Chatbot and Conversational AI API, With Generative Models

What are Chatbots and Conversational AI and Why Use Generative AI?

Conversational AI is a central sub-field of Natural Language Processing that makes it possible for a human to have a conversation with a machine. Everytime the human says or asks something to the AI, the whole conversation history is sent too, so the AI can have the context in memory and make relevant responses. Modern chabots leverage conversational AI and can do more than simply having a conversation. For example they can detect customer intents, search documents, understand the customer tone and adapt their own tone (anger, joy, sarcasm...).

LLaMA 3, Dolphin, ChatDolphin, Yi 34B, and Mixtral 8x7B are advanced alternatives to GPT-4 and ChatGPT, available on NLP Cloud. These models are so complex that they can adapt to many situations, and perfectly sound like a humans. For advanced use cases, it is possible to fine-tune these models (train them with your own data), which is a great way to get a chatbot that is perfectly tailored to your company/product/industry.

Generative models have no "memory". So you should help them by re-sending the conversation history in every request you're making. We actually wrote a dedicated blog article about how to build a chatbot with a generative model, feel free to read it!

If you want to build a chatbot that answers technical questions about your own domain knowledge, you will have to couple your chatbot with a semantic search / RAG model. Here is a guide about coupling RAG with generative AI.

Chatbot and conversational AI

Why Use Chatbots and Conversational AI?

More and more companies want to leverage chatbots, either to build an advanced product based on AI, or improve their internal productivity. Here are a couple of examples:

Support Efficiency

The most popular chatbot application is to automatically help customers without having to rely on a support person. It dramatically improves reactivity, and it alleviates the support team so they can focus on very advanced questions only. A good support chatbot is able to search documents for customers, answer contract or technical questions, detect customer tone and intent...

Video Games

Some video games now include conversational AI capabilities, so players can naturally discuss with the machine. It makes modern games much more interactive, especially because modern conversational AIs can adapt their tone to the situation (anger, joy, sarcasm...).

Product Suggestion

It's sometimes hard for a user to find what he's looking for, especially if there are a lot of products or if the products are complex. In that case, building a chatbot to help customers and point them to the right product is a very good solution.

Medical Assistant

The healthcare industry leverages chatbots in order to discuss with patients and automatically make a diagnostic.

NLP Cloud's Chatbot/Conversational AI API

NLP Cloud proposes a chatbot and conversational AI API based on generative models that give you the opportunity to perform conversational AI out of the box, with breathtaking results. These models are Dolphin, ChatDolphin, Yi 34B, and Mixtral 8x7B. They are powerful alternatives to ChatGPT, GPT-3.5 and GPT-4 by OpenAI. If the pretrained models are not enough, you can also fine-tune/train your own generative models on NLP Cloud and automatically deploy the new models to production with only one click.

For more details, see our documentation about chatbots and conversational AI with generative models here. For advanced usage, see the text generation API endpoint here. And easily test chatbots and conversational AI on our playground.

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Frequently Asked Questions

Can generative AI chatbots understand and respond in multiple languages?

Yes, generative AI chatbots like those based on GPT-4, ChatDolphin, LLaMA 3, Mixtral, etc. can understand and respond in multiple languages, thanks to their extensive training on diverse language datasets from across the globe. This enables them to engage in conversations and provide responses in various languages with a high degree of fluency.

Are generative AI chatbots capable of learning from interactions?

Generative AI chatbots, such as those based on models like GPT-4, ChatDolphin, LLaMA 3, Mixtral, etc. do not learn from individual interactions in real-time due to architecture reasons. In order to "simulate" some sort of learning, the developer should maintain a history and re-send this history in every request to the chatbot.

What are the limitations of current generative AI chatbots?

Current generative AI chatbots, despite their advanced capabilities, often struggle with understanding complex or ambiguous context and can sometimes generate inaccurate or nonsensical responses. Additionally, they may inadvertently produce biased or offensive content if not properly monitored and adjusted.

How do generative AI chatbots handle sensitive or personal information?

It is the responsibility of the chatbot vendor to handle sensitive information with great care. NLP Cloud does not store any information sent to the chatbots and is HIPAA and GDPR compliant.

Are there any biases in responses generated by generatie AI chatbots? If so, how are they addressed?

Yes, responses generated by generative AI chatbots, like those based on GPT-4, ChatDolphin, LLaMA 3, Mixtral, etc. can reflect biases present in their training data. These biases are addressed through continual model training with diverse datasets, rigorous bias testing, and implementing feedback mechanisms to correct skewed responses.

Can these chatbots be customized for specific business needs?

Yes, chatbots based on generative AI can be extensively customized to meet specific business needs, including adapting their responses, tone, and even the knowledge base from which they draw information, making them highly versatile for different industries and applications.

How can developers integrate generative AI chatbots into existing platforms or applications?

Developers can integrate generative AI chatbots into existing platforms or applications by using APIs provided by the AI vendor such as NLP Cloud, which allow for seamless communication between the chatbot and the platform's backend. This involves sending user input to the AI via the API, receiving the AI-generated response, and presenting it through the application's user interface.

What is the typical response time for a chatbot based on generative AI like GPT-4?

The typical response time for a chatbot based on generative AI like GPT-4, ChatDolphin, LLaMA 3, Mixtral, etc. can vary but is usually within a few seconds, depending on the complexity of the query and the processing power available.

Can I try the chatbot API for free?

Yes, like all the models on NLP Cloud, the chatbot API endpoint can be tested for free

What are the supported languages or locales for this chatbot API?

Our chatbot API supports 200 languages
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