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"""
PROMPT> python -m src.expert.expert_orchestrator
"""
import logging
from llama_index.core.llms.llm import LLM
from src.expert.expert_finder import ExpertFinder
from src.expert.expert_criticism import ExpertCriticism
from src.expert.markdown_with_criticism_from_experts import markdown_rows_with_info_about_one_expert, markdown_rows_with_criticism_from_one_expert
logger = logging.getLogger(__name__)
class ExpertOrchestrator:
def __init__(self):
self.phase1_post_callback = None
self.phase2_post_callback = None
self.expert_finder: ExpertFinder = None
self.expert_criticism_list: list[ExpertCriticism] = []
self.max_expert_count = 2
def execute(self, llm: LLM, query: str) -> None:
logger.info("Finding experts that can provide criticism...")
self.expert_finder = ExpertFinder.execute(llm, query)
if self.phase1_post_callback:
self.phase1_post_callback(self.expert_finder)
expert_finder = self.expert_finder
all_expert_list = expert_finder.expert_list
all_expert_count = len(all_expert_list)
expert_list_truncated = all_expert_list[:self.max_expert_count]
expert_list_truncated_count = len(expert_list_truncated)
if all_expert_count != expert_list_truncated_count:
logger.info(f"Truncated expert list from {all_expert_count} to {expert_list_truncated_count} experts.")
logger.info(f"Asking {expert_list_truncated_count} experts for criticism...")
for expert_index, expert_dict in enumerate(expert_list_truncated):
expert_copy = expert_dict.copy()
expert_copy.pop('id')
expert_title = expert_copy.get('title', 'Missing title')
logger.info(f"Getting criticism from expert {expert_index + 1} of {expert_list_truncated_count}. expert_title: {expert_title}")
system_prompt = ExpertCriticism.format_system(expert_dict)
expert_criticism = ExpertCriticism.execute(llm, query, system_prompt)
if self.phase2_post_callback:
self.phase2_post_callback(expert_criticism, expert_index)
self.expert_criticism_list.append(expert_criticism)
logger.info(f"Finished collecting criticism from {expert_list_truncated_count} experts.")
def to_markdown(self) -> str:
rows = []
rows.append("# Project Expert Review & Recommendations\n")
rows.append("## A Compilation of Professional Feedback for Project Planning and Execution\n\n")
number_of_experts_with_criticism = len(self.expert_criticism_list)
for expert_index, expert_criticism in enumerate(self.expert_criticism_list):
section_index = expert_index + 1
if expert_index > 0:
rows.append("\n---\n")
expert_details = self.expert_finder.expert_list[expert_index]
rows.extend(markdown_rows_with_info_about_one_expert(section_index, expert_details))
rows.extend(markdown_rows_with_criticism_from_one_expert(section_index, expert_criticism.to_dict()))
if number_of_experts_with_criticism != len(self.expert_finder.expert_list):
rows.append("\n---\n")
rows.append("# The following experts did not provide feedback:")
for expert_index, expert_details in enumerate(self.expert_finder.expert_list):
if expert_index < number_of_experts_with_criticism:
continue
section_index = expert_index + 1
rows.append("")
rows.extend(markdown_rows_with_info_about_one_expert(section_index, expert_details))
return "\n".join(rows)
if __name__ == "__main__":
import logging
from src.llm_factory import get_llm
from src.plan.find_plan_prompt import find_plan_prompt
import json
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[
logging.StreamHandler()
]
)
plan_prompt = find_plan_prompt("4dc34d55-0d0d-4e9d-92f4-23765f49dd29")
llm = get_llm("ollama-llama3.1")
# llm = get_llm("openrouter-paid-gemini-2.0-flash-001")
# llm = get_llm("deepseek-chat")
def phase1_post_callback(expert_finder: ExpertFinder) -> None:
count = len(expert_finder.expert_list)
d = expert_finder.to_dict(include_system_prompt=False, include_user_prompt=False)
pretty = json.dumps(d, indent=2)
print(f"Found {count} expert:\n{pretty}")
def phase2_post_callback(expert_criticism: ExpertCriticism, expert_index: int) -> None:
d = expert_criticism.to_dict(include_query=False)
pretty = json.dumps(d, indent=2)
print(f"Expert {expert_index + 1} criticism:\n{pretty}")
orchestrator = ExpertOrchestrator()
orchestrator.phase1_post_callback = phase1_post_callback
orchestrator.phase2_post_callback = phase2_post_callback
orchestrator.execute(llm, plan_prompt)
print("\n\nMarkdown:")
print(orchestrator.to_markdown())
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