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"""
WBS Level 1: Create a Work Breakdown Structure (WBS) from a project plan.
https://en.wikipedia.org/wiki/Work_breakdown_structure
"""
import os
import json
import time
from math import ceil
from typing import List, Optional
from uuid import uuid4
from dataclasses import dataclass
from pydantic import BaseModel, Field
from llama_index.core.llms.llm import LLM
class WBSLevel1(BaseModel):
"""
Represents the top-level details of a Work Breakdown Structure (WBS)
"""
project_title: str = Field(
description="A clear, overarching title that conveys the primary objective of the project. Serves as the projects strategic anchor, guiding all subsequent tasks and deliverables."
)
final_deliverable: str = Field(
description="A detailed description of the projects ultimate outcome or product upon completion. Clearly states the final state or result that the team aims to achieve."
)
QUERY_PREAMBLE = f"""
The task here:
Create a work breakdown structure level 1 for this project.
Focus on providing the following:
- 'project_title': A 1- to 3-word name, extremely concise.
- 'final_deliverable': A 1- to 3-word result, extremely concise.
The project plan:
"""
@dataclass
class CreateWBSLevel1:
"""
WBS Level 1: Creating a Work Breakdown Structure (WBS) from a project plan.
"""
query: str
response: dict
metadata: dict
id: str
project_title: str
final_deliverable: str
@classmethod
def execute(cls, llm: LLM, query: str) -> 'CreateWBSLevel1':
"""
Invoke LLM to create a Work Breakdown Structure (WBS) from a json representation of a project plan.
"""
if not isinstance(llm, LLM):
raise ValueError("Invalid LLM instance.")
if not isinstance(query, str):
raise ValueError("Invalid query.")
start_time = time.perf_counter()
sllm = llm.as_structured_llm(WBSLevel1)
response = sllm.complete(QUERY_PREAMBLE + query)
json_response = json.loads(response.text)
end_time = time.perf_counter()
duration = int(ceil(end_time - start_time))
metadata = dict(llm.metadata)
metadata["llm_classname"] = llm.class_name()
metadata["duration"] = duration
metadata["query"] = query
project_id = str(uuid4())
result = CreateWBSLevel1(
query=query,
response=json_response,
metadata=metadata,
id=project_id,
project_title=json_response['project_title'],
final_deliverable=json_response['final_deliverable']
)
return result
def raw_response_dict(self, include_metadata=True) -> dict:
d = self.response.copy()
if include_metadata:
d['metadata'] = self.metadata
return d
def cleanedup_dict(self) -> dict:
return {
"id": self.id,
"project_title": self.project_title,
"final_deliverable": self.final_deliverable
}
if __name__ == "__main__":
from llama_index.llms.ollama import Ollama
# TODO: Eliminate hardcoded paths
path = '/Users/neoneye/Desktop/planexe_data/plan.json'
print(f"file: {path}")
with open(path, 'r', encoding='utf-8') as f:
plan_json = json.load(f)
if 'metadata' in plan_json:
del plan_json['metadata']
model_name = "llama3.1:latest"
# model_name = "qwen2.5-coder:latest"
# model_name = "phi4:latest"
llm = Ollama(model=model_name, request_timeout=120.0, temperature=0.5, is_function_calling_model=False)
query = json.dumps(plan_json, indent=2)
print(f"\nQuery: {query}")
result = CreateWBSLevel1.execute(llm, query)
print("\n\nResult:")
print(json.dumps(result.raw_response_dict(), indent=2))
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