estel/dify
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

chore(api/core): apply ruff reformatting (#7624)

Browse Source
This commit is contained in:
Bowen Liang
2024-09-10 17:00:20 +08:00
committed by GitHub
parent 178730266d
commit 2cf1187b32
724 changed files with 21180 additions and 21123 deletions
Download Patch File Download Diff File Expand all files Collapse all files

99
api/core/llm_generator/llm_generator.py
View File

@@ -43,21 +43,16 @@ class LLMGenerator:
with measure_time() as timer:
response = model_instance.invoke_llm(
prompt_messages=prompts,
model_parameters={
"max_tokens": 100,
"temperature": 1
},
stream=False
prompt_messages=prompts, model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
)
answer = response.message.content
cleaned_answer = re.sub(r'^.*(\{.*\}).*$', r'\1', answer, flags=re.DOTALL)
cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
result_dict = json.loads(cleaned_answer)
answer = result_dict['Your Output']
answer = result_dict["Your Output"]
name = answer.strip()
if len(name) > 75:
name = name[:75] + '...'
name = name[:75] + "..."
# get tracing instance
trace_manager = TraceQueueManager(app_id=app_id)
@@ -79,14 +74,9 @@ class LLMGenerator:
output_parser = SuggestedQuestionsAfterAnswerOutputParser()
format_instructions = output_parser.get_format_instructions()
prompt_template = PromptTemplateParser(
template="{{histories}}\n{{format_instructions}}\nquestions:\n"
)
prompt_template = PromptTemplateParser(template="{{histories}}\n{{format_instructions}}\nquestions:\n")
prompt = prompt_template.format({
"histories": histories,
"format_instructions": format_instructions
})
prompt = prompt_template.format({"histories": histories, "format_instructions": format_instructions})
try:
model_manager = ModelManager()
@@ -101,12 +91,7 @@ class LLMGenerator:
try:
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={
"max_tokens": 256,
"temperature": 0
},
stream=False
prompt_messages=prompt_messages, model_parameters={"max_tokens": 256, "temperature": 0}, stream=False
)
questions = output_parser.parse(response.message.content)
@@ -119,32 +104,24 @@ class LLMGenerator:
return questions
@classmethod
def generate_rule_config(cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512) -> dict:
def generate_rule_config(
cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512
) -> dict:
output_parser = RuleConfigGeneratorOutputParser()
error = ""
error_step = ""
rule_config = {
"prompt": "",
"variables": [],
"opening_statement": "",
"error": ""
}
model_parameters = {
"max_tokens": rule_config_max_tokens,
"temperature": 0.01
}
rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
model_parameters = {"max_tokens": rule_config_max_tokens, "temperature": 0.01}
if no_variable:
prompt_template = PromptTemplateParser(
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE
)
prompt_template = PromptTemplateParser(WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE)
prompt_generate = prompt_template.format(
inputs={
"TASK_DESCRIPTION": instruction,
},
remove_template_variables=False
remove_template_variables=False,
)
prompt_messages = [UserPromptMessage(content=prompt_generate)]
@@ -158,13 +135,11 @@ class LLMGenerator:
try:
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=model_parameters,
stream=False
prompt_messages=prompt_messages, model_parameters=model_parameters, stream=False
)
rule_config["prompt"] = response.message.content
except InvokeError as e:
error = str(e)
error_step = "generate rule config"
@@ -179,24 +154,18 @@ class LLMGenerator:
# get rule config prompt, parameter and statement
prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
prompt_template = PromptTemplateParser(
prompt_generate
)
prompt_template = PromptTemplateParser(prompt_generate)
parameter_template = PromptTemplateParser(
parameter_generate
)
parameter_template = PromptTemplateParser(parameter_generate)
statement_template = PromptTemplateParser(
statement_generate
)
statement_template = PromptTemplateParser(statement_generate)
# format the prompt_generate_prompt
prompt_generate_prompt = prompt_template.format(
inputs={
"TASK_DESCRIPTION": instruction,
},
remove_template_variables=False
remove_template_variables=False,
)
prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
@@ -213,9 +182,7 @@ class LLMGenerator:
try:
# the first step to generate the task prompt
prompt_content = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters=model_parameters,
stream=False
prompt_messages=prompt_messages, model_parameters=model_parameters, stream=False
)
except InvokeError as e:
error = str(e)
@@ -230,7 +197,7 @@ class LLMGenerator:
inputs={
"INPUT_TEXT": prompt_content.message.content,
},
remove_template_variables=False
remove_template_variables=False,
)
parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
@@ -240,15 +207,13 @@ class LLMGenerator:
"TASK_DESCRIPTION": instruction,
"INPUT_TEXT": prompt_content.message.content,
},
remove_template_variables=False
remove_template_variables=False,
)
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
try:
parameter_content = model_instance.invoke_llm(
prompt_messages=parameter_messages,
model_parameters=model_parameters,
stream=False
prompt_messages=parameter_messages, model_parameters=model_parameters, stream=False
)
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.content)
except InvokeError as e:
@@ -257,9 +222,7 @@ class LLMGenerator:
try:
statement_content = model_instance.invoke_llm(
prompt_messages=statement_messages,
model_parameters=model_parameters,
stream=False
prompt_messages=statement_messages, model_parameters=model_parameters, stream=False
)
rule_config["opening_statement"] = statement_content.message.content
except InvokeError as e:
@@ -284,18 +247,10 @@ class LLMGenerator:
model_type=ModelType.LLM,
)
prompt_messages = [
SystemPromptMessage(content=prompt),
UserPromptMessage(content=query)
]
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={
'temperature': 0.01,
"max_tokens": 2000
},
stream=False
prompt_messages=prompt_messages, model_parameters={"temperature": 0.01, "max_tokens": 2000}, stream=False
)
answer = response.message.content

20
api/core/llm_generator/output_parser/rule_config_generator.py
View File

@@ -10,9 +10,12 @@ from libs.json_in_md_parser import parse_and_check_json_markdown
class RuleConfigGeneratorOutputParser:
def get_format_instructions(self) -> tuple[str, str, str]:
return RULE_CONFIG_PROMPT_GENERATE_TEMPLATE, RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE, RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE
return (
RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
RULE_CONFIG_PARAMETER_GENERATE_TEMPLATE,
RULE_CONFIG_STATEMENT_GENERATE_TEMPLATE,
)
def parse(self, text: str) -> Any:
try:
@@ -21,16 +24,9 @@ class RuleConfigGeneratorOutputParser:
if not isinstance(parsed["prompt"], str):
raise ValueError("Expected 'prompt' to be a string.")
if not isinstance(parsed["variables"], list):
raise ValueError(
"Expected 'variables' to be a list."
)
raise ValueError("Expected 'variables' to be a list.")
if not isinstance(parsed["opening_statement"], str):
raise ValueError(
"Expected 'opening_statement' to be a str."
)
raise ValueError("Expected 'opening_statement' to be a str.")
return parsed
except Exception as e:
raise OutputParserException(
f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}"
)
raise OutputParserException(f"Parsing text\n{text}\n of rule config generator raised following error:\n{e}")

3
api/core/llm_generator/output_parser/suggested_questions_after_answer.py
View File

@@ -6,7 +6,6 @@ from core.llm_generator.prompts import SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCT
class SuggestedQuestionsAfterAnswerOutputParser:
def get_format_instructions(self) -> str:
return SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT
@@ -15,7 +14,7 @@ class SuggestedQuestionsAfterAnswerOutputParser:
if action_match is not None:
json_obj = json.loads(action_match.group(0).strip())
else:
json_obj= []
json_obj = []
print(f"Could not parse LLM output: {text}")
return json_obj

20
api/core/llm_generator/prompts.py
View File

@@ -66,19 +66,19 @@ SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
"and keeping each question under 20 characters.\n"
"MAKE SURE your output is the SAME language as the Assistant's latest response(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
"The output must be an array in JSON format following the specified schema:\n"
"[\"question1\",\"question2\",\"question3\"]\n"
'["question1","question2","question3"]\n'
)
GENERATOR_QA_PROMPT = (
'<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge in the long text. Please think step by step.'
'Step 1: Understand and summarize the main content of this text.\n'
'Step 2: What key information or concepts are mentioned in this text?\n'
'Step 3: Decompose or combine multiple pieces of information and concepts.\n'
'Step 4: Generate questions and answers based on these key information and concepts.\n'
'<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. '
'You must answer in {language}, in a style that is clear and detailed in {language}. No language other than {language} should be used. \n'
'<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n'
'<QA Pairs>'
"<Task> The user will send a long text. Generate a Question and Answer pairs only using the knowledge in the long text. Please think step by step."
"Step 1: Understand and summarize the main content of this text.\n"
"Step 2: What key information or concepts are mentioned in this text?\n"
"Step 3: Decompose or combine multiple pieces of information and concepts.\n"
"Step 4: Generate questions and answers based on these key information and concepts.\n"
"<Constraints> The questions should be clear and detailed, and the answers should be detailed and complete. "
"You must answer in {language}, in a style that is clear and detailed in {language}. No language other than {language} should be used. \n"
"<Format> Use the following format: Q1:\nA1:\nQ2:\nA2:...\n"
"<QA Pairs>"
)
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE = """