Auto-keyword Auto-question

Use a chat model to generate keywords and questions from the original chunks.

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When selecting a chunking method, you can also enable auto-keyword or auto-question generation to increase retrieval rates. This feature uses a chat model to produce a specified number of keywords and questions from each created chunk, creating a layer of higher-level information from the original content.

NOTE

Enabling this feature increases document indexing time, as all created chunks will be sent to the chat model for keyword or question generation.

• Auto-keyword

  • Definition: The number of additional keywords the LLM generates for each chunk. By supplying synonyms for text that is unfriendly to tokenization or multilingual content, this improves recall for full-text or hybrid retrieval. It can also be used to correct bad cases. Disabling this can significantly accelerate parsing.
  • Common Values:
    • 0: Disabled;
    • 3-5 = Recommended (if a chunk has over a thousand characters, more keywords may be needed);
    • Maximum 30. Note that, as the number increases, the marginal benefit decreases.

• Auto-question

  • Definition: Generates potential FAQ-style questions for each chunk, making retrieval matches more aligned with real user queries (Who/What/Why).
  • Common Values:
    • 0 = disabled;
    • 1–2 = commonly used (if a chunk has thousands of characters, more may be needed);
    • Upper limit 30 (to avoid generating too many at once). Can also be used to correct bad cases.
  • Typical Use Cases: Scenarios requiring FAQ retrieval, such as product manuals, policy documents, etc.

Configuration

On the Configuration page of your knowledge base, you will find the Auto-keyword and Auto-question sliders under Page rank.

NOTE

The Auto-keyword or Auto-question value must be an integer. If you set their value to a non-integer, say 1.7, it will be rounded down to the nearest integer, which in this case is 1.

Best practices

If you are uncertain how to set auto-keyword or auto-question values, here are some best practices gathered from our community:

Use cases or typical scenarios	Document volume/length	Auto_keyword (0–30)	Auto_question (0–30)
1. Internal Process Guidance for Employee Handbook	Small, under 10 pages	0	0
2. Customer Service FAQ Hot Questions	Medium, 10–100 pages	3–7	1–3
3. Technical Whitepapers: Development Standards, Protocol Explanations	Large, over 100 pages	2–4	1–2
4. Contracts / Regulations / Legal Clause Retrieval	Large, over 50 pages	2–5	0–1
5. Multi-repository Layered New Documents + Old Archive	Many	Adjust as appropriate	Adjust as appropriate
6. Social Media Comment Pool: Multilingual & Mixed Spelling	Very large volume of short text	8–12	0
7. Operational Logs for DevOps Troubleshooting	Very large volume of short text	3–6	0
8. Marketing Asset Library: Multilingual Product Descriptions	Medium	6–10	1–2
9. Training Courseware / eBooks	Large	2–5	1–2
10. Maintenance Manual: Equipment Diagrams + Steps	Medium	3–7	1–2