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68 lines
4.3 KiB
Plaintext
68 lines
4.3 KiB
Plaintext
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---
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sidebar_position: 3
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slug: /autokeyword_autoquestion
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---
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# Auto-keyword Auto-question
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import APITable from '@site/src/components/APITable';
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Use a chat model to generate keywords and questions from the original chunks.
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---
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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.
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:::tip NOTE
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Enabling this feature increases document indexing time, as all created chunks will be sent to the chat model for keyword or question generation.
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:::
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- **Auto-keyword**
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- **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.
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- **Common Values:**
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- `0`: Disabled;
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- `3`-`5` = Recommended (if a chunk has over a thousand characters, more keywords may be needed);
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- Maximum `30`. Note that, as the number increases, the marginal benefit decreases.
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- **Auto-question**
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- **Definition:** Generates potential FAQ-style questions for each chunk, making retrieval matches more aligned with real user queries (Who/What/Why).
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- **Common Values:**
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- `0` = disabled;
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- `1–2` = commonly used (if a chunk has thousands of characters, more may be needed);
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- Upper limit `30` (to avoid generating too many at once). Can also be used to correct bad cases.
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- **Typical Use Cases:** Scenarios requiring FAQ retrieval, such as product manuals, policy documents, etc.
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## Configuration
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On the **Configuration** page of your knowledge base, you will find the Auto-keyword and Auto-question sliders under **Page rank**.
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:::tip NOTE
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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.
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:::
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## Best practices
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If you are uncertain how to set auto-keyword or auto-question values, here are some best practices gathered from our community:
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```mdx-code-block
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<APITable>
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```
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| Use cases or typical scenarios | Document volume/length | Auto_keyword (0–30) | Auto_question (0–30) |
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|---------------------------------------------------------------------|---------------------------------|----------------------------|----------------------------|
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| 1. Internal Process Guidance for Employee Handbook | Small, under 10 pages | 0 | 0 |
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| 2. Customer Service FAQ Hot Questions | Medium, 10–100 pages | 3–7 | 1–3 |
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| 3. Technical Whitepapers: Development Standards, Protocol Explanations | Large, over 100 pages | 2–4 | 1–2 |
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| 4. Contracts / Regulations / Legal Clause Retrieval | Large, over 50 pages | 2–5 | 0–1 |
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| 5. Multi-repository Layered New Documents + Old Archive | Many | Adjust as appropriate |Adjust as appropriate |
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| 6. Social Media Comment Pool: Multilingual & Mixed Spelling | Very large volume of short text | 8–12 | 0 |
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| 7. Operational Logs for DevOps Troubleshooting | Very large volume of short text | 3–6 | 0 |
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| 8. Marketing Asset Library: Multilingual Product Descriptions | Medium | 6–10 | 1–2 |
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| 9. Training Courseware / eBooks | Large | 2–5 | 1–2 |
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| 10. Maintenance Manual: Equipment Diagrams + Steps | Medium | 3–7 | 1–2 |
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```mdx-code-block
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</APITable>
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```
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