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"""
Create a pitch for this project.
PROMPT> python -m src.pitch.create_pitch
"""
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
from src.format_json_for_use_in_query import format_json_for_use_in_query
class ProjectPitch(BaseModel):
pitch: str = Field(
description="A compelling pitch for this project."
)
why_this_pitch_works: str = Field(
description="Explanation why this pitch works."
)
target_audience: str = Field(
description="Who this pitch is aimed at, such as investors, stakeholders, or the general public."
)
call_to_action: str = Field(
description="A clear next step for the audience to engage with the project."
)
risks_and_mitigation: str = Field(
description="Address potential challenges and demonstrate readiness to handle them."
)
metrics_for_success: str = Field(
description="Define how the success of the project will be measured beyond its goals."
)
stakeholder_benefits: str = Field(
description="Explicitly state what stakeholders gain from supporting or being involved in the project."
)
ethical_considerations: str = Field(
description="Build trust by showing a commitment to ethical practices."
)
collaboration_opportunities: str = Field(
description="Highlight ways other organizations or individuals can partner with the project."
)
long_term_vision: str = Field(
description="Show the broader impact and sustainability of the project."
)
QUERY_PREAMBLE = f"""
Craft a compelling pitch for this project that starts with an attention-grabbing hook,
presents its purpose clearly, and highlights the benefits or value it brings. Use a tone
that conveys enthusiasm and aligns with the goals and values of the intended audience,
emphasizing why this project matters and how it stands out.
"""
@dataclass
class CreatePitch:
query: str
response: dict
metadata: dict
@classmethod
def format_query(cls, plan_json: dict, wbs_level1_json: dict, wbs_level2_json: list) -> str:
"""
Format the query for creating project pitch.
"""
if not isinstance(plan_json, dict):
raise ValueError("Invalid plan_json.")
if not isinstance(wbs_level1_json, dict):
raise ValueError("Invalid wbs_level1_json.")
if not isinstance(wbs_level2_json, list):
raise ValueError("Invalid wbs_level2_json.")
query = f"""
The project plan:
{format_json_for_use_in_query(plan_json)}
WBS Level 1:
{format_json_for_use_in_query(wbs_level1_json)}
WBS Level 2:
{format_json_for_use_in_query(wbs_level2_json)}
"""
return query
@classmethod
def execute(cls, llm: LLM, query: str) -> 'CreatePitch':
"""
Invoke LLM to create a project pitch.
"""
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(ProjectPitch)
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
result = CreatePitch(
query=query,
response=json_response,
metadata=metadata,
)
return result
def raw_response_dict(self, include_metadata=True, include_query=True) -> dict:
d = self.response.copy()
if include_metadata:
d['metadata'] = self.metadata
if include_query:
d['query'] = self.query
return d
if __name__ == "__main__":
from llama_index.llms.ollama import Ollama
basepath = os.path.join(os.path.dirname(__file__), 'test_data')
def load_json(relative_path: str) -> dict:
path = os.path.join(basepath, relative_path)
print(f"loading file: {path}")
with open(path, 'r', encoding='utf-8') as f:
the_json = json.load(f)
return the_json
plan_json = load_json('lunar_base-project_plan.json')
wbs_level1_json = load_json('lunar_base-wbs_level1.json')
wbs_level2_json = load_json('lunar_base-wbs_level2.json')
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 = CreatePitch.format_query(plan_json, wbs_level1_json, wbs_level2_json)
print(f"Query: {query}")
result = CreatePitch.execute(llm, query)
print("\nResponse:")
json_response = result.raw_response_dict(include_query=False)
print(json.dumps(json_response, indent=2))
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