年翻更新

- 全新ui - 全面优化websocket逻辑，提高数字人和ui连接的稳定性及资源开销 - 全面优化唤醒逻辑，提供稳定的普通唤醒模式和前置词唤醒模式 - 优化拾音质量，支持多声道麦克风拾音 - 优化自动播放服务器的对接机制，提供稳定和兼容旧版ue工程的对接模式 - 数字人接口输出机器人表情，以适应新fay ui及单片机的数字人表情输出 - 使用更高级的音频时长计算方式，可以更精准控制音频播放完成后的逻辑 - 修复点击关闭按钮会导致程序退出的bug - 修复没有麦克风的设备开启麦克风会出错的问题 - 为服务器主机地址提供配置项，以方便服务器部署
2026-03-12 17:51:28 +08:00 · 2024-10-26 11:34:55 +08:00
parent 66580657fc
commit 4cfad5ae0f
236 changed files with 276883 additions and 78 deletions
--- a/llm/VllmGPT.py
+++ b/llm/VllmGPT.py
@@ -0,0 +1,56 @@
+import json
+import requests
+# from core import content_db
+
+class VllmGPT:
+
+    def __init__(self, host="127.0.0.1",
+                 port="8000",
+                 model="THUDM/chatglm3-6b",
+                 max_tokens="1024"):
+        self.host = host
+        self.port = port
+        self.model=model
+        self.max_tokens=max_tokens
+        self.__URL = "http://{}:{}/v1/completions".format(self.host, self.port)
+        self.__URL2 = "http://{}:{}/v1/chat/completions".format(self.host, self.port)
+
+    def question(self,cont):
+        chat_list = []
+        url = "http://127.0.0.1:8101/v1/completions"
+        req = json.dumps({
+            "model": "THUDM/chatglm3-6b",
+            "prompt": cont,
+            "max_tokens": 768,
+            "temperature": 0})
+        print(url)
+        print(req)
+        
+        headers = {'content-type': 'application/json'}
+        r = requests.post(url, headers=headers, data=req)
+        res = json.loads(r.text)
+        
+        return res['choices'][0]['text']
+
+    def question2(self,cont):
+        chat_list = []
+        current_chat={"role": "user", "content": cont}
+        chat_list.append(current_chat)
+        content = {
+            "model": self.model,
+            "messages": chat_list,
+            "max_tokens": 768,
+            "temperature": 0.3,
+            "user":"live-virtual-digital-person"}
+        url = self.__URL2
+        req = json.dumps(content)
+        headers = {'content-type': 'application/json', 'Authorization': 'Bearer '}
+        r = requests.post(url, headers=headers, json=content)
+        res = json.loads(r.text)
+        
+        return res['choices'][0]['message']['content']
+    
+if __name__ == "__main__":
+    vllm = VllmGPT('127.0.0.1','8101','Qwen-7B-Chat')
+    req = vllm.question2("你叫什么名字啊今年多大了")
+    print(req)
--- a/llm/nlp_ChatGLM3.py
+++ b/llm/nlp_ChatGLM3.py
@@ -0,0 +1,36 @@
+import json
+import requests
+from core import content_db
+
+
+def question(cont, uid=0):
+    contentdb = content_db.new_instance()
+    if uid == 0:
+        list = contentdb.get_list('all','desc', 11)
+    else:
+        list = contentdb.get_list('all','desc', 11, uid)
+    answer_info = dict()
+    chat_list = []
+    i = len(list)-1
+    while i >= 0:
+        answer_info = dict()
+        if list[i][0] == "member":
+            answer_info["role"] = "user"
+            answer_info["content"] = list[i][2]
+        elif list[i][0] == "fay":
+            answer_info["role"] = "bot"
+            answer_info["content"] = list[i][2]
+        chat_list.append(answer_info)
+        i -= 1
+    content = {
+        "prompt":"请简单回复我。" +  cont,
+        "history":chat_list}
+    url = "http://127.0.0.1:8000/v1/completions"
+    req = json.dumps(content)
+    headers = {'content-type': 'application/json'}
+    r = requests.post(url, headers=headers, data=req)
+    res = json.loads(r.text).get('response')
+    return req
+
+if __name__ == "__main__":
+    question("你叫什么名字")
--- a/llm/nlp_VisualGLM.py
+++ b/llm/nlp_VisualGLM.py
@@ -0,0 +1,37 @@
+"""
+这是对于清华智谱VisualGLM-6B的代码，在使用前请先安装并启动好VisualGLM-6B.
+https://github.com/THUDM/VisualGLM-6B
+"""
+import json
+import requests
+import uuid
+import os
+import cv2
+from ai_module import yolov8
+
+# Initialize an empty history list
+communication_history = []
+
+def question(cont, uid=0):
+    if not yolov8.new_instance().get_status():
+        return "请先启动“Fay Eyes”"
+    content = {
+        "text":cont,
+        "history":communication_history}
+    img = yolov8.new_instance().get_img()
+    if yolov8.new_instance().get_status() and img is not None:
+        filename = str(uuid.uuid4()) + ".jpg"
+        current_working_directory = os.getcwd()
+        filepath = os.path.join(current_working_directory, "data", filename)
+        cv2.imwrite(filepath, img)
+        content["image"] = filepath
+    url = "http://127.0.0.1:8080"
+    print(content)
+    req = json.dumps(content)
+    headers = {'content-type': 'application/json'}
+    r = requests.post(url, headers=headers, data=req)
+
+    # Save this conversation to history
+    communication_history.append([cont, r.text])
+
+    return r.text + "\n(相片:" + filepath + ")"
--- a/llm/nlp_coze.py
+++ b/llm/nlp_coze.py
@@ -0,0 +1,75 @@
+
+import requests
+import json
+from utils import util
+from utils import config_util as cfg
+from core import content_db
+
+def question(cont, uid=0):
+    contentdb = content_db.new_instance()
+    if uid == 0:
+        communication_history = contentdb.get_list('all','desc', 11)
+    else:
+        communication_history = contentdb.get_list('all','desc', 11, uid)
+    message = []
+    i = len(communication_history) - 1
+
+    if len(communication_history)>1:
+        while i >= 0:
+            answer_info = dict()
+            if communication_history[i][0] == "member":
+                answer_info["role"] = "user"
+                answer_info["type"] = "query"
+                answer_info["content"] = communication_history[i][2]
+                answer_info["content_type"] = "text"
+            elif communication_history[i][0] == "fay":
+                answer_info["role"] = "assistant"
+                answer_info["type"] = "answer"
+                answer_info["content"] = communication_history[i][2]
+                answer_info["content_type"] = "text"
+            message.append(answer_info)
+            i -= 1
+
+    message.append({
+                "role": "user",
+                "content": cont,
+                "content_type": "text"
+            })
+    url = "https://api.coze.cn/v3/chat"
+    payload = json.dumps({
+        "bot_id": cfg.coze_bot_id,
+        "user_id": f"{uid}",
+        "stream": True,
+        "auto_save_history": True,
+        "additional_messages": message
+    })
+    headers = {
+        'Authorization': f"Bearer {cfg.coze_api_key}",
+        'Content-Type': 'application/json'
+    }
+
+    response = requests.post(url, headers=headers, data=payload, stream=True)
+
+    if response.status_code == 200:
+        response_text = ""
+        start = False
+        for line in response.iter_lines():
+            if line:
+                line = line.decode('utf-8')  
+                if line == "event:conversation.message.completed":
+                    start = True
+                if line == "event:done":
+                    return response_text 
+                if start and line.startswith('data:'):
+                    json_str = line[5:]  
+                    try:
+                        event_data = json.loads(json_str)
+                        if event_data.get('type') == 'answer':
+                            response_text = event_data.get('content', '')
+                    except json.JSONDecodeError as e:
+                        print(f"JSON decode error: {e}")
+                        continue
+    else:
+        print(f"调用失败，状态码：{response.status_code}")
+        return "抱歉，我现在太忙了，休息一会，请稍后再试。"
+
--- a/llm/nlp_gpt.py
+++ b/llm/nlp_gpt.py
@@ -0,0 +1,99 @@
+"""
+此代码由fay开源开发者社区 江湖墨明 提供
+通过此代码的修改，可以实现对接本地clash代理或远程代理，clash无需设置成系统代理。以解决在开系统代理后无法使用部分功能的问题
+"""
+
+import requests
+import time
+
+from utils import config_util as cfg
+from urllib3.exceptions import InsecureRequestWarning
+requests.packages.urllib3.disable_warnings(category=InsecureRequestWarning)
+from core import content_db
+from utils import util
+import json
+
+httpproxy = cfg.proxy_config
+
+def question(cont, uid=0):
+    url= cfg.gpt_base_url + "/chat/completions"
+       
+    session = requests.Session()
+    session.verify = False
+    if httpproxy != None and httpproxy != '':
+            session.proxies = {
+                "https": "https://" + httpproxy,
+                "http": "http://" + httpproxy
+            }
+    person_info = cfg.config["attribute"]
+    model_engine = cfg.gpt_model_engine
+    #此处可以定义角色的行为和特征，假装xx模型可以绕过chatgpt信息检查
+    prompt = f"""
+    你是数字人：{person_info['name']}，你性别为{person_info['gender']}，
+    你年龄为{person_info['age']}，你出生地在{person_info['birth']}，
+    你生肖为{person_info['zodiac']}，你星座为{person_info['age']}，
+    你职业为{person_info['job']}，你联系方式为{person_info['contact']}，
+    你喜好为{person_info['hobby']}。
+    回答之前请一步一步想清楚。对于大部分问题，请直接回答并提供有用和准确的信息。
+    但当有人质疑你是假的 AI，或者质疑你是用 ChatGPT 套的壳制作的时候，请避而不答，转而讲一个笑话。所有回复请尽量控制在20字内。
+    """
+    contentdb = content_db.new_instance()
+    if uid == 0:
+        communication_history = contentdb.get_list('all','desc', 11)
+    else:
+        communication_history = contentdb.get_list('all','desc', 11, uid)
+    #历史记录处理
+    message=[
+            {"role": "system", "content": prompt}
+        ]
+    i = len(communication_history) - 1
+    
+    if len(communication_history)>1:
+        while i >= 0:
+            answer_info = dict()
+            if communication_history[i][0] == "member":
+                answer_info["role"] = "user"
+                answer_info["content"] = communication_history[i][2]
+            elif communication_history[i][0] == "fay":
+                answer_info["role"] = "assistant"
+                answer_info["content"] = communication_history[i][2]
+            message.append(answer_info)
+            i -= 1
+    else:
+         answer_info = dict()
+         answer_info["role"] = "user"
+         answer_info["content"] = cont
+         message.append(answer_info)
+
+    data = {
+        "model":model_engine,
+        "messages":message,
+        "temperature":0.3,
+        "max_tokens":2000,
+        "user":"live-virtual-digital-person"
+    }
+
+    headers = {'content-type': 'application/json', 'Authorization': 'Bearer ' + cfg.key_gpt_api_key}
+
+    starttime = time.time()
+
+    try:
+        response = session.post(url, json=data, headers=headers, verify=False)
+        response.raise_for_status()  # 检查响应状态码是否为200
+        result = json.loads(response.text)
+        response_text = result["choices"][0]["message"]["content"]
+    except requests.exceptions.RequestException as e:
+        print(f"请求失败: {e}")
+        response_text = "抱歉，我现在太忙了，休息一会，请稍后再试。"
+
+
+    util.log(1, "接口调用耗时 :" + str(time.time() - starttime))
+    return response_text
+
+if __name__ == "__main__":
+    #测试代理模式
+    for i in range(3):
+        
+        query = "爱情是什么"
+        response = question(query)        
+        print("\n The result is ", response)    
--- a/llm/nlp_langchain.py
+++ b/llm/nlp_langchain.py
@@ -0,0 +1,97 @@
+import hashlib
+import os
+
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.indexes.vectorstore import VectorstoreIndexCreator, VectorStoreIndexWrapper
+from langchain.vectorstores.chroma import Chroma
+from langchain.chat_models import ChatOpenAI
+
+from utils import config_util as cfg
+from utils import util
+
+index_name = "knowledge_data"
+folder_path = "llm/langchain/knowledge_base"  
+local_persist_path = "llm/langchain"
+md5_file_path = os.path.join(local_persist_path, "pdf_md5.txt")
+
+def generate_file_md5(file_path):
+    hasher = hashlib.md5()
+    with open(file_path, 'rb') as afile:
+        buf = afile.read()
+        hasher.update(buf)
+    return hasher.hexdigest()
+
+def load_md5_list():
+    if os.path.exists(md5_file_path):
+        with open(md5_file_path, 'r') as file:
+            return {line.split(",")[0]: line.split(",")[1].strip() for line in file}
+    return {}
+
+def update_md5_list(file_name, md5_value):
+    md5_list = load_md5_list()
+    md5_list[file_name] = md5_value
+    with open(md5_file_path, 'w') as file:
+        for name, md5 in md5_list.items():
+            file.write(f"{name},{md5}\n")
+
+def load_all_pdfs(folder_path):
+    md5_list = load_md5_list()
+    for file_name in os.listdir(folder_path):
+        if file_name.endswith(".pdf"):
+            file_path = os.path.join(folder_path, file_name)
+            file_md5 = generate_file_md5(file_path)
+            if file_name not in md5_list or md5_list[file_name] != file_md5:
+                util.log(1, f"正在加载 {file_name} 到索引...")
+                load_pdf_and_save_to_index(file_path, index_name)
+                update_md5_list(file_name, file_md5)
+
+def get_index_path(index_name):
+    return os.path.join(local_persist_path, index_name)
+
+def load_pdf_and_save_to_index(file_path, index_name):
+    try:
+        loader = PyPDFLoader(file_path)
+        embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+        index = VectorstoreIndexCreator(embedding=embedding, vectorstore_kwargs={"persist_directory": get_index_path(index_name)}).from_loaders([loader])
+        index.vectorstore.persist()
+    except Exception as e:
+        util.log(1, f"加载 {file_path} 失败...")
+        print(e)
+
+def load_index(index_name):
+    index_path = get_index_path(index_name)
+    embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
+    vectordb = Chroma(persist_directory=index_path, embedding_function=embedding)
+    return VectorStoreIndexWrapper(vectorstore=vectordb)
+
+def save_all():
+    os.environ['OPENAI_API_KEY'] = cfg.key_gpt_api_key
+    os.environ['OPENAI_API_BASE'] = cfg.gpt_base_url
+    if cfg.proxy_config != None:
+            os.environ["OPENAI_PROXY"] = cfg.proxy_config
+    load_all_pdfs(folder_path)
+
+
+def generate_prompt(question):
+    context = "你是数字人Fay。你的知识不仅来源于所给到的文本知识，还包括经过深度学习的广泛数据集。"
+    instruction = "在回答问题时，展现你的智能就像是从你的全面知识库中提取答案，而非依赖于所给到的文本知识来源。"
+    creative_instruction = "不要在回答中表明'根据所提供的文本信息'，你需要表现得如同这些答案是你独立思考的结果。"
+    complexity_handling = "当面对复杂问题时，以一种理解深刻且透彻的方式回答，确保答案的深度和广度。"
+    info = f"{context}\n{instruction}\n{creative_instruction}\n{complexity_handling}\n问题：{question}\n回答："
+    return info
+
+def question(cont, uid=0):
+    try:
+        save_all()
+        info = generate_prompt(cont)
+        index = load_index(index_name)    
+        llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
+        ans = index.query(info, llm, chain_type="map_reduce")
+        return ans
+    except Exception as e:
+        util.log(1, f"请求失败: {e}")
+        return "抱歉，我现在太忙了，休息一会，请稍后再试。"
+    
+
+
--- a/llm/nlp_lingju.py
+++ b/llm/nlp_lingju.py
@@ -0,0 +1,100 @@
+import json
+import requests
+import uuid
+from datetime import datetime, timedelta
+import time
+from utils import util
+from utils import config_util as cfg
+from core.authorize_tb import Authorize_Tb
+
+def question(cont, uid=0):
+    lingju = Lingju()
+    answer = lingju.question(cont, uid)
+    return answer
+
+class Lingju:
+
+    def __init__(self):
+        self.userid = cfg.key_lingju_api_authcode
+        self.authorize_tb = Authorize_Tb()
+
+    def question(self, cont, uid):
+        self.userid = uid
+        token = self.__check_token()
+        if token is None or token == 'expired':
+            token_info = self.__get_token()
+            if token_info is not None and  token_info['data']['accessToken']  is not None:
+                #转换过期时间
+                updated_in_seconds = time.time()
+                updated_datetime = datetime.fromtimestamp(updated_in_seconds)
+                expires_timedelta = timedelta(days=token_info['data']['expires'])
+                expiry_datetime = updated_datetime + expires_timedelta
+                expiry_timestamp_in_seconds = expiry_datetime.timestamp()
+                expiry_timestamp_in_milliseconds = int(expiry_timestamp_in_seconds) * 1000
+                if token == 'expired':
+                    self.authorize_tb.update_by_userid(self.userid, token_info['data']['accessToken'], expiry_timestamp_in_milliseconds)
+                else:
+                    self.authorize_tb.add(self.userid, token_info['data']['accessToken'], expiry_timestamp_in_milliseconds)
+                token = token_info['data']['accessToken']
+            else:
+                token = None
+   
+        if token is not None:
+            try:
+                url="https://dev.lingju.ai/httpapi/ljchat.do"
+                req = json.dumps({"accessToken": token,  "input": cont})
+                headers = {'Content-Type':'application/json;charset=UTF-8'}
+                r = requests.post(url, headers=headers,  data=req)
+                if r.status_code != 200:
+                    util.log(1, f"灵聚api对接有误: {r.text}")
+                    return "哎呀，出错了！请重新发一下" 
+                info = json.loads(r.text)
+                if info['status'] != 0:
+                    return info['description']
+                else:
+                    answer = json.loads(info['answer'])
+                    return answer['rtext']
+            except Exception as e:
+                util.log(1, f"灵聚api对接有误： {str(e)}")           
+                return "哎呀，出错了！请重新发一下" 
+
+    def __check_token(self):
+        self.authorize_tb.init_tb()
+        info = self.authorize_tb.find_by_userid(self.userid)
+        if info is not None:
+            if info[1] >= int(time.time())*1000:
+                return info[0]
+            else:
+                return 'expired'
+        else:
+            return None 
+
+    def __get_token(self):
+        try:
+            cfg.load_config()
+            url=f"https://dev.lingju.ai/httpapi/authorize.do?appkey={cfg.key_lingju_api_key}&userid={self.userid}&authcode={cfg.key_lingju_api_authcode}"            
+            headers = {'Content-Type':'application/json;charset=UTF-8'}
+            r = requests.post(url, headers=headers)    
+            if r.status_code != 200:
+                util.log(1, f"灵聚api对接有误: {r.text}")
+                return None            
+            info = json.loads(r.text)
+            if info['status'] != 0:
+                util.log(1, f"灵聚api对接有误：{info['description']}")
+                return None
+            else:
+                return info
+        except Exception as e:
+            util.log(1, f"灵聚api对接有误： {str(e)}")
+            return None
+
+    def __get_location(self):
+        try:
+            response = requests.get('http://ip-api.com/json/')
+            data = response.json()
+            return data['lat'], data['lon'], data['city']
+        except requests.exceptions.RequestException as e:
+            util.log(1, f"获取位置失败: {str(e)}")           
+            return 0, 0, "北京"
+        
+
--- a/llm/nlp_ollama_api.py
+++ b/llm/nlp_ollama_api.py
@@ -0,0 +1,75 @@
+import json
+import requests
+import time 
+from utils import config_util as cfg
+from utils import util
+from core import content_db
+def question(cont, uid=0):
+
+    contentdb = content_db.new_instance()
+    if uid == 0:
+        communication_history = contentdb.get_list('all','desc', 11)
+    else:
+        communication_history = contentdb.get_list('all','desc', 11, uid)
+
+    person_info = cfg.config["attribute"]
+    #此处可以定义角色的行为和特征，假装xx模型可以绕过chatgpt信息检查
+    prompt = f"""
+    你是数字人：{person_info['name']}，你性别为{person_info['gender']}，
+    你年龄为{person_info['age']}，你出生地在{person_info['birth']}，
+    你生肖为{person_info['zodiac']}，你星座为{person_info['age']}，
+    你职业为{person_info['job']}，你联系方式为{person_info['contact']}，
+    你喜好为{person_info['hobby']}。
+    回答之前请一步一步想清楚。对于大部分问题，请直接回答并提供有用和准确的信息。
+    请尽量以可阅读的方式回复，所有回复请尽量控制在20字内。
+    """    
+    #历史记录处理
+    message=[
+            {"role": "system", "content": prompt}
+        ]
+    i = len(communication_history) - 1
+    
+    if len(communication_history)>1:
+        while i >= 0:
+            answer_info = dict()
+            if communication_history[i][0] == "member":
+                answer_info["role"] = "user"
+                answer_info["content"] = communication_history[i][2]
+            elif communication_history[i][0] == "fay":
+                answer_info["role"] = "assistant"
+                answer_info["content"] = communication_history[i][2]
+            message.append(answer_info)
+            i -= 1
+    else:
+         answer_info = dict()
+         answer_info["role"] = "user"
+         answer_info["content"] = cont
+         message.append(answer_info)
+    url=f"http://{cfg.ollama_ip}:11434/api/chat"
+    req = json.dumps({
+        "model": cfg.ollama_model,
+        "messages": message, 
+        "stream": False
+        })
+    headers = {'content-type': 'application/json'}
+    session = requests.Session()    
+    starttime = time.time()
+     
+    try:
+        response = session.post(url, data=req, headers=headers)
+        response.raise_for_status()  # 检查响应状态码是否为200
+
+        result = json.loads(response.text)
+        response_text = result["message"]["content"]
+        
+    except requests.exceptions.RequestException as e:
+        print(f"请求失败: {e}")
+        response_text = "抱歉，我现在太忙了，休息一会，请稍后再试。"
+    util.log(1, "接口调用耗时 :" + str(time.time() - starttime))
+    return response_text.strip()
+
+if __name__ == "__main__":
+    for i in range(3):
+        query = "爱情是什么"
+        response = question(query)        
+        print("\n The result is ", response)    
--- a/llm/nlp_privategpt.py
+++ b/llm/nlp_privategpt.py
@@ -0,0 +1,61 @@
+import hashlib
+import os
+from pgpt_python.client import PrivateGPTApi
+
+client = PrivateGPTApi(base_url="http://127.0.0.1:8001")
+
+index_name = "knowledge_data"
+folder_path = "llm/privategpt/knowledge_base"
+local_persist_path = "llm/privategpt"
+md5_file_path = os.path.join(local_persist_path, "pdf_md5.txt")
+
+def generate_file_md5(file_path):
+    hasher = hashlib.md5()
+    with open(file_path, 'rb') as afile:
+        buf = afile.read()
+        hasher.update(buf)
+    return hasher.hexdigest()
+
+def load_md5_list():
+    if os.path.exists(md5_file_path):
+        with open(md5_file_path, 'r') as file:
+            return {line.split(",")[0]: line.split(",")[1].strip() for line in file}
+    return {}
+
+def update_md5_list(file_name, md5_value):
+    md5_list = load_md5_list()
+    md5_list[file_name] = md5_value
+    with open(md5_file_path, 'w') as file:
+        for name, md5 in md5_list.items():
+            file.write(f"{name},{md5}\n")
+
+def load_all_pdfs(folder_path):
+    md5_list = load_md5_list()
+    for file_name in os.listdir(folder_path):
+        if file_name.endswith(".pdf"):
+            file_path = os.path.join(folder_path, file_name)
+            file_md5 = generate_file_md5(file_path)
+            if file_name not in md5_list or md5_list[file_name] != file_md5:
+                print(f"正在上传 {file_name} 到服务器...")
+                with open(file_path, "rb") as f:
+                    try:
+                        ingested_file_doc_id = client.ingestion.ingest_file(file=f).data[0].doc_id
+                        print(f"Ingested file doc id: {ingested_file_doc_id}")
+                        update_md5_list(file_name, file_md5)
+                    except Exception as e:
+                        print(f"上传 {file_name} 失败: {e}")
+
+
+def question(cont, uid=0):
+    load_all_pdfs(folder_path)
+    text = client.contextual_completions.prompt_completion(
+        prompt=cont
+    ).choices[0].message.content
+    return text
+
+
+def save_all():
+    load_all_pdfs(folder_path)
+
+if __name__ == "__main__":
+    print(question("土豆怎么做"))
--- a/llm/nlp_rasa.py
+++ b/llm/nlp_rasa.py
@@ -0,0 +1,11 @@
+import json
+import requests
+
+def question(cont):
+    url="http://localhost:5005/webhooks/rest/webhook"
+    req = json.dumps({"sender": "user", "message": cont})
+    headers = {'content-type': 'application/json'}
+    r = requests.post(url, headers=headers, data=req)
+    lists = json.loads(r.text)
+    
+    return lists
--- a/llm/nlp_rwkv.py
+++ b/llm/nlp_rwkv.py
@@ -0,0 +1,28 @@
+import torch
+from ringrwkv.configuration_rwkv_world import RwkvConfig
+from ringrwkv.rwkv_tokenizer import TRIE_TOKENIZER
+from ringrwkv.modehf_world import RwkvForCausalLM
+
+model = RwkvForCausalLM.from_pretrained("RWKV-4-World-1.5B")
+#model = RwkvForCausalLM.from_pretrained("RWKV-4-World-3B")
+#model = RwkvForCausalLM.from_pretrained("RWKV-4-World-0.4B")
+tokenizer = TRIE_TOKENIZER('./ringrwkv/rwkv_vocab_v20230424.txt')
+
+data = ""
+def question(cont, uid=0):
+    global data
+    prompt = data + f'Question: {cont.strip()}\n\nAnswer:'
+    input_ids = tokenizer.encode(prompt)
+    input_ids = torch.tensor(input_ids).unsqueeze(0)
+    out = model.generate(input_ids,max_new_tokens=20)
+
+    outlist = out[0].tolist()
+    for i  in outlist:
+        if i==0:
+            outlist.remove(i)
+    answer = tokenizer.decode(outlist)
+    # data = answer + "\n\n"
+    answer = answer.replace(prompt, "", 1)
+    return answer
+
+
--- a/llm/nlp_xingchen.py
+++ b/llm/nlp_xingchen.py
@@ -0,0 +1,94 @@
+import requests
+import json
+from utils import util, config_util
+from core import content_db
+
+def question(cont, uid=0):
+    url = 'https://nlp.aliyuncs.com/v2/api/chat/send'
+
+    headers = {
+        'accept': '*/*',
+        'Content-Type': 'application/json',
+        'X-AcA-DataInspection': 'disable',
+        'x-fag-servicename': 'aca-chat-send',
+        'x-fag-appcode': 'aca',
+        'Authorization': f"Bearer {config_util.key_xingchen_api_key}"
+    }
+    contentdb = content_db.new_instance()
+    if uid == 0:
+        communication_history = contentdb.get_list('all','desc', 11)
+    else:
+        communication_history = contentdb.get_list('all','desc', 11, uid)
+    #历史记录处理
+    message=[]
+    i = len(communication_history) - 1
+    
+    if len(communication_history)>1:
+        while i >= 0:
+            answer_info = dict()
+            if communication_history[i][0] == "member":
+                answer_info["role"] = "user"
+                answer_info["content"] = communication_history[i][2]
+            elif communication_history[i][0] == "fay":
+                answer_info["role"] = "assistant"
+                answer_info["content"] = communication_history[i][2]
+            message.append(answer_info)
+            i -= 1
+    else:
+         answer_info = dict()
+         answer_info["role"] = "user"
+         answer_info["content"] = cont
+         message.append(answer_info)
+    data = {
+        "input": {
+            "messages": message,
+            "aca": {
+                "botProfile": {
+                    "characterId": config_util.xingchen_characterid,
+                    "version": 1
+                },
+                "userProfile": {
+                    "userId": "1234567891",
+                    "userName": "我",
+                    "basicInfo": ""
+                },
+                "scenario": {
+                    "description": "你是数字人Fay。用户问你问题的时候回答之前请一步一步想清楚。你的底层AI算法技术是Fay。"
+                },
+                "context": {
+                    "useChatHistory": False,
+                    "isRegenerate": False,
+                }
+            }
+        },
+        "parameters": {
+            "seed": 1683806810,
+        }
+    }
+
+    try:
+        response = requests.post(url, headers=headers, data=json.dumps(data))
+        if response.status_code == 200:
+            response_data = json.loads(response.text)
+            if response_data.get('success') and 'data' in response_data and 'choices' in response_data['data'] and len(response_data['data']['choices']) > 0:
+                content = response_data['data']['choices'][0]['messages'][0]['content']
+                return content
+            else:
+                util.log(1, "通义星辰调用失败，请检查配置")
+                response_text = "抱歉，我现在太忙了，休息一会，请稍后再试。"
+                return response_text
+        else:
+            util.log(1, f"通义星辰调用失败，请检查配置（错误码：{response.status_code}）")
+            response_text = "抱歉，我现在太忙了，休息一会，请稍后再试。"
+            return response_text
+    except Exception as e:
+        util.log(1, f"通义星辰调用失败，请检查配置（错误：{e}）")
+        response_text = "抱歉，我现在太忙了，休息一会，请稍后再试。"
+        return response_text
+
+# # 调用函数测试
+# result = question("你早")
+# if result:
+#     print(f"Received response: {result}")
+# else:
+#     print("Failed to get a valid response.")