I too was a bit confused on why it wasn’t working like ChatGPT at first.īut, once you understand how it works, it makes much more sense. I will certainly agree that the documentation and even the labeling of functionality is a bit off for the API. The chat completions api makes this even easier as I just have to slice dictionaries out of my messages array, and is way cheaper to boot. I’d built a concatenation history (only learning it was a thing and had that name after completing it) for delivering context, then a summary routine when I realized that would quickly grow out of hand. I built a very cost-per-token expensive version of the chatbot that used the regular completions api before I found the chat completions. To be fair, I’m only about 20 hours into this journey myself. While it’s true that the API reference doesn’t explicitly state exactly what each returned value means, the keys for the values are quite descriptive. It still has to process the context in relation to the prompt, and that processing is where the cost comes in. Your only savings would be the 1s and 0s you saved transferring them from your app to the API. I’m pretty sure that if there were a session that saved your context, the context would still have to be fed into the model and processed. If we want to continue the conversation later, we could just: session = (model='gpt-3.5-turbo', chat_id=chat_id)

Response = session.appendMessages(userMessage) Session = (model='gpt-3.5-turbo', title='example session') For instance, python API could work like this: import openai I would have expected the API for ChatGPT to work exactly like we use the chatgpt web app. As says, there MUST be another way to keep the session alive.

Chatgpt 3 api