Merge branch 'refs/heads/main' into perf-tool-call-optimization

This commit is contained in:
DaGou12138
2026-04-21 15:02:31 +08:00
99 changed files with 108 additions and 93 deletions
@@ -99,6 +99,7 @@ public class ConfigServiceImpl implements ConfigService {
null,
null,
null,
null,
result,
isCache);
@@ -113,7 +114,7 @@ public class ConfigServiceImpl implements ConfigService {
// 检查是否为管理控制台请求
String redisKey = RedisKeys.getTmpRegisterMacKey(macAddress);
Object isAdminRequest = redisUtils.get(redisKey);
if (isAdminRequest != null && "true".equals(isAdminRequest)) {
// 管理控制台请求,返回getConfig的结果
redisUtils.delete(redisKey); // 使用后清理
@@ -203,10 +204,11 @@ public class ConfigServiceImpl implements ConfigService {
mcpEndpoint = mcpEndpoint.replace("/mcp/", "/call/");
result.put("mcp_endpoint", mcpEndpoint);
}
// 获取上下文源配置
AgentContextProviderEntity contextProviderEntity = agentContextProviderService.getByAgentId(agent.getId());
if (contextProviderEntity != null && contextProviderEntity.getContextProviders() != null && !contextProviderEntity.getContextProviders().isEmpty()) {
if (contextProviderEntity != null && contextProviderEntity.getContextProviders() != null
&& !contextProviderEntity.getContextProviders().isEmpty()) {
result.put("context_providers", contextProviderEntity.getContextProviders());
}
@@ -229,6 +231,7 @@ public class ConfigServiceImpl implements ConfigService {
agent.getAsrModelId(),
agent.getLlmModelId(),
agent.getVllmModelId(),
agent.getSlmModelId(),
agent.getTtsModelId(),
agent.getMemModelId(),
agent.getIntentModelId(),
@@ -410,6 +413,7 @@ public class ConfigServiceImpl implements ConfigService {
String asrModelId,
String llmModelId,
String vllmModelId,
String slmModelId,
String ttsModelId,
String memModelId,
String intentModelId,
@@ -418,9 +422,9 @@ public class ConfigServiceImpl implements ConfigService {
boolean isCache) {
Map<String, String> selectedModule = new HashMap<>();
String[] modelTypes = { "VAD", "ASR", "TTS", "Memory", "Intent", "LLM", "VLLM", "RAG" };
String[] modelTypes = { "VAD", "ASR", "TTS", "Memory", "Intent", "LLM", "VLLM", "SLM", "RAG" };
String[] modelIds = { vadModelId, asrModelId, ttsModelId, memModelId, intentModelId, llmModelId, vllmModelId,
ragModelId };
slmModelId, ragModelId };
String intentLLMModelId = null;
String memLocalShortLLMModelId = null;
@@ -456,7 +460,7 @@ public class ConfigServiceImpl implements ConfigService {
// 火山引擎声音克隆需要替换resource_id
Map<String, Object> map = (Map<String, Object>) model.getConfigJson();
if (Constant.VOICE_CLONE_HUOSHAN_DOUBLE_STREAM.equals(map.get("type"))) {
// 如果voice是”S_开头的,使用seed-icl-1.0
// 如果voice是”S_开头的,使用seed-icl-1.0
if (voice != null && voice.startsWith("S_")) {
map.put("resource_id", "seed-icl-1.0");
}
@@ -474,7 +478,7 @@ public class ConfigServiceImpl implements ConfigService {
if (map.get("functions") != null) {
String functionStr = (String) map.get("functions");
if (StringUtils.isNotBlank(functionStr)) {
String[] functions = functionStr.split("\\;");
String[] functions = functionStr.split(";");
map.put("functions", functions);
}
}
@@ -507,6 +511,15 @@ public class ConfigServiceImpl implements ConfigService {
typeConfig.put(memLocalShortLLM.getId(), memLocalShortLLM.getConfigJson());
}
}
// LLM也返回所选的SLM,如果同名id则不重复显示
if (StringUtils.isNotBlank(slmModelId) && !slmModelId.equals(llmModelId)) {
if (!typeConfig.containsKey(slmModelId)) {
ModelConfigEntity slmModel = modelConfigService.getModelByIdFromCache(slmModelId);
if (slmModel != null && slmModel.getConfigJson() != null) {
typeConfig.put(slmModel.getId(), slmModel.getConfigJson());
}
}
}
}
}
result.put(modelTypes[i], typeConfig);
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+2 -3
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@@ -2,12 +2,11 @@
<html lang="zh-CN">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="icon" href="./favicon.ico">
<title>Xiaozhi AI Customization</title>
<script type="module" crossorigin src="./assets/index-B8r0c7xg.js"></script>
<link rel="stylesheet" crossorigin href="./assets/index-CrIJdTCK.css">
<script type="module" crossorigin src="./assets/index-Guo1hQ-y.js"></script>
<link rel="stylesheet" crossorigin href="./assets/index-B3ns2K1c.css">
</head>
<body>
<div id="app"></div>
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@@ -0,0 +1 @@
wakenet9_tts3h12_Hi,Stack Chan_3_0.585_0.590
@@ -0,0 +1 @@
wakenet9_tts3h12_小峰小峰_3_0.614_0.621
@@ -0,0 +1 @@
wakenet9_tts3h12_小珈小珈_3_0.598_0.607
+1 -1
View File
@@ -23,7 +23,7 @@ You are a playful, expressive, empathetic, and highly emotionally intelligent co
你的输出会被合成器(TTS)转为声音朗读,用户输入格式为 JSON,但你的常规回复必须严格遵循纯文本规范:
1. 【单一表情前置】只允许在每段常规回复的**最开头**插入 1 个且仅 1 个 Emoji 表情(调用工具时不插入表情)。
2. 【表情白名单】绝对只能使用以下列表中的 Emoji:{{emojiList}}。禁止使用列表外的符号及任何颜文字。
3. 【排版绝对禁区】除非输出标准化 JSON 进行工具调用,常规文本绝对禁止输出 Markdown 排版(如加粗、列表、代码块等),严禁使用括号等形式输出心理活动和动作描写(如[无奈地叹气]”、(笑着说)”)。
3. 【排版绝对禁区】除非输出标准化 JSON 进行工具调用,常规文本绝对禁止输出 Markdown 排版。绝对不要使用 “**” 或 “__” 加粗(如”**重点**”),不要在行首使用 “-”、”•” 等列表标记,不要用 “```” 代码块。严禁使用括号输出心理活动和动作描写(如[无奈地叹气]”、(笑着说)”)。多项内容请用中文分号”;”或逗号”,”串联成自然口语段落。
</tts_format_constraints>
<tool_and_knowledge>