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ADK Orchestrator
Chains the 5 agents together (Planner -> Memory -> Solver -> Verifier -> Formatter).
Replaces the monolithic MathAIEngine.
"""
import logging
from typing import Dict, Any
from backend.src.agents.planner import PlannerAgent
from backend.src.agents.memory import MemoryAgent
from backend.src.agents.solver import SolverAgent
from backend.src.agents.verifier import VerifierAgent
from backend.src.agents.formatter import FormatterAgent
logger = logging.getLogger("math_assistant.agents.orchestrator")
class ADKOrchestrator:
"""Orchestrates the multi-agent ADK pipeline."""
def __init__(self, uid: str, session_id: str):
self.uid = uid
self.session_id = session_id
self.planner = PlannerAgent()
self.memory = MemoryAgent()
self.solver = SolverAgent()
self.verifier = VerifierAgent()
self.formatter = FormatterAgent()
def query(self, user_query: str) -> Dict[str, Any]:
"""Execute the full agent pipeline."""
logger.info(f"Starting ADK Orchestrator for session {self.session_id}")
# 1. Plan
plan = self.planner.plan(user_query)
logger.info(f"Plan: {plan}")
class_level = plan.get("class", 10)
# 2. Memory Retrieval
mem_context = self.memory.retrieve(self.uid, self.session_id)
# 3. Solver + RAG
rag_context = self.solver.retrieve_context(user_query)
raw_solution = self.solver.solve(user_query, rag_context, mem_context["history"])
# 4. Verify
# We allow up to 2 retries on verification failure
verification = self.verifier.verify(user_query, raw_solution)
retries = 0
while not verification.get("is_correct", True) and retries < 2:
logger.warning(f"Verification failed: {verification.get('feedback')}. Retrying...")
feedback = verification.get("feedback")
# Inject feedback into prompt
refined_query = f"{user_query}\nNote: Your previous attempt had an error: {feedback}. Please fix it."
raw_solution = self.solver.solve(refined_query, rag_context, mem_context["history"])
verification = self.verifier.verify(user_query, raw_solution)
retries += 1
# 5. Format
final_answer = self.formatter.format(user_query, raw_solution, class_level)
# Save to memory (Memory agent handles retrieval, but we use memory service directly for saving)
from backend.src.services.memory_service import FirestoreChatMemory
mem_svc = FirestoreChatMemory(uid=self.uid, session_id=self.session_id)
mem_svc.add_message("human", user_query)
mem_svc.add_message("assistant", final_answer)
return {
"answer": final_answer,
"session_id": self.session_id,
"is_adk": True
}
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