chore: fix formatting (#5326)

.github/workflows/python-CI.yml

@@ -5,6 +5,7 @@ on:
     branches: [main, sessions]
   pull_request:
     paths:
+      - "**/*.py"
       - "src/**"
       - "tests/**"
       - "tutorials/**"

examples/agent_framework_comparison/autogen_multi_agent/main.py

@@ -1,13 +1,12 @@
 import os
 import sys
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
 import gradio as gr
 from openinference.semconv.trace import SpanAttributes
 from opentelemetry import trace
 from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
-
 from router import run_autogen_agents
 from utils.instrument import Framework, instrument
 

examples/agent_framework_comparison/autogen_multi_agent/router.py

@@ -1,19 +1,18 @@
 import os
 import sys
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
-from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager, register_function
+from autogen import AssistantAgent, GroupChat, GroupChatManager, UserProxyAgent, register_function
+from calculator import calculator
+from db.database import get_schema, get_table
 from dotenv import load_dotenv
 from openinference.semconv.trace import SpanAttributes
 from opentelemetry import trace
 from opentelemetry.trace.propagation.tracecontext import TraceContextTextMapPropagator
-
-from calculator import calculator
-from sql_query import run_sql_query
 from prompt_templates.router_template import SYSTEM_PROMPT as MANAGER_SYSTEM_PROMPT
 from prompt_templates.sql_generator_template import SYSTEM_PROMPT as SQL_SYSTEM_PROMPT
-from db.database import get_schema, get_table
+from sql_query import run_sql_query
 
 load_dotenv()
 
@@ -29,26 +28,21 @@ def run_autogen_agents(query, parent_context):
         span.set_attribute(SpanAttributes.INPUT_MIME_TYPE, "application/json")
         span.set_attribute(SpanAttributes.LLM_TOOLS, str(["calculator", "run_sql_query"]))
 
-        config_list = [
-            {
-                "model": "gpt-4o",
-                "api_key": os.environ["OPENAI_API_KEY"]
-            }
-        ]
+        config_list = [{"model": "gpt-4o", "api_key": os.environ["OPENAI_API_KEY"]}]
         llm_config = {"config_list": config_list, "cache_seed": 42}
 
         calculator_agent = AssistantAgent(
             name="Calculator",
             description="Perform basic arithmetic operations (+, -, *, /) on two integers.",
             system_message="You are a helpful assistant that can perform basic arithmetic operations "
-                           "(+, -, *, /) on two integers.",
+            "(+, -, *, /) on two integers.",
             llm_config=llm_config,
         )
         data_analyzer_agent = AssistantAgent(
             name="Data_Analyzer",
             description="Provide insights, trends, or analysis based on the data and prompt.",
             system_message="You are a helpful assistant that can provide insights, trends, or analysis "
-                           "based on the data and prompt.",
+            "based on the data and prompt.",
             llm_config=llm_config,
         )
         sql_query_agent = AssistantAgent(
@@ -59,13 +53,13 @@ def run_autogen_agents(query, parent_context):
         )
 
         system_message = (
-            MANAGER_SYSTEM_PROMPT +
-            "First, identify and make all necessary agent calls based on the user prompt. " +
-            "Ensure that you gather and aggregate the results from these agent calls. " +
-            "Once all agent calls are completed and the final result is ready, return it in a single message. " +
-            "When the task is fully completed, ensure the final message contains the full result, "
-            "followed by 'TERMINATE' at the very end." +
-            "If the task is about finding any trends, use the SQL_Query Agent first, "
+            MANAGER_SYSTEM_PROMPT
+            + "First, identify and make all necessary agent calls based on the user prompt. "
+            + "Ensure that you gather and aggregate the results from these agent calls. "
+            + "Once all agent calls are completed and the final result is ready, return it in a single message. "
+            + "When the task is fully completed, ensure the final message contains the full result, "
+            "followed by 'TERMINATE' at the very end."
+            + "If the task is about finding any trends, use the SQL_Query Agent first, "
             "to retrieve the data for Data_Analyzer Agent."
         )
         manager_agent = AssistantAgent(
@@ -75,7 +69,8 @@ def run_autogen_agents(query, parent_context):
         )
         user_proxy_agent = UserProxyAgent(
             name="User_Proxy",
-            is_termination_msg=lambda msg: msg.get("content") is not None and "TERMINATE" in msg["content"],
+            is_termination_msg=lambda msg: msg.get("content") is not None
+            and "TERMINATE" in msg["content"],
             human_input_mode="NEVER",
             max_consecutive_auto_reply=10,
             code_execution_config={
@@ -91,27 +86,31 @@ def run_autogen_agents(query, parent_context):
             executor=user_proxy_agent,
             name="Calculator_Tool",
             description="A tool that can be used to perform basic arithmetic operations "
-                        "(+, -, *, /) on two integers."
+            "(+, -, *, /) on two integers.",
         )
         register_function(
             run_sql_query,
             caller=sql_query_agent,
             executor=user_proxy_agent,
             name="SQL_Query_Executor_Tool",
-            description="A tool that can be used to run SQL query on the database."
+            description="A tool that can be used to run SQL query on the database.",
         )
 
-        agents = [calculator_agent, data_analyzer_agent, sql_query_agent, manager_agent, user_proxy_agent]
+        agents = [
+            calculator_agent,
+            data_analyzer_agent,
+            sql_query_agent,
+            manager_agent,
+            user_proxy_agent,
+        ]
         groupchat = GroupChat(agents=agents, messages=[], max_round=15)
         groupchat_manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
 
         result = user_proxy_agent.initiate_chat(
             groupchat_manager,
             message=query,
         )
-        last_chat_message = result.chat_history[-1]['content']
+        last_chat_message = result.chat_history[-1]["content"]
 
-        span.set_attribute(
-            SpanAttributes.OUTPUT_VALUE, str(last_chat_message)
-        )
+        span.set_attribute(SpanAttributes.OUTPUT_VALUE, str(last_chat_message))
         return last_chat_message

examples/agent_framework_comparison/autogen_multi_agent/sql_query.py

@@ -2,21 +2,21 @@
 import sys
 from typing import Annotated
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
+from db.database import run_query
 from openinference.semconv.trace import SpanAttributes
 from opentelemetry import trace
 from pydantic import BaseModel, Field
 
-from db.database import run_query
-
 
 class SQLQueryInput(BaseModel):
-    sql_query: Annotated[str, Field(description="The SQL query to retrieve dataset for data analysis.")]
+    sql_query: Annotated[
+        str, Field(description="The SQL query to retrieve dataset for data analysis.")
+    ]
 
 
 def run_sql_query(input: Annotated[SQLQueryInput, "Input to the SQL query executor."]) -> str:
-
     def _sanitize_query(query):
         query = query.strip()
         if query.startswith("```") and query.endswith("```"):

examples/agent_framework_comparison/crewai_multi_agent/calculator.py

@@ -25,8 +25,7 @@ def _run(self, a: int, b: int, operator: Operator) -> int:
         tracer = trace.get_tracer(__name__)
         with tracer.start_as_current_span("calculator_tool") as span:
             span.set_attribute(SpanAttributes.OPENINFERENCE_SPAN_KIND, "CHAIN")
-            span.set_attribute(SpanAttributes.INPUT_VALUE,
-                               f"a={a}, b={b}, operator={operator}")
+            span.set_attribute(SpanAttributes.INPUT_VALUE, f"a={a}, b={b}, operator={operator}")
 
             if operator == "+":
                 result = a + b

examples/agent_framework_comparison/crewai_multi_agent/main.py

@@ -1,10 +1,9 @@
 import os
 import sys
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
 import gradio as gr
-
 from router import run_crewai
 from utils.instrument import Framework, instrument
 

examples/agent_framework_comparison/crewai_multi_agent/router.py

@@ -1,17 +1,16 @@
 import os
 import sys
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
+from calculator import CalculatorTool
 from crewai import Agent, Crew, Process, Task
+from db.database import get_schema, get_table
 from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
-
-from calculator import CalculatorTool
-from sql_query import SQLQueryTool
 from prompt_templates.router_template import SYSTEM_PROMPT as MANAGER_SYSTEM_PROMPT
 from prompt_templates.sql_generator_template import SYSTEM_PROMPT as SQL_SYSTEM_PROMPT
-from db.database import get_schema, get_table
+from sql_query import SQLQueryTool
 
 load_dotenv()
 
@@ -26,20 +25,20 @@ def run_crewai(query):
         role="Calculator",
         goal="Perform basic arithmetic operations (+, -, *, /) on two integers.",
         backstory="You are a helpful assistant that can perform basic arithmetic operations (+, -, *, /) "
-                  "on two integers.",
+        "on two integers.",
         tools=[calculator_tool],
         allow_delegation=False,
         verbose=True,
-        llm=llm
+        llm=llm,
     )
     data_analyzer_agent = Agent(
         role="Data Analyzer",
         goal="Provide insights, trends, or analysis based on the data and prompt.",
         backstory="You are a helpful assistant that can provide insights, trends, or analysis based on "
-                  "the data and prompt.",
+        "the data and prompt.",
         allow_delegation=False,
         verbose=True,
-        llm=llm
+        llm=llm,
     )
     sql_query_agent = Agent(
         role="SQL Query",
@@ -48,7 +47,7 @@ def run_crewai(query):
         tools=[sql_query_tool],
         allow_delegation=False,
         verbose=True,
-        llm=llm
+        llm=llm,
     )
 
     system_message = (
@@ -64,21 +63,21 @@ def run_crewai(query):
         backstory=MANAGER_SYSTEM_PROMPT,
         allow_delegation=True,
         verbose=True,
-        llm=llm
+        llm=llm,
     )
 
     user_query_task = Task(
         description=query,
         expected_output="Once all agent calls are completed and the final result is ready, return it "
-                        "in a single message.",
-        agent=manager_agent
+        "in a single message.",
+        agent=manager_agent,
     )
 
     crew = Crew(
         agents=[calculator_agent, data_analyzer_agent, sql_query_agent],
         tasks=[user_query_task],
         process=Process.sequential,
-        manager_agent=manager_agent
+        manager_agent=manager_agent,
     )
     result = crew.kickoff()
     return result.raw

examples/agent_framework_comparison/crewai_multi_agent/sql_query.py

@@ -2,21 +2,19 @@
 import sys
 from typing import Type
 
-sys.path.insert(1, os.path.join(sys.path[0], '..'))
+sys.path.insert(1, os.path.join(sys.path[0], ".."))
 
 from crewai_tools import BaseTool
+from db.database import run_query
 from openinference.semconv.trace import SpanAttributes
 from opentelemetry import trace
 from pydantic import BaseModel, Field
 
-from db.database import run_query
-
 
 class SQLQueryInput(BaseModel):
     """Input for SQLQueryTool."""
 
-    sql_query: str = Field(
-        description="The SQL query to retrieve dataset for data analysis.")
+    sql_query: str = Field(description="The SQL query to retrieve dataset for data analysis.")
 
 
 class SQLQueryTool(BaseTool):

examples/agent_framework_comparison/utils/instrument.py

@@ -2,9 +2,10 @@
 from enum import Enum
 
 from openinference.instrumentation.langchain import LangChainInstrumentor
+from openinference.instrumentation.litellm import LiteLLMInstrumentor
 from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
 from openinference.instrumentation.openai import OpenAIInstrumentor
-from openinference.instrumentation.litellm import LiteLLMInstrumentor
+
 from phoenix.otel import register
 
 

pyproject.toml

@@ -412,6 +412,7 @@ target-version = "py39"
 
 [tool.ruff.lint.per-file-ignores]
 "*.ipynb" = ["E402", "E501"]
+"examples/**/*.py" = ["E501"]
 
 [tool.ruff.lint]
 select = ["E", "F", "W", "I", "NPY201"]

tox.ini

@@ -157,7 +157,7 @@ commands =
 [testenv:ruff]
 description = Run ruff for formatting and linting
 commands_pre =
-  uv tool install [email protected].0
+  uv tool install [email protected].3
 commands =
   uv tool run ruff format
   uv tool run ruff check --fix