如何通过 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 CSV 文件中存储的数据的问答系统。与使用 SQL 数据库类似，使用 CSV 文件的关键是让 LLM 访问用于查询和与数据交互的工具。主要有两种方法可以做到这一点：

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，它可以在其中使用 Pandas 等库与数据交互。

我们将在本指南中介绍这两种方法。

⚠️ 安全注意事项 ⚠️

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。由于紧密地限定 SQL 连接权限和清理 SQL 查询比沙盒 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["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

如果您还没有 Titanic 数据集，请下载

!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，因为它比任意 Python 更容易限制权限和清理查询。

大多数 SQL 数据库都可以轻松地将 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 agent 创建技术。这是一个关于我们如何使用 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)

API 参考：SQLDatabase

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 agent 与之交互

选择聊天模型

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.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

from langchain_community.agent_toolkits import create_sql_agent

agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)

API 参考：create_sql_agent

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.'}

这种方法很容易推广到多个 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，除非您采取了广泛的安全措施，否则这种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 都接受过足够的 pandas Python 代码训练，它们只需被要求就可以生成代码

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
\`\`\`

我们可以将这种能力与 Python 执行工具结合起来，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，并让工具访问此数据帧

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()")

API 参考：ChatPromptTemplate | PythonAstREPLTool

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，以将函数调用提取为 dict

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"
)

API 参考：JsonOutputKeyToolsParser

{'query': "df[['Age', 'Fare']].corr()"}

并与提示结合，以便我们只需指定问题，而无需在每次调用时都指定数据帧信息

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

就这样，我们有了一个简单的数据分析链。我们可以通过查看 LangSmith 跟踪来了解中间步骤：https://smith.langchain.com/public/b1309290-7212-49b7-bde2-75b39a32b49a/r

我们可以在最后添加一个额外的 LLM 调用来生成对话式响应，这样我们就不仅仅是用工具输出来响应。为此，我们将需要在提示中添加聊天历史记录 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"])
)

API 参考：ToolMessage | StrOutputParser | MessagesPlaceholder | RunnablePassthrough

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

Agent

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很有帮助。这就是 Agent 发挥作用的地方。它们允许 LLM 决定需要调用工具多少次，并跟踪到目前为止已进行的执行。 create_pandas_dataframe_agent 是一个内置 agent，可以轻松处理数据帧

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?"
    }
)

API 参考：create_pandas_dataframe_agent

[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，我们可以传入数据帧列表而不是仅一个。如果我们自己构建链，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，它们提供用于 Python 代码执行的沙盒环境，以实现更安全的代码执行链和 agent。

下一步

对于更高级的数据分析应用，我们建议查看

SQL 教程：处理 SQL 数据库和 CSV 文件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：关于使用调用工具的链和 agent 的一般最佳实践指南
Agents：了解构建 LLM agent 的基本原理。
集成：沙盒环境，如 E2B 和 Bearly，实用程序，如 SQLDatabase，相关 agent，如 Spark DataFrame agent。

⚠️ 安全注意事项 ⚠️​

设置​

SQL​

Pandas​

链​

Agent​

多个 CSV 文件​

沙盒代码执行​

下一步​

此页面是否对您有帮助？

⚠️ 安全注意事项 ⚠️

设置

SQL

Pandas

链

Agent

多个 CSV 文件

沙盒代码执行

下一步