diff --git a/app_chatgpt/data/ai_robot_data.xml b/app_chatgpt/data/ai_robot_data.xml
index 98ae8a95..7bdc64a2 100644
--- a/app_chatgpt/data/ai_robot_data.xml
+++ b/app_chatgpt/data/ai_robot_data.xml
@@ -23,6 +23,7 @@
azurehttps://my.openai.azure.comgpt35
+ 2023-03-15-preview8
@@ -30,6 +31,7 @@
azurehttps://my.openai.azure.comgpt4
+ 2023-03-15-preview9
\ No newline at end of file
diff --git a/app_chatgpt/i18n/zh_CN.po b/app_chatgpt/i18n/zh_CN.po
index b4b143a7..2d891a39 100644
--- a/app_chatgpt/i18n/zh_CN.po
+++ b/app_chatgpt/i18n/zh_CN.po
@@ -1399,7 +1399,7 @@ msgid "Mahjong red dragon"
msgstr ""
#. module: app_chatgpt
-#: model:ir.model.fields,field_description:app_chatgpt.field_ai_robot__max_length
+#: model:ir.model.fields,field_description:app_chatgpt.field_ai_robot__max_tokens
msgid "Max Length"
msgstr ""
diff --git a/app_chatgpt/models/ai_robot.py b/app_chatgpt/models/ai_robot.py
index 0800c6a9..998893ef 100644
--- a/app_chatgpt/models/ai_robot.py
+++ b/app_chatgpt/models/ai_robot.py
@@ -37,8 +37,63 @@ Moderation: A fine-tuned model that can detect whether text may be sensitive or
GPT-3 A set of models that can understand and generate natural language
""")
openapi_api_key = fields.Char(string="API Key", help="Provide the API key here")
- temperature = fields.Float(string='Temperature', default=0.9)
- max_length = fields.Integer('Max Length', default=300)
+ # begin gpt 参数
+ # 1. stop：表示聊天机器人停止生成回复的条件，可以是一段文本或者一个列表，当聊天机器人生成的回复中包含了这个条件，就会停止继续生成回复。
+ # 2. temperature：控制回复的“新颖度”，值越高，聊天机器人生成的回复越不确定和随机，值越低，聊天机器人生成的回复会更加可预测和常规化。
+ # 3. top_p：与temperature有些类似，也是控制回复的“新颖度”。不同的是，top_p控制的是回复中概率最高的几个可能性的累计概率之和，值越小，生成的回复越保守，值越大，生成的回复越新颖。
+ # 4. frequency_penalty：用于控制聊天机器人回复中出现频率过高的词汇的惩罚程度。聊天机器人会尝试避免在回复中使用频率较高的词汇，以提高回复的多样性和新颖度。
+ # 5. presence_penalty：与frequency_penalty相对，用于控制聊天机器人回复中出现频率较低的词汇的惩罚程度。聊天机器人会尝试在回复中使用频率较低的词汇，以提高回复的多样性和新颖度。
+ max_tokens = fields.Integer('Max response', default=600,
+ help="""
+ Set a limit on the number of tokens per model response.
+ The API supports a maximum of 4000 tokens shared between the prompt
+ (including system message, examples, message history, and user query) and the model's response.
+ One token is roughly 4 characters for typical English text.
+ """)
+ temperature = fields.Float(string='Temperature', default=0.9,
+ help="""
+ Controls randomness. Lowering the temperature means that the model will produce
+ more repetitive and deterministic responses.
+ Increasing the temperature will result in more unexpected or creative responses.
+ Try adjusting temperature or Top P but not both.
+ """)
+ top_p = fields.Float('Top probabilities', default=0.6,
+ help="""
+ Similar to temperature, this controls randomness but uses a different method.
+ Lowering Top P will narrow the model’s token selection to likelier tokens.
+ Increasing Top P will let the model choose from tokens with both high and low likelihood.
+ Try adjusting temperature or Top P but not both
+ """)
+ # 避免使用常用词
+ frequency_penalty = fields.Float('Frequency penalty', default=0.5,
+ help="""
+ Reduce the chance of repeating a token proportionally based on how often it has appeared in the text so far.
+ This decreases the likelihood of repeating the exact same text in a response.
+ """)
+ # 避免使用生僻词
+ presence_penalty = fields.Float('Presence penalty', default=0.2,
+ help="""
+ Reduce the chance of repeating any token that has appeared in the text at all so far.
+ This increases the likelihood of introducing new topics in a response.
+ """)
+ # 停止回复的关键词
+ stop = fields.Char('Stop sequences',
+ help="""
+ Use , to separate the stop key word.
+ Make responses stop at a desired point, such as the end of a sentence or list.
+ Specify up to four sequences where the model will stop generating further tokens in a response.
+ The returned text will not contain the stop sequence.
+ """)
+ # 角色设定
+ sys_content = fields.Char('System message',
+ help="""
+ Give the model instructions about how it should behave and any context it should reference when generating a response.
+ You can describe the assistant’s personality,
+ tell it what it should and shouldn’t answer, and tell it how to format responses.
+ There’s no token limit for this section, but it will be included with every API call,
+ so it counts against the overall token limit.
+ """)
+ # end gpt 参数
endpoint = fields.Char('End Point', default='https://api.openai.com/v1/chat/completions')
engine = fields.Char('Engine', help='If use Azure, Please input the Model deployment name.')
api_version = fields.Char('API Version', default='2022-12-01')
@@ -50,32 +105,39 @@ GPT-3 A set of models that can understand and generate natural language
def action_disconnect(self):
requests.delete('https://chatgpt.com/v1/disconnect')
- def get_ai(self, data, sender_id=False, answer_id=False, **kwargs):
+ def get_ai(self, data, author_id=False, answer_id=False, **kwargs):
# 通用方法
- # sender_id: 请求的 partner_id 对象
+ # author_id: 请求的 partner_id 对象
# answer_id: 回答的 partner_id 对象
# kwargs，dict 形式的可变参数
self.ensure_one()
# 前置勾子，一般返回 False，有问题返回响应内容
- res_pre = self.get_ai_pre(data, sender_id, answer_id, **kwargs)
+ res_pre = self.get_ai_pre(data, author_id, answer_id, **kwargs)
if res_pre:
return res_pre
if hasattr(self, 'get_%s' % self.provider):
- res = getattr(self, 'get_%s' % self.provider)(data, sender_id, answer_id, **kwargs)
+ res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, **kwargs)
else:
res = _('No robot provider found')
# 后置勾子，返回处理后的内容，用于处理敏感词等
- res_post = self.get_ai_post(res, sender_id, answer_id, **kwargs)
+ res_post = self.get_ai_post(res, author_id, answer_id, **kwargs)
return res_post
- def get_ai_pre(self, data, sender_id=False, answer_id=False, **kwargs):
+ def get_ai_pre(self, data, author_id=False, answer_id=False, **kwargs):
return False
- def get_ai_post(self, res, sender_id=False, answer_id=False, **kwargs):
- res = self.filter_sensitive_words(res)
+ def get_ai_post(self, res, author_id=False, answer_id=False, **kwargs):
+ # res = self.filter_sensitive_words(res)
return res
+ def get_ai_system(self, content=None):
+ # 获取基础ai角色设定, role system
+ sys_content = content or self.sys_content
+ if sys_content:
+ return {"role": "system", "content": sys_content}
+ return {}
+
def get_ai_model_info(self):
self.ensure_one()
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openapi_api_key}"}
@@ -109,19 +171,63 @@ GPT-3 A set of models that can understand and generate natural language
r_text = 'No response.'
raise UserError(r_text)
- def get_openai(self, data, sender_id, answer_id, *args):
+ def get_openai(self, data, author_id, answer_id, **kwargs):
self.ensure_one()
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openapi_api_key}"}
R_TIMEOUT = self.ai_timeout or 120
o_url = self.endpoint or "https://api.openai.com/v1/chat/completions"
- partner_name = 'odoo'
- # if sender_id:
- # partner_name = sender_id.name
+
+ if self.stop:
+ stop = self.stop.split(',')
+ else:
+ stop = ["Human:", "AI:"]
# 以下处理 open ai
- # 获取模型信息
- # list_model = requests.get("https://api.openai.com/v1/models", headers=headers)
- # model_info = requests.get("https://api.openai.com/v1/models/%s" % ai_model, headers=headers)
- if self.ai_model == 'dall-e2':
+ if self.ai_model in ['gpt-3.5-turbo', 'gpt-3.5-turbo-0301']:
+ messages = [{"role": "user", "content": data}]
+ # Ai角色设定
+ sys_content = self.get_ai_system(kwargs.get('sys_content'))
+ if sys_content:
+ messages.insert(sys_content)
+ pdata = {
+ "model": self.ai_model,
+ "messages": messages,
+ "temperature": self.temperature or 0.9,
+ "max_tokens": self.max_tokens or 1000,
+ "top_p": self.top_p or 0.6,
+ "frequency_penalty": self.frequency_penalty or 0.5,
+ "presence_penalty": self.presence_penalty or 0.2,
+ "stop": stop
+ }
+ _logger.warning('=====================open input pdata: %s' % pdata)
+ response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)
+ try:
+ # todo: 将 res 总结果给 self.get_ai_post，再取实际内容 return，将 tokens 计算写在 post 方法中
+ res = response.json()
+ if 'usage' in res:
+ usage = res['usage']
+ prompt_tokens = usage['prompt_tokens']
+ completion_tokens = usage['completion_tokens']
+ total_tokens = usage['total_tokens']
+ vals = {
+ 'human_prompt_tokens': author_id.human_prompt_tokens + prompt_tokens,
+ 'ai_completion_tokens': author_id.ai_completion_tokens + completion_tokens,
+ 'tokens_total': author_id.tokens_total + total_tokens,
+ 'used_number': author_id.used_number + 1,
+ }
+ if not author_id.first_ask_time:
+ ask_date = response.headers.get("Date")
+ vals.update({
+ 'first_ask_time': ask_date
+ })
+ author_id.write(vals)
+ if 'choices' in res:
+ # for rec in res:
+ # res = rec['message']['content']
+ res = '\n'.join([x['message']['content'] for x in res['choices']])
+ return res
+ except Exception as e:
+ _logger.warning("Get Response Json failed: %s", e)
+ elif self.ai_model == 'dall-e2':
# todo: 处理 图像引擎，主要是返回参数到聊天中
# image_url = response['data'][0]['url']
# https://platform.openai.com/docs/guides/images/introduction
@@ -131,56 +237,15 @@ GPT-3 A set of models that can understand and generate natural language
"size": "1024x1024",
}
return '建设中'
- elif self.ai_model in ['gpt-3.5-turbo', 'gpt-3.5-turbo-0301']:
- pdata = {
- "model": self.ai_model,
- "messages": [{"role": "user", "content": data}],
- "temperature": 0.9,
- "max_tokens": self.max_length or 1000,
- "top_p": 1,
- "frequency_penalty": 0.0,
- "presence_penalty": 0.6,
- "user": partner_name,
- "stop": ["Human:", "AI:"]
- }
- _logger.warning('=====================open input pdata: %s' % pdata)
- response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)
- try:
- res = response.json()
- if 'usage' in res:
- usage = res['usage']
- prompt_tokens = usage['prompt_tokens']
- completion_tokens = usage['completion_tokens']
- total_tokens = usage['total_tokens']
- vals = {
- 'human_prompt_tokens': sender_id.human_prompt_tokens + prompt_tokens,
- 'ai_completion_tokens': sender_id.ai_completion_tokens + completion_tokens,
- 'tokens_total': sender_id.tokens_total + total_tokens,
- 'used_number': sender_id.used_number + 1,
- }
- if not sender_id.first_ask_time:
- ask_date = response.headers.get("Date")
- vals.update({
- 'first_ask_time': ask_date
- })
- sender_id.write(vals)
- if 'choices' in res:
- # for rec in res:
- # res = rec['message']['content']
- res = '\n'.join([x['message']['content'] for x in res['choices']])
- return res
- except Exception as e:
- _logger.warning("Get Response Json failed: %s", e)
else:
pdata = {
"model": self.ai_model,
"prompt": data,
"temperature": 0.9,
- "max_tokens": self.max_length or 1000,
+ "max_tokens": self.max_tokens or 1000,
"top_p": 1,
"frequency_penalty": 0.0,
"presence_penalty": 0.6,
- "user": partner_name,
"stop": ["Human:", "AI:"]
}
response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)
@@ -189,9 +254,9 @@ GPT-3 A set of models that can understand and generate natural language
res = '\n'.join([x['text'] for x in res['choices']])
return res
- return "获取结果超时，请重新跟我聊聊。"
+ return _("Response Timeout, please speak again.")
- def get_azure(self, data, sender_id, answer_id, *args):
+ def get_azure(self, data, author_id, answer_id, **kwargs):
self.ensure_one()
# only for azure
openai.api_type = self.provider
@@ -202,21 +267,29 @@ GPT-3 A set of models that can understand and generate natural language
raise UserError(_("Please Set your AI robot's API Version first."))
openai.api_version = self.api_version
openai.api_key = self.openapi_api_key
- pdata = {
- "engine": self.engine,
- "prompt": data,
- "temperature": self.temperature or 0.9,
- "max_tokens": self.max_length or 600,
- "top_p": 0.5,
- "frequency_penalty": 0,
- "presence_penalty": 0,
- "stop": ["Human:", "AI:"],
- }
- _logger.warning('=====================azure input data: %s' % pdata)
- response = openai.Completion.create(pdata)
-
+ if self.stop:
+ stop = self.stop.split(',')
+ else:
+ stop = ["Human:", "AI:"]
+ if isinstance(data, list):
+ messages = data
+ else:
+ messages = [{"role": "user", "content": data}]
+ # Ai角色设定
+ sys_content = self.get_ai_system(kwargs.get('sys_content'))
+ if sys_content:
+ messages.insert(sys_content)
+ response = openai.ChatCompletion.create(
+ engine=self.engine,
+ messages=messages,
+ temperature=self.temperature or 0.9,
+ max_tokens=self.max_tokens or 600,
+ top_p=self.top_p or 0.6,
+ frequency_penalty=self.frequency_penalty or 0.5,
+ presence_penalty=self.presence_penalty or 0.2,
+ stop=stop)
if 'choices' in response:
- res = response['choices'][0]['text'].replace(' .', '.').strip()
+ res = response['choices'][0]['message']['content'].replace(' .', '.').strip()
return res
else:
_logger.warning('=====================azure output data: %s' % response)
diff --git a/app_chatgpt/models/lib/sensi_words.txt b/app_chatgpt/models/lib/sensi_words.txt
index 2ebf767a..10e15ae2 100644
--- a/app_chatgpt/models/lib/sensi_words.txt
+++ b/app_chatgpt/models/lib/sensi_words.txt
@@ -977,7 +977,6 @@ GY
有容奶大
李总统
操你媽
-GP
你的逼……真紧
GN
GM
@@ -1412,7 +1411,6 @@ blowjobs
耶和華
奶大穴肥多条肉棒难满足
瞿秋白
-gp
dong fang chuan shuo
saga
藏春阁【全免费】
diff --git a/app_chatgpt/models/mail_channel.py b/app_chatgpt/models/mail_channel.py
index 5f3d474f..ec5087df 100644
--- a/app_chatgpt/models/mail_channel.py
+++ b/app_chatgpt/models/mail_channel.py
@@ -13,41 +13,42 @@ _logger = logging.getLogger(__name__)
class Channel(models.Model):
_inherit = 'mail.channel'
-
- @api.model
- def get_openai_context(self, channel_id, partner_chatgpt, current_prompt, seconds=600):
- afterTime = fields.Datetime.now() - datetime.timedelta(seconds=seconds)
+
+ def get_openai_context(self, channel_id, author_id, answer_id, minutes=30):
+ # 上下文处理，要处理群的方式，以及独聊的方式
+ # azure新api 处理
+ context_history = []
+ afterTime = fields.Datetime.now() - datetime.timedelta(minutes=minutes)
message_model = self.env['mail.message'].sudo()
- prompt = [f"Human:{current_prompt}\nAI:", ]
+ # 处理消息： 取最新问题 + 上2次的交互，将之前的交互按时间顺序拼接
+ # 注意： ai 每一次回复都有 parent_id 来处理连续性
+ # 私聊处理
domain = [('res_id', '=', channel_id),
- ('model', '=', 'mail.channel'),
- ('message_type', '!=', 'user_notification'),
- ('parent_id', '=', False),
- ('date', '>=', afterTime),
- ('author_id', '=', self.env.user.partner_id.id)]
- messages = message_model.with_context(tz='UTC').search(domain, order="id desc", limit=15)
- # print('domain:',domain)
- # print('messages:',messages)
- for msg in messages:
- ai_msg = message_model.search([("res_id", "=", channel_id),
- ('model', '=', msg.model),
- ('parent_id', '=', msg.id),
- ('author_id', '=', partner_chatgpt),
- ('body', '!=', '
获取结果超时，请重新跟我聊聊。
')])
- if ai_msg:
- prompt.append("Human:%s\nAI:%s" % (
- msg.body.replace("