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Docs: Miscellaneous editorial updates (#8237)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
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@ -6,11 +6,11 @@ slug: /autokeyword_autoquestion
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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 each chunk in the knowledge base.
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Use a chat model to generate keywords or questions from each chunk in the knowledge base.
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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, generating a layer of "higher-level information" from the original content.
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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, generating an "additional layer of information" from the original content.
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:::caution WARNING
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Enabling this feature increases document indexing time and uses extra tokens, as all created chunks will be sent to the chat model for keyword or question generation.
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@ -23,7 +23,7 @@ Auto-keyword refers to the auto-keyword generation feature of RAGFlow. It uses a
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**Values**:
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- 0: (Default) Disabled.
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- Between 3 and 5 (invlusive): Recommended if you have chunks of approximately 1,000 characters.
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- Between 3 and 5 (inclusive): Recommended if you have chunks of approximately 1,000 characters.
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- 30 (maximum)
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:::tip NOTE
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@ -21,7 +21,7 @@ The auto-tagging feature is *unavailable* on the [Infinity](https://github.com/i
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Auto-tagging applies in situations where chunks are so similar to each other that the intended chunks cannot be distinguished from the rest. For example, when you have a few chunks about iPhone and a majority about iPhone case or iPhone accessaries, it becomes difficult to retrieve those chunks about iPhone without additional information.
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## Create tag set
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## 1. Create tag set
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You can consider a tag set as a closed set, and the tags to attach to the chunks in your dataset (knowledge base) are *exclusively* from the specified tag set. You use a tag set to "inform" RAGFlow which chunks to tag and which tags to apply.
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@ -41,6 +41,10 @@ As a rule of thumb, consider including the following entries in your tag table:
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### Create a tag set
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:::danger IMPORTANT
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A tag set is *not* involved in document indexing or retrieval. Do not specify a tag set when configuring your chat assistant or agent.
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:::
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1. Click **+ Create knowledge base** to create a knowledge base.
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2. Navigate to the **Configuration** page of the created knowledge base and choose **Tag** as the default chunking method.
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3. Navigate to the **Dataset** page and upload and parse your table file in XLSX, CSV, or TXT formats.
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@ -49,11 +53,7 @@ As a rule of thumb, consider including the following entries in your tag table:
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4. Click the **Table** tab to view the tag frequency table:
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:::danger IMPORTANT
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A tag set is *not* involved in document indexing or retrieval. Do not specify a tag set when configuring your chat assistant or agent.
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:::
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## Tag chunks
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## 2. Tag chunks
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Once a tag set is created, you can apply it to your dataset:
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@ -67,7 +67,7 @@ If the tag set is missing from the dropdown, check that it has been created or c
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3. Re-parse your documents to start the auto-tagging process.
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_In an AI chat scenario using auto-tagged datasets, each query will be tagged using the corresponding tag set(s) and chunks with these tags will have a higher chance to be retrieved._
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## Update tag set
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## 3. Update tag set
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Creating a tag set is *not* for once and for all. Oftentimes, you may find it necessary to update or delete existing tags or add new entries.
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@ -3220,16 +3220,16 @@ Failure:
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---
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### Related Questions
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### Generate related questions
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**POST** `/v1/sessions/related_questions`
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Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.
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This operation requires a `Bearer Login Token`, typically expires with in 24 hours. You can find the it in the browser request easily.
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This operation requires a `Bearer Login Token`, which typically expires with in 24 hours. You can find the it in the Request Headers in your browser easily.
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:::tip NOTE
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The chat model dynamically determines the number of questions to generate based on the instruction, typically between five and ten.
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The chat model autonomously determines the number of questions to generate based on the instruction, typically between five and ten.
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:::
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#### Request
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