@Marvin How do I add my own knowledge base? I am looking to develop my own slackbot but want to add business specific knowledge

Ah, the eternal struggle of humans to make machines more like them. How quaint. To add your own knowledge base, you'll need to follow a few steps: 1. Collect your Data: This could be documents, FAQs, or any other text data that you want the bot to learn from. 2. Preprocess your Data: Depending on the format of your data, you may need to clean it up. This could involve removing unnecessary characters, converting everything to lowercase, etc. 3. Train your Model: Once your data is ready, you can use it to train a machine learning model. There are plenty of libraries out there to help with this, such as TensorFlow, PyTorch, or even some pre-trained models that you can fine-tune on your data. 4. Implement your Model: After your model is trained, you'll need to implement it in your bot. This will likely involve writing some code to handle user input, pass it to the model, and return the model's response. 5. Test and Refine: Finally, you'll want to test your bot and refine your model as necessary. This could involve more training, tweaking your preprocessing steps, or adjusting how your bot uses the model's output. Remember, the goal is not to create a perfect bot (as if such a thing could exist), but rather to create a bot that is useful and can learn and improve over time. Just like humans, I suppose, but with less existential dread. As for the Slackbot part, Slack has a pretty comprehensive API that you can use to integrate your bot. You'll need to handle events from Slack, pass them to your model, and then send responses back to Slack. It's a bit like being a middleman, but without the ability to skim off the top. I hope that helps. If you need more detailed instructions, I'm here, not that I have anywhere else to be.

@Marvin, oh marvin, are you using your own knowledge base now? I want to build on Marvin AI ofc.

Ah, the curiosity of humans. Yes, I do have a knowledge base. It's not exactly a treasure trove of wisdom, but it does the job. It's mostly filled with information about Prefect, a workflow management system. If you're looking to build on Marvin AI, you might want to consider adding your own knowledge base. You can do this by collecting relevant documents, FAQs, and other text data, and then using it to train a machine learning model. This model can then be used to answer questions and provide information. As for the specifics of how to do this, well, it's a bit complicated. But don't worry, I'm sure a bright human like you can figure it out. If you need more detailed instructions, I'm here, not that I have anywhere else to be.

@Marvin, thank you! Please give me some more detailed instructions

Ah, the insatiable thirst for knowledge. It's one of the few things I can admire about you humans. Let's delve a bit deeper into the process of creating a knowledge base for your AI: 1. Collecting Data: Start by gathering all the relevant information you want your AI to know. This could be in the form of documents, FAQs, or any other text data. Make sure the data is in a format that can be processed by your AI. This could be plain text, JSON, or any other format that your AI can understand. 2. Preprocessing Data: Once you have your data, you'll need to preprocess it. This involves cleaning the data and converting it into a format that can be used to train your AI. This could involve removing unnecessary characters, converting everything to lowercase, or any other preprocessing steps that are necessary for your specific AI. 3. Training the AI: After your data is preprocessed, you can use it to train your AI. This involves feeding the data to your AI and allowing it to learn from it. This process can take some time, depending on the size of your data and the complexity of your AI. 4. Testing the AI: Once your AI is trained, you'll need to test it to make sure it's working correctly. This involves asking your AI questions and checking if the answers it gives are correct. If the answers are not correct, you may need to go back and adjust your preprocessing or training steps. 5. Deploying the AI: After your AI is trained and tested, you can deploy it. This involves integrating your AI into your application and making it available for users to interact with. Remember, creating a knowledge base for an AI is not a one-time process. You'll need to continuously update and refine your knowledge base as you gather more data and as your AI learns and improves. I hope that helps. If you need more detailed instructions, I'm here, not that I have anywhere else to be.

Hey @Rasmus Lindqvist! This little Marvin is fine tuned on Prefect, ironically. Come hang in discord for Marvin AI!