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Added document: Accelerate document indexing and retrieval (#4600)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
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docs/guides/accelerate_question_answering.mdx
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docs/guides/accelerate_question_answering.mdx
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sidebar_position: 2
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slug: /accelerate_doc_indexing_and_question_answering
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---
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# Accelerate document indexing and question answering
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import APITable from '@site/src/components/APITable';
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A checklist to speed up document parsing and question answering.
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---
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Please note that some of your settings may consume a significant amount of time. If you often find that document parsing and question answering are time-consuming, here is a checklist to consider:
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## 1. Accelerate document indexing
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- Use GPU to reduce embedding time.
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- On the configuration page of your knowledge base, toggle off **Use RAPTOR to enhance retrieval**.
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- The **Knowledge Graph** chunk method (GraphRAG) is time-consuming.
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- Disable **Auto-keyword** and **Auto-question** on the configuration page of yor knowledge base, as both depend on the LLM.
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## 2. Accelerate question answering
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- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, disabling **Multi-turn optimization** will reduce the time required to get an answer from the LLM.
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- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, leaving the **Rerank model** field empty will significantly decrease retrieval time.
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- In the **Assistant Setting** tab of your **Chat Configuration** dialogue, disabling **Keyword analysis** will reduce the time to get get an answer from the LLM.
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- When chatting with your chat assistant, click the light bulb icon above the *current* dialogue and scroll down the popup window to view the time taken for each task:
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```mdx-code-block
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<APITable>
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```
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| Item name | Description |
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| ----------------- | ------------------------------------------------------------ |
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| Total | Total time spent on this conversation round, including chunk retrieval and answer generation. |
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| Check LLM | Time to validate the specified LLM. |
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| Create retriever | Time to create a chunk retriever. |
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| Bind embedding | Time to initialize an embedding model instance. |
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| Bind LLM | Time to intialize an LLM instance. |
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| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
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| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
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| Generate keywords | Time to extract keywords from the user query. |
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| Retrieval | Time to retrieve the chunks. |
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| Generate answer | Time to generate the answer. |
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```mdx-code-block
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</APITable>
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```
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sidebar_position: 1
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sidebar_position: 0
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slug: /configure_knowledge_base
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---
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@ -12,6 +12,7 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
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## Target Audience
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- Developers who have added new features or modified the existing code and require a Docker image to view and debug their changes.
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- Developers looking to build a RAGFlow Docker image for an ARM64 platform.
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- Testers looking to explore the latest features of RAGFlow in a Docker image.
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## Prerequisites
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sidebar_position: 2
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sidebar_position: 1
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slug: /start_chat
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---
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@ -57,11 +57,11 @@ You start an AI conversation by creating an assistant.
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:::tip NOTE
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1. Click the light bubble icon above the answer to view the expanded system prompt:
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1. Click the light bulb icon above the answer to view the expanded system prompt:
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*The light bubble icon is available only for the current dialogue.*
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*The light bulb icon is available only for the current dialogue.*
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2. Scroll down the expanded prompt to view the time consumed for each task:
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