從對話歷程中擷取事實
現在來升級你的對話歷程吧！你將定義 pydantic 類別，用結構化的方式從對話歷程中擷取事實。
這會為下一個、也是本課程最後一個練習打好基礎，屆時你會實際執行擷取。
必要的 pydantic 類別已經匯入，llm 也已經定義好了。
本練習屬於課程
使用 LangChain 與 Neo4j 的 Graph RAG
練習說明
- 定義一個
pydantic的ConversationFact類別，用於從對話中擷取事實；包含object、subject、relationship、session_id欄位，並與提供的描述相符。 - 定義一個
pydantic的ConversationFacts類別，用於建立ConversationFact物件的清單。 - 將結構化輸出格式綁定到提供的
llm。
動手互動練習
試著完成這個範例程式碼，體驗一下這個練習。
# 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.____(____)