Use set_up_logging function across multiple modules

src/2_frameworks/1_react_rag/langfuse_gradio.py

@@ -5,7 +5,6 @@
 
 import asyncio
 import contextlib
-import logging
 import signal
 import sys
 
@@ -22,15 +21,15 @@
     get_weaviate_async_client,
     oai_agent_stream_to_gradio_messages,
     pretty_print,
+    set_up_logging,
     setup_langfuse_tracer,
 )
 from src.utils.langfuse.shared_client import langfuse_client
 
 
 load_dotenv(verbose=True)
 
-
-logging.basicConfig(level=logging.INFO)
+set_up_logging()
 
 AGENT_LLM_NAME = "gemini-2.5-flash"
 

src/2_frameworks/2_multi_agent/efficient.py

@@ -8,7 +8,6 @@
 
 import asyncio
 import contextlib
-import logging
 import signal
 import sys
 
@@ -24,16 +23,15 @@
     Configs,
     get_weaviate_async_client,
     oai_agent_stream_to_gradio_messages,
+    set_up_logging,
     setup_langfuse_tracer,
 )
 from src.utils.langfuse.shared_client import langfuse_client
 
 
 load_dotenv(verbose=True)
 
-
-logging.basicConfig(level=logging.INFO)
-
+set_up_logging()
 
 AGENT_LLM_NAMES = {
     "worker": "gemini-2.5-flash",  # less expensive,
@@ -75,15 +73,15 @@ def _handle_sigint(signum: int, frame: object) -> None:
     name="SearchAgent",
     instructions=(
         "You are a search agent. You receive a single search query as input. "
-        "Use the WebSearchTool to perform a web search, then produce a concise "
+        "Use the search tool to perform a search, then produce a concise "
         "'search summary' of the key findings. Do NOT return raw search results."
     ),
     tools=[
         agents.function_tool(async_knowledgebase.search_knowledgebase),
     ],
     # a faster, smaller model for quick searches
     model=agents.OpenAIChatCompletionsModel(
-        model="gemini-2.5-flash", openai_client=async_openai_client
+        model=AGENT_LLM_NAMES["worker"], openai_client=async_openai_client
     ),
 )
 
@@ -101,7 +99,7 @@ def _handle_sigint(signum: int, frame: object) -> None:
     ],
     # a larger, more capable model for planning and reasoning over summaries
     model=agents.OpenAIChatCompletionsModel(
-        model="gemini-2.5-pro", openai_client=async_openai_client
+        model=AGENT_LLM_NAMES["planner"], openai_client=async_openai_client
     ),
 )
 

src/2_frameworks/3_code_interpreter/app.py

@@ -5,7 +5,6 @@
 You will need your E2B API Key.
 """
 
-import logging
 from pathlib import Path
 
 import agents
@@ -18,15 +17,15 @@
     CodeInterpreter,
     oai_agent_stream_to_gradio_messages,
     pretty_print,
+    set_up_logging,
     setup_langfuse_tracer,
 )
 from src.utils.langfuse.shared_client import langfuse_client
 
 
 load_dotenv(verbose=True)
 
-
-logging.basicConfig(level=logging.INFO)
+set_up_logging()
 
 CODE_INTERPRETER_INSTRUCTIONS = """\
 The `code_interpreter` tool executes Python commands. \