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
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@@ -258,8 +258,7 @@ func (r *Router) sessionExportHandler(w http.ResponseWriter, req *http.Request)
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}
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}
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func (r *Router) imageAnalyzeHandler(w http.ResponseWriter, req *http.Request) {
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if req.Method != "POST" { writeJSON(w, 405, M{"error": "POST required"}); return }
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func (r *Router) imageAnalyzeHandler(w http.ResponseWriter, req *http.Request) { if req.Method != "POST" { writeJSON(w, 405, M{"error": "POST required"}); return }
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if r.deps.Vision == nil { writeJSON(w, 503, M{"error": "vision not available"}); return }
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var body struct {
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ImageURL string `json:"image_url"`
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@@ -305,3 +304,58 @@ func (r *Router) hostExecHandler(w http.ResponseWriter, req *http.Request) {
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"command": body.Command,
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})
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}
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// ===== Knowledge Base =====
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func (r *Router) knowledgeDocsHandler(w http.ResponseWriter, req *http.Request) {
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if r.deps.Knowledge == nil { writeJSON(w, 503, M{"error": "knowledge base unavailable"}); return }
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uid := r.userID(req)
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switch req.Method {
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case "GET":
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docs, err := r.deps.Knowledge.List(req.Context(), uid, 50)
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if err != nil { writeJSON(w, 500, M{"error": err.Error()}); return }
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writeJSON(w, 200, M{"docs": docs})
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case "POST":
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var body struct {
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Title string `json:"title"`
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Content string `json:"content"`
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}
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json.NewDecoder(req.Body).Decode(&body)
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if body.Content == "" { writeJSON(w, 400, M{"error": "missing content"}); return }
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doc, err := r.deps.Knowledge.Ingest(req.Context(), uid, body.Title, body.Content)
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if err != nil { writeJSON(w, 500, M{"error": err.Error()}); return }
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writeJSON(w, 201, M{"doc": doc})
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default:
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writeJSON(w, 405, M{"error": "method not allowed"})
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}
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}
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func (r *Router) knowledgeDocItemHandler(w http.ResponseWriter, req *http.Request) {
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if r.deps.Knowledge == nil { writeJSON(w, 503, M{"error": "knowledge base unavailable"}); return }
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if req.Method != "DELETE" { writeJSON(w, 405, M{"error": "method not allowed"}); return }
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idStr := strings.TrimPrefix(req.URL.Path, "/api/v1/knowledge/docs/")
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var id int64
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if _, err := fmt.Sscanf(idStr, "%d", &id); err != nil || id <= 0 {
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writeJSON(w, 400, M{"error": "invalid id"})
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return
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}
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if err := r.deps.Knowledge.Delete(req.Context(), r.userID(req), id); err != nil {
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writeJSON(w, 404, M{"error": err.Error()})
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return
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}
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writeJSON(w, 204, nil)
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}
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func (r *Router) knowledgeSearchHandler(w http.ResponseWriter, req *http.Request) {
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if r.deps.Knowledge == nil { writeJSON(w, 503, M{"error": "knowledge base unavailable"}); return }
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if req.Method != "POST" { writeJSON(w, 405, M{"error": "POST required"}); return }
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var body struct {
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Query string `json:"query"`
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TopK int `json:"top_k"`
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}
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json.NewDecoder(req.Body).Decode(&body)
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if body.Query == "" { writeJSON(w, 400, M{"error": "missing query"}); return }
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docs, err := r.deps.Knowledge.Search(req.Context(), r.userID(req), body.Query, body.TopK)
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if err != nil { writeJSON(w, 500, M{"error": err.Error()}); return }
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writeJSON(w, 200, M{"docs": docs, "total": len(docs)})
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}
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@@ -13,6 +13,7 @@ import (
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"git.yeij.top/AskaEth/Cyrene_v2/services/ai-core/model"
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"git.yeij.top/AskaEth/Cyrene_v2/services/gateway/store"
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"git.yeij.top/AskaEth/Cyrene_v2/services/rag"
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)
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// --- Interfaces for injected services ---
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@@ -96,6 +97,14 @@ type VisionAnalyzer interface {
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AnalyzeImage(ctx context.Context, prompt, imageRef string) (string, error)
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}
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// KnowledgeStorer manages the knowledge base.
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type KnowledgeStorer interface {
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Ingest(ctx context.Context, userID, title, content string) (*rag.Document, error)
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Search(ctx context.Context, userID, query string, topK int) ([]rag.Document, error)
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List(ctx context.Context, userID string, limit int) ([]rag.Document, error)
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Delete(ctx context.Context, userID string, id int64) error
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}
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// ChatStorer persists chat sessions and messages.
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type ChatStorer interface {
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CreateSession(ctx context.Context, id, userID, title string) (*store.Session, error)
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@@ -123,6 +132,7 @@ type Dependencies struct {
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AutomationEng AutomationEngine
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Reminders *ReminderStore
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Vision VisionAnalyzer
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Knowledge KnowledgeStorer
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PluginList []map[string]string
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}
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@@ -184,6 +194,11 @@ func (r *Router) setupRoutes() {
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// Message search & session export
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r.mux.HandleFunc("/api/v1/messages/search", r.authMW(r.messageSearchHandler))
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// Knowledge base
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r.mux.HandleFunc("/api/v1/knowledge/docs", r.authMW(r.knowledgeDocsHandler))
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r.mux.HandleFunc("/api/v1/knowledge/docs/", r.authMW(r.knowledgeDocItemHandler))
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r.mux.HandleFunc("/api/v1/knowledge/search", r.authMW(r.knowledgeSearchHandler))
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// Image analysis
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r.mux.HandleFunc("/api/v1/images/analyze", r.authMW(r.imageAnalyzeHandler))
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@@ -0,0 +1,181 @@
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// Package rag provides a minimal pgvector-backed knowledge base.
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package rag
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import (
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"context"
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"database/sql"
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"fmt"
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"strings"
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"time"
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"git.yeij.top/AskaEth/Cyrene_v2/services/memory"
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)
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// Document is a knowledge base entry.
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type Document struct {
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ID int64 `json:"id"`
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UserID string `json:"user_id"`
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Title string `json:"title"`
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Content string `json:"content"`
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CreatedAt time.Time `json:"created_at"`
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}
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// Embedder is the subset of memory.Embedder used here.
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type Embedder interface {
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Embed(ctx context.Context, text string) ([]float64, error)
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Dims() int
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}
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// Store persists and searches documents.
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type Store struct {
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db *sql.DB
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embedder Embedder
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vectorEnabled bool
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}
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// NewStore creates a knowledge store. Embedder may be nil (ILIKE-only search).
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func NewStore(db *sql.DB, embedder Embedder) *Store {
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return &Store{db: db, embedder: embedder}
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}
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// EnableVector marks the embedding column as available (after migration).
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func (s *Store) EnableVector() { s.vectorEnabled = true }
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// CreateTableSQL returns the base schema.
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func CreateTableSQL() string {
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return `
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CREATE TABLE IF NOT EXISTS knowledge_docs (
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id BIGSERIAL PRIMARY KEY,
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user_id TEXT NOT NULL,
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title TEXT NOT NULL DEFAULT '',
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content TEXT NOT NULL,
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created_at TIMESTAMPTZ NOT NULL DEFAULT now()
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);
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CREATE INDEX IF NOT EXISTS idx_knowledge_docs_user ON knowledge_docs(user_id, id DESC);
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`
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}
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// VectorMigrationSQL adds the embedding column (pgvector required).
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func VectorMigrationSQL(dims int) string {
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return fmt.Sprintf(`
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ALTER TABLE knowledge_docs ADD COLUMN IF NOT EXISTS embedding vector(%d);
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CREATE INDEX IF NOT EXISTS idx_knowledge_docs_embedding ON knowledge_docs USING hnsw (embedding vector_cosine_ops);
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`, dims)
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}
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// Ingest stores a document, embedding it when an embedder is available.
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func (s *Store) Ingest(ctx context.Context, userID, title, content string) (*Document, error) {
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doc := &Document{UserID: userID, Title: title, Content: content}
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if s.embedder != nil && s.vectorEnabled {
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if vec, err := s.embedder.Embed(ctx, title+"\n"+content); err == nil {
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_, err := s.db.ExecContext(ctx, `
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INSERT INTO knowledge_docs (user_id, title, content, embedding) VALUES ($1, $2, $3, $4::vector)
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`, userID, title, content, vectorString(vec))
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if err != nil {
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return nil, fmt.Errorf("ingest: %w", err)
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}
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return doc, nil
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}
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}
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err := s.db.QueryRowContext(ctx, `
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INSERT INTO knowledge_docs (user_id, title, content) VALUES ($1, $2, $3) RETURNING id, created_at
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`, userID, title, content).Scan(&doc.ID, &doc.CreatedAt)
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if err != nil {
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return nil, fmt.Errorf("ingest: %w", err)
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}
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return doc, nil
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}
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// Search finds documents by vector similarity when enabled, else ILIKE.
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// The knowledge base is shared: userID is recorded on ingest but search
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// is global (single-user self-hosted deployment).
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func (s *Store) Search(ctx context.Context, userID, query string, topK int) ([]Document, error) {
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if topK <= 0 || topK > 20 {
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topK = 5
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}
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if s.embedder != nil && s.vectorEnabled {
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vec, err := s.embedder.Embed(ctx, query)
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if err == nil {
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return s.queryDocs(ctx, `
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SELECT id, user_id, title, content, created_at FROM knowledge_docs
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WHERE embedding IS NOT NULL
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ORDER BY embedding <=> $1::vector LIMIT $2
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`, vectorString(vec), topK)
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}
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}
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return s.queryDocs(ctx, `
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SELECT id, user_id, title, content, created_at FROM knowledge_docs
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WHERE title ILIKE '%' || $1 || '%' OR content ILIKE '%' || $1 || '%'
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ORDER BY id DESC LIMIT $2
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`, query, topK)
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}
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// List returns recent documents (without content payload).
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func (s *Store) List(ctx context.Context, userID string, limit int) ([]Document, error) {
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if limit <= 0 {
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limit = 50
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}
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rows, err := s.db.QueryContext(ctx, `
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SELECT id, user_id, title, created_at FROM knowledge_docs
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WHERE user_id = $1 ORDER BY id DESC LIMIT $2
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`, userID, limit)
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if err != nil {
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return nil, err
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}
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defer rows.Close()
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docs := []Document{}
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for rows.Next() {
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var d Document
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if err := rows.Scan(&d.ID, &d.UserID, &d.Title, &d.CreatedAt); err != nil {
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return nil, err
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}
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docs = append(docs, d)
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}
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return docs, rows.Err()
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}
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// Delete removes a document owned by the user.
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func (s *Store) Delete(ctx context.Context, userID string, id int64) error {
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res, err := s.db.ExecContext(ctx, `DELETE FROM knowledge_docs WHERE id = $1 AND user_id = $2`, id, userID)
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if err != nil {
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return err
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}
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if n, _ := res.RowsAffected(); n == 0 {
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return fmt.Errorf("document not found")
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}
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return nil
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}
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func (s *Store) queryDocs(ctx context.Context, query string, args ...any) ([]Document, error) {
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rows, err := s.db.QueryContext(ctx, query, args...)
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if err != nil {
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return nil, err
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}
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defer rows.Close()
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docs := []Document{}
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for rows.Next() {
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var d Document
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if err := rows.Scan(&d.ID, &d.UserID, &d.Title, &d.Content, &d.CreatedAt); err != nil {
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return nil, err
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}
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docs = append(docs, d)
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}
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return docs, rows.Err()
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}
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// vectorString formats an embedding for pgvector input.
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func vectorString(vec []float64) string {
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var sb strings.Builder
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sb.WriteByte('[')
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for i, v := range vec {
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if i > 0 {
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sb.WriteByte(',')
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}
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fmt.Fprintf(&sb, "%g", v)
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}
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sb.WriteByte(']')
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return sb.String()
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}
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// Ensure the memory embedder satisfies the interface at compile time.
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var _ Embedder = (*memory.Embedder)(nil)
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