Best Practices for Chat Conversation Storage and Context Optimization with OpenAI API 3.5 turbo

API
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Hello OpenAI Forum,

I’m developing a chat application using the OpenAI API 3.5 Turbo and am looking for advice on two key operational challenges:

  1. Efficient Conversation Storage: Currently, I’m storing each part of the conversation separately, which seems inefficient and potentially cumbersome as the application scales. I’m seeking best practices or strategies for storing chat data effectively, especially ways that maintain context integrity without consuming too much space or processing power.
  2. Context Synthesis and Token Optimization: To make the most out of the API interactions without hitting the token limit, I’m looking for ways to efficiently condense or synthesize the conversation context. Any advice on structuring conversations or managing context to keep the chat relevant and within token constraints would be immensely valuable.

I appreciate any insights, resources, or examples you could provide on these topics. Your expertise could greatly assist in refining the application’s backend functionality.

Thank you in advance for your time and assistance!

Best regards,