fix python style check
lizb
1 year ago
parent
012ad52c44
commit
155b682817
@ -1,388 +1,387 @@
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional, cast
|
||||
|
||||
from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
|
||||
from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
|
||||
from core.llm_generator.prompts import (
|
||||
CONVERSATION_TITLE_PROMPT,
|
||||
GENERATOR_QA_PROMPT,
|
||||
JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||
SYSTEM_STRUCTURED_OUTPUT_GENERATE,
|
||||
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
|
||||
)
|
||||
from core.model_manager import ModelManager
|
||||
from core.model_runtime.entities.llm_entities import LLMResult
|
||||
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
|
||||
from core.model_runtime.entities.model_entities import ModelType
|
||||
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
|
||||
from core.ops.entities.trace_entity import TraceTaskName
|
||||
from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
|
||||
from core.ops.utils import measure_time
|
||||
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
|
||||
import json_repair
|
||||
|
||||
from core.workflow.nodes.llm.exc import LLMNodeError
|
||||
|
||||
|
||||
class LLMGenerator:
|
||||
@classmethod
|
||||
def generate_conversation_name(
|
||||
cls, tenant_id: str, query, conversation_id: Optional[str] = None, app_id: Optional[str] = None
|
||||
):
|
||||
prompt = CONVERSATION_TITLE_PROMPT
|
||||
|
||||
if len(query) > 2000:
|
||||
query = query[:300] + "...[TRUNCATED]..." + query[-300:]
|
||||
|
||||
query = query.replace("\n", " ")
|
||||
|
||||
prompt += query + "\n"
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
prompts = [UserPromptMessage(content=prompt)]
|
||||
|
||||
with measure_time() as timer:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompts), model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
|
||||
),
|
||||
)
|
||||
answer = cast(str, response.message.content)
|
||||
cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
|
||||
if cleaned_answer is None:
|
||||
return ""
|
||||
result_dict = json.loads(cleaned_answer)
|
||||
answer = result_dict["Your Output"]
|
||||
name = answer.strip()
|
||||
|
||||
if len(name) > 75:
|
||||
name = name[:75] + "..."
|
||||
|
||||
# get tracing instance
|
||||
trace_manager = TraceQueueManager(app_id=app_id)
|
||||
trace_manager.add_trace_task(
|
||||
TraceTask(
|
||||
TraceTaskName.GENERATE_NAME_TRACE,
|
||||
conversation_id=conversation_id,
|
||||
generate_conversation_name=name,
|
||||
inputs=prompt,
|
||||
timer=timer,
|
||||
tenant_id=tenant_id,
|
||||
)
|
||||
)
|
||||
|
||||
return name
|
||||
|
||||
@classmethod
|
||||
def generate_suggested_questions_after_answer(cls, tenant_id: str, histories: str):
|
||||
output_parser = SuggestedQuestionsAfterAnswerOutputParser()
|
||||
format_instructions = output_parser.get_format_instructions()
|
||||
|
||||
prompt_template = PromptTemplateParser(template="{{histories}}\n{{format_instructions}}\nquestions:\n")
|
||||
|
||||
prompt = prompt_template.format({"histories": histories, "format_instructions": format_instructions})
|
||||
|
||||
try:
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
except InvokeAuthorizationError:
|
||||
return []
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages),
|
||||
model_parameters={"max_tokens": 256, "temperature": 0},
|
||||
stream=False,
|
||||
),
|
||||
)
|
||||
|
||||
questions = output_parser.parse(cast(str, response.message.content))
|
||||
except InvokeError:
|
||||
questions = []
|
||||
except Exception:
|
||||
logging.exception("Failed to generate suggested questions after answer")
|
||||
questions = []
|
||||
|
||||
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:
|
||||
output_parser = RuleConfigGeneratorOutputParser()
|
||||
|
||||
error = ""
|
||||
error_step = ""
|
||||
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_generate = prompt_template.format(
|
||||
inputs={
|
||||
"TASK_DESCRIPTION": instruction,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt_generate)]
|
||||
|
||||
model_manager = ModelManager()
|
||||
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
rule_config["prompt"] = cast(str, response.message.content)
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate rule config"
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||
rule_config["error"] = str(e)
|
||||
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
# get rule config prompt, parameter and statement
|
||||
prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
|
||||
|
||||
prompt_template = PromptTemplateParser(prompt_generate)
|
||||
|
||||
parameter_template = PromptTemplateParser(parameter_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,
|
||||
)
|
||||
prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
|
||||
|
||||
# get model instance
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
try:
|
||||
try:
|
||||
# the first step to generate the task prompt
|
||||
prompt_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate prefix prompt"
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
rule_config["prompt"] = cast(str, prompt_content.message.content)
|
||||
|
||||
if not isinstance(prompt_content.message.content, str):
|
||||
raise NotImplementedError("prompt content is not a string")
|
||||
parameter_generate_prompt = parameter_template.format(
|
||||
inputs={
|
||||
"INPUT_TEXT": prompt_content.message.content,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
|
||||
|
||||
# the second step to generate the task_parameter and task_statement
|
||||
statement_generate_prompt = statement_template.format(
|
||||
inputs={
|
||||
"TASK_DESCRIPTION": instruction,
|
||||
"INPUT_TEXT": prompt_content.message.content,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
|
||||
|
||||
try:
|
||||
parameter_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate variables"
|
||||
|
||||
try:
|
||||
statement_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
rule_config["opening_statement"] = cast(str, statement_content.message.content)
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate conversation opener"
|
||||
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||
rule_config["error"] = str(e)
|
||||
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
@classmethod
|
||||
def generate_code(
|
||||
cls,
|
||||
tenant_id: str,
|
||||
instruction: str,
|
||||
model_config: dict,
|
||||
code_language: str = "javascript",
|
||||
max_tokens: int = 1000,
|
||||
) -> dict:
|
||||
if code_language == "python":
|
||||
prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||
else:
|
||||
prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||
|
||||
prompt = prompt_template.format(
|
||||
inputs={
|
||||
"INSTRUCTION": instruction,
|
||||
"CODE_LANGUAGE": code_language,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||
model_parameters = {"max_tokens": max_tokens, "temperature": 0.01}
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
generated_code = cast(str, response.message.content)
|
||||
return {"code": generated_code, "language": code_language, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
|
||||
except Exception as e:
|
||||
logging.exception(
|
||||
f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
|
||||
)
|
||||
return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
|
||||
|
||||
@classmethod
|
||||
def generate_qa_document(cls, tenant_id: str, query, document_language: str):
|
||||
prompt = GENERATOR_QA_PROMPT.format(language=document_language)
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
|
||||
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
|
||||
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=prompt_messages,
|
||||
model_parameters={"temperature": 0.01, "max_tokens": 2000},
|
||||
stream=False,
|
||||
),
|
||||
)
|
||||
|
||||
answer = cast(str, response.message.content)
|
||||
return answer.strip()
|
||||
|
||||
@classmethod
|
||||
def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
prompt_messages = [
|
||||
SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
|
||||
UserPromptMessage(content=instruction),
|
||||
]
|
||||
model_parameters = model_config.get("model_parameters", {})
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
generated_json_schema = cast(str, response.message.content)
|
||||
try:
|
||||
generated_json_schema = json.loads(generated_json_schema)
|
||||
except json.JSONDecodeError:
|
||||
generated_json_schema = json_repair.loads(generated_json_schema)
|
||||
if not isinstance(generated_json_schema, (dict | list)):
|
||||
raise LLMNodeError(f"Failed to parse structured output from llm: {response.message.content}")
|
||||
generated_json_schema = json.dumps(generated_json_schema, indent=2, ensure_ascii=False)
|
||||
|
||||
return {"output": generated_json_schema, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to invoke LLM model, model: {model_config.get('name')}")
|
||||
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
||||
import json
|
||||
import json_repair
|
||||
import logging
|
||||
import re
|
||||
from typing import Optional, cast
|
||||
|
||||
from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
|
||||
from core.llm_generator.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
|
||||
from core.llm_generator.prompts import (
|
||||
CONVERSATION_TITLE_PROMPT,
|
||||
GENERATOR_QA_PROMPT,
|
||||
JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||
SYSTEM_STRUCTURED_OUTPUT_GENERATE,
|
||||
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
|
||||
)
|
||||
from core.model_manager import ModelManager
|
||||
from core.model_runtime.entities.llm_entities import LLMResult
|
||||
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
|
||||
from core.model_runtime.entities.model_entities import ModelType
|
||||
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
|
||||
from core.workflow.nodes.llm.exc import LLMNodeError
|
||||
from core.ops.entities.trace_entity import TraceTaskName
|
||||
from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
|
||||
from core.ops.utils import measure_time
|
||||
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
|
||||
|
||||
|
||||
class LLMGenerator:
|
||||
@classmethod
|
||||
def generate_conversation_name(
|
||||
cls, tenant_id: str, query, conversation_id: Optional[str] = None, app_id: Optional[str] = None
|
||||
):
|
||||
prompt = CONVERSATION_TITLE_PROMPT
|
||||
|
||||
if len(query) > 2000:
|
||||
query = query[:300] + "...[TRUNCATED]..." + query[-300:]
|
||||
|
||||
query = query.replace("\n", " ")
|
||||
|
||||
prompt += query + "\n"
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
prompts = [UserPromptMessage(content=prompt)]
|
||||
|
||||
with measure_time() as timer:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompts), model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
|
||||
),
|
||||
)
|
||||
answer = cast(str, response.message.content)
|
||||
cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
|
||||
if cleaned_answer is None:
|
||||
return ""
|
||||
result_dict = json.loads(cleaned_answer)
|
||||
answer = result_dict["Your Output"]
|
||||
name = answer.strip()
|
||||
|
||||
if len(name) > 75:
|
||||
name = name[:75] + "..."
|
||||
|
||||
# get tracing instance
|
||||
trace_manager = TraceQueueManager(app_id=app_id)
|
||||
trace_manager.add_trace_task(
|
||||
TraceTask(
|
||||
TraceTaskName.GENERATE_NAME_TRACE,
|
||||
conversation_id=conversation_id,
|
||||
generate_conversation_name=name,
|
||||
inputs=prompt,
|
||||
timer=timer,
|
||||
tenant_id=tenant_id,
|
||||
)
|
||||
)
|
||||
|
||||
return name
|
||||
|
||||
@classmethod
|
||||
def generate_suggested_questions_after_answer(cls, tenant_id: str, histories: str):
|
||||
output_parser = SuggestedQuestionsAfterAnswerOutputParser()
|
||||
format_instructions = output_parser.get_format_instructions()
|
||||
|
||||
prompt_template = PromptTemplateParser(template="{{histories}}\n{{format_instructions}}\nquestions:\n")
|
||||
|
||||
prompt = prompt_template.format({"histories": histories, "format_instructions": format_instructions})
|
||||
|
||||
try:
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
except InvokeAuthorizationError:
|
||||
return []
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages),
|
||||
model_parameters={"max_tokens": 256, "temperature": 0},
|
||||
stream=False,
|
||||
),
|
||||
)
|
||||
|
||||
questions = output_parser.parse(cast(str, response.message.content))
|
||||
except InvokeError:
|
||||
questions = []
|
||||
except Exception:
|
||||
logging.exception("Failed to generate suggested questions after answer")
|
||||
questions = []
|
||||
|
||||
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:
|
||||
output_parser = RuleConfigGeneratorOutputParser()
|
||||
|
||||
error = ""
|
||||
error_step = ""
|
||||
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_generate = prompt_template.format(
|
||||
inputs={
|
||||
"TASK_DESCRIPTION": instruction,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt_generate)]
|
||||
|
||||
model_manager = ModelManager()
|
||||
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
rule_config["prompt"] = cast(str, response.message.content)
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate rule config"
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||
rule_config["error"] = str(e)
|
||||
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
# get rule config prompt, parameter and statement
|
||||
prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
|
||||
|
||||
prompt_template = PromptTemplateParser(prompt_generate)
|
||||
|
||||
parameter_template = PromptTemplateParser(parameter_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,
|
||||
)
|
||||
prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
|
||||
|
||||
# get model instance
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
try:
|
||||
try:
|
||||
# the first step to generate the task prompt
|
||||
prompt_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate prefix prompt"
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
rule_config["prompt"] = cast(str, prompt_content.message.content)
|
||||
|
||||
if not isinstance(prompt_content.message.content, str):
|
||||
raise NotImplementedError("prompt content is not a string")
|
||||
parameter_generate_prompt = parameter_template.format(
|
||||
inputs={
|
||||
"INPUT_TEXT": prompt_content.message.content,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
|
||||
|
||||
# the second step to generate the task_parameter and task_statement
|
||||
statement_generate_prompt = statement_template.format(
|
||||
inputs={
|
||||
"TASK_DESCRIPTION": instruction,
|
||||
"INPUT_TEXT": prompt_content.message.content,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
|
||||
|
||||
try:
|
||||
parameter_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate variables"
|
||||
|
||||
try:
|
||||
statement_content = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
rule_config["opening_statement"] = cast(str, statement_content.message.content)
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
error_step = "generate conversation opener"
|
||||
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||
rule_config["error"] = str(e)
|
||||
|
||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||
|
||||
return rule_config
|
||||
|
||||
@classmethod
|
||||
def generate_code(
|
||||
cls,
|
||||
tenant_id: str,
|
||||
instruction: str,
|
||||
model_config: dict,
|
||||
code_language: str = "javascript",
|
||||
max_tokens: int = 1000,
|
||||
) -> dict:
|
||||
if code_language == "python":
|
||||
prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||
else:
|
||||
prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||
|
||||
prompt = prompt_template.format(
|
||||
inputs={
|
||||
"INSTRUCTION": instruction,
|
||||
"CODE_LANGUAGE": code_language,
|
||||
},
|
||||
remove_template_variables=False,
|
||||
)
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||
model_parameters = {"max_tokens": max_tokens, "temperature": 0.01}
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
generated_code = cast(str, response.message.content)
|
||||
return {"code": generated_code, "language": code_language, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
|
||||
except Exception as e:
|
||||
logging.exception(
|
||||
f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
|
||||
)
|
||||
return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
|
||||
|
||||
@classmethod
|
||||
def generate_qa_document(cls, tenant_id: str, query, document_language: str):
|
||||
prompt = GENERATOR_QA_PROMPT.format(language=document_language)
|
||||
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_default_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
)
|
||||
|
||||
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
|
||||
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=prompt_messages,
|
||||
model_parameters={"temperature": 0.01, "max_tokens": 2000},
|
||||
stream=False,
|
||||
),
|
||||
)
|
||||
|
||||
answer = cast(str, response.message.content)
|
||||
return answer.strip()
|
||||
|
||||
@classmethod
|
||||
def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
|
||||
model_manager = ModelManager()
|
||||
model_instance = model_manager.get_model_instance(
|
||||
tenant_id=tenant_id,
|
||||
model_type=ModelType.LLM,
|
||||
provider=model_config.get("provider", ""),
|
||||
model=model_config.get("name", ""),
|
||||
)
|
||||
|
||||
prompt_messages = [
|
||||
SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
|
||||
UserPromptMessage(content=instruction),
|
||||
]
|
||||
model_parameters = model_config.get("model_parameters", {})
|
||||
|
||||
try:
|
||||
response = cast(
|
||||
LLMResult,
|
||||
model_instance.invoke_llm(
|
||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||
),
|
||||
)
|
||||
|
||||
generated_json_schema = cast(str, response.message.content)
|
||||
try:
|
||||
generated_json_schema = json.loads(generated_json_schema)
|
||||
except json.JSONDecodeError:
|
||||
generated_json_schema = json_repair.loads(generated_json_schema)
|
||||
if not isinstance(generated_json_schema, (dict | list)):
|
||||
raise LLMNodeError(f"Failed to parse structured output from llm: {response.message.content}")
|
||||
generated_json_schema = json.dumps(generated_json_schema, indent=2, ensure_ascii=False)
|
||||
|
||||
return {"output": generated_json_schema, "error": ""}
|
||||
|
||||
except InvokeError as e:
|
||||
error = str(e)
|
||||
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
||||
except Exception as e:
|
||||
logging.exception(f"Failed to invoke LLM model, model: {model_config.get('name')}")
|
||||
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
||||
|
||||
Loading…
Reference in New Issue