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Fix typos (#4662)

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### What problem does this PR solve?

Fix typos in the documents

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: writinwaters <93570324+writinwaters@users.noreply.github.com>
This commit is contained in:
Jin Hai
2025-01-27 15:45:16 +08:00
committed by GitHub
parent ce8658aa84
commit d970d0ef39
13 changed files with 51 additions and 51 deletions
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2
docs/guides/accelerate_question_answering.mdx
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@ -38,7 +38,7 @@ Please note that some of your settings may consume a significant amount of time.
| Check LLM | Time to validate the specified LLM. |
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to intialize an LLM instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |

30
docs/guides/agent/agent_introduction.md
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@ -43,27 +43,27 @@ We also provide templates catered to different business scenarios. You can eithe
![workflow_editor](https://github.com/user-attachments/assets/47b4d5ce-b35a-4d6b-b483-ba495a75a65d)
4. General speaking, now you can do the following:
3. General speaking, now you can do the following:
- Drag and drop a desired component to your workflow,
- Select the knowledge base to use,
- Update settings of specific components,
- Update LLM settings
- Sets the input and output for a specific component, and more.
5. Click **Save** to apply changes to your agent and **Run** to test it.
4. Click **Save** to apply changes to your agent and **Run** to test it.
## Components
Please review the flowing description of the RAG-specific components before you proceed:
| Component | Description |
| -------------- | ------------------------------------------------------------ |
| **Retrieval** | A component that retrieves information from specified knowledge bases and returns 'Empty response' if no information is found. Ensure the correct knowledge bases are selected. |
| **Generate** | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
| **Interact** | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
| Component | Description |
|----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Retrieval** | A component that retrieves information from specified knowledge bases and returns 'Empty response' if no information is found. Ensure the correct knowledge bases are selected. |
| **Generate** | A component that prompts the LLM to generate responses. You must ensure the prompt is set correctly. |
| **Interact** | A component that serves as the interface between human and the bot, receiving user inputs and displaying the agent's responses. |
| **Categorize** | A component that uses the LLM to classify user inputs into predefined categories. Ensure you specify the name, description, and examples for each category, along with the corresponding next component. |
| **Message** | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Interact**, the interface component. |
| **Rewrite** | A component that rewrites a user query from the **Interact** component, based on the context of previous dialogues. |
| **Keyword** | A component that extracts keywords from a user query, with TopN specifying the number of keywords to extract. |
| **Message** | A component that sends out a static message. If multiple messages are supplied, it randomly selects one to send. Ensure its downstream is **Interact**, the interface component. |
| **Rewrite** | A component that rewrites a user query from the **Interact** component, based on the context of previous dialogues. |
| **Keyword** | A component that extracts keywords from a user query, with TopN specifying the number of keywords to extract. |
:::caution NOTE
@ -75,9 +75,9 @@ Please review the flowing description of the RAG-specific components before you
## Basic operations
| Operation | Description |
| ------------------------- | ------------------------------------------------------------ |
| Add a component | Drag and drop the desired component from the left panel onto the canvas. |
| Delete a component | On the canvas, hover over the three dots (...) of the component to display the delete option, then select it to remove the component. |
| Operation | Description |
|---------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| Add a component | Drag and drop the desired component from the left panel onto the canvas. |
| Delete a component | On the canvas, hover over the three dots (...) of the component to display the delete option, then select it to remove the component. |
| Copy a component | On the canvas, hover over the three dots (...) of the component to display the copy option, then select it to make a copy the component. |
| Update component settings | On the canvas, click the desired component to display the component settings. |
| Update component settings | On the canvas, click the desired component to display the component settings. |

30
docs/guides/configure_knowledge_base.md
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@ -22,7 +22,7 @@ _Each time a knowledge base is created, a folder with the same name is generated
## Configure knowledge base
The following screen shot shows the configuration page of a knowledge base. A proper configuration of your knowledge base is crucial for future AI chats. For example, choosing the wrong embedding model or chunk method would cause unexpected semantic loss or mismatched answers in chats.
The following screenshot shows the configuration page of a knowledge base. A proper configuration of your knowledge base is crucial for future AI chats. For example, choosing the wrong embedding model or chunk method would cause unexpected semantic loss or mismatched answers in chats.
![knowledge base configuration](https://github.com/infiniflow/ragflow/assets/93570324/384c671a-8b9c-468c-b1c9-1401128a9b65)
@ -39,18 +39,18 @@ This section covers the following topics:
RAGFlow offers multiple chunking template to facilitate chunking files of different layouts and ensure semantic integrity. In **Chunk method**, you can choose the default template that suits the layouts and formats of your files. The following table shows the descriptions and the compatible file formats of each supported chunk template:
| **Template** | Description | File format |
| ------------ | ------------------------------------------------------------ | ---------------------------------------------------- |
| **Template** | Description | File format |
|--------------|-----------------------------------------------------------------------|------------------------------------------------------|
| General | Files are consecutively chunked based on a preset chunk token number. | DOCX, EXCEL, PPT, PDF, TXT, JPEG, JPG, PNG, TIF, GIF |
| Q&A | | EXCEL, CSV/TXT |
| Manual | | PDF |
| Table | | EXCEL, CSV/TXT |
| Paper | | PDF |
| Book | | DOCX, PDF, TXT |
| Laws | | DOCX, PDF, TXT |
| Presentation | | PDF, PPTX |
| Picture | | JPEG, JPG, PNG, TIF, GIF |
| One | The entire document is chunked as one. | DOCX, EXCEL, PDF, TXT |
| Q&A | | EXCEL, CSV/TXT |
| Manual | | PDF |
| Table | | EXCEL, CSV/TXT |
| Paper | | PDF |
| Book | | DOCX, PDF, TXT |
| Laws | | DOCX, PDF, TXT |
| Presentation | | PDF, PPTX |
| Picture | | JPEG, JPG, PNG, TIF, GIF |
| One | The entire document is chunked as one. | DOCX, EXCEL, PDF, TXT |
You can also change the chunk template for a particular file on the **Datasets** page.
@ -82,7 +82,7 @@ While uploading files directly to a knowledge base seems more convenient, we *hi
### Parse file
File parsing is a crucial topic in knowledge base configuration. The meaning of file parsing in RAGFlow is twofold: chunking files based on file layout and building embedding and full-text (keyword) indexes on these chunks. After having selected the chunk method and embedding model, you can start parsing an file:
File parsing is a crucial topic in knowledge base configuration. The meaning of file parsing in RAGFlow is twofold: chunking files based on file layout and building embedding and full-text (keyword) indexes on these chunks. After having selected the chunk method and embedding model, you can start parsing a file:
![parse file](https://github.com/infiniflow/ragflow/assets/93570324/5311f166-6426-447f-aa1f-bd488f1cfc7b)
@ -121,8 +121,8 @@ You can add keywords to a file chunk to increase its ranking for queries contain
RAGFlow uses multiple recall of both full-text search and vector search in its chats. Prior to setting up an AI chat, consider adjusting the following parameters to ensure that the intended information always turns up in answers:
- Similarity threshold: Chunks with similarities below the threshold will be filtered. Defaultly set to 0.2.
- Vector similarity weight: The percentage by which vector similarity contributes to the overall score. Defaultly set to 0.3.
- Similarity threshold: Chunks with similarities below the threshold will be filtered. By default, it is set to 0.2.
- Vector similarity weight: The percentage by which vector similarity contributes to the overall score. By default, it is set to 0.3.
![retrieval test](https://github.com/infiniflow/ragflow/assets/93570324/c03f06f6-f41f-4b20-a97e-ae405d3a950c)

2
docs/guides/deploy_local_llm.mdx
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@ -39,7 +39,7 @@ time=2024-12-02T02:20:21.360Z level=INFO source=common.go:49 msg="Dynamic LLM li
Ensure ollama is listening on all IP address:
```bash
sudo ss -tunlp|grep 11434
sudo ss -tunlp | grep 11434
tcp LISTEN 0 4096 0.0.0.0:11434 0.0.0.0:* users:(("docker-proxy",pid=794507,fd=4))
tcp LISTEN 0 4096 [::]:11434 [::]:* users:(("docker-proxy",pid=794513,fd=4))
```