Spaces:
Runtime error
Runtime error
File size: 4,628 Bytes
595d60e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | import json
import logging
from app.db.database import SessionLocal
from app.services.conversation_service import ConversationService
from app.agents.dev_agent import DevAgent
from app.db.models import DevSessionModel, ConversationMessageModel
# Disable logging clutter in CLI mode
logging.getLogger("litellm").setLevel(logging.WARNING)
def main():
print("====================================================")
print(" Universal Dev Agent Builder CLI ")
print("====================================================")
print("Describe the agent you want to build.")
print("Type 'exit' or 'quit' to close the session.\n")
db = SessionLocal()
conv_service = ConversationService()
agent = DevAgent()
# 1. Create a persistent session in the database
session_id = conv_service.create_session(db)
try:
while True:
user = input("\nYou: ").strip()
if not user:
continue
if user.lower() in ("exit", "quit"):
print("Exiting builder. Goodbye!")
break
# 2. Save user message
conv_service.add_message(db, session_id, 'user', user)
# 3. Load full history and previous state
session = db.query(DevSessionModel).filter(DevSessionModel.id == session_id).first()
msgs = db.query(ConversationMessageModel).filter(ConversationMessageModel.session_id == session_id).order_by(ConversationMessageModel.created_at.asc()).all()
history = [{"role": m.role, "content": m.content} for m in msgs]
state_init = {
'extracted_requirements': session.extracted_requirements or {},
'missing_information': session.missing_information or [],
'clarification_questions': session.clarification_questions or [],
'draft_spec': session.draft_spec,
'validation_result': session.validation_result,
'status': session.status
}
# 4. Invoke agent workflow
result = agent.process(session_id, user, history, state_init)
# 5. Save assistant response and update state in DB
assistant_content = ""
questions = result.get('clarification_questions', []) or []
if questions:
assistant_content = "\n".join([q['question'] for q in questions])
elif result.get('draft_spec'):
if result.get('status') == 'completed':
assistant_content = "Here is the final generated Agent Spec!"
else:
assistant_content = "I have drafted the Agent Spec, but validation errors occurred."
if assistant_content:
conv_service.add_message(db, session_id, 'assistant', assistant_content)
session.extracted_requirements = result.get('extracted_requirements', {})
session.missing_information = result.get('missing_information', [])
session.clarification_questions = result.get('clarification_questions', [])
session.draft_spec = result.get('draft_spec', None)
session.validation_result = result.get('validation_result', None)
session.status = result.get('status', 'collecting_requirements')
db.commit()
db.refresh(session)
# 6. Display Agent questions or spec/errors
if questions:
for q in questions:
print(f"\nAgent: {q['question']}")
if session.draft_spec:
print("\n--- Current Agent Spec Draft ---")
print(json.dumps(session.draft_spec, indent=2))
print("--------------------------------")
if session.validation_result:
vr = session.validation_result
if vr.get('valid'):
print("\n[SUCCESS] The Agent Spec is fully VALID and generated!")
print("You can copy the JSON above. Exiting...")
break
else:
print("\n[WARNING] Validation errors detected in draft:")
for err in vr.get('errors', []):
print(f" - [{err.get('code')}] {err.get('message')} (path: {err.get('path')})")
print("\nPlease address the validation errors above.")
finally:
db.close()
if __name__ == "__main__":
main()
|