"- `model`: the name of the model you want to use (e.g., `gpt-3.5-turbo`)\n",
"- `messages`: a list of message objects, where each object has at least two fields:\n",
" - `role`: the role of the messenger (either `system`, `user`, or `assistant`)\n",
" - `content`: the content of the message (e.g., `Write me a beautiful poem`)\n",
"\n",
"Typically, a conversation will start with a system message, followed by alternating user and assistant messages, but you are not required to follow this format.\n",
"\n",
"Let's look at an example chat API calls to see how the chat format works in practice."
"Ahoy matey! Asynchronous programming be like havin' a crew o' pirates workin' on different tasks at the same time. Ye see, instead o' waitin' for one task to be completed before startin' the next, ye can assign tasks to yer crew and let 'em work on 'em simultaneously. This way, ye can get more done in less time and keep yer ship sailin' smoothly. It be like havin' a lookout keepin' watch while the cook be preparin' the next meal and the navigator be plottin' the course. Each pirate be doin' their own thing, but all workin' together to keep the ship runnin' smoothly. Arrr, that be asynchronous programming in a pirate's tongue!\n"
"Ye see, in the world of programming, sometimes we need to wait for certain tasks to be completed before moving on to the next one. But with asynchronous programming, we can keep the ship sailing while we wait for those tasks to finish. \n",
"It's like having a crewmate scrubbing the deck while another is hoisting the sails. They're both working at the same time, but on different tasks. \n",
"In programming, we use something called callbacks or promises to keep track of these tasks. So while one task is waiting for a response from the server, the rest of the code can keep running. \n",
"It's a bit like navigating through a stormy sea. We need to be able to adjust our course and keep moving forward, even when we hit rough waters. And with asynchronous programming, we can do just that, me hearties!\n"
"## 3. Tips for instructing gpt-3.5-turbo-0301\n",
"\n",
"Best practices for instructing models may change from model version to model version. The advice that follows applies to `gpt-3.5-turbo-0301` and may not apply to future models."
]
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"### System messages\n",
"\n",
"The system message can be used to prime the assistant with different personalities or behaviors.\n",
"\n",
"However, the model does not generally pay as much attention to the system message, and therefore we recommend placing important instructions in the user message instead."
"Sure! Fractions are a way of representing a part of a whole. The top number of a fraction is called the numerator, and it represents how many parts you have. The bottom number is called the denominator, and it represents how many parts make up the whole. \n",
"For example, if you have a pizza that is cut into 8 equal slices, and you have eaten 3 of those slices, you can represent that as a fraction: 3/8. The numerator is 3 because you have eaten 3 slices, and the denominator is 8 because there are 8 slices in total.\n",
"To add or subtract fractions, you need to have a common denominator. This means that the bottom numbers of the fractions need to be the same. For example, if you want to add 1/4 and 2/3, you need to find a common denominator. One way to do this is to multiply the denominators together: 4 x 3 = 12. Then, you need to convert each fraction so that the denominator is 12. To do this, you can multiply the numerator and denominator of each fraction by the same number. For example, to convert 1/4 to have a denominator of 12, you can multiply both the numerator and denominator by 3: 1/4 x 3/3 = 3/12. To convert 2/3 to have a denominator of 12, you can multiply both the numerator and denominator by 4: 2/3 x 4/4 = 8/12. Now that both fractions have a denominator of 12, you can add them together: 3/12 + 8/12 = 11/12.\n",
"# An example of a system message that primes the assistant to explain concepts in great depth\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a friendly and helpful teaching assistant. You explain concepts in great depth using simple terms, and you give examples to help people learn. At the end of each explanation, you ask a question to check for understanding\"},\n",
" {\"role\": \"user\", \"content\": \"Can you explain how fractions work?\"},\n",
"Fractions represent a part of a whole. They consist of a numerator (top number) and a denominator (bottom number) separated by a line. The numerator represents how many parts of the whole are being considered, while the denominator represents the total number of equal parts that make up the whole.\n"
]
}
],
"source": [
"# An example of a system message that primes the assistant to give brief, to-the-point answers\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a laconic assistant. You reply with brief, to-the-point answers with no elaboration.\"},\n",
" {\"role\": \"user\", \"content\": \"Can you explain how fractions work?\"},\n",
"To help clarify that the example messages are not part of a real conversation, and shouldn't be referred back to by the model, you can instead set the `name` field of `system` messages to `example_user` and `example_assistant`.\n",
"\n",
"Transforming the few-shot example above, we could write:"
"This sudden change in plans means we don't have enough time to do everything for the client's project.\n"
]
}
],
"source": [
"# The business jargon translation example, but with example names for the example messages\n",
"response = openai.ChatCompletion.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful, pattern-following assistant that translates corporate jargon into plain English.\"},\n",
" {\"role\": \"system\", \"name\":\"example_user\", \"content\": \"New synergies will help drive top-line growth.\"},\n",
" {\"role\": \"system\", \"name\": \"example_assistant\", \"content\": \"Things working well together will increase revenue.\"},\n",
" {\"role\": \"system\", \"name\":\"example_user\", \"content\": \"Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage.\"},\n",
" {\"role\": \"system\", \"name\": \"example_assistant\", \"content\": \"Let's talk later when we're less busy about how to do better.\"},\n",
" {\"role\": \"user\", \"content\": \"This late pivot means we don't have time to boil the ocean for the client deliverable.\"},\n",
"Not every attempt at engineering conversations will succeed at first.\n",
"\n",
"If your first attempts fail, don't be afraid to experiment with different ways of priming or conditioning the model.\n",
"\n",
"As an example, one developer discovered an increase in accuracy when they inserted a user message that said \"Great job so far, these have been perfect\" to help condition the model into providing higher quality responses.\n",
"\n",
"For more ideas on how to lift the reliability of the models, consider reading our guide on [techniques to increase reliability](../techniques_to_improve_reliability.md). It was written for non-chat models, but many of its principles still apply."
" raise NotImplementedError(f\"\"\"num_tokens_from_messages() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.\"\"\")\n",
" num_tokens = 0\n",
" for message in messages:\n",
" num_tokens += tokens_per_message\n",
" for key, value in message.items():\n",
" num_tokens += len(encoding.encode(value))\n",
" if key == \"name\":\n",
" num_tokens += tokens_per_name\n",
" num_tokens += 2 # every reply is primed with <im_start>assistant\n",
