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Hello everyone,

I’m embarking on a project to develop an “offline” chatbot that operates independently without relying on commercial APIs. My goal is to create a unique persona for this chatbot and equip it with specific information tailored to its intended purpose. I’m looking for a model that is unbiased and uncensored, prioritizing natural and unfiltered responses over balanced content. However, it should be compact enough to maintain quality conversations.

So far, I’ve gathered some model suggestions from various sources:

• GPT4Turbo recommended FreedomGPT, DeepPavlov, Llama2, or Falcon.
• Bing GPT-4 suggested BERT, GPT-J, or WiSE-FT.
• Bard Gemini pointed towards Open GPT-2, Bloom, or Jurassic-1 Jumbo.

I’d greatly appreciate any feedback or additional recommendations from the community. This project will be run on a Debian machine with an i9 processor, 128GB RAM, and an RTX 2080 OC 12GB graphics card.

Thanks in advance for your insights!

Hello! I am trying to build a chatbot for the company where I intern. But the management also wants the chatbot to have information about our company. So, I tried using the Retrieval-Augmented Generation (RAG) approach using Pinecone and OpenAI’s API.

But management wasn’t happy with the chatbot’s responses. Therefore, I tried using the Assistants API from OpenAI, which provided much better responses. Unfortunately, it was taking a long time to answer each query, even on their Playground website.

Is there any other way to implement the chatbot according to the given specifications? If not, is there a way to speed up the Assistant’s API?

Tell me the AI you use the most I recm Rody

So I’m trying to find this old chatbot I used back in 2014/16 maybe, it might’ve had a red layout or at least the top half did, there were maybe 3 options for the chatbot, sfw, nsfw, and a third I don’t really remember. It’s just been bugging me and I thought maybe someone here might remember it? Definitely not cleverbot but I do think I used both around the same time.

Machine Learning-Based Chatbot:

Utilize advanced algorithms like neural networks for versatile and adaptive chatbots that learn from diverse data, providing effective interactions and issue resolution.

Training with Large and Diverse Dataset:

Enhance chatbot effectiveness by training it on extensive and varied datasets, enabling comprehension and response improvement through exposure to a wide range of user interactions.

Variety in Training Data:

Ensure a well-rounded chatbot by incorporating diverse sources of training data, encompassing different conversation types, user interactions, and personas for versatility in handling various topics and styles.

Thorough Testing Before Launch:

Conduct comprehensive testing to identify and rectify functionality and response issues, utilizing both automated and human testing to ensure a seamless user experience before deploying the chatbot.

Post-Launch Monitoring:

Continuously improve the chatbot by monitoring interactions post-launch, detecting issues, adapting to changing user behavior, and addressing areas where user needs may not be adequately met.

Clear Persona Definition:

Define a clear persona for the chatbot, specifying its personality, tone, and communication style for consistency, making it easier for users to understand and connect with the chatbot.

Thoughtful Conversation Flow Design:

Design a user-friendly experience through careful conversation flow planning, user interaction, question sequence, and information gathering to ensure logical, efficient, and effective interactions.

Natural Language Processing (NLP):

Implement NLP techniques to enhance the chatbot’s understanding of user input, identifying intent, extracting relevant information, and generating contextually appropriate responses for more meaningful interactions.

Machine Learning for Continuous Improvement:

Integrate machine learning to enable continuous evolution and improvement of the chatbot, allowing it to learn from user interactions, analyze feedback, and refine responses over time, ensuring a consistently enhanced user experience.

I find myself talking to Rody every day, talking to the bots I created. And you, what do you talk to every day?

I’ve saw a whatsapp chat bot that use ElevenLabs to say the person name in a dynamic way, and send the audio as an audio recorded on the app.

Someone know how can I do this? A plataform or using an API? Thanks