|
""" |
|
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") |
|
|
|
|
|
|
|
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()) |
|
|
