Merge pull request #17 from langchain-ai/eugene/format_codebase

Reformat entire codebase with ruff
langchain-ai · Nov 16, 2023 · 3a2ef54 · 3a2ef54
2 parents 7b6823d + 9894475
commit 3a2ef54
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Showing 18 changed files with 181 additions and 159 deletions.
diff --git a/Makefile b/Makefile
@@ -21,7 +21,7 @@ test_watch:
 ######################
 
 # Define a variable for Python and notebook files.
-lint format: PYTHON_FILES=langchain_benchmarks tests
+lint format: PYTHON_FILES=.
 lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=. --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
 
 lint lint_diff:

diff --git a/csv-qa/custom_agent.py b/csv-qa/custom_agent.py
@@ -1,22 +1,25 @@
-from langchain.agents import OpenAIFunctionsAgent, AgentExecutor
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.tools import PythonAstREPLTool
 import pandas as pd
+from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
+from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
+    create_retriever_tool,
+)
 from langchain.chat_models import ChatOpenAI
-from langsmith import Client
-from langchain.smith import RunEvalConfig, run_on_dataset
-from pydantic import BaseModel, Field
 from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.smith import RunEvalConfig, run_on_dataset
+from langchain.tools import PythonAstREPLTool
 from langchain.vectorstores import FAISS
-from langchain.agents.agent_toolkits.conversational_retrieval.tool import create_retriever_tool
-
+from langsmith import Client
+from pydantic import BaseModel, Field
 
-pd.set_option('display.max_rows', 20)
-pd.set_option('display.max_columns', 20)
+pd.set_option("display.max_rows", 20)
+pd.set_option("display.max_columns", 20)
 
 embedding_model = OpenAIEmbeddings()
 vectorstore = FAISS.load_local("titanic_data", embedding_model)
-retriever_tool = create_retriever_tool(vectorstore.as_retriever(), "person_name_search", "Search for a person by name")
+retriever_tool = create_retriever_tool(
+    vectorstore.as_retriever(), "person_name_search", "Search for a person by name"
+)
 
 
 TEMPLATE = """You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
@@ -42,7 +45,6 @@
 """
 
 
-
 class PythonInputs(BaseModel):
     query: str = Field(description="code snippet to run")
 
@@ -51,27 +53,33 @@ class PythonInputs(BaseModel):
     df = pd.read_csv("titanic.csv")
     template = TEMPLATE.format(dhead=df.head().to_markdown())
 
-    prompt = ChatPromptTemplate.from_messages([
-        ("system", template),
-        MessagesPlaceholder(variable_name="agent_scratchpad"),
-        ("human", "{input}")
-    ])
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", template),
+            MessagesPlaceholder(variable_name="agent_scratchpad"),
+            ("human", "{input}"),
+        ]
+    )
 
     def get_chain():
-        repl = PythonAstREPLTool(locals={"df": df}, name="python_repl",
-                                 description="Runs code and returns the output of the final line",
-                                 args_schema=PythonInputs)
+        repl = PythonAstREPLTool(
+            locals={"df": df},
+            name="python_repl",
+            description="Runs code and returns the output of the final line",
+            args_schema=PythonInputs,
+        )
         tools = [repl, retriever_tool]
-        agent = OpenAIFunctionsAgent(llm=ChatOpenAI(temperature=0, model="gpt-4"), prompt=prompt, tools=tools)
-        agent_executor = AgentExecutor(agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate")
+        agent = OpenAIFunctionsAgent(
+            llm=ChatOpenAI(temperature=0, model="gpt-4"), prompt=prompt, tools=tools
+        )
+        agent_executor = AgentExecutor(
+            agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate"
+        )
         return agent_executor
 
-
     client = Client()
     eval_config = RunEvalConfig(
-        evaluators=[
-            "qa"
-        ],
+        evaluators=["qa"],
     )
     chain_results = run_on_dataset(
         client,

diff --git a/csv-qa/pandas_agent_gpt_35.py b/csv-qa/pandas_agent_gpt_35.py
@@ -1,9 +1,9 @@
 import pandas as pd
-from langchain.chat_models import ChatOpenAI
 from langchain.agents.agent_toolkits import create_pandas_dataframe_agent
 from langchain.agents.agent_types import AgentType
-from langsmith import Client
+from langchain.chat_models import ChatOpenAI
 from langchain.smith import RunEvalConfig, run_on_dataset
+from langsmith import Client
 
 if __name__ == "__main__":
     df = pd.read_csv("titanic.csv")
@@ -18,20 +18,17 @@ def get_chain():
             df,
             agent_type=AgentType.OPENAI_FUNCTIONS,
             agent_executor_kwargs=agent_executor_kwargs,
-            max_iterations=5
+            max_iterations=5,
         )
         return agent
 
-
     client = Client()
     eval_config = RunEvalConfig(
-        evaluators=[
-            "qa"
-        ],
+        evaluators=["qa"],
     )
     chain_results = run_on_dataset(
         client,
         dataset_name="Titanic CSV Data",
         llm_or_chain_factory=get_chain,
         evaluation=eval_config,
-    )
+    )
diff --git a/csv-qa/pandas_agent_gpt_4.py b/csv-qa/pandas_agent_gpt_4.py
@@ -1,14 +1,13 @@
 import pandas as pd
-from langchain.chat_models import ChatOpenAI
 from langchain.agents.agent_toolkits import create_pandas_dataframe_agent
 from langchain.agents.agent_types import AgentType
-from langsmith import Client
+from langchain.chat_models import ChatOpenAI
 from langchain.smith import RunEvalConfig, run_on_dataset
+from langsmith import Client
 
 if __name__ == "__main__":
     df = pd.read_csv("titanic.csv")
 
-
     def get_chain():
         llm = ChatOpenAI(temperature=0, model="gpt-4")
         agent_executor_kwargs = {
@@ -19,20 +18,17 @@ def get_chain():
             df,
             agent_type=AgentType.OPENAI_FUNCTIONS,
             agent_executor_kwargs=agent_executor_kwargs,
-            max_iterations=5
+            max_iterations=5,
         )
         return agent
 
-
     client = Client()
     eval_config = RunEvalConfig(
-        evaluators=[
-            "qa"
-        ],
+        evaluators=["qa"],
     )
     chain_results = run_on_dataset(
         client,
         dataset_name="Titanic CSV Data",
         llm_or_chain_factory=get_chain,
         evaluation=eval_config,
-    )
+    )
diff --git a/csv-qa/pandas_agent_instruct.py b/csv-qa/pandas_agent_instruct.py
@@ -1,22 +1,25 @@
-from langchain.agents import ZeroShotAgent, AgentExecutor
-from langchain.prompts import PromptTemplate
-from langchain.tools import PythonAstREPLTool
 import pandas as pd
+from langchain.agents import AgentExecutor, ZeroShotAgent
+from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
+    create_retriever_tool,
+)
+from langchain.embeddings import OpenAIEmbeddings
 from langchain.llms import OpenAI
-from langsmith import Client
+from langchain.prompts import PromptTemplate
 from langchain.smith import RunEvalConfig, run_on_dataset
-from pydantic import BaseModel, Field
-from langchain.embeddings import OpenAIEmbeddings
+from langchain.tools import PythonAstREPLTool
 from langchain.vectorstores import FAISS
-from langchain.agents.agent_toolkits.conversational_retrieval.tool import create_retriever_tool
-
+from langsmith import Client
+from pydantic import BaseModel, Field
 
-pd.set_option('display.max_rows', 20)
-pd.set_option('display.max_columns', 20)
+pd.set_option("display.max_rows", 20)
+pd.set_option("display.max_columns", 20)
 
 embedding_model = OpenAIEmbeddings()
 vectorstore = FAISS.load_local("titanic_data", embedding_model)
-retriever_tool = create_retriever_tool(vectorstore.as_retriever(), "person_name_search", "Search for a person by name")
+retriever_tool = create_retriever_tool(
+    vectorstore.as_retriever(), "person_name_search", "Search for a person by name"
+)
 
 
 TEMPLATE = """You are working with a pandas dataframe in Python. The name of the dataframe is `df`.
@@ -41,7 +44,6 @@
 <logic>Use `python_repl` since even though the question is about a person, you don't know their name so you can't include it.</logic>"""
 
 
-
 class PythonInputs(BaseModel):
     query: str = Field(description="code snippet to run")
 
@@ -50,22 +52,27 @@ class PythonInputs(BaseModel):
     df = pd.read_csv("titanic.csv")
     template = TEMPLATE.format(dhead=df.head().to_markdown())
 
-
     def get_chain():
-        repl = PythonAstREPLTool(locals={"df": df}, name="python_repl",
-                                 description="Runs code and returns the output of the final line",
-                                 args_schema=PythonInputs)
+        repl = PythonAstREPLTool(
+            locals={"df": df},
+            name="python_repl",
+            description="Runs code and returns the output of the final line",
+            args_schema=PythonInputs,
+        )
         tools = [repl, retriever_tool]
-        agent = ZeroShotAgent.from_llm_and_tools(llm=OpenAI(temperature=0, model="gpt-3.5-turbo-instruct"), tools=tools, prefix=template)
-        agent_executor = AgentExecutor(agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate")
+        agent = ZeroShotAgent.from_llm_and_tools(
+            llm=OpenAI(temperature=0, model="gpt-3.5-turbo-instruct"),
+            tools=tools,
+            prefix=template,
+        )
+        agent_executor = AgentExecutor(
+            agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate"
+        )
         return agent_executor
 
-
     client = Client()
     eval_config = RunEvalConfig(
-        evaluators=[
-            "qa"
-        ],
+        evaluators=["qa"],
     )
     chain_results = run_on_dataset(
         client,

diff --git a/csv-qa/pandas_ai.py b/csv-qa/pandas_ai.py
@@ -1,44 +1,47 @@
 import pandas as pd
-from langchain.chat_models import ChatOpenAI
 from langchain.agents.agent_toolkits import create_pandas_dataframe_agent
 from langchain.agents.agent_types import AgentType
-from langsmith import Client
-from langchain.smith import RunEvalConfig, run_on_dataset
-import pandas as pd
-from pandasai import PandasAI
-
 from langchain.chat_models import ChatOpenAI
 from langchain.prompts import ChatPromptTemplate
 from langchain.schema.output_parser import StrOutputParser
+from langchain.smith import RunEvalConfig, run_on_dataset
+from langsmith import Client
+from pandasai import PandasAI
 
 if __name__ == "__main__":
     df = pd.read_csv("titanic.csv")
 
     pandas_ai = PandasAI(ChatOpenAI(temperature=0, model="gpt-4"), enable_cache=False)
-    prompt = ChatPromptTemplate.from_messages([
-        ("system",
-         "Answer the users question about some data. A data scientist will run some code and the results will be returned to you to use in your answer"),
-        ("human", "Question: {input}"),
-        ("human", "Data Scientist Result: {result}"),
-    ])
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                "Answer the users question about some data. A data scientist will run some code and the results will be returned to you to use in your answer",
+            ),
+            ("human", "Question: {input}"),
+            ("human", "Data Scientist Result: {result}"),
+        ]
+    )
 
     def get_chain():
-        chain = {
-                    "input": lambda x: x["input_question"],
-                    "result": lambda x: pandas_ai(df, prompt=x['input_question'])
-                } | prompt | ChatOpenAI(temperature=0, model="gpt-4") | StrOutputParser()
+        chain = (
+            {
+                "input": lambda x: x["input_question"],
+                "result": lambda x: pandas_ai(df, prompt=x["input_question"]),
+            }
+            | prompt
+            | ChatOpenAI(temperature=0, model="gpt-4")
+            | StrOutputParser()
+        )
         return chain
 
-
     client = Client()
     eval_config = RunEvalConfig(
-        evaluators=[
-            "qa"
-        ],
+        evaluators=["qa"],
     )
     chain_results = run_on_dataset(
         client,
         dataset_name="Titanic CSV Data",
         llm_or_chain_factory=get_chain,
         evaluation=eval_config,
-    )
+    )