Messages stored in thread did it const for each message for each run

if i have thread that store almost 15 messages for each new question did it take cost for each old question like old time we will send all conversation that it take cost for each previous message.

did there is any other way to reduce cost ?

I see, the question seems to be about OpenAI’s pricing model for using the threads feature in their API.

As of my last update, OpenAI’s pricing model charges based on the number of tokens processed. A “thread” refers to a conversation or a series of related messages/requests to the API. When using threads in the OpenAI API, you send the entire conversation with each new API call, which means you’re processing all messages each time you send a new message. This can indeed result in higher costs, as you are repeatedly sending the old messages along with the new one.

Here’s an attempt to clarify the original question:

  • “if i have a thread that stores almost 15 messages for each new question”: The user is referencing a conversation thread where each new input or question generates around 15 messages/responses.
  • “did it take cost for each old question like old time we will send all conversation that it take cost for each previous message.”: The user is asking if they will incur a cost for each message in the thread, including old messages, every time they send the entire conversation history to the API.
  • “did there is any other way to reduce cost?”: They are looking for alternative methods to minimize the cost of using the API with threads.

To answer the question, yes, if you send the entire thread each time, it will count all the tokens in the thread towards your usage, which can add up quickly if the threads are long.

To reduce costs, you might consider the following:

  1. Truncate the thread: Only send the most recent messages that are necessary for context, rather than the entire conversation.
  2. Summarize the context: Instead of sending all previous messages, summarize the context in fewer tokens.
  3. Streamline tokens: Ensure that messages are as concise as possible to reduce the token count.
  4. Use caching: Store responses on the client side and reference them as needed, rather than sending them to the API again.

These strategies should help in reducing the number of tokens processed per API call and therefore the overall cost of using the OpenAI API with threads.

Yeah, you still get charged for all tokens in and out. To reduce cost, you’ll want to look at implementing your own optimized RAG system…

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