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README.md
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## How to Contribute Your Chatbot
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1. **Fork the Repository**
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2. **Clone Your Fork**
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- Clone your fork to your local machine.
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3. **Add Your Chatbot**
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- If using a Hugging Face model, specify the model ID in the appropriate section.
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4. **Test Your Chatbot**
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- Run the application locally and test your chatbot
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5. **Submit a Pull Request**
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6. **Review Process**
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- Your submission will be reviewed
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## Example Code for Adding a Chatbot
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def respond(message, history, system_message, max_tokens, temperature, top_p):
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# Your chatbot logic here
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---
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title: Chatbots
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emoji: 💬
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colorFrom: yellow
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colorTo: purple
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sdk: gradio
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sdk_version: 4.39.0
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app_file: app.py
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pinned: false
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---
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An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
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---
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title: Your Chatbot Space Title
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emoji: 💬
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colorFrom: blue
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colorTo: green
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sdk: gradio
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sdk_version: "3.30.0" # Replace with the version of Gradio you are using
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app_file: app.py
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pinned: false
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---
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# Welcome to Vector Database Cloud Chatbot Space
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This space hosts a variety of chatbots built using different models. Users can interact with the chatbots and even contribute their own by following the guidelines below.
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## How to Contribute Your Chatbot
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1. **Fork the Repository**
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- Fork this repository to your own GitHub account.
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2. **Clone Your Fork**
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- Clone your fork to your local machine.
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3. **Add Your Chatbot**
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- Modify the `app.py` file to integrate your chatbot. Ensure you include the necessary configurations for your model.
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4. **Test Your Chatbot**
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- Run the application locally and test the functionality of your chatbot.
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5. **Submit a Pull Request**
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- Push your changes to your fork and submit a pull request for review.
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6. **Review Process**
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- Your submission will be reviewed and, if accepted, merged into the main repository.
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## Example Code for Adding a Chatbot
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def respond(message, history, system_message, max_tokens, temperature, top_p):
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# Your chatbot logic here
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