How to Create the Support Chatbot in Just 3 Steps Using OpenAI API

Is there any system that has already nailed its grasp of NLP?

LLMs’ examples:

GPT-3.5 / GPT-4 (Generative Pretrained Transformer) – from OpenAI.

BERT (Bidirectional Encoder Representations from Transformers) – from Google.

RoBERTa (Robustly Optimized BERT Approach) – from Facebook AI.

Megatron-Turing – from NVIDIA

GPT-3.5 models can comprehend and generate both natural language and code. Among the GPT-3.5 variants, gpt-3.5-turbo stands out as the most proficient and cost-effective model. It is specifically fine-tuned for chat applications through the Chat completions API, yet it also performs effectively in conventional completion tasks.

GPT-4 currently processes text inputs and produces corresponding text outputs. This model stands out in effectively tackling intricate problems, showcasing superior accuracy in comparison to our prior models. Much like gpt-3.5-turbo, GPT-4 is finely calibrated for chat-based applications and delivers excellent performance in typical completion tasks through the Chat completions API.

GPT base models possess the ability to comprehend and generate both natural language and code, albeit without specific instruction following training. These models are designed as alternatives to our initial GPT-3 base models and utilize the traditional Completions API.

DALL·E is an AI system capable of generating realistic images and artwork based on a natural language description.

Embeddings are numerical representations of text, enabling the assessment of the correlation between two blocks of text. OpenAI's latest embedding model, text-embedding-ada-002, represents a second-generation advancement intended to supplant the 16 initial first-generation embedding models.

Moderation models are created to validate content adherence to OpenAI's usage policies. These models offer classification features to identify content falling into specific categories, including hate, threatening language, self-harm, sexual content, content involving minors, violence, and graphic violence.

First Step

Invoke the model by passing the user's query and a predefined set of functions through the 'functions' parameter.

The model can choose a function, and if it does, the resulting content will be a JSON object, following a specified custom schema.

In your code, parse the string into JSON, and if the provided arguments exist, proceed to call your function using those arguments.

Second Step

Third Step

Conclusion