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从会话历史中抽取事实

现在让您的会话历史更上一层楼！您将定义用于以结构化方式从会话历史中抽取事实的 pydantic 类。

这会为下一道也是本课程最后一道练习打下基础，届时您将实际执行抽取。

所需的 pydantic 类已经导入，并且已定义了一个 llm。

本练习是课程的一部分

使用 LangChain 和 Neo4j 的 Graph RAG

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练习说明

  • 定义一个 pydantic 的 ConversationFact 类，用于从会话中抽取事实；包含与说明相匹配的字段 object、subject、relationship 和 session_id。
  • 定义一个 pydantic 的 ConversationFacts 类，用于创建 ConversationFact 对象列表。
  • 将结构化输出格式绑定到提供的 llm。

交互式实操练习

通过完成这段示例代码来试试这个练习。

# Define the ConversationFact with the correct fields
class ____(____):
    """
    A class that holds the facts from a conversation in a format of object, subject, predicate.

    For example, if the conversation includes the fact that the user likes ice creamthe facts would be:
    - object: "Adam"
    - subject: "ice cream"
    - relationship: "LIKES"
    The class also includes a session ID to identify the conversation.
    """
    ____: str = Field(description="The session ID of the conversation.")
    ____: str = Field(description="The object of the fact. For example, 'Adam' ")
    ____: str = Field(description="The subject of the fact. For example, 'Ice cream'")
    ____: str = Field(description="The relationship between the object and the subject.  This should be a single word verb in upper case. For example, 'LIKES' or 'OWNS'")

# Define a ConversationFacts class for creating lists of ConversationFact objects
class ____(____):
    """A class that holds a list of ConversationFact objects."""
    facts: list[____] = Field(description="A list of ConversationFact objects.")

# Bind the output to the llm provided
llm_with_output = llm.____(____)
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