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BoxRetriever

这将帮助您开始使用 Box 检索器。有关所有 BoxRetriever 功能和配置的详细文档,请访问 API 参考

概述

BoxRetriever 类帮助您以 Langchain 的 Document 格式从 Box 获取非结构化内容。您可以通过基于全文搜索查找文件或使用 Box AI 检索包含针对文件的 AI 查询结果的 Document。这需要包含一个 List[str],其中包含 Box 文件 ID,即 `[

info

Box AI 需要企业增强版许可证

没有文本表示的文件将被跳过。

集成细节

1: 自带数据(即,索引和搜索自定义文档语料库):

检索器自托管云服务包名
BoxRetrieverlangchain-box

设置

要使用 Box 包,您需要一些东西:

  • 一个 Box 账户 — 如果您不是当前的 Box 客户或想在生产 Box 实例之外进行测试,您可以使用一个 免费开发者账户
  • 一个 Box 应用 — 这在 开发者控制台 中配置,对于 Box AI,必须启用 Manage AI 范围。在这里,您还将选择您的认证方法。
  • 应用必须由 管理员启用。对于免费开发者账户,这就是注册账户的人。

凭证

在这些示例中,我们将使用 令牌认证。这可以与任何 认证方法 一起使用。只需使用任何方法获取令牌。如果您想了解如何使用其他认证类型与 langchain-box,请访问 Box 大模型供应商 文档。

import getpass
import os

box_developer_token = getpass.getpass("Enter your Box Developer Token: ")
Enter your Box Developer Token:  ········

如果您想从单个查询中获取自动追踪,您还可以通过取消下面的注释来设置您的 LangSmith API 密钥:

# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

安装

该检索器位于 langchain-box 包中:

%pip install -qU langchain-box

实例化

现在我们可以实例化我们的检索器:

搜索

<!--IMPORTS:[{"imported": "BoxRetriever", "source": "langchain_box", "docs": "https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html", "title": "BoxRetriever"}]-->
from langchain_box import BoxRetriever

retriever = BoxRetriever(box_developer_token=box_developer_token)

为了更细粒度的搜索,我们提供了一系列选项来帮助您过滤结果。这使用 langchain_box.utilities.SearchOptions 结合 langchain_box.utilities.SearchTypeFilterlangchain_box.utilities.DocumentFiles 枚举来过滤创建日期、搜索文件的哪个部分,甚至限制搜索范围到特定文件夹。

有关更多信息,请查看 API 参考

<!--IMPORTS:[{"imported": "BoxSearchOptions", "source": "langchain_box.utilities", "docs": "https://python.langchain.com/api_reference/box/utilities/langchain_box.utilities.box.BoxSearchOptions.html", "title": "BoxRetriever"}, {"imported": "DocumentFiles", "source": "langchain_box.utilities", "docs": "https://python.langchain.com/api_reference/box/utilities/langchain_box.utilities.box.DocumentFiles.html", "title": "BoxRetriever"}, {"imported": "SearchTypeFilter", "source": "langchain_box.utilities", "docs": "https://python.langchain.com/api_reference/box/utilities/langchain_box.utilities.box.SearchTypeFilter.html", "title": "BoxRetriever"}]-->
from langchain_box.utilities import BoxSearchOptions, DocumentFiles, SearchTypeFilter

box_folder_id = "260931903795"

box_search_options = BoxSearchOptions(
ancestor_folder_ids=[box_folder_id],
search_type_filter=[SearchTypeFilter.FILE_CONTENT],
created_date_range=["2023-01-01T00:00:00-07:00", "2024-08-01T00:00:00-07:00,"],
k=200,
size_range=[1, 1000000],
updated_data_range=None,
)

retriever = BoxRetriever(
box_developer_token=box_developer_token, box_search_options=box_search_options
)

retriever.invoke("AstroTech Solutions")
[Document(metadata={'source': 'https://dl.boxcloud.com/api/2.0/internal_files/1514555423624/versions/1663171610024/representations/extracted_text/content/', 'title': 'Invoice-A5555_txt'}, page_content='Vendor: AstroTech Solutions\nInvoice Number: A5555\n\nLine Items:\n    - Gravitational Wave Detector Kit: $800\n    - Exoplanet Terrarium: $120\nTotal: $920')]

Box AI

<!--IMPORTS:[{"imported": "BoxRetriever", "source": "langchain_box", "docs": "https://python.langchain.com/api_reference/box/retrievers/langchain_box.retrievers.box.BoxRetriever.html", "title": "BoxRetriever"}]-->
from langchain_box import BoxRetriever

box_file_ids = ["1514555423624", "1514553902288"]

retriever = BoxRetriever(
box_developer_token=box_developer_token, box_file_ids=box_file_ids
)

用法

query = "What was the most expensive item purchased"

retriever.invoke(query)
[Document(metadata={'source': 'Box AI', 'title': 'Box AI What was the most expensive item purchased'}, page_content='The most expensive item purchased was the **Gravitational Wave Detector Kit** from AstroTech Solutions, which cost $800.')]

在链中使用

与其他检索器一样,BoxRetriever 可以通过 集成到 LLM 应用中。

我们需要一个大型语言模型或聊天模型:

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")
openai_key = getpass.getpass("Enter your OpenAI key: ")
Enter your OpenAI key:  ········
<!--IMPORTS:[{"imported": "StrOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html", "title": "BoxRetriever"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "BoxRetriever"}, {"imported": "RunnablePassthrough", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html", "title": "BoxRetriever"}]-->
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

box_search_options = BoxSearchOptions(
ancestor_folder_ids=[box_folder_id],
search_type_filter=[SearchTypeFilter.FILE_CONTENT],
created_date_range=["2023-01-01T00:00:00-07:00", "2024-08-01T00:00:00-07:00,"],
k=200,
size_range=[1, 1000000],
updated_data_range=None,
)

retriever = BoxRetriever(
box_developer_token=box_developer_token, box_search_options=box_search_options
)

context = "You are a finance professional that handles invoices and purchase orders."
question = "Show me all the items purchased from AstroTech Solutions"

prompt = ChatPromptTemplate.from_template(
"""Answer the question based only on the context provided.

Context: {context}

Question: {question}"""
)


def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)


chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
chain.invoke(question)
'- Gravitational Wave Detector Kit: $800\n- Exoplanet Terrarium: $120'

作为代理工具使用

与其他检索器一样,BoxRetriever 也可以作为工具添加到 LangGraph 代理中。

pip install -U langsmith
<!--IMPORTS:[{"imported": "AgentExecutor", "source": "langchain.agents", "docs": "https://python.langchain.com/api_reference/langchain/agents/langchain.agents.agent.AgentExecutor.html", "title": "BoxRetriever"}, {"imported": "create_openai_tools_agent", "source": "langchain.agents", "docs": "https://python.langchain.com/api_reference/langchain/agents/langchain.agents.openai_tools.base.create_openai_tools_agent.html", "title": "BoxRetriever"}, {"imported": "create_retriever_tool", "source": "langchain.tools.retriever", "docs": "https://python.langchain.com/api_reference/core/tools/langchain_core.tools.retriever.create_retriever_tool.html", "title": "BoxRetriever"}]-->
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.tools.retriever import create_retriever_tool
box_search_options = BoxSearchOptions(
ancestor_folder_ids=[box_folder_id],
search_type_filter=[SearchTypeFilter.FILE_CONTENT],
created_date_range=["2023-01-01T00:00:00-07:00", "2024-08-01T00:00:00-07:00,"],
k=200,
size_range=[1, 1000000],
updated_data_range=None,
)

retriever = BoxRetriever(
box_developer_token=box_developer_token, box_search_options=box_search_options
)

box_search_tool = create_retriever_tool(
retriever,
"box_search_tool",
"This tool is used to search Box and retrieve documents that match the search criteria",
)
tools = [box_search_tool]
prompt = hub.pull("hwchase17/openai-tools-agent")
prompt.messages

llm = ChatOpenAI(temperature=0, openai_api_key=openai_key)

agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
/Users/shurrey/local/langchain/.venv/lib/python3.11/site-packages/langsmith/client.py:312: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API
warnings.warn(
result = agent_executor.invoke(
{
"input": "list the items I purchased from AstroTech Solutions from most expensive to least expensive"
}
)
print(f"result {result['output']}")
result The items you purchased from AstroTech Solutions from most expensive to least expensive are:

1. Gravitational Wave Detector Kit: $800
2. Exoplanet Terrarium: $120

Total: $920

API 参考

有关 BoxRetriever 所有功能和配置的详细文档,请访问 API 参考

帮助

如果您有问题,可以查看我们的 开发者文档 或在我们的 开发者社区 中与我们联系。

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