mirror of
https://github.com/saymrwulf/KnowledgeRefinery.git
synced 2026-05-14 20:47:51 +00:00
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
46 lines
1,021 B
Go
46 lines
1,021 B
Go
package api
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import (
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"encoding/json"
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"net/http"
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"github.com/go-chi/chi/v5"
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"github.com/oho/knowledge-refinery-daemon/internal/pipeline"
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)
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type startIngestRequest struct {
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Paths []string `json:"paths"`
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}
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func IngestRouter(orch *pipeline.Orchestrator) chi.Router {
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r := chi.NewRouter()
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r.Post("/start", func(w http.ResponseWriter, r *http.Request) {
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if orch.IsRunning() {
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http.Error(w, "Pipeline is already running", http.StatusConflict)
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return
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}
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var req startIngestRequest
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json.NewDecoder(r.Body).Decode(&req) // may be empty body
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jobID, err := orch.RunPipeline(req.Paths)
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if err != nil {
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http.Error(w, err.Error(), http.StatusConflict)
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return
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}
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w.Header().Set("Content-Type", "application/json")
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json.NewEncoder(w).Encode(map[string]string{
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"job_id": jobID,
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"status": "started",
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})
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})
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r.Get("/status", func(w http.ResponseWriter, r *http.Request) {
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w.Header().Set("Content-Type", "application/json")
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json.NewEncoder(w).Encode(orch.GetStatus())
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})
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return r
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}
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