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Updated max_tokens descriptions (#6751)

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### What problem does this PR solve?

#6721 

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- [x] Documentation Update
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2025-04-02 13:56:55 +08:00
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docs/guides/chat/start_chat.md
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@ -48,10 +48,25 @@ You start an AI conversation by creating an assistant.
4. Update **Model Setting**:
- In **Model**: you select the chat model. Though you have selected the default chat model in **System Model Settings**, RAGFlow allows you to choose an alternative chat model for your dialogue.
- **Preset configurations** refers to the level that the LLM improvises. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
- **Temperature**: Level of the prediction randomness of the LLM. The higher the value, the more creative the LLM is.
- **Top P** is also known as "nucleus sampling". See [here](https://en.wikipedia.org/wiki/Top-p_sampling) for more information.
- **Max Tokens**: The maximum length of the LLM's responses. Note that the responses may be curtailed if this value is set too low.
- **Freedom**: A shortcut to **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty** settings, indicating the freedom level of the model. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
This parameter has three options:
- **Improvise**: Produces more creative responses.
- **Precise**: (Default) Produces more conservative responses.
- **Balance**: A middle ground between **Improvise** and **Precise**.
- **Temperature**: The randomness level of the model's output.
Defaults to 0.1.
- Lower values lead to more deterministic and predictable outputs.
- Higher values lead to more creative and varied outputs.
- A temperature of zero results in the same output for the same prompt.
- **Top P**: Nucleus sampling.
- Reduces the likelihood of generating repetitive or unnatural text by setting a threshold *P* and restricting the sampling to tokens with a cumulative probability exceeding *P*.
- Defaults to 0.3.
- **Presence penalty**: Encourages the model to include a more diverse range of tokens in the response.
- A higher **presence penalty** value results in the model being more likely to generate tokens not yet been included in the generated text.
- Defaults to 0.4.
- **Frequency penalty**: Discourages the model from repeating the same words or phrases too frequently in the generated text.
- A higher **frequency penalty** value results in the model being more conservative in its use of repeated tokens.
- Defaults to 0.7.
5. Now, let's start the show: