[数据分析] Format method for combining and organizing Q&A content and multi-turn dialogue content in a RAG knowledge base

When preparing a local knowledge base, we usually deal with question-and-answer pairs and multi-turn dialogues, especially in customer service systems. We want RAG to handle rigorous one-question-one-answer interactions, while also hoping it can chat better and understand dialogue content. In this case, we need to process these data at the same time.

Specific method
We use a CSV table as an example; by using group_id to distinguish different dialogue groups, we can easily integrate single-turn Q&A and multi-turn dialogue.

group_id	role	content
1	user	Hello, what type of music do you like?
1	assistant	I like listening to pop music. What about you?
1	user	I also like pop music. Recently I've been listening to Jay Chou's songs.
1	assistant	I like his songs very much too. His music always has a strong sense of story.
2	user	Do you like traveling?
2	assistant	I like traveling, especially going to places I've never been to. What about you?
2	user	I also like traveling. Recently I'm planning to go to Japan to see cherry blossoms.
2	assistant	That sounds great! I've always wanted to go to Japan to see cherry blossoms too.
3	user	Have you eaten?
3	assistant	Yes, I have. What about you?
4	user	Hello, the weather is really nice today.
4	assistant	Yes, the weather today is very suitable for outdoor activities.

Table explanation
The group_id values 1, 2, 3, and 4 represent different groups. Groups 1 and 2 contain multiple rows of data and represent chat dialogues, while groups 3 and 4 contain only one question and one answer and represent Q&A.