fix style of LF to CRLF
lizb
1 year ago
parent
664ef7044f
commit
f09469df50
@ -1,393 +1,393 @@
|
|||||||
import json
|
import json
|
||||||
import logging
|
import logging
|
||||||
import re
|
import re
|
||||||
from typing import Optional, cast
|
from typing import Optional, cast
|
||||||
|
|
||||||
import json_repair
|
import json_repair
|
||||||
|
|
||||||
from core.llm_generator.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
|
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.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
|
||||||
from core.llm_generator.prompts import (
|
from core.llm_generator.prompts import (
|
||||||
CONVERSATION_TITLE_PROMPT,
|
CONVERSATION_TITLE_PROMPT,
|
||||||
GENERATOR_QA_PROMPT,
|
GENERATOR_QA_PROMPT,
|
||||||
JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
|
JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||||
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
|
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
|
||||||
SYSTEM_STRUCTURED_OUTPUT_GENERATE,
|
SYSTEM_STRUCTURED_OUTPUT_GENERATE,
|
||||||
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
|
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
|
||||||
)
|
)
|
||||||
from core.model_manager import ModelManager
|
from core.model_manager import ModelManager
|
||||||
from core.model_runtime.entities.llm_entities import LLMResult
|
from core.model_runtime.entities.llm_entities import LLMResult
|
||||||
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
|
from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
|
||||||
from core.model_runtime.entities.model_entities import ModelType
|
from core.model_runtime.entities.model_entities import ModelType
|
||||||
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
|
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
|
||||||
from core.ops.entities.trace_entity import TraceTaskName
|
from core.ops.entities.trace_entity import TraceTaskName
|
||||||
from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
|
from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
|
||||||
from core.ops.utils import measure_time
|
from core.ops.utils import measure_time
|
||||||
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
|
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
|
||||||
from core.workflow.nodes.llm.exc import LLMNodeError
|
from core.workflow.nodes.llm.exc import LLMNodeError
|
||||||
|
|
||||||
|
|
||||||
class LLMGenerator:
|
class LLMGenerator:
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_conversation_name(
|
def generate_conversation_name(
|
||||||
cls, tenant_id: str, query, conversation_id: Optional[str] = None, app_id: Optional[str] = None
|
cls, tenant_id: str, query, conversation_id: Optional[str] = None, app_id: Optional[str] = None
|
||||||
):
|
):
|
||||||
prompt = CONVERSATION_TITLE_PROMPT
|
prompt = CONVERSATION_TITLE_PROMPT
|
||||||
|
|
||||||
if len(query) > 2000:
|
if len(query) > 2000:
|
||||||
query = query[:300] + "...[TRUNCATED]..." + query[-300:]
|
query = query[:300] + "...[TRUNCATED]..." + query[-300:]
|
||||||
|
|
||||||
query = query.replace("\n", " ")
|
query = query.replace("\n", " ")
|
||||||
|
|
||||||
prompt += query + "\n"
|
prompt += query + "\n"
|
||||||
|
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_default_model_instance(
|
model_instance = model_manager.get_default_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
)
|
)
|
||||||
prompts = [UserPromptMessage(content=prompt)]
|
prompts = [UserPromptMessage(content=prompt)]
|
||||||
|
|
||||||
with measure_time() as timer:
|
with measure_time() as timer:
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompts), model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
|
prompt_messages=list(prompts), model_parameters={"max_tokens": 100, "temperature": 1}, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
answer = cast(str, response.message.content)
|
answer = cast(str, 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)
|
||||||
if cleaned_answer is None:
|
if cleaned_answer is None:
|
||||||
return ""
|
return ""
|
||||||
result_dict = json.loads(cleaned_answer)
|
result_dict = json.loads(cleaned_answer)
|
||||||
answer = result_dict["Your Output"]
|
answer = result_dict["Your Output"]
|
||||||
name = answer.strip()
|
name = answer.strip()
|
||||||
|
|
||||||
if len(name) > 75:
|
if len(name) > 75:
|
||||||
name = name[:75] + "..."
|
name = name[:75] + "..."
|
||||||
|
|
||||||
# get tracing instance
|
# get tracing instance
|
||||||
trace_manager = TraceQueueManager(app_id=app_id)
|
trace_manager = TraceQueueManager(app_id=app_id)
|
||||||
trace_manager.add_trace_task(
|
trace_manager.add_trace_task(
|
||||||
TraceTask(
|
TraceTask(
|
||||||
TraceTaskName.GENERATE_NAME_TRACE,
|
TraceTaskName.GENERATE_NAME_TRACE,
|
||||||
conversation_id=conversation_id,
|
conversation_id=conversation_id,
|
||||||
generate_conversation_name=name,
|
generate_conversation_name=name,
|
||||||
inputs=prompt,
|
inputs=prompt,
|
||||||
timer=timer,
|
timer=timer,
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
)
|
)
|
||||||
)
|
)
|
||||||
|
|
||||||
return name
|
return name
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_suggested_questions_after_answer(cls, tenant_id: str, histories: str):
|
def generate_suggested_questions_after_answer(cls, tenant_id: str, histories: str):
|
||||||
output_parser = SuggestedQuestionsAfterAnswerOutputParser()
|
output_parser = SuggestedQuestionsAfterAnswerOutputParser()
|
||||||
format_instructions = output_parser.get_format_instructions()
|
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:
|
try:
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_default_model_instance(
|
model_instance = model_manager.get_default_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
)
|
)
|
||||||
except InvokeAuthorizationError:
|
except InvokeAuthorizationError:
|
||||||
return []
|
return []
|
||||||
|
|
||||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||||
|
|
||||||
try:
|
try:
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompt_messages),
|
prompt_messages=list(prompt_messages),
|
||||||
model_parameters={"max_tokens": 256, "temperature": 0},
|
model_parameters={"max_tokens": 256, "temperature": 0},
|
||||||
stream=False,
|
stream=False,
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
|
|
||||||
questions = output_parser.parse(cast(str, response.message.content))
|
questions = output_parser.parse(cast(str, response.message.content))
|
||||||
except InvokeError:
|
except InvokeError:
|
||||||
questions = []
|
questions = []
|
||||||
except Exception:
|
except Exception:
|
||||||
logging.exception("Failed to generate suggested questions after answer")
|
logging.exception("Failed to generate suggested questions after answer")
|
||||||
questions = []
|
questions = []
|
||||||
|
|
||||||
return questions
|
return questions
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_rule_config(
|
def generate_rule_config(
|
||||||
cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512
|
cls, tenant_id: str, instruction: str, model_config: dict, no_variable: bool, rule_config_max_tokens: int = 512
|
||||||
) -> dict:
|
) -> dict:
|
||||||
output_parser = RuleConfigGeneratorOutputParser()
|
output_parser = RuleConfigGeneratorOutputParser()
|
||||||
|
|
||||||
error = ""
|
error = ""
|
||||||
error_step = ""
|
error_step = ""
|
||||||
rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
|
rule_config = {"prompt": "", "variables": [], "opening_statement": "", "error": ""}
|
||||||
model_parameters = {"max_tokens": rule_config_max_tokens, "temperature": 0.01}
|
model_parameters = {"max_tokens": rule_config_max_tokens, "temperature": 0.01}
|
||||||
|
|
||||||
if no_variable:
|
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(
|
prompt_generate = prompt_template.format(
|
||||||
inputs={
|
inputs={
|
||||||
"TASK_DESCRIPTION": instruction,
|
"TASK_DESCRIPTION": instruction,
|
||||||
},
|
},
|
||||||
remove_template_variables=False,
|
remove_template_variables=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
prompt_messages = [UserPromptMessage(content=prompt_generate)]
|
prompt_messages = [UserPromptMessage(content=prompt_generate)]
|
||||||
|
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
|
|
||||||
model_instance = model_manager.get_default_model_instance(
|
model_instance = model_manager.get_default_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
)
|
)
|
||||||
|
|
||||||
try:
|
try:
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
|
|
||||||
rule_config["prompt"] = cast(str, response.message.content)
|
rule_config["prompt"] = cast(str, response.message.content)
|
||||||
|
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
error_step = "generate rule config"
|
error_step = "generate rule config"
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||||
rule_config["error"] = str(e)
|
rule_config["error"] = str(e)
|
||||||
|
|
||||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||||
|
|
||||||
return rule_config
|
return rule_config
|
||||||
|
|
||||||
# get rule config prompt, parameter and statement
|
# get rule config prompt, parameter and statement
|
||||||
prompt_generate, parameter_generate, statement_generate = output_parser.get_format_instructions()
|
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
|
# format the prompt_generate_prompt
|
||||||
prompt_generate_prompt = prompt_template.format(
|
prompt_generate_prompt = prompt_template.format(
|
||||||
inputs={
|
inputs={
|
||||||
"TASK_DESCRIPTION": instruction,
|
"TASK_DESCRIPTION": instruction,
|
||||||
},
|
},
|
||||||
remove_template_variables=False,
|
remove_template_variables=False,
|
||||||
)
|
)
|
||||||
prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
|
prompt_messages = [UserPromptMessage(content=prompt_generate_prompt)]
|
||||||
|
|
||||||
# get model instance
|
# get model instance
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_model_instance(
|
model_instance = model_manager.get_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
provider=model_config.get("provider", ""),
|
provider=model_config.get("provider", ""),
|
||||||
model=model_config.get("name", ""),
|
model=model_config.get("name", ""),
|
||||||
)
|
)
|
||||||
|
|
||||||
try:
|
try:
|
||||||
try:
|
try:
|
||||||
# the first step to generate the task prompt
|
# the first step to generate the task prompt
|
||||||
prompt_content = cast(
|
prompt_content = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
error_step = "generate prefix prompt"
|
error_step = "generate prefix prompt"
|
||||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||||
|
|
||||||
return rule_config
|
return rule_config
|
||||||
|
|
||||||
rule_config["prompt"] = cast(str, prompt_content.message.content)
|
rule_config["prompt"] = cast(str, prompt_content.message.content)
|
||||||
|
|
||||||
if not isinstance(prompt_content.message.content, str):
|
if not isinstance(prompt_content.message.content, str):
|
||||||
raise NotImplementedError("prompt content is not a string")
|
raise NotImplementedError("prompt content is not a string")
|
||||||
parameter_generate_prompt = parameter_template.format(
|
parameter_generate_prompt = parameter_template.format(
|
||||||
inputs={
|
inputs={
|
||||||
"INPUT_TEXT": prompt_content.message.content,
|
"INPUT_TEXT": prompt_content.message.content,
|
||||||
},
|
},
|
||||||
remove_template_variables=False,
|
remove_template_variables=False,
|
||||||
)
|
)
|
||||||
parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
|
parameter_messages = [UserPromptMessage(content=parameter_generate_prompt)]
|
||||||
|
|
||||||
# the second step to generate the task_parameter and task_statement
|
# the second step to generate the task_parameter and task_statement
|
||||||
statement_generate_prompt = statement_template.format(
|
statement_generate_prompt = statement_template.format(
|
||||||
inputs={
|
inputs={
|
||||||
"TASK_DESCRIPTION": instruction,
|
"TASK_DESCRIPTION": instruction,
|
||||||
"INPUT_TEXT": prompt_content.message.content,
|
"INPUT_TEXT": prompt_content.message.content,
|
||||||
},
|
},
|
||||||
remove_template_variables=False,
|
remove_template_variables=False,
|
||||||
)
|
)
|
||||||
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
|
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
|
||||||
|
|
||||||
try:
|
try:
|
||||||
parameter_content = cast(
|
parameter_content = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
|
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))
|
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
error_step = "generate variables"
|
error_step = "generate variables"
|
||||||
|
|
||||||
try:
|
try:
|
||||||
statement_content = cast(
|
statement_content = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
|
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
rule_config["opening_statement"] = cast(str, statement_content.message.content)
|
rule_config["opening_statement"] = cast(str, statement_content.message.content)
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
error_step = "generate conversation opener"
|
error_step = "generate conversation opener"
|
||||||
|
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
|
||||||
rule_config["error"] = str(e)
|
rule_config["error"] = str(e)
|
||||||
|
|
||||||
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
|
||||||
|
|
||||||
return rule_config
|
return rule_config
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_code(
|
def generate_code(
|
||||||
cls,
|
cls,
|
||||||
tenant_id: str,
|
tenant_id: str,
|
||||||
instruction: str,
|
instruction: str,
|
||||||
model_config: dict,
|
model_config: dict,
|
||||||
code_language: str = "javascript",
|
code_language: str = "javascript",
|
||||||
max_tokens: int = 1000,
|
max_tokens: int = 1000,
|
||||||
) -> dict:
|
) -> dict:
|
||||||
if code_language == "python":
|
if code_language == "python":
|
||||||
prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
|
prompt_template = PromptTemplateParser(PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||||
else:
|
else:
|
||||||
prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
|
prompt_template = PromptTemplateParser(JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE)
|
||||||
|
|
||||||
prompt = prompt_template.format(
|
prompt = prompt_template.format(
|
||||||
inputs={
|
inputs={
|
||||||
"INSTRUCTION": instruction,
|
"INSTRUCTION": instruction,
|
||||||
"CODE_LANGUAGE": code_language,
|
"CODE_LANGUAGE": code_language,
|
||||||
},
|
},
|
||||||
remove_template_variables=False,
|
remove_template_variables=False,
|
||||||
)
|
)
|
||||||
|
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_model_instance(
|
model_instance = model_manager.get_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
provider=model_config.get("provider", ""),
|
provider=model_config.get("provider", ""),
|
||||||
model=model_config.get("name", ""),
|
model=model_config.get("name", ""),
|
||||||
)
|
)
|
||||||
|
|
||||||
prompt_messages = [UserPromptMessage(content=prompt)]
|
prompt_messages = [UserPromptMessage(content=prompt)]
|
||||||
model_parameters = {"max_tokens": max_tokens, "temperature": 0.01}
|
model_parameters = {"max_tokens": max_tokens, "temperature": 0.01}
|
||||||
|
|
||||||
try:
|
try:
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
|
|
||||||
generated_code = cast(str, response.message.content)
|
generated_code = cast(str, response.message.content)
|
||||||
return {"code": generated_code, "language": code_language, "error": ""}
|
return {"code": generated_code, "language": code_language, "error": ""}
|
||||||
|
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
|
return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logging.exception(
|
logging.exception(
|
||||||
f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
|
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)}"}
|
return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_qa_document(cls, tenant_id: str, query, document_language: str):
|
def generate_qa_document(cls, tenant_id: str, query, document_language: str):
|
||||||
prompt = GENERATOR_QA_PROMPT.format(language=document_language)
|
prompt = GENERATOR_QA_PROMPT.format(language=document_language)
|
||||||
|
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_default_model_instance(
|
model_instance = model_manager.get_default_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
)
|
)
|
||||||
|
|
||||||
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
|
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
|
||||||
|
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=prompt_messages,
|
prompt_messages=prompt_messages,
|
||||||
model_parameters={"temperature": 0.01, "max_tokens": 2000},
|
model_parameters={"temperature": 0.01, "max_tokens": 2000},
|
||||||
stream=False,
|
stream=False,
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
|
|
||||||
answer = cast(str, response.message.content)
|
answer = cast(str, response.message.content)
|
||||||
return answer.strip()
|
return answer.strip()
|
||||||
|
|
||||||
@classmethod
|
@classmethod
|
||||||
def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
|
def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
|
||||||
model_manager = ModelManager()
|
model_manager = ModelManager()
|
||||||
model_instance = model_manager.get_model_instance(
|
model_instance = model_manager.get_model_instance(
|
||||||
tenant_id=tenant_id,
|
tenant_id=tenant_id,
|
||||||
model_type=ModelType.LLM,
|
model_type=ModelType.LLM,
|
||||||
provider=model_config.get("provider", ""),
|
provider=model_config.get("provider", ""),
|
||||||
model=model_config.get("name", ""),
|
model=model_config.get("name", ""),
|
||||||
)
|
)
|
||||||
|
|
||||||
prompt_messages = [
|
prompt_messages = [
|
||||||
SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
|
SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
|
||||||
UserPromptMessage(content=instruction),
|
UserPromptMessage(content=instruction),
|
||||||
]
|
]
|
||||||
model_parameters = model_config.get("model_parameters", {})
|
model_parameters = model_config.get("model_parameters", {})
|
||||||
|
|
||||||
try:
|
try:
|
||||||
response = cast(
|
response = cast(
|
||||||
LLMResult,
|
LLMResult,
|
||||||
model_instance.invoke_llm(
|
model_instance.invoke_llm(
|
||||||
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
|
||||||
),
|
),
|
||||||
)
|
)
|
||||||
|
|
||||||
raw_content = response.message.content
|
raw_content = response.message.content
|
||||||
|
|
||||||
if not isinstance(raw_content, str):
|
if not isinstance(raw_content, str):
|
||||||
raise LLMNodeError(f"LLM response content must be a string, got: {type(raw_content)}")
|
raise LLMNodeError(f"LLM response content must be a string, got: {type(raw_content)}")
|
||||||
|
|
||||||
try:
|
try:
|
||||||
parsed_content = json.loads(raw_content)
|
parsed_content = json.loads(raw_content)
|
||||||
except json.JSONDecodeError:
|
except json.JSONDecodeError:
|
||||||
parsed_content = json_repair.loads(raw_content)
|
parsed_content = json_repair.loads(raw_content)
|
||||||
|
|
||||||
if not isinstance(parsed_content, dict | list):
|
if not isinstance(parsed_content, dict | list):
|
||||||
raise LLMNodeError(f"Failed to parse structured output from llm: {raw_content}")
|
raise LLMNodeError(f"Failed to parse structured output from llm: {raw_content}")
|
||||||
|
|
||||||
generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
|
generated_json_schema = json.dumps(parsed_content, indent=2, ensure_ascii=False)
|
||||||
return {"output": generated_json_schema, "error": ""}
|
return {"output": generated_json_schema, "error": ""}
|
||||||
|
|
||||||
except InvokeError as e:
|
except InvokeError as e:
|
||||||
error = str(e)
|
error = str(e)
|
||||||
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
logging.exception(f"Failed to invoke LLM model, model: {model_config.get('name')}")
|
logging.exception(f"Failed to invoke LLM model, model: {model_config.get('name')}")
|
||||||
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}
|
||||||
|
|||||||
Loading…
Reference in New Issue