diff --git a/.gitignore b/.gitignore
index 56fa312b..61cea7bc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -141,8 +141,8 @@ music/
# Cython debug symbols
cython_debug/
*.iml
-model.pt
tmp
+.history
.DS_Store
main/xiaozhi-server/data
main/manager-web/node_modules
@@ -151,5 +151,8 @@ main/manager-web/node_modules
.private_config.yaml
.env.development
+# model files
+main/xiaozhi-server/models/SenseVoiceSmall/model.pt
+main/xiaozhi-server/models/sherpa-onnx*
/main/xiaozhi-server/audio_ref/
/audio_ref/
diff --git a/README.md b/README.md
index cdf31847..b59d9d45 100644
--- a/README.md
+++ b/README.md
@@ -201,6 +201,7 @@ server:
| TTS | CosyVoiceSiliconflow | 接口调用 | 消耗 token | 需申请硅基流动 API 密钥；输出格式为 wav |
| TTS | TTS302AI | 接口调用 | 消耗 token | [点击创建密钥](https://dash.302.ai/apis/list) |
| TTS | CozeCnTTS | 接口调用 | 消耗 token | 需提供 Coze API key；输出格式为 wav |
+| TTS | GizwitsTTS | 接口调用 | 消耗 token | [点击创建密钥](https://agentrouter.gizwitsapi.com) |
| TTS | ACGNTTS | 接口调用 | 消耗 token | [联系网站管理员购买密钥](www.ttson.cn) |
| TTS | OpenAITTS | 接口调用 | 消耗 token | 境外使用，境外购买 |
| TTS | FishSpeech | 接口调用 | 免费/自定义 | 本地启动 TTS 服务；启动方法见配置文件内说明 |
@@ -223,8 +224,10 @@ server:
| 类型 | 平台名称 | 使用方式 | 收费模式 | 备注 |
|:---:|:---------:|:----:|:----:|:--:|
| ASR | FunASR | 本地使用 | 免费 | |
+| ASR | SherpaASR | 本地使用 | 免费 | |
| ASR | DoubaoASR | 接口调用 | 收费 | |
+
---
### Memory 记忆存储
diff --git a/README_en.md b/README_en.md
index 8c251a44..88306675 100644
--- a/README_en.md
+++ b/README_en.md
@@ -165,6 +165,7 @@ In fact, any LLM that supports OpenAI API calls can be integrated.
| Type | Platform Name | Usage Method | Pricing Model | Remarks |
|:----:|:-------------------:|:------------:|:-------------:|:-------:|
| ASR | FunASR | Local | Free | |
+| ASR | SherpaASR | Local | Free | |
| ASR | DoubaoASR | API call | Paid | |
---
diff --git a/main/manager-web/src/apis/module/user.js b/main/manager-web/src/apis/module/user.js
index 444d5d76..bf3eea34 100755
--- a/main/manager-web/src/apis/module/user.js
+++ b/main/manager-web/src/apis/module/user.js
@@ -18,20 +18,6 @@ export default {
})
}).send()
},
- // 获取用户信息
- getUserInfo(callback) {
- RequestService.sendRequest().url(`${getServiceUrl()}/api/v1/user/info`)
- .method('GET')
- .success((res) => {
- RequestService.clearRequestTime()
- callback(res)
- })
- .fail(() => {
- RequestService.reAjaxFun(() => {
- this.getUserInfo()
- })
- }).send()
- },
// 获取设备信息
getHomeList(callback) {
RequestService.sendRequest().url(`${getServiceUrl()}/api/v1/user/device/bind`)
@@ -173,5 +159,52 @@ export default {
});
}).send();
},
+ // 获取智能体列表
+ getAgentList(callback) {
+ RequestService.sendRequest()
+ .url(`${getServiceUrl()}/api/v1/user/agent`)
+ .method('GET')
+ .success((res) => {
+ RequestService.clearRequestTime();
+ callback(res);
+ })
+ .fail(() => {
+ RequestService.reAjaxFun(() => {
+ this.getAgentList(callback);
+ });
+ }).send();
+ },
+
+ getUserInfo(callback) {
+ RequestService.sendRequest()
+ .url(`${getServiceUrl()}/api/v1/user/info`)
+ .method('GET')
+ .success((res) => {
+ RequestService.clearRequestTime()
+ callback(res)
+ })
+ .fail((err) => {
+ console.error('接口请求失败:', err)
+ RequestService.reAjaxFun(() => {
+ this.getUserInfo(callback)
+ })
+ }).send()
+ },
+ // 添加智能体
+ addAgent(agentName, callback) {
+ RequestService.sendRequest()
+ .url(`${getServiceUrl()}/api/v1/user/agent`)
+ .method('POST')
+ .data({ name: agentName })
+ .success((res) => {
+ RequestService.clearRequestTime();
+ callback(res);
+ })
+ .fail(() => {
+ RequestService.reAjaxFun(() => {
+ this.addAgent(agentName, callback);
+ });
+ }).send();
+ },
}
diff --git a/main/manager-web/src/components/AddWisdomBodyDialog.vue b/main/manager-web/src/components/AddWisdomBodyDialog.vue
index fef13d50..cca026ef 100644
--- a/main/manager-web/src/components/AddWisdomBodyDialog.vue
+++ b/main/manager-web/src/components/AddWisdomBodyDialog.vue
@@ -29,6 +29,9 @@
diff --git a/main/manager-web/src/views/home.vue b/main/manager-web/src/views/home.vue
index 4b9c6eb5..0c914e77 100644
--- a/main/manager-web/src/views/home.vue
+++ b/main/manager-web/src/views/home.vue
@@ -1,7 +1,7 @@
-
@@ -47,22 +47,22 @@
import DeviceItem from '@/components/DeviceItem.vue'
import AddWisdomBodyDialog from '@/components/AddWisdomBodyDialog.vue'
import HeaderBar from '@/components/HeaderBar.vue'
+
export default {
name: 'HomePage',
components: { DeviceItem, AddWisdomBodyDialog, HeaderBar },
data() {
return {
addDeviceDialogVisible: false,
- // 此处模拟设备列表（10条数据）
- devices: Array.from({ length: 10 }, (_, i) => ({
- id: i,
- mac: 'CC:ba:97:11:a6:ac',
- model: 'esp32-s3-touch-amoled-1.8',
- voiceModel: 'esp32-s3-touch-amoled-1.8',
- lastConversation: '6天前',
- }))
+ devices: [],
+ originalDevices: [],
}
},
+
+ mounted() {
+ this.fetchAgentList();
+ },
+
methods: {
showAddDialog() {
this.addDeviceDialogVisible = true
@@ -71,13 +71,28 @@ export default {
// 点击配置角色后跳转到角色配置页
this.$router.push('/role-config')
},
- handleWisdomBodyAdded(name) {
- console.log('新增智慧体名称：', name)
- this.addDeviceDialogVisible = false
+ handleWisdomBodyAdded(res) {
+ console.log('新增智能体响应：', res);
+ this.fetchAgentList();
+ this.addDeviceDialogVisible = false;
},
handleDeviceManage() {
this.$router.push('/device-management');
},
+ // 获取智能体列表
+ fetchAgentList() {
+ import('@/apis/module/user').then(({ default: userApi }) => {
+ userApi.getAgentList(({data}) => {
+ this.originalDevices = data.data;
+ this.devices = data.data;
+ });
+ });
+ },
+ // 搜索更新智能体列表
+ handleSearchResult(filteredList) {
+ this.devices = filteredList; // 更新设备列表
+ }
+
}
}
diff --git a/main/xiaozhi-server/config.yaml b/main/xiaozhi-server/config.yaml
index f3ca5706..27a2f6c1 100644
--- a/main/xiaozhi-server/config.yaml
+++ b/main/xiaozhi-server/config.yaml
@@ -125,6 +125,10 @@ ASR:
type: fun_local
model_dir: models/SenseVoiceSmall
output_dir: tmp/
+ SherpaASR:
+ type: sherpa_onnx_local
+ model_dir: models/sherpa-onnx-sense-voice-zh-en-ja-ko-yue-2024-07-17
+ output_dir: tmp/
DoubaoASR:
type: doubao
appid: 你的火山引擎语音合成服务appid
@@ -138,6 +142,7 @@ VAD:
min_silence_duration_ms: 700 # 如果说话停顿比较长，可以把这个值设置大一些
LLM:
+ # 所有openai类型均可以修改超参，以AliLLM为例
# 当前支持的type为openai、dify、ollama，可自行适配
AliLLM:
# 定义LLM API类型
@@ -146,6 +151,11 @@ LLM:
base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
model_name: qwen-turbo
api_key: 你的deepseek web key
+ temperature: 0.7 # 温度值
+ max_tokens: 500 # 最大生成token数
+ top_p: 1
+ top_k: 50
+ frequency_penalty: 0 # 频率惩罚
DoubaoLLM:
# 定义LLM API类型
type: openai
@@ -181,8 +191,12 @@ LLM:
type: dify
# 建议使用本地部署的dify接口，国内部分区域访问dify公有云接口可能会受限
# 如果使用DifyLLM，配置文件里prompt(提示词)是无效的，需要在dify控制台设置提示词
- base_url: https://api.dify.cn/v1
+ base_url: https://api.dify.ai/v1
api_key: 你的DifyLLM web key
+ # 使用的对话模式 可以选择工作流 workflows/run 对话模式 chat-messages 文本生成 completion-messages
+ # 使用workflows进行返回的时候输入参数为 query 返回参数的名字要设置为 answer
+ # 文本生成的默认输入参数也是query
+ mode: chat-messages
GeminiLLM:
type: gemini
# 谷歌Gemini API，需要先在Google Cloud控制台创建API密钥并获取api_key
@@ -190,7 +204,9 @@ LLM:
# token申请地址： https://aistudio.google.com/apikey
# 若部署地无法访问接口，需要开启科学上网
api_key: 你的gemini web key
- model_name: "gemini-1.5-pro" # gemini-1.5-pro 是免费的
+ model_name: "gemini-2.0-flash"
+ http_proxy: "" #"http://127.0.0.1:10808"
+ https_proxy: "" #http://127.0.0.1:10808"
CozeLLM:
# 定义LLM API类型
type: coze
@@ -396,9 +412,21 @@ TTS:
type: doubao
api_url: https://api.302ai.cn/doubao/tts_hd
authorization: "Bearer "
+ # 湾湾小何音色
voice: "zh_female_wanwanxiaohe_moon_bigtts"
output_dir: tmp/
access_token: "你的302API密钥"
+ GizwitsTTS:
+ type: doubao
+ # 火山引擎作为基座，可以完全使用企业级火山引擎语音合成服务
+ # 前一万名注册的用户，将送5元体验金额
+ # 获取API Key地址：https://agentrouter.gizwitsapi.com/panel/token
+ api_url: https://bytedance.gizwitsapi.com/api/v1/tts
+ authorization: "Bearer "
+ # 湾湾小何音色
+ voice: "zh_female_wanwanxiaohe_moon_bigtts"
+ output_dir: tmp/
+ access_token: "你的机智云API key"
ACGNTTS:
#在线网址：https://acgn.ttson.cn/
#token购买：www.ttson.cn
diff --git a/main/xiaozhi-server/core/providers/asr/sherpa_onnx_local.py b/main/xiaozhi-server/core/providers/asr/sherpa_onnx_local.py
new file mode 100644
index 00000000..3dfa3162
--- /dev/null
+++ b/main/xiaozhi-server/core/providers/asr/sherpa_onnx_local.py
@@ -0,0 +1,164 @@
+import time
+import wave
+import os
+import sys
+import io
+from config.logger import setup_logging
+from typing import Optional, Tuple, List
+import uuid
+import opuslib_next
+from core.providers.asr.base import ASRProviderBase
+
+import numpy as np
+import sherpa_onnx
+
+from modelscope.hub.file_download import model_file_download
+
+TAG = __name__
+logger = setup_logging()
+
+
+# 捕获标准输出
+class CaptureOutput:
+ def __enter__(self):
+ self._output = io.StringIO()
+ self._original_stdout = sys.stdout
+ sys.stdout = self._output
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ sys.stdout = self._original_stdout
+ self.output = self._output.getvalue()
+ self._output.close()
+
+ # 将捕获到的内容通过 logger 输出
+ if self.output:
+ logger.bind(tag=TAG).info(self.output.strip())
+
+
+class ASRProvider(ASRProviderBase):
+ def __init__(self, config: dict, delete_audio_file: bool):
+ self.model_dir = config.get("model_dir")
+ self.output_dir = config.get("output_dir")
+ self.delete_audio_file = delete_audio_file
+
+ # 确保输出目录存在
+ os.makedirs(self.output_dir, exist_ok=True)
+
+ # 初始化模型文件路径
+ model_files = {
+ "model.int8.onnx": os.path.join(self.model_dir, "model.int8.onnx"),
+ "tokens.txt": os.path.join(self.model_dir, "tokens.txt")
+ }
+
+ # 下载并检查模型文件
+ try:
+ for file_name, file_path in model_files.items():
+ if not os.path.isfile(file_path):
+ logger.bind(tag=TAG).info(f"正在下载模型文件: {file_name}")
+ model_file_download(
+ model_id="pengzhendong/sherpa-onnx-sense-voice-zh-en-ja-ko-yue",
+ file_path=file_name,
+ local_dir=self.model_dir
+ )
+
+ if not os.path.isfile(file_path):
+ raise FileNotFoundError(f"模型文件下载失败: {file_path}")
+
+ self.model_path = model_files["model.int8.onnx"]
+ self.tokens_path = model_files["tokens.txt"]
+
+ except Exception as e:
+ logger.bind(tag=TAG).error(f"模型文件处理失败: {str(e)}")
+ raise
+
+ with CaptureOutput():
+ self.model = sherpa_onnx.OfflineRecognizer.from_sense_voice(
+ model=self.model_path,
+ tokens=self.tokens_path,
+ num_threads=2,
+ sample_rate=16000,
+ feature_dim=80,
+ decoding_method="greedy_search",
+ debug=False,
+ use_itn=True,
+ )
+
+ def save_audio_to_file(self, opus_data: List[bytes], session_id: str) -> str:
+ """将Opus音频数据解码并保存为WAV文件"""
+ file_name = f"asr_{session_id}_{uuid.uuid4()}.wav"
+ file_path = os.path.join(self.output_dir, file_name)
+
+ decoder = opuslib_next.Decoder(16000, 1) # 16kHz, 单声道
+ pcm_data = []
+
+ for opus_packet in opus_data:
+ try:
+ pcm_frame = decoder.decode(opus_packet, 960) # 960 samples = 60ms
+ pcm_data.append(pcm_frame)
+ except opuslib_next.OpusError as e:
+ logger.bind(tag=TAG).error(f"Opus解码错误: {e}", exc_info=True)
+
+ with wave.open(file_path, "wb") as wf:
+ wf.setnchannels(1)
+ wf.setsampwidth(2) # 2 bytes = 16-bit
+ wf.setframerate(16000)
+ wf.writeframes(b"".join(pcm_data))
+
+ return file_path
+
+ def read_wave(self, wave_filename: str) -> Tuple[np.ndarray, int]:
+ """
+ Args:
+ wave_filename:
+ Path to a wave file. It should be single channel and each sample should
+ be 16-bit. Its sample rate does not need to be 16kHz.
+ Returns:
+ Return a tuple containing:
+ - A 1-D array of dtype np.float32 containing the samples, which are
+ normalized to the range [-1, 1].
+ - sample rate of the wave file
+ """
+
+ with wave.open(wave_filename) as f:
+ assert f.getnchannels() == 1, f.getnchannels()
+ assert f.getsampwidth() == 2, f.getsampwidth() # it is in bytes
+ num_samples = f.getnframes()
+ samples = f.readframes(num_samples)
+ samples_int16 = np.frombuffer(samples, dtype=np.int16)
+ samples_float32 = samples_int16.astype(np.float32)
+
+ samples_float32 = samples_float32 / 32768
+ return samples_float32, f.getframerate()
+
+ async def speech_to_text(self, opus_data: List[bytes], session_id: str) -> Tuple[Optional[str], Optional[str]]:
+ """语音转文本主处理逻辑"""
+ file_path = None
+ try:
+ # 保存音频文件
+ start_time = time.time()
+ file_path = self.save_audio_to_file(opus_data, session_id)
+ logger.bind(tag=TAG).debug(f"音频文件保存耗时: {time.time() - start_time:.3f}s | 路径: {file_path}")
+
+ # 语音识别
+ start_time = time.time()
+ s = self.model.create_stream()
+ samples, sample_rate = self.read_wave(file_path)
+ s.accept_waveform(sample_rate, samples)
+ self.model.decode_stream(s)
+ text = s.result.text
+ logger.bind(tag=TAG).debug(f"语音识别耗时: {time.time() - start_time:.3f}s | 结果: {text}")
+
+ return text, file_path
+
+ except Exception as e:
+ logger.bind(tag=TAG).error(f"语音识别失败: {e}", exc_info=True)
+ return "", None
+
+ finally:
+ # 文件清理逻辑
+ if self.delete_audio_file and file_path and os.path.exists(file_path):
+ try:
+ os.remove(file_path)
+ logger.bind(tag=TAG).debug(f"已删除临时音频文件: {file_path}")
+ except Exception as e:
+ logger.bind(tag=TAG).error(f"文件删除失败: {file_path} | 错误: {e}")
diff --git a/main/xiaozhi-server/core/providers/llm/dify/dify.py b/main/xiaozhi-server/core/providers/llm/dify/dify.py
index f3c62639..5300fc79 100644
--- a/main/xiaozhi-server/core/providers/llm/dify/dify.py
+++ b/main/xiaozhi-server/core/providers/llm/dify/dify.py
@@ -9,6 +9,7 @@ logger = setup_logging()
class LLMProvider(LLMProviderBase):
def __init__(self, config):
self.api_key = config["api_key"]
+ self.mode = config.get("mode", "chat-messages")
self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip('/')
self.session_conversation_map = {} # 存储session_id和conversation_id的映射
@@ -19,27 +20,59 @@ class LLMProvider(LLMProviderBase):
conversation_id = self.session_conversation_map.get(session_id)
# 发起流式请求
- with requests.post(
- f"{self.base_url}/chat-messages",
- headers={"Authorization": f"Bearer {self.api_key}"},
- json={
+ if self.mode == "chat-messages":
+ request_json = {
"query": last_msg["content"],
"response_mode": "streaming",
"user": session_id,
"inputs": {},
"conversation_id": conversation_id
- },
+ }
+ elif self.mode == "workflows/run":
+ request_json = {
+ "inputs": {"query": last_msg["content"]},
+ "response_mode": "streaming",
+ "user": session_id
+ }
+ elif self.mode == "completion-messages":
+ request_json = {
+ "inputs": {"query": last_msg["content"]},
+ "response_mode": "streaming",
+ "user": session_id
+ }
+
+ with requests.post(
+ f"{self.base_url}/{self.mode}",
+ headers={"Authorization": f"Bearer {self.api_key}"},
+ json=request_json,
stream=True
) as r:
- for line in r.iter_lines():
- if line.startswith(b'data: '):
- event = json.loads(line[6:])
- # 如果没有找到conversation_id，则获取此次conversation_id
- if not conversation_id:
- conversation_id = event.get('conversation_id')
- self.session_conversation_map[session_id] = conversation_id # 更新映射
- if event.get('answer'):
- yield event['answer']
+ if self.mode == "chat-messages":
+ for line in r.iter_lines():
+ if line.startswith(b'data: '):
+ event = json.loads(line[6:])
+ # 如果没有找到conversation_id，则获取此次conversation_id
+ if not conversation_id:
+ conversation_id = event.get('conversation_id')
+ self.session_conversation_map[session_id] = conversation_id # 更新映射
+ if event.get('answer'):
+ yield event['answer']
+ elif self.mode == "workflows/run":
+ for line in r.iter_lines():
+ # logger.bind(tag=TAG).info(f"chat message response: {line}")
+ if line.startswith(b'data: '):
+ event = json.loads(line[6:])
+ if event.get('event') == "workflow_finished":
+ if event['data']['status'] == "succeeded":
+ yield event['data']['outputs']['answer']
+ else:
+ yield "【服务响应异常】"
+ elif self.mode == "completion-messages":
+ for line in r.iter_lines():
+ if line.startswith(b'data: '):
+ event = json.loads(line[6:])
+ if event.get('answer'):
+ yield event['answer']
except Exception as e:
logger.bind(tag=TAG).error(f"Error in response generation: {e}")
diff --git a/main/xiaozhi-server/core/providers/llm/gemini/gemini.py b/main/xiaozhi-server/core/providers/llm/gemini/gemini.py
index f753ac3e..93c7dcb5 100644
--- a/main/xiaozhi-server/core/providers/llm/gemini/gemini.py
+++ b/main/xiaozhi-server/core/providers/llm/gemini/gemini.py
@@ -1,14 +1,19 @@
import google.generativeai as genai
from core.utils.util import check_model_key
from core.providers.llm.base import LLMProviderBase
-
+from config.logger import setup_logging
+import requests
+import json
+TAG = __name__
+logger = setup_logging()
class LLMProvider(LLMProviderBase):
def __init__(self, config):
"""初始化Gemini LLM Provider"""
self.model_name = config.get("model_name", "gemini-1.5-pro")
self.api_key = config.get("api_key")
-
+ self.http_proxy=config.get("http_proxy")
+ self.https_proxy = config.get("https_proxy")
have_key = check_model_key("LLM", self.api_key)
if not have_key:
@@ -16,6 +21,19 @@ class LLMProvider(LLMProviderBase):
try:
# 初始化Gemini客户端
+ # 配置代理（如果提供了代理配置）
+ self.proxies=None
+ if self.http_proxy is not "" or self.https_proxy is not "":
+
+ self.proxies = {
+ "http": self.http_proxy,
+ "https": self.https_proxy,
+ }
+ logger.bind(tag=TAG).info(f"Gemini set proxys:{self.proxies}")
+ # 使用猴子补丁修改 google-generativeai 库的请求会话
+
+ # 使用 session 对象配置 genai
+
genai.configure(api_key=self.api_key)
self.model = genai.GenerativeModel(self.model_name)
@@ -46,26 +64,54 @@ class LLMProvider(LLMProviderBase):
if content:
chat_history.append({
"role": role,
- "parts": [content]
+ "parts": [{"text":content}]
+
})
# 获取当前消息
current_msg = dialogue[-1]["content"]
- # 创建新的聊天会话
- chat = self.model.start_chat(history=chat_history)
+ # 构建请求体
+ request_body = {
+ "contents": chat_history + [{"role": "user", "parts": [{"text":current_msg}]}],
+ "generationConfig": self.generation_config
+ }
- # 发送消息并获取流式响应
- response = chat.send_message(
- current_msg,
- stream=True,
- generation_config=self.generation_config
- )
+ # 构建请求URL
+ url = f"https://generativelanguage.googleapis.com/v1beta/models/{self.model_name}:generateContent?key={self.api_key}"
- # 处理流式响应
- for chunk in response:
- if hasattr(chunk, 'text') and chunk.text:
- yield chunk.text
+ # 构建请求头
+ headers = {
+ "Content-Type": "application/json",
+ }
+
+ # 发送POST请求,经测试手动 request 无法使用 stream 模式
+ if self.proxies:
+ response = requests.post(url, headers=headers, json=request_body, stream=False, proxies=self.proxies)
+ try:
+ data = response.json() # 直接解析JSON
+ if 'candidates' in data and data['candidates']:
+ yield data['candidates'][0]['content']['parts'][0]['text']
+ else:
+ yield "未找到候选回复。"
+ except json.JSONDecodeError as e:
+ yield f"JSON解码错误：{e}"
+ except Exception as e:
+ yield f"发生错误：{e}"
+ else:
+ logger.bind(tag=TAG).info(f"Gemini stream mode ")
+ chat = self.model.start_chat(history=chat_history)
+
+ # 发送消息并获取流式响应
+ response = chat.send_message(
+ current_msg,
+ stream=True,
+ generation_config=self.generation_config
+ )
+ # 处理流式响应
+ for chunk in response:
+ if hasattr(chunk, 'text') and chunk.text:
+ yield chunk.text
except Exception as e:
error_msg = str(e)
@@ -78,3 +124,13 @@ class LLMProvider(LLMProviderBase):
yield "【Gemini API key无效】"
else:
yield f"【Gemini服务响应异常: {error_msg}】"
+
+
+
+
+ except requests.exceptions.RequestException as e:
+ yield f"请求失败：{e}"
+ except json.JSONDecodeError as e:
+ yield f"JSON解码错误：{e}"
+ except Exception as e:
+ yield f"发生错误：{e}"
diff --git a/main/xiaozhi-server/core/providers/llm/openai/openai.py b/main/xiaozhi-server/core/providers/llm/openai/openai.py
index 0cce1da6..b1a9a24e 100644
--- a/main/xiaozhi-server/core/providers/llm/openai/openai.py
+++ b/main/xiaozhi-server/core/providers/llm/openai/openai.py
@@ -1,8 +1,11 @@
import openai
+from config.logger import setup_logging
from core.utils.util import check_model_key
from core.providers.llm.base import LLMProviderBase
TAG = __name__
+logger = setup_logging()
+
class LLMProvider(LLMProviderBase):
def __init__(self, config):
@@ -12,6 +15,8 @@ class LLMProvider(LLMProviderBase):
self.base_url = config.get("base_url")
else:
self.base_url = config.get("url")
+ self.max_tokens = config.get("max_tokens", 500)
+
check_model_key("LLM", self.api_key)
self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
@@ -20,9 +25,10 @@ class LLMProvider(LLMProviderBase):
responses = self.client.chat.completions.create(
model=self.model_name,
messages=dialogue,
- stream=True
+ stream=True,
+ max_tokens=self.max_tokens,
)
-
+
is_active = True
for chunk in responses:
try:
@@ -43,7 +49,7 @@ class LLMProvider(LLMProviderBase):
yield content
except Exception as e:
- self.logger.bind(tag=TAG).error(f"Error in response generation: {e}")
+ logger.bind(tag=TAG).error(f"Error in response generation: {e}")
def response_with_functions(self, session_id, dialogue, functions=None):
try:
@@ -51,12 +57,12 @@ class LLMProvider(LLMProviderBase):
model=self.model_name,
messages=dialogue,
stream=True,
- tools=functions,
+ tools=functions
)
-
+
for chunk in stream:
yield chunk.choices[0].delta.content, chunk.choices[0].delta.tool_calls
-
+
except Exception as e:
self.logger.bind(tag=TAG).error(f"Error in function call streaming: {e}")
- yield {"type": "content", "content": f"【OpenAI服务响应异常: {e}】"}
\ No newline at end of file
+ yield {"type": "content", "content": f"【OpenAI服务响应异常: {e}】"}
diff --git a/main/xiaozhi-server/requirements.txt b/main/xiaozhi-server/requirements.txt
index 81d0cdfd..6afec6b5 100755
--- a/main/xiaozhi-server/requirements.txt
+++ b/main/xiaozhi-server/requirements.txt
@@ -20,3 +20,5 @@ requests==2.32.3
cozepy==0.12.0
mem0ai==0.1.62
bs4==0.0.2
+modelscope==1.23.2
+sherpa_onnx==1.11.0