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40f02cf | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | from smolagents import (
CodeAgent,
VisitWebpageTool,
WebSearchTool,
WikipediaSearchTool,
PythonInterpreterTool,
FinalAnswerTool,
)
from groq import Groq
from vision_tool import image_reasoning_tool
import os
import time
# ---- TOOLS ----
# ---- GROQ MODEL WRAPPER ----
class GroqModel:
def __init__(self, model_name=""):
self.model_name = model_name
self.client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
def __call__(self, prompt, max_tokens=8096):
if isinstance(prompt, str):
messages = [{"role": "user", "content": prompt}]
else:
messages = prompt
response = None
for attempt in range(3):
try:
response = self.client.chat.completions.create(
messages=messages,
model=self.model_name,
stream=False,
max_tokens=max_tokens,
)
break
except Exception as e:
msg = str(e).lower()
if "rate limit" in msg and attempt < 2:
wait = 10 * (attempt + 1)
time.sleep(wait)
continue
raise
if response is None:
response = self.client.chat.completions.create(
messages=messages,
model=self.model_name,
stream=False,
max_tokens=max_tokens,
)
choice = response.choices[0]
if hasattr(choice, "message"):
content = choice.message.content
else:
# Fallback for text-only completions
if hasattr(choice, "text"):
content = choice.text
elif isinstance(choice, str):
content = choice
else:
content = str(choice)
# token usage is calculated but currently unused
if hasattr(response, "usage") and response.usage is not None:
_ = response.usage.total_tokens
return content
def generate(self, prompt, max_tokens=8096, **kwargs):
# For compatibility with agent frameworks
return self.__call__(prompt, max_tokens=max_tokens)
# ---- MULTI-AGENT SYSTEM ----
class MultyAgentSystem:
def __init__(self):
self.primary_model_name = "deepseek-r1-distill-llama-70b"
self.fallback_model_name = "llama3-70b-8k"
self.deepseek_model = GroqModel(self.primary_model_name)
qwen_model = GroqModel("qwen-qwq-32b")
self.verification_limit = int(os.getenv("VERIFY_WORD_LIMIT", "75"))
# --- Web agent definition ---
self.web_agent = CodeAgent(
model=qwen_model,
tools=[WebSearchTool(), VisitWebpageTool(), WikipediaSearchTool()],
name="web_agent",
description=(
"You are a web browsing agent. Whenever the given {task} involves browsing "
"the web or a specific website such as Wikipedia or YouTube, you will use "
"the provided tools. For web-based factual and retrieval tasks, be as precise and source-reliable as possible."
),
additional_authorized_imports=[
"markdownify",
"json",
"requests",
"urllib.request",
"urllib.parse",
"wikipedia-api",
],
verbosity_level=0,
max_steps=10,
)
# --- Info agent definition ---
self.info_agent = CodeAgent(
model=qwen_model,
tools=[PythonInterpreterTool(), image_reasoning_tool],
name="info_agent",
description=(
"You are an agent tasked with cleaning, parsing, calculating information, and performing OCR if images are provided in the {task}. "
"You can also analyze images using a vision model. You handle all math, code, and data manipulation. Use numpy, math, and available libraries. "
"For image or chess tasks, use pytesseract, PIL, chess, or the image_reasoning_tool as required."
),
additional_authorized_imports=[
"numpy",
"math",
"pytesseract",
"PIL",
"chess",
],
)
# --- Manager agent definition ---
manager_planning_interval = int(os.getenv("MANAGER_PLANNING_INTERVAL", "3"))
manager_max_steps = int(os.getenv("MANAGER_MAX_STEPS", "8"))
self.manager_agent = CodeAgent(
model=qwen_model,
tools=[FinalAnswerTool()],
managed_agents=[self.web_agent, self.info_agent],
name="manager_agent",
description=(
"You are the manager. Given a {task}, plan which agent to use: "
"If web data is needed, delegate to web_agent. If math, parsing, image reasoning, or code is needed, use info_agent. "
"After collecting outputs, optionally cross-validate and check correctness, then finalize and submit the best answer using FinalAnswerTool. "
"For each task, explicitly explain your planning steps and reasons for choosing which agent, and always prefer the most accurate and complete answer possible."
),
additional_authorized_imports=[
"json",
"pandas",
"numpy",
],
planning_interval=manager_planning_interval,
verbosity_level=2,
max_steps=manager_max_steps,
)
# runtime tracking for fallback switching
self.total_runtime = 0.0
self.first_call_duration = None
self.model_switched = False
def _switch_to_fallback(self):
if self.model_switched:
return
self.manager_agent.model = GroqModel(self.fallback_model_name)
self.model_switched = True
def run(self, question, high_stakes: bool = False, **kwargs):
start_time = time.time()
print("Generating initial answer with Qwen-32B")
initial_answer = self.manager_agent(question, **kwargs)
call_duration = time.time() - start_time
answer = initial_answer
if high_stakes or len(initial_answer.split()) > self.verification_limit:
print("Verifying answer using DeepSeek-70B")
verification_prompt = (
"Review the following answer for accuracy and rewrite if needed:"
f"\n\n{initial_answer}"
)
try:
answer = self.deepseek_model(verification_prompt)
except Exception as e:
print(f"Verification failed: {e}. Using initial answer.")
answer = initial_answer
if self.first_call_duration is None:
self.first_call_duration = call_duration
if self.first_call_duration > 30:
self._switch_to_fallback()
self.total_runtime += call_duration
if self.total_runtime > 300 and not self.model_switched:
self._switch_to_fallback()
return answer
def __call__(self, question, high_stakes: bool = False, **kwargs):
return self.run(question, high_stakes=high_stakes, **kwargs)
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