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import json
from agents import GroqClient, SerpClient
from prompts import build_master_prompt
GENERATOR_MODEL = "llama-3.1-70b-versatile"
VERIFIER_MODEL = "gemma2-27b-it"
FORMATTER_MODEL = "mixtral-8x7b-32768"
class MultiAgentOrchestrator:
def __init__(self, groq: GroqClient, serp: SerpClient):
self.groq = groq
self.serp = serp
def run_pipeline(self, subject, stream, partA, partB, partC, syllabus_text, ref_qp_text):
# 1) Real-time search
serp_results = self.serp.search(f"{subject} latest 2024 2025")
snippets = "\n".join([r.get("snippet", "") for r in serp_results[:3]])
# 2) Build master prompt
master_prompt = build_master_prompt(
subject, stream, partA, partB, partC,
syllabus_text, ref_qp_text, snippets
)
# 3) AGENT 1 — GENERATOR
gen_output = self.groq.ask(
[{"role": "system", "content": "Exam generator agent."},
{"role": "user", "content": master_prompt}],
model=GENERATOR_MODEL
)
# Extract JSON from generator
try:
json_start = gen_output.rfind("{")
qp_json = json.loads(gen_output[json_start:])
except:
qp_json = {"raw": gen_output}
# 4) AGENT 2 — VERIFIER
ver_prompt = f"Check this QP JSON for errors and propose corrections:\n{json.dumps(qp_json)}"
verifier_output = self.groq.ask(
[{"role": "system", "content": "Verifier agent."},
{"role": "user", "content": ver_prompt}],
model=VERIFIER_MODEL
)
try:
corrections = json.loads(verifier_output)
except:
corrections = {"notes": verifier_output}
# 5) AGENT 3 — FORMATTER
fmt_prompt = f"""
Apply corrections to QP JSON and produce:
1. final_qp
2. answers
3. obe
All inside ONE valid JSON object.
Original QP:
{json.dumps(qp_json)}
Corrections:
{json.dumps(corrections)}
"""
final_output = self.groq.ask(
[{"role": "system", "content": "Formatter agent."},
{"role": "user", "content": fmt_prompt}],
model=FORMATTER_MODEL
)
try:
final_json = json.loads(final_output)
except:
final_json = {"raw": final_output}
return {
"generator_raw": gen_output,
"qp_json": qp_json,
"verifier": corrections,
"final": final_json
}
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