如何对 CSV 文件进行问答
LLM 非常适合构建基于各种类型数据源的问答系统。在本节中,我们将介绍如何构建基于存储在 CSV 文件中的数据的问答系统。与使用 SQL 数据库类似,使用 CSV 文件的关键是让 LLM 访问查询和与数据交互的工具。实现此目的的两种主要方法是:
- 推荐:将 CSV 文件加载到 SQL 数据库中,并使用 SQL 教程中概述的方法。
- 让 LLM 访问 Python 环境,在该环境中,它可以使用 Pandas 等库与数据交互。
我们将在本指南中介绍这两种方法。
⚠️ 安全提示 ⚠️
上述两种方法都存在重大风险。使用 SQL 需要执行模型生成的 SQL 查询。使用像 Pandas 这样的库需要让模型执行 Python 代码。由于限制 SQL 连接权限和清理 SQL 查询比沙盒化 Python 环境更容易,我们强烈建议通过 SQL 与 CSV 数据交互。 有关一般安全最佳实践的更多信息,请参阅此处。
设置
本指南的依赖项
%pip install -qU langchain langchain-openai langchain-community langchain-experimental pandas
设置所需的环境变量
# Using LangSmith is recommended but not required. Uncomment below lines to use.
# import os
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
如果您还没有,请下载泰坦尼克号数据集
!wget https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv -O titanic.csv
import pandas as pd
df = pd.read_csv("titanic.csv")
print(df.shape)
print(df.columns.tolist())
(887, 8)
['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Siblings/Spouses Aboard', 'Parents/Children Aboard', 'Fare']
SQL
使用 SQL 与 CSV 数据交互是推荐的方法,因为与任意 Python 相比,它更容易限制权限和清理查询。
大多数 SQL 数据库可以轻松地将 CSV 文件加载为表(DuckDB,SQLite 等)。完成此操作后,您可以使用SQL 教程中概述的所有链和代理创建技术。以下是一个使用 SQLite 执行此操作的快速示例
from langchain_community.utilities import SQLDatabase
from sqlalchemy import create_engine
engine = create_engine("sqlite:///titanic.db")
df.to_sql("titanic", engine, index=False)
887
db = SQLDatabase(engine=engine)
print(db.dialect)
print(db.get_usable_table_names())
print(db.run("SELECT * FROM titanic WHERE Age < 2;"))
sqlite
['titanic']
[(1, 2, 'Master. Alden Gates Caldwell', 'male', 0.83, 0, 2, 29.0), (0, 3, 'Master. Eino Viljami Panula', 'male', 1.0, 4, 1, 39.6875), (1, 3, 'Miss. Eleanor Ileen Johnson', 'female', 1.0, 1, 1, 11.1333), (1, 2, 'Master. Richard F Becker', 'male', 1.0, 2, 1, 39.0), (1, 1, 'Master. Hudson Trevor Allison', 'male', 0.92, 1, 2, 151.55), (1, 3, 'Miss. Maria Nakid', 'female', 1.0, 0, 2, 15.7417), (0, 3, 'Master. Sidney Leonard Goodwin', 'male', 1.0, 5, 2, 46.9), (1, 3, 'Miss. Helene Barbara Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 3, 'Miss. Eugenie Baclini', 'female', 0.75, 2, 1, 19.2583), (1, 2, 'Master. Viljo Hamalainen', 'male', 0.67, 1, 1, 14.5), (1, 3, 'Master. Bertram Vere Dean', 'male', 1.0, 1, 2, 20.575), (1, 3, 'Master. Assad Alexander Thomas', 'male', 0.42, 0, 1, 8.5167), (1, 2, 'Master. Andre Mallet', 'male', 1.0, 0, 2, 37.0042), (1, 2, 'Master. George Sibley Richards', 'male', 0.83, 1, 1, 18.75)]
并创建一个SQL 代理与之交互
pip install -qU langchain-openai
import getpass
import os
if not os.environ.get("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
from langchain_community.agent_toolkits import create_sql_agent
agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)
agent_executor.invoke({"input": "what's the average age of survivors"})
[1m> Entering new SQL Agent Executor chain...[0m
[32;1m[1;3m
Invoking: `sql_db_list_tables` with `{}`
[0m[38;5;200m[1;3mtitanic[0m[32;1m[1;3m
Invoking: `sql_db_schema` with `{'table_names': 'titanic'}`
[0m[33;1m[1;3m
CREATE TABLE titanic (
"Survived" BIGINT,
"Pclass" BIGINT,
"Name" TEXT,
"Sex" TEXT,
"Age" FLOAT,
"Siblings/Spouses Aboard" BIGINT,
"Parents/Children Aboard" BIGINT,
"Fare" FLOAT
)
/*
3 rows from titanic table:
Survived Pclass Name Sex Age Siblings/Spouses Aboard Parents/Children Aboard Fare
0 3 Mr. Owen Harris Braund male 22.0 1 0 7.25
1 1 Mrs. John Bradley (Florence Briggs Thayer) Cumings female 38.0 1 0 71.2833
1 3 Miss. Laina Heikkinen female 26.0 0 0 7.925
*/[0m[32;1m[1;3m
Invoking: `sql_db_query` with `{'query': 'SELECT AVG(Age) AS Average_Age FROM titanic WHERE Survived = 1'}`
[0m[36;1m[1;3m[(28.408391812865496,)][0m[32;1m[1;3mThe average age of survivors in the Titanic dataset is approximately 28.41 years.[0m
[1m> Finished chain.[0m
{'input': "what's the average age of survivors",
'output': 'The average age of survivors in the Titanic dataset is approximately 28.41 years.'}
此方法可以轻松地推广到多个 CSV 文件,因为我们可以将每个文件作为其自己的表加载到我们的数据库中。 请参阅下面的多个 CSV 文件部分。
Pandas
除了 SQL,我们还可以使用像 pandas 这样的数据分析库和 LLM 的代码生成能力来与 CSV 数据交互。同样,除非您有广泛的安全措施,否则此方法不适合生产用例。因此,我们的代码执行实用程序和构造函数位于 langchain-experimental
包中。
链
大多数 LLM 都经过了足够的 pandas Python 代码训练,它们只需被要求就可以生成它
ai_msg = llm.invoke(
"I have a pandas DataFrame 'df' with columns 'Age' and 'Fare'. Write code to compute the correlation between the two columns. Return Markdown for a Python code snippet and nothing else."
)
print(ai_msg.content)
\`\`\`python
correlation = df['Age'].corr(df['Fare'])
correlation
\`\`\`
我们可以将此功能与 Python 执行工具结合使用,以创建一个简单的数据分析链。我们首先要将 CSV 表加载为数据帧,并授予该工具对此数据帧的访问权限
import pandas as pd
from langchain_core.prompts import ChatPromptTemplate
from langchain_experimental.tools import PythonAstREPLTool
df = pd.read_csv("titanic.csv")
tool = PythonAstREPLTool(locals={"df": df})
tool.invoke("df['Fare'].mean()")
32.30542018038331
为了帮助强制正确使用我们的 Python 工具,我们将使用工具调用
llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)
response = llm_with_tools.invoke(
"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns"
)
response
AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_SBrK246yUbdnJemXFC8Iod05', 'function': {'arguments': '{"query":"df.corr()[\'Age\'][\'Fare\']"}', 'name': 'python_repl_ast'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 125, 'total_tokens': 138}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_3b956da36b', 'finish_reason': 'stop', 'logprobs': None}, id='run-1fd332ba-fa72-4351-8182-d464e7368311-0', tool_calls=[{'name': 'python_repl_ast', 'args': {'query': "df.corr()['Age']['Fare']"}, 'id': 'call_SBrK246yUbdnJemXFC8Iod05'}])
response.tool_calls
[{'name': 'python_repl_ast',
'args': {'query': "df.corr()['Age']['Fare']"},
'id': 'call_SBrK246yUbdnJemXFC8Iod05'}]
我们将添加一个工具输出解析器来提取函数调用作为字典
from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsParser
parser = JsonOutputKeyToolsParser(key_name=tool.name, first_tool_only=True)
(llm_with_tools | parser).invoke(
"I have a dataframe 'df' and want to know the correlation between the 'Age' and 'Fare' columns"
)
{'query': "df[['Age', 'Fare']].corr()"}
并与提示结合使用,以便我们只需指定问题,而无需每次调用都指定数据帧信息
system = f"""You have access to a pandas dataframe `df`. \
Here is the output of `df.head().to_markdown()`:
\`\`\`
{df.head().to_markdown()}
\`\`\`
Given a user question, write the Python code to answer it. \
Return ONLY the valid Python code and nothing else. \
Don't assume you have access to any libraries other than built-in Python ones and pandas."""
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", "{question}")])
code_chain = prompt | llm_with_tools | parser
code_chain.invoke({"question": "What's the correlation between age and fare"})
{'query': "df[['Age', 'Fare']].corr()"}
最后,我们将添加我们的 Python 工具,以便实际执行生成的代码
chain = prompt | llm_with_tools | parser | tool
chain.invoke({"question": "What's the correlation between age and fare"})
0.11232863699941621
这样,我们就有了一个简单的数据分析链。我们可以通过查看 LangSmith 跟踪来查看中间步骤:https://smith.langchain.com/public/b1309290-7212-49b7-bde2-75b39a32b49a/r
我们可以在最后添加额外的 LLM 调用来生成对话响应,这样我们就不仅仅是用工具输出进行响应。为此,我们要在提示中添加一个聊天记录 MessagesPlaceholder
from operator import itemgetter
from langchain_core.messages import ToolMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import MessagesPlaceholder
from langchain_core.runnables import RunnablePassthrough
system = f"""You have access to a pandas dataframe `df`. \
Here is the output of `df.head().to_markdown()`:
\`\`\`
{df.head().to_markdown()}
\`\`\`
Given a user question, write the Python code to answer it. \
Don't assume you have access to any libraries other than built-in Python ones and pandas.
Respond directly to the question once you have enough information to answer it."""
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
system,
),
("human", "{question}"),
# This MessagesPlaceholder allows us to optionally append an arbitrary number of messages
# at the end of the prompt using the 'chat_history' arg.
MessagesPlaceholder("chat_history", optional=True),
]
)
def _get_chat_history(x: dict) -> list:
"""Parse the chain output up to this point into a list of chat history messages to insert in the prompt."""
ai_msg = x["ai_msg"]
tool_call_id = x["ai_msg"].additional_kwargs["tool_calls"][0]["id"]
tool_msg = ToolMessage(tool_call_id=tool_call_id, content=str(x["tool_output"]))
return [ai_msg, tool_msg]
chain = (
RunnablePassthrough.assign(ai_msg=prompt | llm_with_tools)
.assign(tool_output=itemgetter("ai_msg") | parser | tool)
.assign(chat_history=_get_chat_history)
.assign(response=prompt | llm | StrOutputParser())
.pick(["tool_output", "response"])
)
chain.invoke({"question": "What's the correlation between age and fare"})
{'tool_output': 0.11232863699941616,
'response': 'The correlation between age and fare is approximately 0.1123.'}
这是此运行的 LangSmith 跟踪:https://smith.langchain.com/public/14e38d70-45b1-4b81-8477-9fd2b7c07ea6/r
代理
对于复杂的问题,LLM 能够迭代执行代码,同时保持其先前执行的输入和输出可能会有所帮助。这就是代理发挥作用的地方。它们允许 LLM 决定需要调用工具的次数,并跟踪到目前为止的执行情况。create_pandas_dataframe_agent 是一个内置代理,可以轻松地使用数据帧
from langchain_experimental.agents import create_pandas_dataframe_agent
agent = create_pandas_dataframe_agent(
llm, df, agent_type="openai-tools", verbose=True, allow_dangerous_code=True
)
agent.invoke(
{
"input": "What's the correlation between age and fare? is that greater than the correlation between fare and survival?"
}
)
[1m> Entering new AgentExecutor chain...[0m
[32;1m[1;3m
Invoking: `python_repl_ast` with `{'query': "df[['Age', 'Fare']].corr().iloc[0,1]"}`
[0m[36;1m[1;3m0.11232863699941621[0m[32;1m[1;3m
Invoking: `python_repl_ast` with `{'query': "df[['Fare', 'Survived']].corr().iloc[0,1]"}`
[0m[36;1m[1;3m0.2561785496289603[0m[32;1m[1;3mThe correlation between Age and Fare is approximately 0.112, and the correlation between Fare and Survival is approximately 0.256.
Therefore, the correlation between Fare and Survival (0.256) is greater than the correlation between Age and Fare (0.112).[0m
[1m> Finished chain.[0m
{'input': "What's the correlation between age and fare? is that greater than the correlation between fare and survival?",
'output': 'The correlation between Age and Fare is approximately 0.112, and the correlation between Fare and Survival is approximately 0.256.\n\nTherefore, the correlation between Fare and Survival (0.256) is greater than the correlation between Age and Fare (0.112).'}
这是此运行的 LangSmith 跟踪:https://smith.langchain.com/public/6a86aee2-4f22-474a-9264-bd4c7283e665/r
多个 CSV 文件
要处理多个 CSV 文件(或数据帧),我们只需要将多个数据帧传递给我们的 Python 工具。我们的 create_pandas_dataframe_agent
构造函数可以开箱即用地做到这一点,我们可以传入一个数据帧列表,而不是只有一个。如果我们自己构造一个链,我们可以这样做
df_1 = df[["Age", "Fare"]]
df_2 = df[["Fare", "Survived"]]
tool = PythonAstREPLTool(locals={"df_1": df_1, "df_2": df_2})
llm_with_tool = llm.bind_tools(tools=[tool], tool_choice=tool.name)
df_template = """\`\`\`python
{df_name}.head().to_markdown()
>>> {df_head}
\`\`\`"""
df_context = "\n\n".join(
df_template.format(df_head=_df.head().to_markdown(), df_name=df_name)
for _df, df_name in [(df_1, "df_1"), (df_2, "df_2")]
)
system = f"""You have access to a number of pandas dataframes. \
Here is a sample of rows from each dataframe and the python code that was used to generate the sample:
{df_context}
Given a user question about the dataframes, write the Python code to answer it. \
Don't assume you have access to any libraries other than built-in Python ones and pandas. \
Make sure to refer only to the variables mentioned above."""
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", "{question}")])
chain = prompt | llm_with_tool | parser | tool
chain.invoke(
{
"question": "return the difference in the correlation between age and fare and the correlation between fare and survival"
}
)
0.14384991262954416
这是此运行的 LangSmith 跟踪:https://smith.langchain.com/public/cc2a7d7f-7c5a-4e77-a10c-7b5420fcd07f/r
沙盒代码执行
有许多工具(如 E2B 和 Bearly)为 Python 代码执行提供沙盒环境,以允许更安全的代码执行链和代理。
下一步
对于更高级的数据分析应用程序,我们建议查看
- SQL 教程:使用 SQL 数据库和 CSV 的许多挑战对于任何结构化数据类型都是通用的,因此即使您使用 Pandas 进行 CSV 数据分析,阅读 SQL 技术也很有用。
- 工具使用:有关在使用调用工具的链和代理时的一般最佳实践的指南
- 代理:了解构建 LLM 代理的基础知识。
- 集成:像 E2B 和 Bearly 这样的沙盒环境,像 SQLDatabase 这样的实用程序,以及像 Spark DataFrame 代理这样的相关代理。