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lizhongxiang
2025-03-20 10:14:49 +08:00
parent b153bc11f2 27df97d710
commit 1136ca4b24
14 changed files with 473 additions and 79 deletions
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.gitignore
+4 -1
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@@ -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/
README.md
+3
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@@ -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
README_en.md
+1
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@@ -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 | |
---
main/manager-web/src/apis/module/user.js
+47 -14
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@@ -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(`${getServiceUrl()}/api/v1/user/agent`)
.method('POST')
.data({ name: agentName })
.success((res) => {
RequestService.clearRequestTime();
callback(res);
})
.fail(() => {
RequestService.reAjaxFun(() => {
this.addAgent(agentName, callback);
});
}).send();
},
}
main/manager-web/src/components/AddWisdomBodyDialog.vue
+13 -3
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@@ -29,6 +29,9 @@
</template>
<script>
import userApi from '@/apis/module/user';
export default {
name: 'AddWisdomBodyDialog',
props: {
@@ -39,9 +42,16 @@ export default {
},
methods: {
confirm() {
this.$emit('update:visible', false)
this.$emit('confirmed', this.wisdomBodyName)
this.wisdomBodyName = ""
if (!this.wisdomBodyName.trim()) {
this.$message.error('请输入智慧体名称');
return;
}
userApi.addAgent(this.wisdomBodyName, (res) => {
this.$message.success('添加成功');
this.$emit('confirm', res);
this.$emit('update:visible', false);
this.wisdomBodyName = "";
});
},
cancel() {
this.$emit('update:visible', false)
main/manager-web/src/components/DeviceItem.vue
+6 -6
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@@ -1,8 +1,8 @@
<template>
<div class="device-item">
<div style="display: flex;justify-content: space-between;">
<div style="font-weight: 700;font-size: 24px;text-align: left;color: #3d4566;">
{{ device.mac }}
<div style="font-weight: 700;font-size: 18px;text-align: left;color: #3d4566;">
{{ device.agentName }}
</div>
<div>
<img src="@/assets/home/delete.png" alt=""
@@ -11,10 +11,10 @@
</div>
</div>
<div class="device-name">
设备型号：{{ device.model }}
设备型号：{{ device.ttsModelName }}
</div>
<div class="device-name">
音色模型：{{ device.voiceModel }}
音色模型：{{ device.ttsVoiceName }}
</div>
<div style="display: flex;gap: 10px;align-items: center;">
<div class="settings-btn" @click="$emit('configure')">
@@ -31,7 +31,7 @@
</div>
</div>
<div class="version-info">
<div>最近对话：{{ device.lastConversation }}</div>
<div>最近对话：{{ device.lastConnectedAt }}</div>
</div>
</div>
</template>
@@ -58,7 +58,7 @@ export default {
.device-name {
margin: 7px 0 10px;
font-weight: 400;
font-size: 10px;
font-size: 11px;
color: #3d4566;
text-align: left;
}
main/manager-web/src/components/HeaderBar.vue
+44 -4
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@@ -19,13 +19,13 @@
</div>
<div style="display: flex;align-items: center;gap: 7px; margin-top: 2px;">
<div class="serach-box">
<el-input placeholder="输入名称搜索.." v-model="serach" style="border: none; background: transparent;" />
<el-input placeholder="输入名称搜索.." v-model="serach" style="border: none; background: transparent;" @keyup.enter.native="handleSearch" />
<img src="@/assets/home/search.png" alt=""
style="width: 14px;height: 14px;margin-right: 11px;cursor: pointer;" />
style="width: 14px;height: 14px;margin-right: 11px;cursor: pointer;" @click="handleSearch" />
</div>
<img src="@/assets/home/avatar.png" alt="" style="width: 21px;height: 21px;" />
<div class="user-info">
158 3632 4642
{{ userInfo.mobile || '加载中...' }}
</div>
</div>
</div>
@@ -33,17 +33,57 @@
</template>
<script>
import userApi from '@/apis/module/user'
export default {
name: 'HeaderBar',
props: ['devices'], // 接收父组件设备列表
data() {
return { serach: '' }
return {
serach: '',
userInfo: {
mobile: ''
}
}
},
mounted() {
this.fetchUserInfo()
},
methods: {
goHome() {
// 跳转到首页
this.$router.push('/')
},
// 获取用户信息
fetchUserInfo() {
userApi.getUserInfo(({data}) => {
this.userInfo = data.data
})
},
// 处理搜索
handleSearch() {
const searchValue = this.serach.trim();
let filteredDevices;
if (!searchValue) {
// 当搜索内容为空时，显示原始完整列表
filteredDevices = this.$parent.originalDevices;
} else {
// 过滤逻辑
filteredDevices = this.devices.filter(device => {
return device.agentName.includes(searchValue) ||
device.ttsModelName.includes(searchValue) ||
device.ttsVoiceName.includes(searchValue);
});
}
this.$emit('search-result', filteredDevices);
}
}
}
</script>
main/manager-web/src/views/home.vue
+28 -13
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@@ -1,7 +1,7 @@
<template>
<div class="welcome">
<!-- 公共头部 -->
<HeaderBar />
<HeaderBar :devices="devices" @search-result="handleSearchResult" />
<el-main style="padding: 20px;display: flex;flex-direction: column;">
<div>
<!-- 首页内容 -->
@@ -30,7 +30,7 @@
</div>
</div>
</div>
<div style="display: flex;flex-wrap: wrap;margin-top: 20px;gap: 20px;justify-content: space-between;box-sizing: border-box;">
<div style="display: flex;flex-wrap: wrap;margin-top: 20px;gap: 20px;justify-content: flex-start;box-sizing: border-box;">
<DeviceItem v-for="(item,index) in devices" :key="index" :device="item" @configure="goToRoleConfig" @deviceManage="handleDeviceManage" />
</div>
</div>
@@ -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
}
}
}
</script>
main/xiaozhi-server/config.yaml
+30 -2
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@@ -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
main/xiaozhi-server/core/providers/asr/sherpa_onnx_local.py
+164
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@@ -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}")
main/xiaozhi-server/core/providers/llm/dify/dify.py
+47 -14
View File
@@ -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}")
main/xiaozhi-server/core/providers/llm/gemini/gemini.py
+71 -15
View File
@@ -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}"
main/xiaozhi-server/core/providers/llm/openai/openai.py
+13 -7
View File
@@ -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}】"}
yield {"type": "content", "content": f"【OpenAI服务响应异常: {e}】"}
main/xiaozhi-server/requirements.txt
+2
View File
@@ -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