KnowledgeRefinery/daemon-go/internal/api/concepts.go
oho 38a99476d6 Knowledge Refinery: local-first semantic search & 3D concept visualization
macOS app for corpus ingestion, semantic search, and concept universe
visualization powered by local LLMs via LM Studio.

Architecture:
- Go daemon (17MB single binary, zero dependencies)
  - chi router, pure-Go SQLite, tiktoken tokenizer
  - 6-stage pipeline: scan → extract → chunk → embed → annotate → conceptualize
  - Brute-force cosine vector search in memory
  - 89 tests across 8 packages
- SwiftUI app (macOS 15+)
  - Multi-workspace management with auto-start daemons
  - Live pipeline progress, search, concept browser
  - WebGPU 3D universe renderer with Canvas2D fallback
  - Custom crystal app icon
2026-02-13 18:09:46 +01:00

215 lines
5.7 KiB
Go

package api
import (
"encoding/json"
"fmt"
"net/http"
"strconv"
"github.com/go-chi/chi/v5"
"github.com/oho/knowledge-refinery-daemon/internal/pipeline"
"github.com/oho/knowledge-refinery-daemon/internal/storage"
)
func ConceptsRouter(db *storage.Database, conceptualizer *pipeline.Conceptualizer) chi.Router {
r := chi.NewRouter()
r.Get("/list", func(w http.ResponseWriter, r *http.Request) {
var levelPtr *int
if l := r.URL.Query().Get("level"); l != "" {
if n, err := strconv.Atoi(l); err == nil {
levelPtr = &n
}
}
concepts, err := db.GetConceptNodes(levelPtr)
if err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
resp := make([]map[string]any, len(concepts))
for i, c := range concepts {
var exemplars any
if c.ExemplarChunkIDs != nil {
json.Unmarshal([]byte(*c.ExemplarChunkIDs), &exemplars)
}
if exemplars == nil {
exemplars = []any{}
}
resp[i] = map[string]any{
"id": c.ID,
"level": c.Level,
"label": c.Label,
"description": c.Description,
"parent_id": c.ParentID,
"exemplar_chunk_ids": exemplars,
"model_id": c.ModelID,
"created_at": c.CreatedAt,
}
}
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(resp)
})
r.Get("/{concept_id}", func(w http.ResponseWriter, r *http.Request) {
conceptID := chi.URLParam(r, "concept_id")
node, err := db.GetConceptNodeByID(conceptID)
if err != nil || node == nil {
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]string{"error": "Concept not found"})
return
}
memberIDs, _ := db.GetMemberChunkIDs(conceptID)
var members []map[string]any
limit := 20
if limit > len(memberIDs) {
limit = len(memberIDs)
}
for _, mid := range memberIDs[:limit] {
chunk, _ := db.GetChunk(mid)
if chunk != nil {
ann, _ := db.GetCurrentAnnotation(mid)
member := map[string]any{
"chunk_id": mid,
"text": truncate(chunk.ChunkText, 200),
"asset_id": chunk.AssetID,
}
if ann != nil {
member["summary"] = ann.Summary
}
members = append(members, member)
}
}
if members == nil {
members = []map[string]any{}
}
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]any{
"id": node.ID,
"level": node.Level,
"label": node.Label,
"description": node.Description,
"parent_id": node.ParentID,
"member_count": len(memberIDs),
"members": members,
})
})
r.Post("/refine", func(w http.ResponseWriter, r *http.Request) {
conceptID := r.URL.Query().Get("concept_id")
nSub := 5
if n := r.URL.Query().Get("n_sub"); n != "" {
if v, err := strconv.Atoi(n); err == nil && v >= 2 && v <= 20 {
nSub = v
}
}
if conceptualizer == nil {
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]string{"error": "Conceptualizer not available"})
return
}
subConcepts := conceptualizer.RefineConcept(conceptID, nSub)
var subs []map[string]any
for _, sc := range subConcepts {
subs = append(subs, map[string]any{
"id": sc.ID,
"label": sc.Label,
"description": sc.Description,
"level": sc.Level,
})
}
if subs == nil {
subs = []map[string]any{}
}
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]any{
"parent_concept_id": conceptID,
"sub_concepts": subs,
})
})
r.Get("/{concept_id}/why", func(w http.ResponseWriter, r *http.Request) {
conceptID := chi.URLParam(r, "concept_id")
node, _ := db.GetConceptNodeByID(conceptID)
if node == nil {
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]string{"error": "Concept not found"})
return
}
var exemplarIDs []string
if node.ExemplarChunkIDs != nil {
json.Unmarshal([]byte(*node.ExemplarChunkIDs), &exemplarIDs)
}
var evidence []map[string]any
for _, eid := range exemplarIDs {
chunk, _ := db.GetChunk(eid)
if chunk != nil {
asset, _ := db.GetFileAsset(chunk.AssetID)
ann, _ := db.GetCurrentAnnotation(eid)
var anchor any
if chunk.EvidenceAnchor != "" {
json.Unmarshal([]byte(chunk.EvidenceAnchor), &anchor)
}
if anchor == nil {
anchor = map[string]any{}
}
var topics any
if ann != nil && ann.TopicsJSON != nil {
json.Unmarshal([]byte(*ann.TopicsJSON), &topics)
}
if topics == nil {
topics = []any{}
}
ev := map[string]any{
"chunk_id": eid,
"chunk_text": truncate(chunk.ChunkText, 300),
"evidence_anchor": anchor,
"topics": topics,
}
if asset != nil {
ev["asset_path"] = asset.Path
ev["asset_filename"] = asset.Filename
}
if ann != nil {
ev["annotation_summary"] = ann.Summary
}
evidence = append(evidence, ev)
}
}
if evidence == nil {
evidence = []map[string]any{}
}
labelStr := ptrOr(node.Label, "unknown")
modelStr := ptrOr(node.ModelID, "unknown model")
explanation := fmt.Sprintf(
"This concept '%s' was formed by clustering %d text chunks based on embedding similarity using %s. The label was generated by analyzing representative excerpts.",
labelStr, len(exemplarIDs), modelStr,
)
w.Header().Set("Content-Type", "application/json")
json.NewEncoder(w).Encode(map[string]any{
"concept_id": conceptID,
"label": node.Label,
"description": node.Description,
"pipeline_version": node.PipelineVersion,
"model_id": node.ModelID,
"evidence": evidence,
"explanation": explanation,
})
})
return r
}