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# meta_prompt_utils.py
import json
import logging
import io
from typing import Any, Dict, List, Optional, Union
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from meta_prompt import *
from meta_prompt.sample_generator import TaskDescriptionGenerator
from pythonjsonlogger import jsonlogger
from app.config import MetaPromptConfig, RoleMessage
from confz import BaseConfig, CLArgSource, EnvSource, FileSource
def prompt_templates_confz2langchain(
prompt_templates: Dict[str, Dict[str, List[RoleMessage]]]
) -> Dict[str, ChatPromptTemplate]:
return {
node: ChatPromptTemplate.from_messages(
[
(role_message.role, role_message.message)
for role_message in role_messages
]
)
for node, role_messages in prompt_templates.items()
}
class LLMModelFactory:
_instance = None
def __new__(cls):
if not cls._instance:
cls._instance = super(LLMModelFactory, cls).__new__(cls)
return cls._instance
def create(self, model_type: str, **kwargs) -> BaseLanguageModel:
model_class = globals()[model_type]
return model_class(**kwargs)
def chat_log_2_chatbot_list(chat_log: str) -> List[List[str]]:
chatbot_list = []
if chat_log is None or chat_log == '':
return chatbot_list
for line in chat_log.splitlines():
try:
json_line = json.loads(line)
if 'action' in json_line:
if json_line['action'] == 'invoke':
chatbot_list.append([json_line['message'], None])
if json_line['action'] == 'response':
chatbot_list.append([None, json_line['message']])
except json.decoder.JSONDecodeError as e:
print(f"Error decoding JSON log output: {e}")
print(line)
except KeyError as e:
print(f"Error accessing key in JSON log output: {e}")
print(line)
return chatbot_list
def get_current_model(simple_model_name: str,
advanced_model_name: str,
expert_model_name: str,
expert_model_config: Optional[Dict[str, Any]] = None,
config: MetaPromptConfig = None,
active_model_tab: str = "Simple") -> BaseLanguageModel:
model_mapping = {
"Simple": simple_model_name,
"Advanced": advanced_model_name,
"Expert": expert_model_name
}
try:
model_name = model_mapping.get(active_model_tab, simple_model_name)
model = config.llms[model_name]
model_type = model.type
model_config = model.model_dump(exclude={'type'})
if active_model_tab == "Expert" and expert_model_config:
model_config.update(expert_model_config)
return LLMModelFactory().create(model_type, **model_config)
except KeyError as e:
logging.error(f"Configuration key error: {e}")
raise ValueError(f"Invalid model name or configuration: {e}")
except Exception as e:
logging.error(f"An unexpected error occurred: {e}")
raise RuntimeError(f"Failed to retrieve the model: {e}")
def evaluate_system_message(system_message, user_message, simple_model,
advanced_executor_model, expert_executor_model,
expert_executor_model_temperature=0.1,
config: MetaPromptConfig = None,
active_model_tab: str = "Simple"):
llm = get_current_model(simple_model, advanced_executor_model,
expert_executor_model,
{"temperature": expert_executor_model_temperature},
config, active_model_tab)
template = ChatPromptTemplate.from_messages([
("system", "{system_message}"),
("human", "{user_message}")
])
try:
output = llm.invoke(template.format(
system_message=system_message, user_message=user_message))
return output.content if hasattr(output, 'content') else ""
except Exception as e:
raise Exception(f"Error: {e}")
def generate_acceptance_criteria(user_message, expected_output,
simple_model, advanced_executor_model,
expert_prompt_acceptance_criteria_model,
expert_prompt_acceptance_criteria_temperature=0.1,
prompt_template_group: Optional[str] = None,
config: MetaPromptConfig = None,
active_model_tab: str = "Simple"):
log_stream = io.StringIO()
logger = logging.getLogger(MetaPromptGraph.__name__) if config.verbose else None
log_handler = logging.StreamHandler(log_stream) if logger else None
if log_handler:
log_handler.setFormatter(
jsonlogger.JsonFormatter('%(asctime)s %(name)s %(levelname)s %(message)s')
)
logger.addHandler(log_handler)
llm = get_current_model(simple_model, advanced_executor_model,
expert_prompt_acceptance_criteria_model,
{"temperature": expert_prompt_acceptance_criteria_temperature},
config, active_model_tab)
if prompt_template_group is None:
prompt_template_group = 'default'
prompt_templates = prompt_templates_confz2langchain(
config.prompt_templates[prompt_template_group]
)
acceptance_criteria_graph = MetaPromptGraph(llms={
NODE_ACCEPTANCE_CRITERIA_DEVELOPER: llm
}, prompts=prompt_templates,
verbose=config.verbose, logger=logger)
state = AgentState(
user_message=user_message,
expected_output=expected_output
)
output_state = acceptance_criteria_graph.run_acceptance_criteria_graph(state)
if log_handler:
log_handler.close()
log_output = log_stream.getvalue()
else:
log_output = None
return output_state.get('acceptance_criteria', ""), chat_log_2_chatbot_list(log_output)
def generate_initial_system_message(
user_message: str,
expected_output: str,
simple_model: str,
advanced_executor_model: str,
expert_prompt_initial_developer_model: str,
expert_prompt_initial_developer_temperature: float = 0.1,
prompt_template_group: Optional[str] = None,
config: MetaPromptConfig = None,
active_model_tab: str = "Simple"
) -> tuple:
log_stream = io.StringIO()
logger = logging.getLogger(MetaPromptGraph.__name__) if config.verbose else None
log_handler = logging.StreamHandler(log_stream) if logger else None
if log_handler:
log_handler.setFormatter(
jsonlogger.JsonFormatter('%(asctime)s %(name)s %(levelname)s %(message)s')
)
logger.addHandler(log_handler)
llm = get_current_model(
simple_model,
advanced_executor_model,
expert_prompt_initial_developer_model,
{"temperature": expert_prompt_initial_developer_temperature},
config,
active_model_tab
)
if prompt_template_group is None:
prompt_template_group = 'default'
prompt_templates = prompt_templates_confz2langchain(
config.prompt_templates[prompt_template_group]
)
initial_system_message_graph = MetaPromptGraph(
llms={NODE_PROMPT_INITIAL_DEVELOPER: llm},
prompts=prompt_templates,
verbose=config.verbose,
logger=logger
)
state = AgentState(
user_message=user_message,
expected_output=expected_output
)
output_state = initial_system_message_graph.run_prompt_initial_developer_graph(state)
if log_handler:
log_handler.close()
log_output = log_stream.getvalue()
else:
log_output = None
system_message = output_state.get('system_message', "")
return system_message, chat_log_2_chatbot_list(log_output)
def process_message(
user_message: str, expected_output: str, acceptance_criteria: str,
initial_system_message: str, recursion_limit: int, max_output_age: int,
llms: Union[BaseLanguageModel, Dict[str, BaseLanguageModel]],
prompt_template_group: Optional[str] = None,
aggressive_exploration: bool = False,
config: MetaPromptConfig = None
) -> tuple:
input_state = AgentState(
user_message=user_message,
expected_output=expected_output,
acceptance_criteria=acceptance_criteria,
system_message=initial_system_message,
max_output_age=max_output_age
)
log_stream = io.StringIO()
logger = logging.getLogger(MetaPromptGraph.__name__) if config.verbose else None
log_handler = logging.StreamHandler(log_stream) if logger else None
if log_handler:
log_handler.setFormatter(jsonlogger.JsonFormatter(
'%(asctime)s %(name)s %(levelname)s %(message)s'))
logger.addHandler(log_handler)
if prompt_template_group is None:
prompt_template_group = 'default'
prompt_templates = prompt_templates_confz2langchain(config.prompt_templates[prompt_template_group])
meta_prompt_graph = MetaPromptGraph(llms=llms, prompts=prompt_templates,
aggressive_exploration=aggressive_exploration,
verbose=config.verbose, logger=logger)
try:
output_state = meta_prompt_graph(input_state, recursion_limit=recursion_limit)
except Exception as e:
raise Exception(f"Error: {e}")
if log_handler:
log_handler.close()
log_output = log_stream.getvalue()
else:
log_output = None
system_message = output_state.get(
'best_system_message', "Error: The output state does not contain a valid 'best_system_message'")
output = output_state.get(
'best_output', "Error: The output state does not contain a valid 'best_output'")
analysis = output_state.get(
'analysis', "Error: The output state does not contain a valid 'analysis'")
acceptance_criteria = output_state.get(
'acceptance_criteria', "Error: The output state does not contain a valid 'acceptance_criteria'")
return (system_message, output, analysis, acceptance_criteria, chat_log_2_chatbot_list(log_output))
def initialize_llm(model_name: str, model_config: Optional[Dict[str, Any]] = None, config: MetaPromptConfig = None) -> Any:
try:
llm_config = config.llms[model_name]
model_type = llm_config.type
dumped_config = llm_config.model_dump(exclude={'type'})
if model_config:
dumped_config.update(model_config)
return LLMModelFactory().create(model_type, **dumped_config)
except KeyError:
raise KeyError(f"No configuration exists for the model name: {model_name}")
except NotImplementedError:
raise NotImplementedError(
f"Unrecognized type configured for the language model: {model_type}"
)
# Sample generator functions
def process_json(input_json, model_name, generating_batch_size, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.process(input_json, generating_batch_size)
description = result["description"]
suggestions = result["suggestions"]
examples_directly = [[example["input"], example["output"]]
for example in result["examples_directly"]["examples"]]
input_analysis = result["examples_from_briefs"]["input_analysis"]
new_example_briefs = result["examples_from_briefs"]["new_example_briefs"]
examples_from_briefs = [[example["input"], example["output"]]
for example in result["examples_from_briefs"]["examples"]]
examples = [[example["input"], example["output"]]
for example in result["additional_examples"]]
return description, suggestions, examples_directly, input_analysis, new_example_briefs, examples_from_briefs, examples
except Exception as e:
raise Exception(f"An error occurred: {str(e)}. Returning default values.")
def generate_description_only(input_json, model_name, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.generate_description(input_json)
description = result["description"]
suggestions = result["suggestions"]
return description, suggestions
except Exception as e:
raise Exception(f"An error occurred: {str(e)}")
def analyze_input(description, model_name, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
input_analysis = generator.analyze_input(description)
return input_analysis
except Exception as e:
raise Exception(f"An error occurred: {str(e)}")
def generate_briefs(description, input_analysis, generating_batch_size, model_name, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
briefs = generator.generate_briefs(
description, input_analysis, generating_batch_size)
return briefs
except Exception as e:
raise Exception(f"An error occurred: {str(e)}")
def generate_examples_from_briefs(description, new_example_briefs, input_str, generating_batch_size, model_name, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.generate_examples_from_briefs(
description, new_example_briefs, input_str, generating_batch_size)
examples = [[example["input"], example["output"]]
for example in result["examples"]]
return examples
except Exception as e:
raise Exception(f"An error occurred: {str(e)}")
def generate_examples_directly(description, raw_example, generating_batch_size, model_name, temperature, config: MetaPromptConfig = None):
try:
model = ChatOpenAI(
model=model_name, temperature=temperature, max_retries=3)
generator = TaskDescriptionGenerator(model)
result = generator.generate_examples_directly(
description, raw_example, generating_batch_size)
examples = [[example["input"], example["output"]]
for example in result["examples"]]
return examples
except Exception as e:
raise Exception(f"An error occurred: {str(e)}")
class FileConfig(BaseConfig):
config_file: str = 'config.yml' # default path
def load_config():
pre_config_sources = [
EnvSource(prefix='METAPROMPT_', allow_all=True),
CLArgSource()
]
pre_config = FileConfig(config_sources=pre_config_sources)
config_sources = [
FileSource(file=pre_config.config_file, optional=True),
EnvSource(prefix='METAPROMPT_', allow_all=True),
CLArgSource()
]
return MetaPromptConfig(config_sources=config_sources)
# Add any additional utility functions here if needed |