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import os
from ..core.module import BaseModule
from typing import Optional, Literal, Dict, Any, List
from pydantic import Field, BaseModel
import json
from dotenv import load_dotenv
import time
from ..models import OpenAILLM, OpenAILLMConfig, BaseLLM
from ..models.model_configs import LLMConfig
from ..prompts.workflow.workflow_editor import WORKFLOW_EDITOR_PROMPT
from ..core.logging import logger
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
class MockLLMConfig(LLMConfig):
"""Mock LLM configuration for testing purposes"""
llm_type: str = "MockLLM"
model: str = "mock-model"
class MockLLM(BaseLLM):
"""Mock LLM implementation for testing purposes that passes pydantic type validation"""
def __init__(self, config: MockLLMConfig = None, **kwargs):
if config is None:
config = MockLLMConfig(
llm_type="MockLLM",
model="mock-model",
output_response=True
)
super().__init__(config, **kwargs)
def init_model(self):
"""Initialize the mock model (no-op)"""
pass
def formulate_messages(self, prompts: List[str], system_messages: Optional[List[str]] = None) -> List[List[dict]]:
"""Mock implementation of formulate_messages"""
result = []
for prompt in prompts:
messages = []
if system_messages:
for sys_msg in system_messages:
messages.append({"role": "system", "content": sys_msg})
messages.append({"role": "user", "content": prompt})
result.append(messages)
return result
def single_generate(self, messages: List[dict], **kwargs) -> str:
"""Mock implementation that returns a simple JSON response"""
return '{"nodes": [], "edges": []}'
def batch_generate(self, batch_messages: List[List[dict]], **kwargs) -> List[str]:
"""Mock implementation for batch generation"""
return [self.single_generate(messages, **kwargs) for messages in batch_messages]
async def single_generate_async(self, messages: List[dict], **kwargs) -> str:
"""Mock async implementation"""
return self.single_generate(messages, **kwargs)
def default_llm_config():
"""
Create default LLM configuration. Uses MockLLM in testing environments
or when OPENAI_API_KEY is not available.
"""
# Check if we're in a testing environment or if API key is missing
is_testing = (
os.getenv("PYTEST_CURRENT_TEST") is not None or # pytest environment
os.getenv("CI") is not None or # CI environment
OPENAI_API_KEY is None or # no API key
OPENAI_API_KEY.strip() == "" # empty API key
)
if is_testing:
# Return MockLLM for testing environments
mock_config = MockLLMConfig(
llm_type="MockLLM",
model="mock-model",
output_response=True
)
return MockLLM(mock_config)
else:
# Return real OpenAI LLM for production environments
llm_config = OpenAILLMConfig(
model="gpt-4o",
openai_key=OPENAI_API_KEY,
stream=True,
output_response=True
)
return OpenAILLM(llm_config)
class WorkFlowEditorReturn(BaseModel):
"""
The return of the workflow editor.
"""
status: Literal["success", "failed", "exceeded_max_retries"] = Field(
description="The status of the workflow editing operation"
)
workflow_json: Dict[str, Any] | None = Field(
description="The workflow JSON structure after editing"
)
workflow_json_path: str | None = Field(
description="The file path where the workflow JSON is saved"
)
error_message: Optional[str] | None = Field(
default=None,
description="Error message if the operation failed"
)
class WorkFlowEditor(BaseModule):
"""
This is a API oriented version of HITLOutsideConversationAgent, it can be used to edit the workflow json structure but in a interaction-free way.
Attributes:
save_dir (str): The directory to save the workflow json file.
llm (BaseLLM): The LLM model to use for editing the workflow json file.
max_retries (int): The maximum number of retries to edit the workflow json file.
"""
save_dir: str
llm: Optional[BaseLLM] = Field(default=default_llm_config())
max_retries: Optional[int] = Field(default=3)
def init_module(self):
pass
async def edit_workflow(self, file_path: str, instruction: str, new_file_path: Optional[str] = None):
"""
optimize or modify the workflow json file according to the instruction, using LLM's ability.
Args:
file_path (str): The path to the workflow json file or the file name in the save_dir.
instruction (str): The instruction to edit the workflow json file.
new_file_path (Optional[str]): The path to the new workflow json file.
Returns:
new_json_path (str): The path to the new workflow json file.
"""
if new_file_path is None:
new_file_path = "new_json_for__" + os.path.split(file_path)[-1] + "__" + time.strftime("%Y%m%d_%H%M%S") + ".json"
new_file_path = os.path.join(self.save_dir, new_file_path)
else:
# check if new_file_path is a file name or a path
path_split = os.path.split(new_file_path)
if not path_split[0]:
new_file_path = os.path.join(self.save_dir, new_file_path)
else:
if os.path.exists(path_split[0]) and path_split[1][:-5] == ".json":
new_file_path = new_file_path
else:
raise FileNotFoundError(f"The directory {path_split[0]} does not exist or the file name is not a json file name.")
# load the workflow json file
with open(file_path, "r") as f:
workflow_json = json.load(f)
optimization_prompt = WORKFLOW_EDITOR_PROMPT.format(
current_workflow_json=json.dumps(workflow_json, indent=2, ensure_ascii=False),
user_advice=instruction
)
messages = [
{"role": "system", "content": "You are a helpful assistant that can optimize the workflow json structure."},
{"role": "user", "content": optimization_prompt}
]
try:
response = await self.llm.single_generate_async(messages=messages, response_format={"type": "json_object"})
# try to parse the LLM response
optimized_json = json.loads(response)
except Exception as e:
logger.error(f"LLM optimization failed: {e}")
optimized_json = None
if not optimized_json:
return WorkFlowEditorReturn(
status="failed",
workflow_json=None,
workflow_json_path=None,
error_message="LLM optimization failed"
)
# check workflow json structure
try:
from ..workflow.workflow import WorkFlow
from ..workflow.workflow_graph import WorkFlowGraph
# create the workflow graph from the json
graph = WorkFlowGraph.from_dict(optimized_json)
# create the workflow instance
workflow = WorkFlow(graph=graph, llm=self.llm)
except Exception as e:
logger.error(f"Workflow json structure check failed: {e}")
return WorkFlowEditorReturn(
status="failed",
workflow_json=None,
workflow_json_path=None,
error_message="Workflow json structure check failed"
)
del workflow
# save the workflow json file
with open(new_file_path, "w") as f:
json.dump(optimized_json, f, indent=2, ensure_ascii=False)
return WorkFlowEditorReturn(
status="success",
workflow_json=optimized_json,
workflow_json_path=new_file_path,
error_message=None
) |