mirror of
https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-22 07:03:53 +08:00
Merge branch 'xinnan-tech:main' into main
This commit is contained in:
ljwwd2
@@ -32,7 +32,7 @@ INSERT INTO `ai_model_config` VALUES ('TTS_EdgeTTS', 'TTS', 'EdgeTTS', 'Edge语
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INSERT INTO `ai_model_config` VALUES ('TTS_DoubaoTTS', 'TTS', 'DoubaoTTS', '豆包语音合成', 0, 1, '{\"type\": \"doubao\", \"api_url\": \"https://openspeech.bytedance.com/api/v1/tts\", \"voice\": \"BV001_streaming\", \"output_dir\": \"tmp/\", \"authorization\": \"Bearer;\", \"appid\": \"\", \"access_token\": \"\", \"cluster\": \"volcano_tts\"}', NULL, NULL, 2, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_CosyVoiceSiliconflow', 'TTS', 'CosyVoiceSiliconflow', '硅基流动语音合成', 0, 1, '{\"type\": \"siliconflow\", \"model\": \"FunAudioLLM/CosyVoice2-0.5B\", \"voice\": \"FunAudioLLM/CosyVoice2-0.5B:alex\", \"output_dir\": \"tmp/\", \"access_token\": \"\", \"response_format\": \"wav\"}', NULL, NULL, 3, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_CozeCnTTS', 'TTS', 'CozeCnTTS', 'Coze中文语音合成', 0, 1, '{\"type\": \"cozecn\", \"voice\": \"7426720361733046281\", \"output_dir\": \"tmp/\", \"access_token\": \"\", \"response_format\": \"wav\"}', NULL, NULL, 4, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_FishSpeech', 'TTS', 'FishSpeech', 'FishSpeech语音合成', 0, 1, '{\"type\": \"fishspeech\", \"output_dir\": \"tmp/\", \"response_format\": \"wav\", \"reference_id\": null, \"reference_audio\": [\"/tmp/test.wav\"], \"reference_text\": [\"你弄来这些吟词宴曲来看，还是这些混话来欺负我。\"], \"normalize\": true, \"max_new_tokens\": 1024, \"chunk_length\": 200, \"top_p\": 0.7, \"repetition_penalty\": 1.2, \"temperature\": 0.7, \"streaming\": false, \"use_memory_cache\": \"on\", \"seed\": null, \"channels\": 1, \"rate\": 44100, \"api_key\": \"\", \"api_url\": \"http://127.0.0.1:8080/v1/tts\"}', NULL, NULL, 5, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_FishSpeech', 'TTS', 'FishSpeech', 'FishSpeech语音合成', 0, 1, '{\"type\": \"fishspeech\", \"output_dir\": \"tmp/\", \"response_format\": \"wav\", \"reference_id\": null, \"reference_audio\": [\"/tmp/test.wav\"], \"reference_text\": [\"你弄来这些吟词宴曲来看，还是这些混话来欺负我。\"], \"normalize\": true, \"max_new_tokens\": 1024, \"chunk_length\": 200, \"top_p\": 0.7, \"repetition_penalty\": 1.2, \"temperature\": 0.7, \"streaming\": false, \"use_memory_cache\": \"on\", \"seed\": 1, \"channels\": 1, \"rate\": 44100, \"api_key\": \"\", \"api_url\": \"http://127.0.0.1:8080/v1/tts\"}', NULL, NULL, 5, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_GPT_SOVITS_V2', 'TTS', 'GPT_SOVITS_V2', 'GPT-SoVITS V2', 0, 1, '{\"type\": \"gpt_sovits_v2\", \"url\": \"http://127.0.0.1:9880/tts\", \"output_dir\": \"tmp/\", \"text_lang\": \"auto\", \"ref_audio_path\": \"caixukun.wav\", \"prompt_text\": \"\", \"prompt_lang\": \"zh\", \"top_k\": 5, \"top_p\": 1, \"temperature\": 1, \"text_split_method\": \"cut0\", \"batch_size\": 1, \"batch_threshold\": 0.75, \"split_bucket\": true, \"return_fragment\": false, \"speed_factor\": 1.0, \"streaming_mode\": false, \"seed\": -1, \"parallel_infer\": true, \"repetition_penalty\": 1.35, \"aux_ref_audio_paths\": []}', NULL, NULL, 6, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_GPT_SOVITS_V3', 'TTS', 'GPT_SOVITS_V3', 'GPT-SoVITS V3', 0, 1, '{\"type\": \"gpt_sovits_v3\", \"url\": \"http://127.0.0.1:9880\", \"output_dir\": \"tmp/\", \"text_language\": \"auto\", \"refer_wav_path\": \"caixukun.wav\", \"prompt_language\": \"zh\", \"prompt_text\": \"\", \"top_k\": 15, \"top_p\": 1.0, \"temperature\": 1.0, \"cut_punc\": \"\", \"speed\": 1.0, \"inp_refs\": [], \"sample_steps\": 32, \"if_sr\": false}', NULL, NULL, 7, NULL, NULL, NULL, NULL);
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INSERT INTO `ai_model_config` VALUES ('TTS_MinimaxTTS', 'TTS', 'MinimaxTTS', 'MiniMax语音合成', 0, 1, '{\"type\": \"minimax\", \"output_dir\": \"tmp/\", \"group_id\": \"\", \"api_key\": \"你的api_key\", \"model\": \"speech-01-turbo\", \"voice_id\": \"female-shaonv\"}', NULL, NULL, 8, NULL, NULL, NULL, NULL);
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@@ -26,7 +26,7 @@
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<div class="main-wrapper">
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<div class="content-panel">
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<div class="content-area">
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<el-card class="params-card" shadow="never">
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<el-card class="provider-card" shadow="never">
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<el-table ref="providersTable" :data="filteredProvidersList" class="transparent-table" v-loading="loading"
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element-loading-text="拼命加载中" element-loading-spinner="el-icon-loading"
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element-loading-background="rgba(255, 255, 255, 0.7)" :header-cell-class-name="headerCellClassName">
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@@ -218,6 +218,15 @@ class IntentProvider(IntentProviderBase):
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f"llm 识别到意图: {function_name}, 参数: {function_args}"
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)
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# 如果是继续聊天，清理工具调用相关的历史消息
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if function_name == "continue_chat":
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# 保留非工具相关的消息
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clean_history = [
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msg for msg in conn.dialogue.dialogue
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if msg.role not in ["tool", "function"]
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]
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conn.dialogue.dialogue = clean_history
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# 添加到缓存
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self.intent_cache[cache_key] = {
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"intent": intent,
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@@ -21,27 +21,67 @@ class LLMProvider(LLMProviderBase):
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api_key="ollama" # Ollama doesn't need an API key but OpenAI client requires one
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)
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# 检查是否是qwen3模型
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self.is_qwen3 = self.model_name and self.model_name.lower().startswith("qwen3")
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def response(self, session_id, dialogue):
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try:
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# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
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if self.is_qwen3:
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# 复制对话列表，避免修改原始对话
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dialogue_copy = dialogue.copy()
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# 找到最后一条用户消息
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for i in range(len(dialogue_copy) - 1, -1, -1):
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if dialogue_copy[i]["role"] == "user":
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# 在用户消息前添加/no_think指令
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dialogue_copy[i]["content"] = "/no_think " + dialogue_copy[i]["content"]
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logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
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break
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# 使用修改后的对话
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dialogue = dialogue_copy
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responses = self.client.chat.completions.create(
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model=self.model_name,
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messages=dialogue,
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stream=True
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)
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is_active=True
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is_active = True
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# 用于处理跨chunk的标签
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buffer = ""
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for chunk in responses:
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try:
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delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
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content = delta.content if hasattr(delta, 'content') else ''
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if content:
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if '<think>' in content:
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# 将内容添加到缓冲区
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buffer += content
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# 处理缓冲区中的标签
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while '<think>' in buffer and '</think>' in buffer:
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# 找到完整的<think></think>标签并移除
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pre = buffer.split('<think>', 1)[0]
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post = buffer.split('</think>', 1)[1]
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buffer = pre + post
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# 处理只有开始标签的情况
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if '<think>' in buffer:
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is_active = False
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content = content.split('<think>')[0]
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if '</think>' in content:
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buffer = buffer.split('<think>', 1)[0]
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# 处理只有结束标签的情况
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if '</think>' in buffer:
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is_active = True
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content = content.split('</think>')[-1]
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if is_active:
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yield content
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buffer = buffer.split('</think>', 1)[1]
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# 如果当前处于活动状态且缓冲区有内容，则输出
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if is_active and buffer:
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yield buffer
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buffer = "" # 清空缓冲区
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
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@@ -51,6 +91,22 @@ class LLMProvider(LLMProviderBase):
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def response_with_functions(self, session_id, dialogue, functions=None):
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try:
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# 如果是qwen3模型，在用户最后一条消息中添加/no_think指令
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if self.is_qwen3:
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# 复制对话列表，避免修改原始对话
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dialogue_copy = dialogue.copy()
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# 找到最后一条用户消息
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for i in range(len(dialogue_copy) - 1, -1, -1):
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if dialogue_copy[i]["role"] == "user":
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# 在用户消息前添加/no_think指令
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dialogue_copy[i]["content"] = "/no_think " + dialogue_copy[i]["content"]
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logger.bind(tag=TAG).debug(f"为qwen3模型添加/no_think指令")
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break
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# 使用修改后的对话
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dialogue = dialogue_copy
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stream = self.client.chat.completions.create(
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model=self.model_name,
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messages=dialogue,
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@@ -58,8 +114,49 @@ class LLMProvider(LLMProviderBase):
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tools=functions,
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)
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is_active = True
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buffer = ""
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for chunk in stream:
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yield chunk.choices[0].delta.content, chunk.choices[0].delta.tool_calls
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try:
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delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
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content = delta.content if hasattr(delta, 'content') else None
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tool_calls = delta.tool_calls if hasattr(delta, 'tool_calls') else None
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# 如果是工具调用，直接传递
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if tool_calls:
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yield None, tool_calls
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continue
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# 处理文本内容
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if content:
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# 将内容添加到缓冲区
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buffer += content
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# 处理缓冲区中的标签
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while '<think>' in buffer and '</think>' in buffer:
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# 找到完整的<think></think>标签并移除
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pre = buffer.split('<think>', 1)[0]
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post = buffer.split('</think>', 1)[1]
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buffer = pre + post
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# 处理只有开始标签的情况
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if '<think>' in buffer:
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is_active = False
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buffer = buffer.split('<think>', 1)[0]
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# 处理只有结束标签的情况
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if '</think>' in buffer:
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is_active = True
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buffer = buffer.split('</think>', 1)[1]
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# 如果当前处于活动状态且缓冲区有内容，则输出
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if is_active and buffer:
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yield buffer, None
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buffer = "" # 清空缓冲区
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing function chunk: {e}")
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continue
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in Ollama function call: {e}")
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