from openai import OpenAI # Initialize client client = OpenAI( base_url="http://localhost:8010/v1", api_key="dummy", # Not required for local server ) # Make research request response = client.chat.completions.create( model="sgr-agent", messages=[{"role": "user", "content": "Research BMW X6 2025 prices in Russia"}], stream=True, temperature=0.4, ) # Print streaming response for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="")

import json from openai import OpenAI client = OpenAI(base_url="http://localhost:8010/v1", api_key="dummy") # Step 1: Initial research request print("Starting research...") response = client.chat.completions.create( model="sgr-agent", messages=[{"role": "user", "content": "Research AI market trends"}], stream=True, temperature=0, ) agent_id = None clarification_questions = [] # Process streaming response for chunk in response: # Extract agent ID from model field if chunk.model and chunk.model.startswith("sgr_agent_"): agent_id = chunk.model print(f"\nAgent ID: {agent_id}") # Check for clarification requests if chunk.choices[0].delta.tool_calls: for tool_call in chunk.choices[0].delta.tool_calls: if tool_call.function and tool_call.function.name == "clarification": args = json.loads(tool_call.function.arguments) clarification_questions = args.get("questions", []) # Print content if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="") # Step 2: Handle clarification if needed if clarification_questions and agent_id: print(f"\n\nClarification needed:") for i, question in enumerate(clarification_questions, 1): print(f"{i}. {question}") # Provide clarification clarification = "Focus on LLM market trends for 2024-2025, global perspective" print(f"\nProviding clarification: {clarification}") # Continue with agent ID response = client.chat.completions.create( model=agent_id, # Use agent ID as model messages=[{"role": "user", "content": clarification}], stream=True, temperature=0, ) # Print final response for chunk in response: if chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="") print("\n\nResearch completed!")