mirror of
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482 lines
24 KiB
Python
482 lines
24 KiB
Python
# -*- coding: utf-8 -*-
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import requests
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import json
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import base64
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import logging
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from openai import OpenAI
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from openai import AzureOpenAI
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from odoo import api, fields, models, modules, tools, _
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from odoo.exceptions import UserError
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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 = 'Ai 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')],
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required=True, default='openai', change_default=True)
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# update ai_robot set ai_model=set_ai_model
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ai_model = fields.Char(string="AI Model", required=True, default='auto', help='Customize input')
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set_ai_model = fields.Selection(string="Quick Set Model", selection=[
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('gpt-3.5-turbo-0125', 'gpt-3.5-turbo-0125(Default and Latest)'),
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('gpt-3.5-turbo-0613', 'gpt-3.5-turbo-0613'),
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('gpt-3.5-turbo-0125', 'gpt-3.5-turbo-0125'),
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('gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-16k-0613(Big text)'),
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('gpt-4', 'Chatgpt 4'),
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('gpt-4-32k', 'Chatgpt 4 32k'),
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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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], default='gpt-4o',
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help="""
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GPT-4o: It is multimodal (accepting text or image inputs and outputting text), and it has the same high intelligence as GPT-4 Turbo but is much more efficient—it generates text 2x faster and is 50% cheaper.
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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：0-2，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：0-1，语言连贯性，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：-2~2，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：-2~2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=1,
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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=1,
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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=1,
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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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max_send_char = fields.Integer('Max Send Char', help='Max Send Prompt Length', default=8000)
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image_avatar = fields.Image('Avatar')
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partner_ids = fields.One2many('res.partner', 'gpt_id', string='Partner')
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partner_count = fields.Integer('#Partner', compute='_compute_partner_count', store=False)
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active = fields.Boolean('Active', default=True)
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def _compute_partner_count(self):
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for rec in self:
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rec.partner_count = len(rec.partner_ids)
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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_pre(self, data, author_id=False, answer_id=False, param={}):
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# hook，都正常
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return False
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def get_ai(self, data, author_id=False, answer_id=False, param={}):
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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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# param，dict 形式的参数
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# 调整输出为2个参数：res_post详细内容，is_ai是否ai的响应
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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，用于处理敏感词等
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res_pre = self.get_ai_pre(data, author_id, answer_id, param)
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if res_pre:
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# 有错误内容，则返回上级内容及 is_ai为假
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return res_pre, {}, False
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if not hasattr(self, 'get_%s' % self.provider):
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res = _('No robot provider found')
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return res, {}, False
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res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, param)
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# 后置勾子，返回处理后的内容
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res_post, usage, is_ai = self.get_ai_post(res, author_id, answer_id, param)
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return res_post, usage, is_ai
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def get_ai_origin(self, data, author_id=False, answer_id=False, param={}):
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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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# param，dict 形式的参数
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# 调整输出为2个参数：res_post详细内容，is_ai是否ai的响应
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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，用于处理敏感词等
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res_pre = self.get_ai_pre(data, author_id, answer_id, param)
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if res_pre:
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# 有错误内容，则返回上级内容及 is_ai为假
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return res_pre, {}, False
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if not hasattr(self, 'get_%s' % self.provider):
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res = _('No robot provider found')
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return res, {}, False
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res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, param)
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# 后置勾子，返回处理后的内容
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res_post, usage, is_ai = self.get_ai_post(res, author_id, answer_id, param)
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return res
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def get_ai_post(self, res, author_id=False, answer_id=False, param=None):
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# hook，高级版要替代
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if param is None:
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param = {}
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if not res or not author_id or (not isinstance(res, list) and not isinstance(res, dict)):
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return res, False, False
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usage = content = data = None
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try:
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if self.provider == 'openai':
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# openai 格式处理
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usage = res['usage']
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content = res['choices'][0]['message']['content']
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# _logger.warning('===========Ai响应:%s' % content)
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elif self.provider == 'azure':
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# azure 格式
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usage = res['usage']
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content = res['choices'][0]['message']['content']
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else:
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usage = False
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content = res
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data = content.replace(' .', '.').strip()
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return data, usage, True
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except Exception as e:
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_logger.error('==========app_chatgpt get_ai_post Error: %s' % e)
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return res, False, False
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# if res and author_id and isinstance(res, openai.openai_object.OpenAIObject) or isinstance(res, list) or isinstance(res, dict):
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# # 返回是个对象，那么就是ai
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# # if isinstance(res, dict):
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# if self.provider == 'openai':
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# # openai 格式处理
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# usage = res['usage']
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# content = res['choices'][0]['message']['content']
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# # _logger.warning('===========Ai响应:%s' % content)
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# elif self.provider == 'azure':
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# # azure 格式
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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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# else:
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# usage = False
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# content = res
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# data = content.replace(' .', '.').strip()
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# return data, usage, True
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# else:
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# # 直接返回错误语句，那么就是非ai
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# return res, False, False
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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, param={}):
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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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# 处理传参，传过来的优先于 robot 默认的
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max_tokens = param.get('max_tokens') if param.get('max_tokens') else self.max_tokens
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temperature = param.get('temperature') if param.get('temperature') else self.temperature
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top_p = param.get('top_p') if param.get('top_p') else self.top_p
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frequency_penalty = param.get('frequency_penalty') if param.get('frequency_penalty') else self.frequency_penalty
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presence_penalty = param.get('presence_penalty') if param.get('presence_penalty') else self.presence_penalty
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request_timeout = param.get('request_timeout') if param.get('request_timeout') else self.ai_timeout
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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 == '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": 1,
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"max_tokens": max_tokens,
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"top_p": 0.6,
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"frequency_penalty": 0.1,
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"presence_penalty": 0.1,
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"stop": stop
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}
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client = OpenAI(
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api_key=self.openapi_api_key,
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timeout=R_TIMEOUT
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)
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response = client.chat.completions.create(
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messages=data,
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model=self.ai_model,
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)
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res = response.model_dump()
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if 'choices' in res:
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return res
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else:
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_logger.warning('=====================openai output data: %s' % response.json())
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return _("Response Timeout, please speak again.")
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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，如果没设定则再处理
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# if messages[0].get('role') != 'system':
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# sys_content = self.get_ai_system(param.get('sys_content'))
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# if sys_content:
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# messages.insert(0, sys_content)
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# # todo: 当前反向代理方式不通，要调整为 远程主机中接受请求，post到openai，再将结果返回给请求者
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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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# # # 返回的回答数量
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# # n=1,
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# # max_tokens=max_tokens,
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# # temperature=temperature,
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# # top_p=top_p,
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# # frequency_penalty=frequency_penalty,
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# # presence_penalty=presence_penalty,
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# # stop=stop,
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# # request_timeout=request_timeout,
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# # )
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# # if 'choices' in response:
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# # return response
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# # todo: 两种方式一样，要调整 v 服务器的二次处理 /root/toai.py
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# pdata = {
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# "model": self.ai_model,
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# "messages": messages,
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# "max_tokens": max_tokens,
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# "temperature": temperature,
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# "top_p": top_p,
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# "frequency_penalty": frequency_penalty,
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# "presence_penalty": presence_penalty,
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# "stop": stop
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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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# try:
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# res = response.json()
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# if 'choices' in res:
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# return res
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# except Exception as e:
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# _logger.warning("Get Response Json failed: %s", e)
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# else:
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# _logger.warning('=====================Openai output data: %s' % response.json())
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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": 1,
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# "max_tokens": max_tokens,
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# "top_p": 0.6,
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# "frequency_penalty": 0.1,
|
||
# "presence_penalty": 0.1,
|
||
# "stop": stop
|
||
# }
|
||
# response = openai.ChatCompletion.create(
|
||
# model=self.ai_model,
|
||
# messages=data
|
||
# )
|
||
# # response = requests.post(o_url, data=json.dumps(pdata), headers=headers, timeout=R_TIMEOUT)
|
||
# if 'choices' in response:
|
||
# return response
|
||
# else:
|
||
# _logger.warning('=====================openai output data: %s' % response.json())
|
||
|
||
# return _("Response Timeout, please speak again.")
|
||
|
||
def get_azure(self, data, author_id, answer_id, param={}):
|
||
self.ensure_one()
|
||
# only for azure
|
||
if not self.endpoint:
|
||
raise UserError(_("Please Set your AI robot's endpoint first."))
|
||
|
||
if not self.api_version:
|
||
raise UserError(_("Please Set your AI robot's API Version first."))
|
||
|
||
if self.stop:
|
||
stop = self.stop.split(',')
|
||
else:
|
||
stop = ["Human:", "AI:"]
|
||
if isinstance(data, list):
|
||
messages = data
|
||
else:
|
||
messages = [{"role": "user", "content": data}]
|
||
|
||
# 处理传参，传过来的优先于 robot 默认的
|
||
max_tokens = param.get('max_tokens') if param.get('max_tokens') else self.max_tokens
|
||
temperature = param.get('temperature') if param.get('temperature') else self.temperature
|
||
top_p = param.get('top_p') if param.get('top_p') else self.top_p
|
||
frequency_penalty = param.get('frequency_penalty') if param.get('frequency_penalty') else self.frequency_penalty
|
||
presence_penalty = param.get('presence_penalty') if param.get('presence_penalty') else self.presence_penalty
|
||
request_timeout= param.get('request_timeout') if param.get('request_timeout') else self.ai_timeout
|
||
|
||
# Ai角色设定，如果没设定则再处理
|
||
if messages[0].get('role') != 'system':
|
||
sys_content = self.get_ai_system(param.get('sys_content'))
|
||
if sys_content:
|
||
messages.insert(0, sys_content)
|
||
# 暂时不变
|
||
|
||
client = AzureOpenAI(
|
||
api_version=self.api_version,
|
||
azure_endpoint=self.endpoint,
|
||
api_key=self.openapi_api_key,
|
||
timeout=request_timeout
|
||
)
|
||
response = client.chat.completions.create(
|
||
model=self.engine,
|
||
messages=messages,
|
||
# 返回的回答数量
|
||
n=1,
|
||
max_tokens=max_tokens,
|
||
temperature=temperature,
|
||
top_p=top_p,
|
||
frequency_penalty=frequency_penalty,
|
||
presence_penalty=presence_penalty,
|
||
stop=None,
|
||
)
|
||
res = response.model_dump()
|
||
if 'choices' in res:
|
||
return res
|
||
else:
|
||
_logger.warning('=====================azure output data: %s' % response.json())
|
||
return _("Response Timeout, please speak again.")
|
||
|
||
@api.onchange('provider')
|
||
def _onchange_provider(self):
|
||
if self.provider == 'openai':
|
||
self.endpoint = 'https://api.openai.com/v1/chat/completions'
|
||
elif self.provider == 'azure':
|
||
self.endpoint = 'https://odoo.openai.azure.com'
|
||
|
||
if self.provider:
|
||
# 取头像
|
||
module_path = modules.get_module_path('app_chatgpt', display_warning=False)
|
||
if module_path:
|
||
path = modules.check_resource_path(module_path, ('static/description/src/%s.png' % self.provider))
|
||
if path:
|
||
image_file = tools.file_open(path, 'rb')
|
||
self.image_avatar = base64.b64encode(image_file.read())
|
||
|
||
@api.onchange('set_ai_model')
|
||
def _onchange_set_ai_model(self):
|
||
if self.set_ai_model:
|
||
self.ai_model = self.set_ai_model
|
||
else:
|
||
self.ai_model = None
|