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Hello guys, I’m working on a chatbot that answers questions about a specific area of legislation. The available documents include legal texts and other related materials. I’m exploring possible ways to represent the complexity of relationships between various texts.

So far, I’ve found: – Llama index’s indexing (especially the keyword tablet index) – Building a relational DB

In your experience, do you have any other possible methodologies? Thanks!

Integrating a chatbot with ChatGPT can create a seamless and powerful conversational experience for users. Here’s a general outline of how you can achieve this:

1. Define Objectives and Use Cases: Clearly outline the objectives you want to achieve with the chatbot and ChatGPT integration. Identify the specific use cases you want to address, such as customer support, lead generation, or product recommendations.
2. Choose a Chatbot Platform: Select a chatbot platform or framework that aligns with your business needs and technical capabilities. Some popular options include Dialogflow, Botpress, Rasa, or custom-built solutions.
3. Train and Deploy ChatGPT: Ensure that ChatGPT is trained on the relevant data and is capable of understanding user queries and providing appropriate responses. You can use the OpenAI API or deploy a pre-trained model depending on your requirements.
4. Design Conversational Flows: Design conversational flows for the chatbot, outlining how it will handle different types of user queries. Consider the scenarios where the chatbot can handle the interactions independently and where it needs to call on ChatGPT for assistance.
5. User Testing and Iteration: Conduct extensive user testing to identify any potential issues and gather user feedback. Iterate and improve the integration based on the feedback received.
6. Data Privacy and Security: Ensure that the integration complies with data privacy and security regulations. Safeguard user data and use it responsibly within the scope of your business operations.

By integrating a chatbot with ChatGPT, you can create a seamless conversational experience that combines the efficiency of a chatbot with the natural language capabilities of ChatGPT. Users can interact with the system more naturally, receive accurate and helpful responses, and have a more satisfying experience overall. This integration can lead to increased customer engagement, improved support services, and better overall business outcomes.

I worked for less than a year as a Data Engineer. I decided to look for other challenges and got a job as an AI engineer developing language models.

The product of the company that hired me is related to data and metadata management. My tasks will be to introduce features to the product, including a chat function that will allow for asking questions about data. Other tasks will include research and proposing additional AI-related functionalities to the product (on premise). I have a two weeks left to start work and I need to prepare a bit. My job will involve implementing ready-made solutions and conducting research (high level – I need to implement valuable features and no one cares how).

What are the most important things I should learn before starting work?

First of all, I replicated a few applications from this blog: https://blog.streamlit.io/tag/llms/

Then I have focused on Langchain. I’m also in the middle of a course on Udemy about Next-Gen AI projects – Beginner friendly – Langchain, Pinecone – OpenAI, HuggingFace & LLAMA 2 models

I need a roadmap that will guide me a bit. I’m looking for blogs/materials/courses that will give me practical knowledge in this matter.

I’m writing a cybercrime thriller series, and when I jailbroke Claude, it helped me write very good scenes for my MC. It unbroke somehow and I really enjoyed it’s insights. I could use some help finding one I could talk about various aspects of cybercrime.

Hi,

I want to develop an app with real-time conversational capabilities. Basically it would run as follows:

1. The user would say something.
2. The app would then (1) convert voice-to-text (2) generating a reply via an LLM (3) convert the reply via text-to-speech to generate an output
3. Repeat steps 1 & 2 in real-time throughout the conversation

I know all three of the requires pieces here (text-to-speech, speech-to-text, LLMs) are somewhat of a commodity these days, but I am wondering if there are any open source tools or projects which have glued these altogether?

Thanks!

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A few friends and I built this first version of a climate action advisors in the form a ChatGPT plugin called climesumer.

It’s powered by the climesumer.com database, a website that shows you how to live the most sustainable lifestyle possible and take effective individual climate action. Check it out here: Climesumer.com/plugin

Atm you can ask it to suggest sustainable and eco friendly alternatives. We hope to turn the Climesumer plugin into an actual personal sustainability advisor that can help you with all your questions about personal climate action by all 12 categories and 500+ resources from climesumer.com.

Do you think a personal climate action chatbot makes sense? How would you use it ?

Joyland (joyland.ai) is bringing to life the science-fiction dream of open-ended conversations and collaborations with Artificial Intelligence.

Our mission is to build the next generation of dialog chatbots that can serve a long list of applications spanning entertainment, education, general question-answering, and more.

Our chatbots are powered by our own proprietary technology based on large language modeling, built and trained from the ground up with the goal of creating as human-like dialog partners as possible.

How does the Joyland work?

The Joyland is based on neural language models. A supercomputer reads huge amounts of text and learns to predict what words might come next in any given situation. The more the computer reads, the more natural and logical the conversation with the human user can flow. Models like these have many uses, including auto-complete and machine translation.

At Joyland, you collaborate with the computer to create your own experience. Start with any topic and the computer will create a response based on your input and the parameters of the chatbot’s character, giving you a human-like dialog experience.

Needless to say, our chatbots are not a source of reliable information, nothing like a classic Google search. Still, we hope that you find Joyland a useful tool for imagination, brainstorming, language learning, and a host of other purposes we have not yet imagined ourselves 🙂