Add core implementation for AI-powered question answering

Introduce multiple agents, tools, and utilities for processing, extracting, and answering user-provided questions using LLMs and external data. Key features include knowledge processing, question splitting, search term generation, and contextual knowledge handling.

pkg/agents/shared/modeltracker.go

@@ -0,0 +1,42 @@
+package shared
+
+import (
+	"context"
+	"errors"
+	"sync/atomic"
+
+	gollm "gitea.stevedudenhoeffer.com/steve/go-llm"
+)
+
+type ModelTracker struct {
+	parent  gollm.ChatCompletion
+	maximum int64
+	calls   int64
+}
+
+var _ gollm.ChatCompletion = &ModelTracker{}
+
+// NewModelTracker creates a new model tracker that will limit the number of calls to the parent.
+// Set to 0 to disable the limit.
+func NewModelTracker(parent gollm.ChatCompletion, maximum int64) *ModelTracker {
+	return &ModelTracker{parent: parent, maximum: maximum}
+}
+
+var ErrModelCapacity = errors.New("maximum model capacity reached")
+
+func (m *ModelTracker) ChatComplete(ctx context.Context, req gollm.Request) (gollm.Response, error) {
+	if m.maximum > 0 && atomic.AddInt64(&m.calls, 1) >= m.maximum {
+		return gollm.Response{}, ErrModelCapacity
+	}
+
+	return m.parent.ChatComplete(ctx, req)
+}
+
+// ResetCalls resets the number of calls made to the parent.
+func (m *ModelTracker) ResetCalls() {
+	atomic.StoreInt64(&m.calls, 0)
+}
+
+func (m *ModelTracker) GetCalls() int64 {
+	return atomic.LoadInt64(&m.calls)
+}