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如何创建自定义检索器

概述​

许多大型语言模型应用涉及使用 检索器 从外部数据源检索信息。

检索器负责检索与给定用户 查询 相关的 文档 列表。

检索到的文档通常被格式化为提示词，输入到大型语言模型中，使其能够利用这些信息生成适当的响应（例如，根据知识库回答用户问题）。

接口​

要创建自己的检索器，您需要扩展 BaseRetriever 类并实现以下方法：

方法描述必需/可选
_get_relevant_documents获取与查询相关的文档。必需
_aget_relevant_documents实现以提供异步原生支持。可选

在 _get_relevant_documents 内部的逻辑可以涉及对数据库或使用请求访问网络的任意调用。

tip

通过从 BaseRetriever 继承，您的检索器将自动成为 LangChain Runnable，并将获得标准的 Runnable 功能！

info

您可以使用 RunnableLambda 或 RunnableGenerator 来实现检索器。

将检索器实现为 BaseRetriever 与 RunnableLambda（自定义 可运行函数）的主要好处在于，BaseRetriever 是一个众所周知的 LangChain 实体，因此某些监控工具可能会为检索器实现专门的行为。 另一个区别是，BaseRetriever 在某些 API 中的行为与 RunnableLambda 会略有不同；例如，astream_events API 中的 start 事件将是 on_retriever_start 而不是 on_chain_start。 一个 BaseRetriever 在某些 API 中的行为会与 RunnableLambda 略有不同；例如，start 事件 astream_events API 中将是 on_retriever_start 而不是 on_chain_start。

示例​

让我们实现一个玩具检索器，返回所有文本包含用户查询文本的文档。

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from typing import List

from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever


class ToyRetriever(BaseRetriever):
    """A toy retriever that contains the top k documents that contain the user query.

    This retriever only implements the sync method _get_relevant_documents.

    If the retriever were to involve file access or network access, it could benefit
    from a native async implementation of `_aget_relevant_documents`.

    As usual, with Runnables, there's a default async implementation that's provided
    that delegates to the sync implementation running on another thread.
    """

    documents: List[Document]
    """List of documents to retrieve from."""
    k: int
    """Number of top results to return"""

    def _get_relevant_documents(
        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
    ) -> List[Document]:
        """Sync implementations for retriever."""
        matching_documents = []
        for document in documents:
            if len(matching_documents) > self.k:
                return matching_documents

            if query.lower() in document.page_content.lower():
                matching_documents.append(document)
        return matching_documents

    # Optional: Provide a more efficient native implementation by overriding
    # _aget_relevant_documents
    # async def _aget_relevant_documents(
    #     self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
    # ) -> List[Document]:
    #     """Asynchronously get documents relevant to a query.

    #     Args:
    #         query: String to find relevant documents for
    #         run_manager: The callbacks handler to use

    #     Returns:
    #         List of relevant documents
    #     """

测试它 🧪​

documents = [
    Document(
        page_content="Dogs are great companions, known for their loyalty and friendliness.",
        metadata={"type": "dog", "trait": "loyalty"},
    ),
    Document(
        page_content="Cats are independent pets that often enjoy their own space.",
        metadata={"type": "cat", "trait": "independence"},
    ),
    Document(
        page_content="Goldfish are popular pets for beginners, requiring relatively simple care.",
        metadata={"type": "fish", "trait": "low maintenance"},
    ),
    Document(
        page_content="Parrots are intelligent birds capable of mimicking human speech.",
        metadata={"type": "bird", "trait": "intelligence"},
    ),
    Document(
        page_content="Rabbits are social animals that need plenty of space to hop around.",
        metadata={"type": "rabbit", "trait": "social"},
    ),
]
retriever = ToyRetriever(documents=documents, k=3)
retriever.invoke("that")
[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'type': 'cat', 'trait': 'independence'}),
 Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'type': 'rabbit', 'trait': 'social'})]

它是一个 可运行的，因此将受益于标准的运行接口！ 🤩

await retriever.ainvoke("that")
[Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'type': 'cat', 'trait': 'independence'}),
 Document(page_content='Rabbits are social animals that need plenty of space to hop around.', metadata={'type': 'rabbit', 'trait': 'social'})]
retriever.batch(["dog", "cat"])
[[Document(page_content='Dogs are great companions, known for their loyalty and friendliness.', metadata={'type': 'dog', 'trait': 'loyalty'})],
 [Document(page_content='Cats are independent pets that often enjoy their own space.', metadata={'type': 'cat', 'trait': 'independence'})]]
async for event in retriever.astream_events("bar", version="v1"):
    print(event)
{'event': 'on_retriever_start', 'run_id': 'f96f268d-8383-4921-b175-ca583924d9ff', 'name': 'ToyRetriever', 'tags': [], 'metadata': {}, 'data': {'input': 'bar'}}
{'event': 'on_retriever_stream', 'run_id': 'f96f268d-8383-4921-b175-ca583924d9ff', 'tags': [], 'metadata': {}, 'name': 'ToyRetriever', 'data': {'chunk': []}}
{'event': 'on_retriever_end', 'name': 'ToyRetriever', 'run_id': 'f96f268d-8383-4921-b175-ca583924d9ff', 'tags': [], 'metadata': {}, 'data': {'output': []}}

贡献​

我们欢迎有趣的检索器贡献！

这是一个检查清单，帮助确保您的贡献被添加到 LangChain：

文档：

  • 检索器包含所有初始化参数的文档字符串，因为这些将在 API 参考 中显示。
  • 模型的类文档字符串包含指向检索器使用的任何相关 API 的链接（例如，如果检索器是从维基百科检索的，链接到维基百科 API 将是很好的！）

测试：

  • 添加单元或集成测试以验证 invoke 和 ainvoke 的工作。

优化：

如果检索器连接到外部数据源（例如，API 或文件），它几乎肯定会从异步原生优化中受益！

  • 提供 _aget_relevant_documents 的原生异步实现（由 ainvoke 使用）

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