Garfield Dai
2 years ago
committed by
GitHub
parent
2d1cb076c6
commit
42a5b3ec17
@ -0,0 +1,26 @@
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from flask_restful import Resource, reqparse
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from controllers.console import api
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from controllers.console.setup import setup_required
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from controllers.console.wraps import account_initialization_required
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from libs.login import login_required
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from services.advanced_prompt_template_service import AdvancedPromptTemplateService
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class AdvancedPromptTemplateList(Resource):
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@setup_required
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@login_required
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@account_initialization_required
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def get(self):
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parser = reqparse.RequestParser()
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parser.add_argument('app_mode', type=str, required=True, location='args')
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parser.add_argument('model_mode', type=str, required=True, location='args')
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parser.add_argument('has_context', type=str, required=False, default='true', location='args')
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parser.add_argument('model_name', type=str, required=True, location='args')
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args = parser.parse_args()
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service = AdvancedPromptTemplateService()
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return service.get_prompt(args)
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api.add_resource(AdvancedPromptTemplateList, '/app/prompt-templates')
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CONTEXT = "Use the following context as your learned knowledge, inside <context></context> XML tags.\n\n<context>\n{{#context#}}\n</context>\n\nWhen answer to user:\n- If you don't know, just say that you don't know.\n- If you don't know when you are not sure, ask for clarification.\nAvoid mentioning that you obtained the information from the context.\nAnd answer according to the language of the user's question.\n"
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BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找到的材料，给出全面专业的解释，但不会过度演绎。同时回答中不会暴露引用的材料：\n\n```\n{{#context#}}\n```\n\n"
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CHAT_APP_COMPLETION_PROMPT_CONFIG = {
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"completion_prompt_config": {
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"prompt": {
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"text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "
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},
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"conversation_histories_role": {
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"user_prefix": "Human",
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"assistant_prefix": "Assistant"
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}
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}
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}
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CHAT_APP_CHAT_PROMPT_CONFIG = {
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"chat_prompt_config": {
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"prompt": [{
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"role": "system",
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"text": "{{#pre_prompt#}}"
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}]
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}
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}
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COMPLETION_APP_CHAT_PROMPT_CONFIG = {
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"chat_prompt_config": {
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"prompt": [{
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"role": "user",
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"text": "{{#pre_prompt#}}"
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}]
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}
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}
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COMPLETION_APP_COMPLETION_PROMPT_CONFIG = {
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"completion_prompt_config": {
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"prompt": {
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"text": "{{#pre_prompt#}}"
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}
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}
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}
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BAICHUAN_CHAT_APP_COMPLETION_PROMPT_CONFIG = {
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"completion_prompt_config": {
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"prompt": {
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"text": "{{#pre_prompt#}}\n\n用户和助手的历史对话内容如下：\n```\n{{#histories#}}\n```\n\n\n\n用户：{{#query#}}"
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},
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"conversation_histories_role": {
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"user_prefix": "用户",
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"assistant_prefix": "助手"
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}
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}
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}
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BAICHUAN_CHAT_APP_CHAT_PROMPT_CONFIG = {
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"chat_prompt_config": {
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"prompt": [{
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"role": "system",
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"text": "{{#pre_prompt#}}"
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}]
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}
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}
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BAICHUAN_COMPLETION_APP_CHAT_PROMPT_CONFIG = {
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"chat_prompt_config": {
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"prompt": [{
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"role": "user",
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"text": "{{#pre_prompt#}}"
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}]
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}
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}
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BAICHUAN_COMPLETION_APP_COMPLETION_PROMPT_CONFIG = {
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"completion_prompt_config": {
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"prompt": {
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"text": "{{#pre_prompt#}}"
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}
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}
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}
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@ -1,38 +1,24 @@
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import re
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from langchain.schema import BaseMessage, SystemMessage, AIMessage, HumanMessage
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from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, AIMessagePromptTemplate
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from langchain.schema import BaseMessage
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from core.prompt.prompt_template import JinjaPromptTemplate
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from core.prompt.prompt_template import PromptTemplateParser
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class PromptBuilder:
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@classmethod
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def parse_prompt(cls, prompt: str, inputs: dict) -> str:
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prompt_template = PromptTemplateParser(prompt)
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prompt_inputs = {k: inputs[k] for k in prompt_template.variable_keys if k in inputs}
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prompt = prompt_template.format(prompt_inputs)
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return prompt
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@classmethod
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def to_system_message(cls, prompt_content: str, inputs: dict) -> BaseMessage:
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prompt_template = JinjaPromptTemplate.from_template(prompt_content)
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system_prompt_template = SystemMessagePromptTemplate(prompt=prompt_template)
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prompt_inputs = {k: inputs[k] for k in system_prompt_template.input_variables if k in inputs}
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system_message = system_prompt_template.format(**prompt_inputs)
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return system_message
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return SystemMessage(content=cls.parse_prompt(prompt_content, inputs))
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@classmethod
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def to_ai_message(cls, prompt_content: str, inputs: dict) -> BaseMessage:
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prompt_template = JinjaPromptTemplate.from_template(prompt_content)
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ai_prompt_template = AIMessagePromptTemplate(prompt=prompt_template)
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prompt_inputs = {k: inputs[k] for k in ai_prompt_template.input_variables if k in inputs}
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ai_message = ai_prompt_template.format(**prompt_inputs)
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return ai_message
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return AIMessage(content=cls.parse_prompt(prompt_content, inputs))
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@classmethod
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def to_human_message(cls, prompt_content: str, inputs: dict) -> BaseMessage:
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prompt_template = JinjaPromptTemplate.from_template(prompt_content)
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human_prompt_template = HumanMessagePromptTemplate(prompt=prompt_template)
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human_message = human_prompt_template.format(**inputs)
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return human_message
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@classmethod
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def process_template(cls, template: str):
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processed_template = re.sub(r'\{{2}(.+)\}{2}', r'{\1}', template)
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# processed_template = re.sub(r'\{([a-zA-Z_]\w+?)\}', r'\1', template)
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# processed_template = re.sub(r'\{\{([a-zA-Z_]\w+?)\}\}', r'{\1}', processed_template)
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return processed_template
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return HumanMessage(content=cls.parse_prompt(prompt_content, inputs))
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@ -1,79 +1,39 @@
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import re
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from typing import Any
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from jinja2 import Environment, meta
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from langchain import PromptTemplate
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from langchain.formatting import StrictFormatter
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REGEX = re.compile(r"\{\{([a-zA-Z_][a-zA-Z0-9_]{1,29}|#histories#|#query#|#context#)\}\}")
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class JinjaPromptTemplate(PromptTemplate):
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template_format: str = "jinja2"
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"""The format of the prompt template. Options are: 'f-string', 'jinja2'."""
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class PromptTemplateParser:
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"""
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Rules:
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@classmethod
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def from_template(cls, template: str, **kwargs: Any) -> PromptTemplate:
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"""Load a prompt template from a template."""
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env = Environment()
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template = template.replace("{{}}", "{}")
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ast = env.parse(template)
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input_variables = meta.find_undeclared_variables(ast)
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if "partial_variables" in kwargs:
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partial_variables = kwargs["partial_variables"]
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input_variables = {
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var for var in input_variables if var not in partial_variables
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}
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return cls(
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input_variables=list(sorted(input_variables)), template=template, **kwargs
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)
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class OutLinePromptTemplate(PromptTemplate):
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@classmethod
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def from_template(cls, template: str, **kwargs: Any) -> PromptTemplate:
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"""Load a prompt template from a template."""
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input_variables = {
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v for _, v, _, _ in OneLineFormatter().parse(template) if v is not None
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}
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return cls(
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input_variables=list(sorted(input_variables)), template=template, **kwargs
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)
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def format(self, **kwargs: Any) -> str:
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"""Format the prompt with the inputs.
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1. Template variables must be enclosed in `{{}}`.
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2. The template variable Key can only be: letters + numbers + underscore, with a maximum length of 16 characters,
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and can only start with letters and underscores.
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3. The template variable Key cannot contain new lines or spaces, and must comply with rule 2.
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4. In addition to the above, 3 types of special template variable Keys are accepted:
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`{{#histories#}}` `{{#query#}}` `{{#context#}}`. No other `{{##}}` template variables are allowed.
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"""
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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def __init__(self, template: str):
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self.template = template
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self.variable_keys = self.extract()
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Returns:
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A formatted string.
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def extract(self) -> list:
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# Regular expression to match the template rules
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return re.findall(REGEX, self.template)
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Example:
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def format(self, inputs: dict, remove_template_variables: bool = True) -> str:
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def replacer(match):
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key = match.group(1)
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value = inputs.get(key, match.group(0)) # return original matched string if key not found
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.. code-block:: python
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if remove_template_variables:
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return PromptTemplateParser.remove_template_variables(value)
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return value
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prompt.format(variable1="foo")
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"""
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kwargs = self._merge_partial_and_user_variables(**kwargs)
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return OneLineFormatter().format(self.template, **kwargs)
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return re.sub(REGEX, replacer, self.template)
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class OneLineFormatter(StrictFormatter):
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def parse(self, format_string):
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last_end = 0
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results = []
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for match in re.finditer(r"{([a-zA-Z_]\w*)}", format_string):
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field_name = match.group(1)
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start, end = match.span()
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literal_text = format_string[last_end:start]
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last_end = end
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results.append((literal_text, field_name, '', None))
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remaining_literal_text = format_string[last_end:]
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if remaining_literal_text:
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results.append((remaining_literal_text, None, None, None))
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return results
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@classmethod
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def remove_template_variables(cls, text: str):
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return re.sub(REGEX, r'{\1}', text)
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from events.message_event import message_was_created
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from tasks.generate_conversation_summary_task import generate_conversation_summary_task
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@message_was_created.connect
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def handle(sender, **kwargs):
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message = sender
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conversation = kwargs.get('conversation')
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is_first_message = kwargs.get('is_first_message')
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if not is_first_message and conversation.mode == 'chat' and not conversation.summary:
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history_message_count = conversation.message_count
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if history_message_count >= 5:
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generate_conversation_summary_task.delay(conversation.id)
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"""add advanced prompt templates
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Revision ID: b3a09c049e8e
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Revises: 2e9819ca5b28
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Create Date: 2023-10-10 15:23:23.395420
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"""
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from alembic import op
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import sqlalchemy as sa
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# revision identifiers, used by Alembic.
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revision = 'b3a09c049e8e'
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down_revision = '2e9819ca5b28'
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branch_labels = None
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depends_on = None
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def upgrade():
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# ### commands auto generated by Alembic - please adjust! ###
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with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
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batch_op.add_column(sa.Column('prompt_type', sa.String(length=255), nullable=False, server_default='simple'))
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batch_op.add_column(sa.Column('chat_prompt_config', sa.Text(), nullable=True))
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batch_op.add_column(sa.Column('completion_prompt_config', sa.Text(), nullable=True))
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batch_op.add_column(sa.Column('dataset_configs', sa.Text(), nullable=True))
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# ### end Alembic commands ###
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def downgrade():
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# ### commands auto generated by Alembic - please adjust! ###
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with op.batch_alter_table('app_model_configs', schema=None) as batch_op:
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batch_op.drop_column('dataset_configs')
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batch_op.drop_column('completion_prompt_config')
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batch_op.drop_column('chat_prompt_config')
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batch_op.drop_column('prompt_type')
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# ### end Alembic commands ###
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import copy
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from core.prompt.advanced_prompt_templates import CHAT_APP_COMPLETION_PROMPT_CONFIG, CHAT_APP_CHAT_PROMPT_CONFIG, COMPLETION_APP_CHAT_PROMPT_CONFIG, COMPLETION_APP_COMPLETION_PROMPT_CONFIG, \
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BAICHUAN_CHAT_APP_COMPLETION_PROMPT_CONFIG, BAICHUAN_CHAT_APP_CHAT_PROMPT_CONFIG, BAICHUAN_COMPLETION_APP_COMPLETION_PROMPT_CONFIG, BAICHUAN_COMPLETION_APP_CHAT_PROMPT_CONFIG, CONTEXT, BAICHUAN_CONTEXT
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class AdvancedPromptTemplateService:
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def get_prompt(self, args: dict) -> dict:
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app_mode = args['app_mode']
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model_mode = args['model_mode']
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model_name = args['model_name']
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has_context = args['has_context']
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if 'baichuan' in model_name:
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return self.get_baichuan_prompt(app_mode, model_mode, has_context)
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else:
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return self.get_common_prompt(app_mode, model_mode, has_context)
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def get_common_prompt(self, app_mode: str, model_mode:str, has_context: bool) -> dict:
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if app_mode == 'chat':
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if model_mode == 'completion':
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return self.get_completion_prompt(copy.deepcopy(CHAT_APP_COMPLETION_PROMPT_CONFIG), has_context, CONTEXT)
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elif model_mode == 'chat':
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return self.get_chat_prompt(copy.deepcopy(CHAT_APP_CHAT_PROMPT_CONFIG), has_context, CONTEXT)
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elif app_mode == 'completion':
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if model_mode == 'completion':
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return self.get_completion_prompt(copy.deepcopy(COMPLETION_APP_COMPLETION_PROMPT_CONFIG), has_context, CONTEXT)
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elif model_mode == 'chat':
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return self.get_chat_prompt(copy.deepcopy(COMPLETION_APP_CHAT_PROMPT_CONFIG), has_context, CONTEXT)
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def get_completion_prompt(self, prompt_template: str, has_context: bool, context: str) -> dict:
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if has_context == 'true':
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prompt_template['completion_prompt_config']['prompt']['text'] = context + prompt_template['completion_prompt_config']['prompt']['text']
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return prompt_template
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def get_chat_prompt(self, prompt_template: str, has_context: bool, context: str) -> dict:
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if has_context == 'true':
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prompt_template['chat_prompt_config']['prompt'][0]['text'] = context + prompt_template['chat_prompt_config']['prompt'][0]['text']
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return prompt_template
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def get_baichuan_prompt(self, app_mode: str, model_mode:str, has_context: bool) -> dict:
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if app_mode == 'chat':
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if model_mode == 'completion':
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return self.get_completion_prompt(copy.deepcopy(BAICHUAN_CHAT_APP_COMPLETION_PROMPT_CONFIG), has_context, BAICHUAN_CONTEXT)
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elif model_mode == 'chat':
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return self.get_chat_prompt(copy.deepcopy(BAICHUAN_CHAT_APP_CHAT_PROMPT_CONFIG), has_context, BAICHUAN_CONTEXT)
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elif app_mode == 'completion':
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if model_mode == 'completion':
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return self.get_completion_prompt(copy.deepcopy(BAICHUAN_COMPLETION_APP_COMPLETION_PROMPT_CONFIG), has_context, BAICHUAN_CONTEXT)
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elif model_mode == 'chat':
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return self.get_chat_prompt(copy.deepcopy(BAICHUAN_COMPLETION_APP_CHAT_PROMPT_CONFIG), has_context, BAICHUAN_CONTEXT)
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import logging
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import time
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import click
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from celery import shared_task
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from werkzeug.exceptions import NotFound
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from core.generator.llm_generator import LLMGenerator
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from core.model_providers.error import LLMError, ProviderTokenNotInitError
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from extensions.ext_database import db
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from models.model import Conversation, Message
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@shared_task(queue='generation')
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def generate_conversation_summary_task(conversation_id: str):
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"""
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Async Generate conversation summary
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:param conversation_id:
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Usage: generate_conversation_summary_task.delay(conversation_id)
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"""
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logging.info(click.style('Start generate conversation summary: {}'.format(conversation_id), fg='green'))
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start_at = time.perf_counter()
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conversation = db.session.query(Conversation).filter(Conversation.id == conversation_id).first()
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if not conversation:
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raise NotFound('Conversation not found')
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try:
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# get conversation messages count
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history_message_count = conversation.message_count
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if history_message_count >= 5 and not conversation.summary:
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app_model = conversation.app
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if not app_model:
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return
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history_messages = db.session.query(Message).filter(Message.conversation_id == conversation.id) \
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.order_by(Message.created_at.asc()).all()
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conversation.summary = LLMGenerator.generate_conversation_summary(app_model.tenant_id, history_messages)
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db.session.add(conversation)
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db.session.commit()
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except (LLMError, ProviderTokenNotInitError):
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conversation.summary = '[No Summary]'
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db.session.commit()
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pass
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except Exception as e:
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conversation.summary = '[No Summary]'
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db.session.commit()
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||||
logging.exception(e)
|
||||
|
||||
end_at = time.perf_counter()
|
||||
logging.info(
|
||||
click.style('Conversation summary generated: {} latency: {}'.format(conversation_id, end_at - start_at),
|
||||
fg='green'))
|
||||
Loading…
Reference in New Issue