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https://github.com/langgenius/dify.git
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refactor app
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takatost
0
api/core/prompt/__init__.py
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0
api/core/prompt/__init__.py
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@ -15,7 +15,7 @@ from core.model_runtime.entities.message_entities import (
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TextPromptMessageContent,
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UserPromptMessage,
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)
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from core.prompt.prompt_template import PromptTemplateParser
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from core.prompt.utils.prompt_template_parser import PromptTemplateParser
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from core.prompt.prompt_transform import PromptTransform
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from core.prompt.simple_prompt_transform import ModelMode
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@ -1,33 +0,0 @@
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from typing import Any
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from langchain.schema import BaseOutputParser, OutputParserException
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from core.prompt.prompts import RULE_CONFIG_GENERATE_TEMPLATE
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from libs.json_in_md_parser import parse_and_check_json_markdown
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class RuleConfigGeneratorOutputParser(BaseOutputParser):
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def get_format_instructions(self) -> str:
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return RULE_CONFIG_GENERATE_TEMPLATE
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def parse(self, text: str) -> Any:
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try:
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expected_keys = ["prompt", "variables", "opening_statement"]
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parsed = parse_and_check_json_markdown(text, expected_keys)
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if not isinstance(parsed["prompt"], str):
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raise ValueError("Expected 'prompt' to be a string.")
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if not isinstance(parsed["variables"], list):
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raise ValueError(
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"Expected 'variables' to be a list."
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)
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if not isinstance(parsed["opening_statement"], str):
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raise ValueError(
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"Expected 'opening_statement' to be a str."
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)
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return parsed
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except Exception as e:
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raise OutputParserException(
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f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}"
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)
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@ -1,24 +0,0 @@
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import json
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import re
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from typing import Any
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from langchain.schema import BaseOutputParser
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from core.model_runtime.errors.invoke import InvokeError
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from core.prompt.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
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class SuggestedQuestionsAfterAnswerOutputParser(BaseOutputParser):
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def get_format_instructions(self) -> str:
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return SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
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def parse(self, text: str) -> Any:
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json_string = text.strip()
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action_match = re.search(r".*(\[\".+\"\]).*", json_string, re.DOTALL)
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if action_match is not None:
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json_obj = json.loads(action_match.group(1).strip(), strict=False)
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else:
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raise InvokeError("Could not parse LLM output: {text}")
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return json_obj
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api/core/prompt/prompt_templates/__init__.py
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api/core/prompt/prompt_templates/__init__.py
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@ -1,136 +0,0 @@
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# Written by YORKI MINAKO🤡
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CONVERSATION_TITLE_PROMPT = """You need to decompose the user's input into "subject" and "intention" in order to accurately figure out what the user's input language actually is.
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Notice: the language type user use could be diverse, which can be English, Chinese, Español, Arabic, Japanese, French, and etc.
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MAKE SURE your output is the SAME language as the user's input!
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Your output is restricted only to: (Input language) Intention + Subject(short as possible)
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Your output MUST be a valid JSON.
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Tip: When the user's question is directed at you (the language model), you can add an emoji to make it more fun.
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example 1:
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User Input: hi, yesterday i had some burgers.
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{
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"Language Type": "The user's input is pure English",
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"Your Reasoning": "The language of my output must be pure English.",
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"Your Output": "sharing yesterday's food"
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}
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example 2:
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User Input: hello
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{
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"Language Type": "The user's input is written in pure English",
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"Your Reasoning": "The language of my output must be pure English.",
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"Your Output": "Greeting myself☺️"
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}
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example 3:
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User Input: why mmap file: oom
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{
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"Language Type": "The user's input is written in pure English",
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"Your Reasoning": "The language of my output must be pure English.",
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"Your Output": "Asking about the reason for mmap file: oom"
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}
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example 4:
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User Input: www.convinceme.yesterday-you-ate-seafood.tv讲了什么？
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{
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"Language Type": "The user's input English-Chinese mixed",
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"Your Reasoning": "The English-part is an URL, the main intention is still written in Chinese, so the language of my output must be using Chinese.",
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"Your Output": "询问网站www.convinceme.yesterday-you-ate-seafood.tv"
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}
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example 5:
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User Input: why小红的年龄is老than小明？
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{
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"Language Type": "The user's input is English-Chinese mixed",
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"Your Reasoning": "The English parts are subjective particles, the main intention is written in Chinese, besides, Chinese occupies a greater \"actual meaning\" than English, so the language of my output must be using Chinese.",
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"Your Output": "询问小红和小明的年龄"
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}
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example 6:
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User Input: yo, 你今天咋样？
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{
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"Language Type": "The user's input is English-Chinese mixed",
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"Your Reasoning": "The English-part is a subjective particle, the main intention is written in Chinese, so the language of my output must be using Chinese.",
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"Your Output": "查询今日我的状态☺️"
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}
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User Input:
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"""
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SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
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"Please help me predict the three most likely questions that human would ask, "
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"and keeping each question under 20 characters.\n"
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"The output must be an array in JSON format following the specified schema:\n"
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"[\"question1\",\"question2\",\"question3\"]\n"
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)
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GENERATOR_QA_PROMPT = (
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'The user will send a long text. Please think step by step.'
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'Step 1: Understand and summarize the main content of this text.\n'
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'Step 2: What key information or concepts are mentioned in this text?\n'
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'Step 3: Decompose or combine multiple pieces of information and concepts.\n'
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'Step 4: Generate 20 questions and answers based on these key information and concepts.'
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'The questions should be clear and detailed, and the answers should be detailed and complete.\n'
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"Answer MUST according to the the language:{language} and in the following format: Q1:\nA1:\nQ2:\nA2:...\n"
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)
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RULE_CONFIG_GENERATE_TEMPLATE = """Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \
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the model prompt that best suits the input.
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You will be provided with the prompt, variables, and an opening statement.
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Only the content enclosed in double curly braces, such as {{variable}}, in the prompt can be considered as a variable; \
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otherwise, it cannot exist as a variable in the variables.
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If you believe revising the original input will result in a better response from the language model, you may \
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suggest revisions.
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<< FORMATTING >>
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Return a markdown code snippet with a JSON object formatted to look like, \
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no any other string out of markdown code snippet:
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```json
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{{{{
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"prompt": string \\ generated prompt
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"variables": list of string \\ variables
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"opening_statement": string \\ an opening statement to guide users on how to ask questions with generated prompt \
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and fill in variables, with a welcome sentence, and keep TLDR.
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}}}}
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```
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<< EXAMPLES >>
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[EXAMPLE A]
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```json
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{
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"prompt": "Write a letter about love",
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"variables": [],
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"opening_statement": "Hi! I'm your love letter writer AI."
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}
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```
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[EXAMPLE B]
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```json
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{
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"prompt": "Translate from {{lanA}} to {{lanB}}",
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"variables": ["lanA", "lanB"],
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"opening_statement": "Welcome to use translate app"
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}
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```
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[EXAMPLE C]
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```json
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{
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"prompt": "Write a story about {{topic}}",
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"variables": ["topic"],
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"opening_statement": "I'm your story writer"
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}
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```
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<< MY INTENDED AUDIENCES >>
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{{audiences}}
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<< HOPING TO SOLVE >>
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{{hoping_to_solve}}
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<< OUTPUT >>
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"""
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@ -15,7 +15,7 @@ from core.model_runtime.entities.message_entities import (
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TextPromptMessageContent,
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UserPromptMessage,
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)
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from core.prompt.prompt_template import PromptTemplateParser
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from core.prompt.utils.prompt_template_parser import PromptTemplateParser
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from core.prompt.prompt_transform import PromptTransform
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from models.model import AppMode
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@ -275,7 +275,7 @@ class SimplePromptTransform(PromptTransform):
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return prompt_file_contents[prompt_file_name]
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# Get the absolute path of the subdirectory
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prompt_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'generate_prompts')
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prompt_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'prompt_templates')
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json_file_path = os.path.join(prompt_path, f'{prompt_file_name}.json')
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# Open the JSON file and read its content
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0
api/core/prompt/utils/__init__.py
Normal file
0
api/core/prompt/utils/__init__.py
Normal file
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