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GPT API: Difference between revisions
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'''Uses:''' | '''Uses:''' | ||
#When you receive responses from the model you can append the response to the ''messages'' array before the next ''user'' message. | #When you receive responses from the model you can append the response to the ''[[#messages|messages]]'' array before the next ''[[#user|user]]'' message. | ||
#You can supply the ''assistant'' messages to show the model examples. | #You can supply the ''assistant'' messages to show the model examples. | ||
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===stream=== | ===stream=== | ||
<pre> | |||
stream: true, | |||
</pre> | |||
If the stream is true, the model's response can be shown while it is still generated. We no longer need to wait for the whole response to be generated. | |||
[[OpenAI]] uses server-sent events for streaming. How you process the stream depends on your tech stack, But the idea is the same, you receive a stream of chunks. | |||
Chunks are strings that start with data: followed by an object. The first chunk looks like this: | |||
<pre> | |||
'data: {"id":"chatcmpl-xxxx","object":"chat.completion.chunk","created":1688198627,"model":"gpt-3.5-turbo-0613","choices":[{"index":0,"delta":{"role":"assistant","content":""},"finish_reason":null}]}' | |||
</pre> | |||
After that, you'll receive one last chunk with the string "data: [DONE]". | |||
One thing we lose with streaming is the usage field. So if you need to know how many tokens the request used you'll need to count them yourself.<ref name="”1”">https://gpt.pomb.us/</ref> | |||
===temperature=== | |||
[[temperature]] accepts a value between 0 and 2. It impacts the randomness of the predictions made by the model. A lower temperature (e.g., close to 0) will cause the model to make more deterministic and confident predictions, picking the most likely next words or phrases. Higher temperatures (e.g., 1 or more) make the output more random and the model may generate less likely but more diverse outputs. | |||
*0 = least random, 2 = most random | |||
*The default value of temperature is 1 | |||
===top_p=== | |||
[[top-p]] is a value between 0 and 1. It also adds randomness into the model's predictions. Instead of choosing the most likely next word prediction, the model creates a subset (the "nucleus") of the next-word predictions that have a cumulative probability greater than the chosen 'p' value. The next word is then randomly selected from this subset. For instance, if 'p' is set to 0.9, the model will pick the smallest set of words whose cumulative probability exceeds 0.9, and the next word will be randomly selected from this set. | |||
*0 = least random, 1 = most random | |||
*default value is 1 | |||
===n=== | |||
'''n''' is a number value that allows you to get multiple responses. Each response will be a different object inside the ''[[#choices|choices]]'' array. | |||
*Note that the content of each choice may be the same, especially for short answers or if you're using a low ''[[#temperature|temperature]]''.<ref name="”1”"></ref> | |||
===stop=== | |||
'''stop''' is an array of strings that tells the model to stop generating text when it encounters one of the strings. You can provide up to 4 strings to the stop array. The stop string found will Not be included in the response. | |||
===max_tokens=== | |||
<pre> | |||
max_tokens: 100, | |||
</pre> | |||
'''max_tokens''' is a number value that indicates the maximum number of [[tokens]] the model will generate before stopping. For example, if the max_tokens is 100, the model will generate 100 tokens (approximately 75 words) before stopping. | |||
===presence_penalty=== | |||
'''presence_penalty''' | |||
===frequency_penalty=== | |||
'''frequency_penalty''' | |||
==Response Fields== | ==Response Fields== | ||