56 lines
3.4 KiB
Markdown
56 lines
3.4 KiB
Markdown
# Issue Classification
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### 1. Definition
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By defining classification descriptions, the issue classifier can infer and match user inputs to the corresponding categories and output the classification results.
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***
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### 2. Scenarios
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Common use cases include **customer service conversation intent classification, product review classification, and bulk email classification**.
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In a typical product customer service Q&A scenario, the issue classifier can serve as a preliminary step before knowledge base retrieval. It classifies the user's input question, directing it to different downstream knowledge base queries to accurately respond to the user's question.
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The following diagram is an example workflow template for a product customer service scenario:
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<figure><img src="/en/.gitbook/assets/guides/workflow/node/question_classifier/image (2) (1) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption></figcaption></figure>
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In this scenario, we set up three classification labels/descriptions:
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* Category 1: **Questions related to after-sales service**
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* Category 2: **Questions related to product usage**
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* Category 3: **Other questions**
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When users input different questions, the issue classifier will automatically classify them based on the set classification labels/descriptions:
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* "**How to set up contacts on iPhone 14?**" —> "**Questions related to product usage**"
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* "**What is the warranty period?**" —> "**Questions related to after-sales service**"
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* "**How's the weather today?**" —> "**Other questions**"
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***
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### 3. How to Configure
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<figure><img src="/en/.gitbook/assets/guides/workflow/node/question_classifier/image (3) (1) (1) (1) (1) (1) (1) (1) (1).png" alt=""><figcaption></figcaption></figure>
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**Configuration Steps:**
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1. **Select Input Variable**: This refers to the content to be classified, usually the user's question in a customer service Q&A scenario, e.g., `sys.query`.
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2. **Choose Inference Model**: The issue classifier leverages the natural language classification and inference capabilities of large language models. Selecting an appropriate model can enhance classification effectiveness.
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3. **Write Classification Labels/Descriptions**: You can manually add multiple classifications by writing keywords or descriptive statements for each category, helping the large language model better understand the classification criteria.
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4. **Choose Corresponding Downstream Nodes**: After classification, the issue classification node can direct the flow to different paths based on the relationship between the classification and downstream nodes.
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#### Advanced Settings:
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**Instructions**: In **Advanced Settings - Instructions**, you can add supplementary instructions, such as more detailed classification criteria, to enhance the classifier's capabilities.
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**Memory**: When enabled, each input to the issue classifier will include chat history from the conversation to help the LLM understand the context and improve question comprehension in interactive dialogues.
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**Memory Window**: When the memory window is closed, the system dynamically filters the amount of chat history passed based on the model's context window; when open, users can precisely control the amount of chat history passed (in terms of numbers).
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**Output Variable**:
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`class_name`
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This is the classification name output after classification. You can use the classification result variable in downstream nodes as needed. |