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334 lines
17 KiB
Python
334 lines
17 KiB
Python
# -*- coding: utf-8 -*-
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import openai.openai_object
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import requests, json
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import openai
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from odoo import api, fields, models, _
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from odoo.exceptions import UserError
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from .lib.WordsSearch import WordsSearch
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import logging
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_logger = logging.getLogger(__name__)
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class AiRobot(models.Model):
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_name = 'ai.robot'
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_description = 'Gpt Robot'
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_order = 'sequence, name'
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name = fields.Char(string='Name', translate=True, required=True)
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provider = fields.Selection(string="AI Provider", selection=[('openai', 'OpenAI'), ('azure', 'Azure')], required=True, default='openai')
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ai_model = fields.Selection(string="AI Model", selection=[
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('gpt-4', 'Chatgpt 4'),
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('gpt-3.5-turbo', 'Chatgpt 3.5 Turbo'),
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('gpt-3.5-turbo-0301', 'Chatgpt 3.5 Turbo on 20230301'),
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('text-davinci-003', 'Chatgpt 3 Davinci'),
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('code-davinci-002', 'Chatgpt 2 Code Optimized'),
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('text-davinci-002', 'Chatgpt 2 Davinci'),
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('dall-e2', 'Dall-E Image'),
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], required=True, default='gpt-3.5-turbo',
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help="""
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GPT-4: Can understand Image, generate natural language or code.
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GPT-3.5: A set of models that improve on GPT-3 and can understand as well as generate natural language or code
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DALL·E: A model that can generate and edit images given a natural language prompt
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Whisper: A model that can convert audio into text
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Embeddings: A set of models that can convert text into a numerical form
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CodexLimited: A set of models that can understand and generate code, including translating natural language to code
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Moderation: A fine-tuned model that can detect whether text may be sensitive or unsafe
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GPT-3 A set of models that can understand and generate natural language
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""")
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openapi_api_key = fields.Char(string="API Key", help="Provide the API key here")
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# begin gpt 参数
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# 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。
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# 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。
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# 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。
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# 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。
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# 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。
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max_tokens = fields.Integer('Max response', default=600,
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help="""
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Set a limit on the number of tokens per model response.
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The API supports a maximum of 4000 tokens shared between the prompt
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(including system message, examples, message history, and user query) and the model's response.
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One token is roughly 4 characters for typical English text.
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""")
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temperature = fields.Float(string='Temperature', default=0.9,
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help="""
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Controls randomness. Lowering the temperature means that the model will produce
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more repetitive and deterministic responses.
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Increasing the temperature will result in more unexpected or creative responses.
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Try adjusting temperature or Top P but not both.
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""")
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top_p = fields.Float('Top probabilities', default=0.6,
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help="""
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Similar to temperature, this controls randomness but uses a different method.
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Lowering Top P will narrow the model’s token selection to likelier tokens.
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Increasing Top P will let the model choose from tokens with both high and low likelihood.
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Try adjusting temperature or Top P but not both
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""")
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# 避免使用常用词
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frequency_penalty = fields.Float('Frequency penalty', default=0.5,
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help="""
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Reduce the chance of repeating a token proportionally based on how often it has appeared in the text so far.
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This decreases the likelihood of repeating the exact same text in a response.
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""")
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# 越大模型就趋向于生成更新的话题，惩罚已经出现过的文本
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presence_penalty = fields.Float('Presence penalty', default=0.5,
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help="""
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Reduce the chance of repeating any token that has appeared in the text at all so far.
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This increases the likelihood of introducing new topics in a response.
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""")
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# 停止回复的关键词
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stop = fields.Char('Stop sequences',
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help="""
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Use , to separate the stop key word.
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Make responses stop at a desired point, such as the end of a sentence or list.
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Specify up to four sequences where the model will stop generating further tokens in a response.
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The returned text will not contain the stop sequence.
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""")
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# 角色设定
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sys_content = fields.Char('System message',
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help="""
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Give the model instructions about how it should behave and any context it should reference when generating a response.
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You can describe the assistant’s personality,
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tell it what it should and shouldn’t answer, and tell it how to format responses.
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There’s no token limit for this section, but it will be included with every API call,
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so it counts against the overall token limit.
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""")
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# end gpt 参数
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endpoint = fields.Char('End Point', default='https://api.openai.com/v1/chat/completions')
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engine = fields.Char('Engine', help='If use Azure, Please input the Model deployment name.')
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api_version = fields.Char('API Version', default='2022-12-01')
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ai_timeout = fields.Integer('Timeout(seconds)', help="Connect timeout for Ai response", default=120)
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sequence = fields.Integer('Sequence', help="Determine the display order", default=10)
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sensitive_words = fields.Text('Sensitive Words Plus', help='Sensitive word filtering. Separate keywords with a carriage return.')
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is_filtering = fields.Boolean('Filter Sensitive Words', default=False, help='Use base Filter in dir models/lib/sensi_words.txt')
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def action_disconnect(self):
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requests.delete('https://chatgpt.com/v1/disconnect')
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def get_ai(self, data, author_id=False, answer_id=False, **kwargs):
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# 通用方法
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# author_id: 请求的 partner_id 对象
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# answer_id: 回答的 partner_id 对象
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# kwargs，dict 形式的可变参数
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self.ensure_one()
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# 前置勾子，一般返回 False，有问题返回响应内容
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res_pre = self.get_ai_pre(data, author_id, answer_id, **kwargs)
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if res_pre:
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return res_pre
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if hasattr(self, 'get_%s' % self.provider):
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res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, **kwargs)
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else:
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res = _('No robot provider found')
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# 后置勾子，返回处理后的内容，用于处理敏感词等
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res_post = self.get_ai_post(res, author_id, answer_id, **kwargs)
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return res_post
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def get_ai_pre(self, data, author_id=False, answer_id=False, **kwargs):
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search = WordsSearch()
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search.SetKeywords([])
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content = data[0]['content']
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sensi = search.FindFirst(content)
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if sensi is not None:
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return _('温馨提示：您发送的内容含有敏感词，请修改内容后再向我发送。')
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else:
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return False
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def get_ai_post(self, res, author_id=False, answer_id=False, **kwargs):
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if res and author_id and isinstance(res, openai.openai_object.OpenAIObject) or isinstance(res, list):
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usage = json.loads(json.dumps(res['usage']))
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content = json.loads(json.dumps(res['choices'][0]['message']['content']))
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data = content.replace(' .', '.').strip()
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if usage:
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# todo: 不是写到 user ，是要写到指定 m2m 相关模型， 如： res.partner.ai.use
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user_id = author_id.mapped('user_ids')[:1]
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prompt_tokens = usage['prompt_tokens']
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completion_tokens = usage['completion_tokens']
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total_tokens = usage['total_tokens']
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vals = {
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'human_prompt_tokens': user_id.human_prompt_tokens + prompt_tokens,
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'ai_completion_tokens': user_id.ai_completion_tokens + completion_tokens,
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'tokens_total': user_id.tokens_total + total_tokens,
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'used_number': user_id.used_number + 1,
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}
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if not user_id.first_ask_time:
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ask_date = fields.Datetime.now()
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vals.update({
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'first_ask_time': ask_date
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})
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user_id.write(vals)
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# res = self.filter_sensitive_words(data)
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else:
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data = res
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return data
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def get_ai_system(self, content=None):
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# 获取基础ai角色设定, role system
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sys_content = content or self.sys_content
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if sys_content:
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return {"role": "system", "content": sys_content}
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return {}
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def get_ai_model_info(self):
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self.ensure_one()
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headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openapi_api_key}"}
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R_TIMEOUT = self.ai_timeout or 120
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o_url = "https://api.openai.com/v1/models/%s" % self.ai_model
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if self.endpoint:
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o_url = self.endpoint.replace("/chat/completions", "") + "/models/%s" % self.ai_model
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response = requests.get(o_url, headers=headers, timeout=R_TIMEOUT)
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response.close()
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if response:
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res = response.json()
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r_text = json.dumps(res, indent=2)
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else:
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r_text = 'No response.'
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raise UserError(r_text)
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def get_ai_list_model(self):
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self.ensure_one()
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headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openapi_api_key}"}
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R_TIMEOUT = self.ai_timeout or 120
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o_url = "https://api.openai.com/v1/models"
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if self.endpoint:
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o_url = self.endpoint.replace("/chat/completions", "") + "/models"
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response = requests.get(o_url, headers=headers, timeout=R_TIMEOUT)
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response.close()
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if response:
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res = response.json()
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r_text = json.dumps(res, indent=2)
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else:
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r_text = 'No response.'
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raise UserError(r_text)
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def get_openai(self, data, author_id, answer_id, **kwargs):
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self.ensure_one()
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headers = {"Content-Type": "application/json", "Authorization": f"Bearer {self.openapi_api_key}"}
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R_TIMEOUT = self.ai_timeout or 120
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o_url = self.endpoint or "https://api.openai.com/v1/chat/completions"
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if self.stop:
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stop = self.stop.split(',')
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else:
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stop = ["Human:", "AI:"]
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# 以下处理 open ai
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if self.ai_model in ['gpt-3.5-turbo', 'gpt-3.5-turbo-0301']:
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# 基本与 azure 同，要处理 api_base
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openai.api_key = self.openapi_api_key
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openai.api_base = o_url.replace('/chat/completions', '')
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if isinstance(data, list):
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messages = data
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else:
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messages = [{"role": "user", "content": data}]
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# Ai角色设定
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sys_content = self.get_ai_system(kwargs.get('sys_content'))
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if sys_content:
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messages.insert(0, sys_content)
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response = openai.ChatCompletion.create(
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model=self.ai_model,
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messages=messages,
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n=1,
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temperature=self.temperature or 0.9,
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max_tokens=self.max_tokens or 600,
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top_p=self.top_p or 0.6,
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frequency_penalty=self.frequency_penalty or 0.5,
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presence_penalty=self.presence_penalty or 0.5,
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stop=stop,
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request_timeout=self.ai_timeout or 120,
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)
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if 'choices' in response:
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return response
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else:
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_logger.warning('=====================Openai output data: %s' % response)
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elif self.ai_model == 'dall-e2':
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# todo: 处理 图像引擎，主要是返回参数到聊天中
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# image_url = response['data'][0]['url']
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# https://platform.openai.com/docs/guides/images/introduction
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pdata = {
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"prompt": data,
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"n": 3,
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"size": "1024x1024",
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}
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return '建设中'
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else:
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pdata = {
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"model": self.ai_model,
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"prompt": data,
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"temperature": 0.9,
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"max_tokens": self.max_tokens or 1000,
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"top_p": 1,
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"frequency_penalty": 0.0,
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"presence_penalty": 0.6,
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"stop": ["Human:", "AI:"]
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}
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response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)
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res = response.json()
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if 'choices' in res:
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res = '\n'.join([x['text'] for x in res['choices']])
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return res
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return _("Response Timeout, please speak again.")
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def get_azure(self, data, author_id, answer_id, **kwargs):
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self.ensure_one()
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# only for azure
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openai.api_type = self.provider
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if not self.endpoint:
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raise UserError(_("Please Set your AI robot's endpoint first."))
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openai.api_base = self.endpoint
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if not self.api_version:
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raise UserError(_("Please Set your AI robot's API Version first."))
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openai.api_version = self.api_version
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openai.api_key = self.openapi_api_key
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if self.stop:
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stop = self.stop.split(',')
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else:
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stop = ["Human:", "AI:"]
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if isinstance(data, list):
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messages = data
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else:
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messages = [{"role": "user", "content": data}]
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# Ai角色设定
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sys_content = self.get_ai_system(kwargs.get('sys_content'))
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if sys_content:
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messages.insert(0, sys_content)
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response = openai.ChatCompletion.create(
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engine=self.engine,
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messages=messages,
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# 返回的回答数量
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n=1,
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temperature=self.temperature or 0.9,
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max_tokens=self.max_tokens or 600,
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top_p=self.top_p or 0.6,
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frequency_penalty=self.frequency_penalty or 0.5,
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presence_penalty=self.presence_penalty or 0.5,
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stop=stop,
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request_timeout=self.ai_timeout or 120,
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)
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if 'choices' in response:
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return response
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else:
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_logger.warning('=====================azure output data: %s' % response)
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return _("Response Timeout, please speak again.")
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@api.onchange('provider')
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def _onchange_provider(self):
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if self.provider == 'openai':
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self.endpoint = 'https://api.openai.com/v1/chat/completions'
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elif self.provider == 'azure':
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self.endpoint = 'https://odoo.openai.azure.com'
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def filter_sensitive_words(self, data):
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if self.is_filtering:
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search = WordsSearch()
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s = self.sensitive_words
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if s:
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search.SetKeywords(s.split('\n'))
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else:
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search.SetKeywords([])
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data = search.Replace(text=data)
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return data
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else:
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return data
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