Add Baidu Wenxin Bot

.gitignore

@@ -1,6 +1,8 @@
 .DS_Store
 .idea
 .vscode
+.venv
+.vs
 .wechaty/
 __pycache__/
 venv*
@@ -22,6 +24,8 @@ plugins/**/
 !plugins/tool
 !plugins/banwords
 !plugins/banwords/**/
+plugins/banwords/__pycache__
+plugins/banwords/lib/__pycache__
 !plugins/hello
 !plugins/role
 !plugins/keyword

bot/baidu/baidu_wenxin.py

@@ -0,0 +1,97 @@
+# encoding:utf-8
+
+import requests, json
+import pdb
+from bot.bot import Bot
+from bridge.reply import Reply, ReplyType
+from bot.session_manager import SessionManager
+from bridge.context import ContextType
+from bridge.reply import Reply, ReplyType
+from common.log import logger
+from config import conf
+from bot.baidu.baidu_wenxin_session import BaiduWenxinSession
+
+BAIDU_API_KEY = conf().get("baidu_wenxin_api_key")
+BAIDU_SECRET_KEY = conf().get("baidu_wenxin_api_key")
+
+class BaiduWenxinBot(Bot):
+
+    def __init__(self):
+        super().__init__()
+        self.sessions = SessionManager(BaiduWenxinSession, model=conf().get("baidu_wenxin_model") or "eb-instant")
+
+    def reply(self, query, context=None):
+        # acquire reply content
+        if context and context.type:
+            if context.type == ContextType.TEXT:
+                logger.info("[BAIDU] query={}".format(query))
+                session_id = context["session_id"]
+                reply = None
+                if query == "#清除记忆":
+                    self.sessions.clear_session(session_id)
+                    reply = Reply(ReplyType.INFO, "记忆已清除")
+                elif query == "#清除所有":
+                    self.sessions.clear_all_session()
+                    reply = Reply(ReplyType.INFO, "所有人记忆已清除")
+                else:
+                    session = self.sessions.session_query(query, session_id)
+                    result = self.reply_text(session)
+                    total_tokens, completion_tokens, reply_content = (
+                        result["total_tokens"],
+                        result["completion_tokens"],
+                        result["content"],
+                    )
+                    logger.debug(
+                        "[BAIDU] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(session.messages, session_id, reply_content, completion_tokens)
+                    )
+
+                    if total_tokens == 0:
+                        reply = Reply(ReplyType.ERROR, reply_content)
+                    else:
+                        self.sessions.session_reply(reply_content, session_id, total_tokens)
+                        reply = Reply(ReplyType.TEXT, reply_content)
+                return reply
+            elif context.type == ContextType.IMAGE_CREATE:
+                ok, retstring = self.create_img(query, 0)
+                reply = None
+                if ok:
+                    reply = Reply(ReplyType.IMAGE_URL, retstring)
+                else:
+                    reply = Reply(ReplyType.ERROR, retstring)
+                return reply
+
+    def reply_text(self, session: BaiduWenxinSession, retry_count=0):
+        try:
+            access_token = self.get_access_token()
+            url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/" + session.model + "?access_token=" + access_token
+            headers = {
+                'Content-Type': 'application/json'
+            }
+            payload = {'messages': session.messages}
+            response = requests.request("POST", url, headers=headers, data=json.dumps(payload))
+            response_text = json.loads(response.text)
+            res_content = response_text["result"]
+            total_tokens = response_text["usage"]["total_tokens"]
+            completion_tokens = response_text["usage"]["completion_tokens"]
+            logger.info("[BAIDU] reply={}".format(res_content))
+            return {
+                "total_tokens": total_tokens,
+                "completion_tokens": completion_tokens,
+                "content": res_content,
+            }
+        except Exception as e:
+            need_retry = retry_count < 2
+            logger.warn("[BAIDU] Exception: {}".format(e))
+            need_retry = False
+            self.sessions.clear_session(session.session_id)
+            result = {"completion_tokens": 0, "content": "出错了: {}".format(e)}
+            return result
+
+    def get_access_token(self):
+        """
+        使用 AK，SK 生成鉴权签名（Access Token）
+        :return: access_token，或是None(如果错误)
+        """
+        url = "https://aip.baidubce.com/oauth/2.0/token"
+        params = {"grant_type": "client_credentials", "client_id": BAIDU_API_KEY, "client_secret": BAIDU_SECRET_KEY}
+        return str(requests.post(url, params=params).json().get("access_token"))

bot/baidu/baidu_wenxin_session.py

@@ -0,0 +1,87 @@
+from bot.session_manager import Session
+from common.log import logger
+
+"""
+    e.g.  [
+        {"role": "user", "content": "Who won the world series in 2020?"},
+        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
+        {"role": "user", "content": "Where was it played?"}
+    ]
+"""
+
+class BaiduWenxinSession(Session):
+    def __init__(self, session_id, system_prompt=None, model="gpt-3.5-turbo"):
+        super().__init__(session_id, system_prompt)
+        self.model = model
+        # 百度文心不支持system prompt
+        # self.reset()
+
+    def discard_exceeding(self, max_tokens, cur_tokens=None):
+        # pdb.set_trace()
+        precise = True
+        try:
+            cur_tokens = self.calc_tokens()
+        except Exception as e:
+            precise = False
+            if cur_tokens is None:
+                raise e
+            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
+        while cur_tokens > max_tokens:
+            if len(self.messages) > 2:
+                self.messages.pop(1)
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
+                self.messages.pop(1)
+                if precise:
+                    cur_tokens = self.calc_tokens()
+                else:
+                    cur_tokens = cur_tokens - max_tokens
+                break
+            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
+                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
+                break
+            else:
+                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens, len(self.messages)))
+                break
+            if precise:
+                cur_tokens = self.calc_tokens()
+            else:
+                cur_tokens = cur_tokens - max_tokens
+        return cur_tokens
+
+    def calc_tokens(self):
+        return num_tokens_from_messages(self.messages, self.model)
+
+
+# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
+def num_tokens_from_messages(messages, model):
+    """Returns the number of tokens used by a list of messages."""
+    import tiktoken
+
+    if model in ["gpt-3.5-turbo-0301", "gpt-35-turbo"]:
+        return num_tokens_from_messages(messages, model="gpt-3.5-turbo")
+    elif model in ["gpt-4-0314", "gpt-4-0613", "gpt-4-32k", "gpt-4-32k-0613", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-35-turbo-16k"]:
+        return num_tokens_from_messages(messages, model="gpt-4")
+
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        logger.debug("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model == "gpt-3.5-turbo":
+        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_name = -1  # if there's a name, the role is omitted
+    elif model == "gpt-4":
+        tokens_per_message = 3
+        tokens_per_name = 1
+    else:
+        logger.warn(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo.")
+        return num_tokens_from_messages(messages, model="gpt-3.5-turbo")
+    num_tokens = 0
+    for message in messages:
+        num_tokens += tokens_per_message
+        for key, value in message.items():
+            num_tokens += len(encoding.encode(value))
+            if key == "name":
+                num_tokens += tokens_per_name
+    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+    return num_tokens

bot/bot_factory.py

@@ -11,10 +11,13 @@ def create_bot(bot_type):
     :return: bot instance
     """
     if bot_type == const.BAIDU:
-        # Baidu Unit对话接口
-        from bot.baidu.baidu_unit_bot import BaiduUnitBot
+        # 替换Baidu Unit为Baidu文心千帆对话接口
+        # from bot.baidu.baidu_unit_bot import BaiduUnitBot
+        # return BaiduUnitBot()
 
-        return BaiduUnitBot()
+        from bot.baidu.baidu_wenxin import BaiduWenxinBot
+
+        return BaiduWenxinBot()
 
     elif bot_type == const.CHATGPT:
         # ChatGPT 网页端web接口

bridge/bridge.py

@@ -23,6 +23,8 @@ class Bridge(object):
             self.btype["chat"] = const.OPEN_AI
         if conf().get("use_azure_chatgpt", False):
             self.btype["chat"] = const.CHATGPTONAZURE
+        if conf().get("use_baidu_wenxin", False):
+            self.btype["chat"] = const.BAIDU
         if conf().get("use_linkai") and conf().get("linkai_api_key"):
             self.btype["chat"] = const.LINKAI
         self.bots = {}

config.py

@@ -19,6 +19,7 @@ available_setting = {
     "model": "gpt-3.5-turbo",
     "use_azure_chatgpt": False,  # 是否使用azure的chatgpt
     "azure_deployment_id": "",  # azure 模型部署名称
+    "use_baidu_wenxin": False,  # 是否使用baidu文心一言，优先级次于azure
     # Bot触发配置
     "single_chat_prefix": ["bot", "@bot"],  # 私聊时文本需要包含该前缀才能触发机器人回复
     "single_chat_reply_prefix": "[bot] ",  # 私聊时自动回复的前缀，用于区分真人
@@ -50,6 +51,10 @@ available_setting = {
     "presence_penalty": 0,
     "request_timeout": 60,  # chatgpt请求超时时间，openai接口默认设置为600，对于难问题一般需要较长时间
     "timeout": 120,  # chatgpt重试超时时间，在这个时间内，将会自动重试
+    # Baidu 文心一言参数
+    "baidu_wenxin_model": "eb-instant", # 默认使用ERNIE-Bot-turbo模型
+    "baidu_wenxin_api_key": "", # Baidu api key
+    "baidu_wenxin_secret_key": "", # Baidu secret key
     # 语音设置
     "speech_recognition": False,  # 是否开启语音识别
     "group_speech_recognition": False,  # 是否开启群组语音识别