A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

Correct Answer: C
To provide a stateful, conversational experience with the 'COMPLETE (SNOWFLAKE.CORTEX)' function, all previous user prompts and model responses must be passed as part of the argument. This argument accepts an array of objects, where each object represents a turn and contains a 'role' ('system', 'user', or 'assistant') and a 'content' key, presented in chronological order. In Streamlit, st.session_states is the standard and recommended mechanism for storing and managing data across reruns of the application, making it ideal for maintaining chat history. Option A is inefficient and incorrect because 'COMPLETE does not inherently manage history from external tables. Option B is incorrect as 'COMPLETE does not retain state between calls; history must be explicitly managed. Option D is less effective than structured history, as it loses the semantic role distinction and can be less accurate for LLMs. Option E describes a non-existent parameter for the 'COMPLETE' function.