStore(db *sql.DB, embedder Embedder) *Store {
+	return &Store{db: db, embedder: embedder}
+}
+
+// EnableVector marks the embedding column as available (after migration).
+func (s *Store) EnableVector() { s.vectorEnabled = true }
+
+// CreateTableSQL returns the base schema.
+func CreateTableSQL() string {
+	return `
+CREATE TABLE IF NOT EXISTS knowledge_docs (
+    id         BIGSERIAL PRIMARY KEY,
+    user_id    TEXT NOT NULL,
+    title      TEXT NOT NULL DEFAULT '',
+    content    TEXT NOT NULL,
+    created_at TIMESTAMPTZ NOT NULL DEFAULT now()
+);
+CREATE INDEX IF NOT EXISTS idx_knowledge_docs_user ON knowledge_docs(user_id, id DESC);
+`
+}
+
+// VectorMigrationSQL adds the embedding column (pgvector required).
+func VectorMigrationSQL(dims int) string {
+	return fmt.Sprintf(`
+	ALTER TABLE knowledge_docs ADD COLUMN IF NOT EXISTS embedding vector(%d);
+	CREATE INDEX IF NOT EXISTS idx_knowledge_docs_embedding ON knowledge_docs USING hnsw (embedding vector_cosine_ops);
+	`, dims)
+}
+
+// Ingest stores a document, embedding it when an embedder is available.
+func (s *Store) Ingest(ctx context.Context, userID, title, content string) (*Document, error) {
+	doc := &Document{UserID: userID, Title: title, Content: content}
+	if s.embedder != nil && s.vectorEnabled {
+		if vec, err := s.embedder.Embed(ctx, title+"\n"+content); err == nil {
+			_, err := s.db.ExecContext(ctx, `
+				INSERT INTO knowledge_docs (user_id, title, content, embedding) VALUES ($1, $2, $3, $4::vector)
+			`, userID, title, content, vectorString(vec))
+			if err != nil {
+				return nil, fmt.Errorf("ingest: %w", err)
+			}
+			return doc, nil
+		}
+	}
+	err := s.db.QueryRowContext(ctx, `
+		INSERT INTO knowledge_docs (user_id, title, content) VALUES ($1, $2, $3) RETURNING id, created_at
+	`, userID, title, content).Scan(&doc.ID, &doc.CreatedAt)
+	if err != nil {
+		return nil, fmt.Errorf("ingest: %w", err)
+	}
+	return doc, nil
+}
+
+// Search finds documents by vector similarity when enabled, else ILIKE.
+// The knowledge base is shared: userID is recorded on ingest but search
+// is global (single-user self-hosted deployment).
+func (s *Store) Search(ctx context.Context, userID, query string, topK int) ([]Document, error) {
+	if topK <= 0 || topK > 20 {
+		topK = 5
+	}
+	if s.embedder != nil && s.vectorEnabled {
+		vec, err := s.embedder.Embed(ctx, query)
+		if err == nil {
+			return s.queryDocs(ctx, `
+				SELECT id, user_id, title, content, created_at FROM knowledge_docs
+				WHERE embedding IS NOT NULL
+				ORDER BY embedding <=> $1::vector LIMIT $2
+			`, vectorString(vec), topK)
+		}
+	}
+	return s.queryDocs(ctx, `
+		SELECT id, user_id, title, content, created_at FROM knowledge_docs
+		WHERE title ILIKE '%' || $1 || '%' OR content ILIKE '%' || $1 || '%'
+		ORDER BY id DESC LIMIT $2
+	`, query, topK)
+}
+
+// List returns recent documents (without content payload).
+func (s *Store) List(ctx context.Context, userID string, limit int) ([]Document, error) {
+	if limit <= 0 {
+		limit = 50
+	}
+	rows, err := s.db.QueryContext(ctx, `
+		SELECT id, user_id, title, created_at FROM knowledge_docs
+		WHERE user_id = $1 ORDER BY id DESC LIMIT $2
+	`, userID, limit)
+	if err != nil {
+		return nil, err
+	}
+	defer rows.Close()
+	docs := []Document{}
+	for rows.Next() {
+		var d Document
+		if err := rows.Scan(&d.ID, &d.UserID, &d.Title, &d.CreatedAt); err != nil {
+			return nil, err
+		}
+		docs = append(docs, d)
+	}
+	return docs, rows.Err()
+}
+
+// Delete removes a document owned by the user.
+func (s *Store) Delete(ctx context.Context, userID string, id int64) error {
+	res, err := s.db.ExecContext(ctx, `DELETE FROM knowledge_docs WHERE id = $1 AND user_id = $2`, id, userID)
+	if err != nil {
+		return err
+	}
+	if n, _ := res.RowsAffected(); n == 0 {
+		return fmt.Errorf("document not found")
+	}
+	return nil
+}
+
+func (s *Store) queryDocs(ctx context.Context, query string, args ...any) ([]Document, error) {
+	rows, err := s.db.QueryContext(ctx, query, args...)
+	if err != nil {
+		return nil, err
+	}
+	defer rows.Close()
+	docs := []Document{}
+	for rows.Next() {
+		var d Document
+		if err := rows.Scan(&d.ID, &d.UserID, &d.Title, &d.Content, &d.CreatedAt); err != nil {
+			return nil, err
+		}
+		docs = append(docs, d)
+	}
+	return docs, rows.Err()
+}
+
+// vectorString formats an embedding for pgvector input.
+func vectorString(vec []float64) string {
+	var sb strings.Builder
+	sb.WriteByte('[')
+	for i, v := range vec {
+		if i > 0 {
+			sb.WriteByte(',')
+		}
+		fmt.Fprintf(&sb, "%g", v)
+	}
+	sb.WriteByte(']')
+	return sb.String()
+}
+
+// Ensure the memory embedder satisfies the interface at compile time.
+var _ Embedder = (*memory.Embedder)(nil)