Agent-Mode mit Aufgabenteilung und automatischer Recherche

internal/engine/source_inbox.go

@@ -0,0 +1,120 @@
+package engine
+
+import (
+	"context"
+	"fmt"
+	"log/slog"
+	"strings"
+	"time"
+
+	"github.com/local/glpi-neural-brain/internal/model"
+	"github.com/local/glpi-neural-brain/internal/ollama"
+	"github.com/local/glpi-neural-brain/internal/sourceagent"
+)
+
+func (e *Engine) SetSourceInbox(store *sourceagent.Store) { e.SourceInbox = store }
+
+func (e *Engine) evidenceAcquisitionEnabled() bool {
+	return (e.SourceInbox != nil && e.Cfg.SourceInboxEnabled) || e.ResearchEnabledForRuntime()
+}
+
+func (e *Engine) sourceInboxLoop(ctx context.Context) {
+	ticker := time.NewTicker(e.Cfg.SourceInboxInterval)
+	defer ticker.Stop()
+	for {
+		select {
+		case <-ctx.Done():
+			return
+		case <-ticker.C:
+			e.processSourceInbox(ctx)
+		}
+	}
+}
+
+func (e *Engine) processSourceInbox(ctx context.Context) {
+	if e.SourceInbox == nil || !e.Cfg.SourceInboxEnabled {
+		return
+	}
+	items, err := e.SourceInbox.ClaimInbox(ctx, e.Cfg.SourceInboxBatchSize)
+	if err != nil {
+		slog.Warn("source inbox claim failed", "error", err)
+		return
+	}
+	if len(items) == 0 {
+		return
+	}
+	texts := make([]string, 0, len(items))
+	for _, item := range items {
+		text := item.Document.Title + "\n" + strings.Join(item.Document.Categories, " · ") + "\n" + item.Document.Text
+		if len([]rune(text)) > 12000 {
+			text = string([]rune(text)[:12000])
+		}
+		texts = append(texts, text)
+	}
+	cctx, cancel := context.WithTimeout(ollama.WithLowPriority(ctx), 4*time.Minute)
+	vectors, embedErr := e.Ollama.Embed(cctx, texts)
+	cancel()
+	if embedErr != nil || len(vectors) != len(items) {
+		for _, item := range items {
+			_ = e.SourceInbox.ReleaseInbox(ctx, item.ID, fmt.Sprint(embedErr))
+		}
+		return
+	}
+	candidateCount := 0
+	for i, item := range items {
+		hits, stats := e.similarKnowledge(vectors[i], 1, e.effectiveLearningFilter(), 0)
+		if len(hits) == 0 {
+			_ = e.SourceInbox.ReleaseInbox(ctx, item.ID, "knowledge vectors are not ready")
+			continue
+		}
+		relevance := 0.0
+		matched := ""
+		if len(hits) > 0 {
+			relevance = hits[0].Score
+			matched = hits[0].NodeID
+		}
+		status := "archived"
+		if relevance >= e.Cfg.SourceInboxMinSimilarity {
+			status = "candidate"
+			candidateCount++
+		}
+		meta := make(map[string]any, len(item.Metadata)+4)
+		for key, value := range item.Metadata {
+			meta[key] = value
+		}
+		meta["processing_mode"] = e.RuntimeSettings().ProcessingMode
+		meta["exact_comparisons"] = stats.ExactComparisons
+		meta["coarse_comparisons"] = stats.CoarseComparisons
+		meta["candidate_pool"] = stats.CandidatePool
+		_ = e.SourceInbox.CompleteClassification(ctx, item.ID, status, relevance, matched, meta)
+	}
+	if e.Broker != nil {
+		e.Broker.Publish(model.Activity{Type: "source.inbox.classified", Source: "brain", Phase: "source-inbox", Message: fmt.Sprintf("Source-Inbox: %d Dokumente geprüft · %d als Wissenskandidaten vorgemerkt", len(items), candidateCount), Strength: .36, Metadata: map[string]any{"documents": len(items), "candidates": candidateCount, "minimum_similarity": e.Cfg.SourceInboxMinSimilarity}})
+	}
+}
+
+func (e *Engine) sourceInboxResearch(ctx context.Context, query string, limit int, freshnessSensitive bool) []model.ResearchResult {
+	if e.SourceInbox == nil || !e.Cfg.SourceInboxEnabled || limit < 1 {
+		return nil
+	}
+	maxAge := time.Duration(0)
+	if freshnessSensitive {
+		maxAge = e.Cfg.SourceInboxFreshMaxAge
+	}
+	items, err := e.SourceInbox.SearchCandidates(ctx, query, limit, maxAge)
+	if err != nil {
+		return nil
+	}
+	out := make([]model.ResearchResult, 0, len(items))
+	for _, item := range items {
+		if item.QueryScore < .18 {
+			continue
+		}
+		d := item.Document
+		out = append(out, model.ResearchResult{Title: d.Title, URL: d.CanonicalURL, Snippet: clamp(d.Text, 1000), Content: d.Text, ContentType: d.ContentType, Query: query, Language: d.Language, Fetched: true, Relevant: true, Relevance: item.QueryScore, SourceQuality: "source_inbox", SourceQualityScore: .68, AssessmentReason: "Vorab durch Source-Agent gesammelt und vom Brain als thematisch passend zur Knowledgebase klassifiziert."})
+	}
+	if len(out) > 0 && e.Broker != nil {
+		e.Broker.Publish(model.Activity{Type: "article.research.inbox", Source: "brain", Phase: "knowledge-research-routing", Message: fmt.Sprintf("Source-Inbox liefert %d bereits gecrawlte Kandidaten vor SearXNG", len(out)), Strength: .56, Metadata: map[string]any{"query": query, "results": len(out), "freshness_sensitive": freshnessSensitive}})
+	}
+	return out
+}