聊天自动回复 Chat Completion

# Note: you need to be using OpenAI Python v0.27.0 for the code below to work
import openai

openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"},
        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
        {"role": "user", "content": "Where was it played?"}
    ]
)

{
 'id': 'chatcmpl-6p9XYPYSTTRi0xEviKjjilqrWU2Ve',
 'object': 'chat.completion',
 'created': 1677649420,
 'model': 'gpt-3.5-turbo',
 'usage': {'prompt_tokens': 56, 'completion_tokens': 31, 'total_tokens': 87},
 'choices': [
   {
    'message': {
      'role': 'assistant',
      'content': 'The 2020 World Series was played in Arlington, Texas at the Globe Life Field, which was the new home stadium for the Texas Rangers.'},
    'finish_reason': 'stop',
    'index': 0
   }
  ]
}

def num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301"):
  """Returns the number of tokens used by a list of messages."""
  try:
      encoding = tiktoken.encoding_for_model(model)
  except KeyError:
      encoding = tiktoken.get_encoding("cl100k_base")
  if model == "gpt-3.5-turbo-0301":  # note: future models may deviate from this
      num_tokens = 0
      for message in messages:
          num_tokens += 4  # every message follows <im_start>{role/name}\n{content}<im_end>\n
          for key, value in message.items():
              num_tokens += len(encoding.encode(value))
              if key == "name":  # if there's a name, the role is omitted
                  num_tokens += -1  # role is always required and always 1 token
      num_tokens += 2  # every reply is primed with <im_start>assistant
      return num_tokens
  else:
      raise NotImplementedError(f"""num_tokens_from_messages() is not presently implemented for model {model}.
  See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens.""")

messages = [
  {"role": "system", "content": "You are a helpful, pattern-following assistant that translates corporate jargon into plain English."},
  {"role": "system", "name":"example_user", "content": "New synergies will help drive top-line growth."},
  {"role": "system", "name": "example_assistant", "content": "Things working well together will increase revenue."},
  {"role": "system", "name":"example_user", "content": "Let's circle back when we have more bandwidth to touch base on opportunities for increased leverage."},
  {"role": "system", "name": "example_assistant", "content": "Let's talk later when we're less busy about how to do better."},
  {"role": "user", "content": "This late pivot means we don't have time to boil the ocean for the client deliverable."},
]

model = "gpt-3.5-turbo-0301"

print(f"{num_tokens_from_messages(messages, model)} prompt tokens counted.")
# Should show ~126 total_tokens

# example token count from the OpenAI API
import openai

response = openai.ChatCompletion.create(
    model=model,
    messages=messages,
    temperature=0,
)

print(f'{response["usage"]["prompt_tokens"]} prompt tokens used.')

Translate the following English text to French: "{text}"

[
  { role: 'system', content: 'You are a helpful assistant that translates English to French.' },
  { role: 'user', content: 'Translate the following English text to French: "{text}"' }
]

[
  { role: 'user', content: 'Translate the following English text to French: "{text}"' }
]