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搜索文件或使用 Box AI 检索包含针对文件的 AI 查询结果的Document 来执行此操作。这需要包含一个包含 Box 文件 ID 的List[str]，例如["12345","67890"]

信息

Box AI 需要 Enterprise Plus 许可证

将跳过没有文本表示的文件。

集成详细信息

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
BoxRetriever	❌	✅	langchain-box

设置

为了使用 Box 包，您需要以下几样东西

• Box 帐户 — 如果您不是当前的 Box 客户或希望在生产 Box 实例之外进行测试，您可以使用 免费开发者帐户。
• Box 应用 — 这是在 开发者控制台 中配置的，对于 Box AI，必须启用Manage AI 范围。在这里，您还将选择您的身份验证方法
• 该应用必须由 管理员启用。对于免费开发者帐户，这是注册帐户的任何人。

凭据

对于这些示例，我们将使用 令牌身份验证。这可与任何 身份验证方法 一起使用。只需使用任何方法获取令牌即可。如果您想了解有关如何将其他身份验证类型与langchain-box一起使用的更多信息，请访问 Box 提供程序 文档。

import getpass
import os

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

如果您希望从单个查询中获取自动跟踪，您还可以通过取消以下注释来设置 LangSmith API 密钥

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

安装

此检索器位于langchain-box包中

%pip install -qU langchain-box

Note: you may need to restart the kernel to use updated packages.

实例化

现在我们可以实例化我们的检索器

搜索

from langchain_box import BoxRetriever

retriever = BoxRetriever(box_developer_token=box_developer_token)

API 参考：BoxRetriever

对于更细粒度的搜索，我们提供了一系列选项来帮助您缩小结果范围。这使用langchain_box.utilities.SearchOptions与langchain_box.utilities.SearchTypeFilter和langchain_box.utilities.DocumentFiles枚举结合使用，以根据创建日期、搜索文件的哪个部分甚至将搜索范围限制到特定文件夹等内容进行筛选。

有关更多信息，请查看 API 参考。

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")

API 参考：BoxSearchOptions | DocumentFiles | SearchTypeFilter

[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

from langchain_box import BoxRetriever

box_file_ids = ["1514555423624", "1514553902288"]

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

API 参考：BoxRetriever

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 is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.')]

引用

使用 Box AI 和BoxRetriever，您可以返回提示的答案，返回 Box 用于获取该答案的引用，或同时返回两者。无论您选择如何使用 Box AI，检索器都会返回一个List[Document]对象。我们通过两个bool参数answer和citations提供了这种灵活性。Answer 默认为True，citations 默认为False，因此如果您只需要答案，则可以省略两者。如果您想要两者，则只需包含citations=True，如果您只需要引用，则需要包含answer=False和citations=True

同时获取

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

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 is the **Gravitational Wave Detector Kit** from AstroTech Solutions, which costs **$800**.'),
 Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\nInvoice Number: A5555\n\nLine Items:\n    - Gravitational Wave Detector Kit: $800\n    - Exoplanet Terrarium: $120\nTotal: $920')]

仅引用

retriever = BoxRetriever(
    box_developer_token=box_developer_token,
    box_file_ids=box_file_ids,
    answer=False,
    citations=True,
)

retriever.invoke(query)

[Document(metadata={'source': 'Box AI What was the most expensive item purchased', 'file_name': 'Invoice-A5555.txt', 'file_id': '1514555423624', 'file_type': 'file'}, page_content='Vendor: AstroTech Solutions\nInvoice Number: A5555\n\nLine Items:\n    - Gravitational Wave Detector Kit: $800\n    - Exoplanet Terrarium: $120\nTotal: $920')]

在链中使用

与其他检索器一样，BoxRetriever 可以通过 链 整合到 LLM 应用程序中。

我们将需要一个 LLM 或聊天模型

OpenAI

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")

Anthropic

pip install -qU langchain-anthropic

import getpass
import os

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

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")

Azure

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

Google

pip install -qU langchain-google-vertexai

import getpass
import os

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

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-1.5-flash")

Cohere

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r-plus")

NVIDIA

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

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

from langchain import ChatNVIDIA

llm = ChatNVIDIA(model="meta/llama3-70b-instruct")

FireworksAI

pip install -qU langchain-fireworks

import getpass
import os

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

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

Groq

pip install -qU langchain-groq

import getpass
import os

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

from langchain_groq import ChatGroq

llm = ChatGroq(model="llama3-8b-8192")

MistralAI

pip install -qU langchain-mistralai

import getpass
import os

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

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

openai_key = getpass.getpass("Enter your OpenAI key: ")

Enter your OpenAI key:  ········

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()
)

API 参考：StrOutputParser | ChatPromptTemplate | RunnablePassthrough

chain.invoke(question)

'- Gravitational Wave Detector Kit: $800\n- Exoplanet Terrarium: $120'

用作代理工具

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

from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain.tools.retriever import create_retriever_tool

API 参考：AgentExecutor | create_openai_tools_agent | 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

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

帮助

如果您有任何疑问，您可以查看我们的 开发者文档 或在我们的 开发者社区 中与我们联系。

相关

• 检索器 概念指南
• 检索器 操作指南