To implement conversation history in OpenAI's GPT models, you need to manage the dialogue context by storing and updating previous messages in the conversation. This allows the model to maintain continuity and coherence throughout the interaction. The most common way to do this is by creating a list or an array to hold the messages exchanged between the user and the model. Each time a new message is sent, you append it to this list, ensuring you keep the most relevant context within the input limit of the model.
When constructing the input for the GPT model, include both the user’s previous messages and the model’s responses. Often, you'll format this input in a conversational style, alternating between user and AI messages. For example, if the conversation history looks like this:
- User: "What's the weather like today?"
- AI: "It's sunny with a high of 75°F."
- User: "What about tomorrow?"
You can format the next input as:
"User: What's the weather like today? AI: It's sunny with a high of 75°F. User: What about tomorrow?" This complete input maintains the context, allowing the model to generate relevant and contextual responses.
Keep in mind the token limit for the specific GPT model you are using. If the conversation history exceeds this limit, you will need to truncate older messages or distill the information into shorter summaries to fit within the constraints. This method ensures that you don't lose meaningful context while staying within operational limits. By thoughtfully managing the conversation history, you can offer a more natural chat experience, where users feel like they're having a fluid conversation rather than just isolated exchanges.