- 根因: loop/agent.decodeToolCalls 只读 resp.Message.ToolCallName/resp.Content JSON信封, 从不看 api 原生解析出的 resp.ToolCalls(Anthropic tool_use / OpenAI tool_calls) → 模型返回 thinking+tool_use(无text) 被判定'无工具调用' → 空 Content 当最终 text → res.Text="" → no reply produced; 纯聊天消息不调工具直接出 text, 故表现为'部分消息无回复' - agent/step.go: ToolCall 增 ID; ExtractToolCalls 导出并优先 resp.ToolCalls(保留原生id+归一化nil Args), 回落两旧约定 - loop/request.go: decodeToolCalls 委托 agent.ExtractToolCalls(流式/非流式共用) - loop/tool_history.go(新): 原生批次回填 assistant tool_use + 带tool_use_id的tool_result; 旧JSON信封批次保持 RoleTool 纯文本(向后兼容) - openai.go: tool_call parts 编成 tool_calls + tool_call_id - 新增 native_toolcall_test + native_toolcall_http_test(httptest假Anthropic SSE: round1 tool_use(无text)→round2含tool_use_id/tool_result→res.Text非空) - build/vet/test绿+交叉编译
174 lines
6.0 KiB
Go
174 lines
6.0 KiB
Go
package loop
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import (
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"context"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/agent"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/capacity"
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"git.yeij.top/AskaEth/Cyrene_v2/internal/runtime/model"
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)
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// buildRequest assembles a model.Request from the agent's public surface,
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// mirroring agent.buildRequest so the streaming path behaves identically to
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// RunStep (leading instruction message, tool schemas, model routing).
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func (l *Loop) buildRequest(ctx context.Context, messages []model.Message) model.Request {
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req := model.Request{
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Model: l.agent.Model(),
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Messages: l.assembleMessages(messages),
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Temperature: l.temperature(),
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MaxTokens: l.agent.MaxTokens(),
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Purpose: l.agent.Purpose(),
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Tools: l.toolSchemas(ctx, messages),
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}
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if l.agent.Router() != nil {
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if m, _ := l.agent.Router().RouteRequest(req); m != "" {
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req.Model = m
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}
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}
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return req
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}
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// assembleMessages prepends the agent's instructions as a leading system message
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// when they are non-empty, otherwise returns the input slice unchanged.
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func (l *Loop) assembleMessages(messages []model.Message) []model.Message {
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instr := l.agent.Instructions()
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if instr == "" {
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return messages
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}
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out := make([]model.Message, 0, len(messages)+1)
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out = append(out, model.TextMessage(model.RoleSystem, instr))
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out = append(out, messages...)
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return out
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}
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// temperature returns a pointer to the configured temperature, mapping zero to
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// nil so the provider default applies (matching the agent package).
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func (l *Loop) temperature() *float64 {
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t := l.agent.Temperature()
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if t == 0 {
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return nil
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}
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return &t
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}
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// toolSchemas advertises the registered tool schemas to the model, or nil when no
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// dispatcher is configured. When a capacity decider is wired and enabled, it
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// classifies the current round and returns only the allowed set for that state
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// (a view over the dispatcher, per design/14 §4): the model sees/invokes only
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// the allowed tools, while Invoke itself stays unblocked. A nil or disabled
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// decider keeps the pre-existing behavior (every tool advertised).
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//
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// The design/11 §3 security gate runs last over whatever the capacity view left:
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// in a group/stranger environment only tools whose editable marker allows that
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// environment are advertised (default closed), and the owner environment keeps
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// the full set. Security is a hard red line, not a convenience hint, so it can
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// only shrink the surface — it never re-admits a tool capacity excluded.
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func (l *Loop) toolSchemas(ctx context.Context, messages []model.Message) []model.ToolSpec {
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d := l.dispatcher()
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if d == nil {
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return nil
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}
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schemas := d.ToolSchemas()
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if l.capacity == nil || !l.capacity.Enabled() {
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return l.restrictSchemas(ctx, d, schemas)
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}
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names := make([]string, 0, len(schemas))
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for _, s := range schemas {
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names = append(names, s.Name)
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}
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acc := capState(ctx)
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dec := l.capacity.Decide(ctx, capacity.Input{
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Messages: capacityTexts(messages),
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CurrentState: acc.state,
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AllToolNames: names,
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Recall: l.capacityRecall(ctx, messages),
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})
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acc.state = dec.State
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if l.onCapacity != nil && !isMutedBroadcast(ctx) {
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sessionID := loopSession(ctx)
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for _, ev := range dec.Events {
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ev.SessionID = sessionID
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l.onCapacity(ev)
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}
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}
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allowed := make(map[string]bool, len(dec.AllowedTools))
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for _, n := range dec.AllowedTools {
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allowed[n] = true
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}
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out := make([]model.ToolSpec, 0, len(dec.AllowedTools))
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for _, s := range schemas {
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if allowed[s.Name] {
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out = append(out, s)
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}
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}
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return l.restrictSchemas(ctx, d, out)
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}
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// capacityTexts extracts the recent conversation text for the capacity decider:
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// the last few non-system/non-tool turns, in chronological order (oldest first),
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// so the decider's current-turn window sees the latest user message last.
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func capacityTexts(messages []model.Message) []string {
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texts := make([]string, 0, 6)
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for i := len(messages) - 1; i >= 0 && len(texts) < 6; i-- {
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m := messages[i]
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if m.Role == model.RoleSystem || m.Role == model.RoleTool {
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continue
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}
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if t := messageText(m); t != "" {
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texts = append(texts, t)
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}
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}
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for i, j := 0, len(texts)-1; i < j; i, j = i+1, j-1 {
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texts[i], texts[j] = texts[j], texts[i]
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}
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return texts
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}
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// capacityRecallTopK bounds how many recalled habit memories the capacity L4 layer
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// considers per round.
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const capacityRecallTopK = 5
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// capacityRecall retrieves the owner's relevant long-term memories for the capacity
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// L4 habit-alignment layer (design §2.2 L4), the memory feed that lets the decider
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// learn the user's task-expression habits. It returns nil (keeping L4 silent) when
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// no retriever/persona is configured — backward compatible — or in a restricted
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// (group chat / stranger) context, per the isolation red line: L4 learns the owner's
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// habits and must never read owner-sensitive memory into a group or stranger intent
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// decision (design §2.2 红线: 不读陌生人敏感).
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func (l *Loop) capacityRecall(ctx context.Context, messages []model.Message) []capacity.RecallItem {
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if l.retriever == nil || l.personaID == "" {
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return nil
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}
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env := sessionEnvFrom(ctx)
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if env.Restricted() {
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return nil
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}
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results, err := l.retriever.Retrieve(ctx, l.personaID, queryFromMessages(messages), capacityRecallTopK)
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if err != nil {
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l.log.Warn("capacity recall", "error", err, "env", env.ActorEnv)
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return nil
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}
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items := make([]capacity.RecallItem, 0, len(results))
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for _, r := range results {
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items = append(items, capacity.RecallItem{
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Content: r.Item.Content,
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Scope: string(r.Item.Scope),
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Kind: string(r.Item.Kind),
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Tags: r.Item.Tags,
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Score: r.Score,
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})
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}
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return items
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}
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// decodeToolCalls decodes tool calls from a model.Response via the agent's shared
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// decoder, so the streaming and non-streaming paths agree on the provider-native
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// tool calls the adapters emit (Anthropic tool_use / OpenAI tool_calls) as well
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// as the legacy JSON-envelope convention. It reports ok=true (with a non-empty
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// slice) when a call is decoded.
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func decodeToolCalls(resp model.Response) ([]agent.ToolCall, bool) {
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return agent.ExtractToolCalls(resp)
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}
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