Description
Given a list of messages representing a conversation history, runs LLM inference to produce the next message.
Details
Like completions, chat completions involve an LLM's response to input. However, chat completions take a conversation history as input, instead of a single prompt, which enables the LLM to create responses that take past context into account.
Messages
The primary input to the LLM is a list of messages represented by the messages array, which forms the conversation. The messages array must contain at least one message object.
Each message object is attributed to a specific entity through its role. The available roles are:
user: Represents the human querying the model. -assistant: Represents the model responding to user. -system: Represents a non-user entity that provides information to guide the behavior of the assistant.
When the role of a message is set to user, assistant, or system, the message must also contain a content field which is a string representing the actual text of the message itself. Semantically, when the role is user, content contains the user's query. When the role is assistant, content is the model's response to the user. When the role is system, content represents the instruction for the assistant.
Instructions
You may provide instructions to the assistant by supplying by supplying instructions in the HTTP request body or by specifying a message with role set to system in the messages array. By convention, the system message should be the first message in the array. Do not specify both an instruction and a system message in the messages array.