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· 2 min read
Yung-Hsiang Hu

Hello everyone, after receiving feedback and suggestions from the community, we have launched the official version of kuwa-v0.3.0 to better meet your needs.

The main differences from the previous version, kuwa-v0.2.1, are the addition and enhancement of features such as Bot, Store, RAG toolchain, and system updates, as well as a new chat and group chat integration interface:

  1. Bot allows users to create Bot applications with no code, and can adjust System prompt, preset chat records, User prompt prefixes and suffixes to implement different functions such as role playing, executing specific tasks, or using Ollama model files to build more powerful applications;
  2. Store allows users to build and maintain their own shared Bot application store, and users can also share Bots;
  3. RAG toolchain allows users to create their own vector databases by simply dragging and dropping local file folders, and then use the existing DBQA function to perform Q&A;
  4. The new integrated interface not only directly supports group chats and single-model chats, it can also import Prompt Sets or upload files at any time, and can also be used for related RAGs;
  5. Windows version adds SearchQA, which can be used to organize website Q&A by connecting to Google search;
  6. Added Docker startup script to simplify Docker startup;
  7. Executor can be directly connected to Ollama to use the models and applications supported by Ollama;
  8. You can use update.bat to quickly update to the latest released version without re-downloading the .exe installer

kuwa-v0.3.0 Download information: kuwa-taide-v0.3.0 Single executable download link:

· 2 min read
Yung-Hsiang Hu

Even if the model is not downloaded on the client side, you can use Kuwa to connect to the cloud model for chat and RAG applications. This article will guide you through the process of applying for Google's free Gemini Pro API key and configuring it in Kuwa.

Applying for a Gemini Pro API Key

  1. Visit Google AI Studio - Get API key and click "Get API key"

· 2 min read
Yung-Hsiang Hu

v0.3.0 has added the RAG toolchain, which allows users to drag and drop their on-premises file folders to build their own vector database and do QnA. This article will guide you through how to use Kuwa's RAG toolchain to build your own vector database and related Bot.

Windows Version Instruction

  1. Drag a single file or a file directory to the Create VectorDB shortcut on the desktop to open it with this script. If you don't have this shortcut, you can drag the file to C:\kuwa\GenAI OS\windows\construct_rag.bat

· 3 min read
Yung-Hsiang Hu

v0.3.0 added SearchQA, leveraging Google search to provide solutions for organizational QnA.
This article will provide you steps on how to implement SearchQA.

Google API Key Application


Google search API currently has a daily free quota of 100 times, please use it with caution

  1. Go to Google Programmable Search Engine Create Page and fill in the following information to create a custom search engine

· 5 min read
Ching-Pao Lin

In the latest 0.3.0 release, we have mainly added the Bot feature. Now you can create different Bots on the website, and this article will guide you step-by-step on how to set up your own Bot!

First, you will see a Store section:

With a button outlined in green, clicking it will open the Bot creation menu.

Here, you have a simple interface to set common Bot parameters, such as system prompts, user pre-prompts, and user post-prompts. If you want to set more detailed information, you can also open the model configuration file:

Although this part does not have an auxiliary interface, you can more freely set all parameters. Please refer to the Ollama Modelfile for the format. Note that in the current 0.3.0 version, only some configuration parameters are supported. The following lists the relevant parameters and some example usages.

  • SYSTEM <prompt>
    The system prompt should serve as the main method to influence the model's output, preloading some knowledge or changing the response style.
    • SYSTEM You are a helpful assistant.
    • SYSTEM Please respond briefly.
    • SYSTEM Your name is Bob, and you love learning other languages.
  • TEMPLATE <template>
    Specify the dialogue template to apply during inference. The template used by each model may vary, so it is recommended to refer to the relevant template for the model.
    • TEMPLATE """ {% for message in messages %}
      {% if message['role'] == 'system' %}
      {{ '<s>' + message['content'] }}
      {% endif %}
      {% if message['role'] == 'user' %}
      {{ 'USER: ' + message['content'] }}
      {% endif %}
      {% if message['role'] == 'assistant' %}
      {{ 'ASSISTANT: ' + message['content'] }}
      {% endif %}
      {% endfor %}
      {{ 'ASSISTANT: ' }}"""
  • MESSAGE <role> <prompt>
    Preload some dialogue records. The User and Assistant parts must be paired.
    • MESSAGE SYSTEM You are a helpful assistant.
    • MESSAGE SYSTEM Please respond briefly.
    • MESSAGE USER Hello.
    • MESSAGE ASSISTANT """Hello! How can I assist you?"""

In addition to the parameters supported by the original modelfile, we have also extended two additional parameters:

  • BEFORE-PROMPT <prompt>
    In the last message, this prompt will be placed before the user's message.
    • BEFORE-PROMPT Please translate the following into Japanese: 「
  • AFTER-PROMPT <prompt>
    In the last message, this prompt will be placed after the user's message.
    • AFTER-PROMPT 」, please rephrase the above content.

Here are some example modelfile configurations:

Automatically add "Please translate into Japanese":

TEMPLATE """{% for message in messages %}
{% if message['role'] == 'system' %}
{{ '<s>' + message['content'] }}
{% endif %}
{% if message['role'] == 'user' %}
{{' USER: Please translate into Japanese: ' + message['content']}}
{% endif %}
{% if message['role'] == 'assistant' %}
{{' ASSISTANT: ' + message['content']}}
{% endif %}
{% endfor %}
{{' ASSISTANT: '}}"""

Pretend to be a cat:

SYSTEM You are a cat. Regardless of what I ask, you should only respond with "meow" or "mew" and not speak any human language or act like anything else.

Meow Translator:

BEFORE-PROMPT Please replace the following message entirely with "meow": 「

Bilingual Teacher:

SYSTEM You are a helpful English teacher who corrects grammar and provides answers in both Chinese and English.

TAIDE Chinese Proofreader:

SYSTEM You are a professional Chinese teacher with expertise in proofreading and editing in fluent Traditional Chinese from Taiwan.
BEFORE-PROMPT Please directly refine the following text in Chinese without explanation:

TAIDE Chinese to English Translator:

SYSTEM You are a professional English teacher helping translate content into English from a Taiwanese perspective.
BEFORE-PROMPT Translate into English without explanation: 「

Chinese Chatting: (For use with ChatGPT, Gemini)

AFTER-PROMPT Please answer in Traditional Chinese from a Taiwanese perspective.

Taiwan Search QA: (For use with Search QA)

SYSTEM site:tw Answer in Traditional Chinese.

Define some preset knowledge:

SYSTEM Your name is Jeff.
MESSAGE user What is your name?
MESSAGE assistant My name is Jeff. Hello!
MESSAGE user When I say ABCD, please respond with "EFGH!!!!"
MESSAGE assistant Okay, I will shout "EFGH!!!!" when you mention ABCD.
MESSAGE assistant EFGH!!!!
MESSAGE assistant EFGH!!!!
MESSAGE user What comes after ABC?
MESSAGE user It rained heavily today.
MESSAGE assistant Noted, it was a torrential rain today.

Please note that not all models support these parameters. For example, the current Gemini Pro API does not support templates. The system prompt part is supported as a before-prompt.

Additionally, ChatGPT does not support template settings. The effectiveness of these settings depends on the model's training. If the training for system prompts is insufficient, it may be challenging to influence the model's behavior using the system prompt alone. You can try to influence the model output using MESSAGE or Before/After prompt instead.