75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
from typing import Callable, Iterator
|
|
import openai
|
|
import tiktoken
|
|
|
|
from .llm_engine import LlmEngine
|
|
|
|
class OpenAILlmEngine(LlmEngine):
|
|
"""
|
|
LLM Engine implementation using OpenAI's API.
|
|
Supports streaming responses from chat completion models.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
temperature: float,
|
|
token_limit: int,
|
|
api_key: str,
|
|
):
|
|
"""
|
|
Initialize the OpenAI LLM Engine.
|
|
|
|
Args:
|
|
model: OpenAI model to use
|
|
temperature: Temperature for sampling
|
|
api_key: OpenAI API key
|
|
token_limit: Maximum number of tokens to generate
|
|
"""
|
|
self._model = model
|
|
self._temperature = temperature
|
|
self._token_limit = token_limit
|
|
|
|
self._client = openai.Client(
|
|
api_key=api_key,
|
|
)
|
|
|
|
def infer(self, system_prompt: str, main_context: str, should_stop: Callable[[], bool] = lambda: False) -> Iterator[str]:
|
|
messages = [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": main_context}
|
|
]
|
|
|
|
stream = self._client.chat.completions.create(
|
|
model=self._model,
|
|
messages=messages,
|
|
temperature=self._temperature,
|
|
stream=True,
|
|
)
|
|
|
|
try:
|
|
for chunk in stream:
|
|
if should_stop():
|
|
break
|
|
if content := chunk.choices[0].delta.content:
|
|
yield content
|
|
finally:
|
|
stream.close()
|
|
#stream.response.close()
|
|
|
|
def token_count(self, system_prompt: str, main_context: str) -> int:
|
|
"""
|
|
Calculate the total token count for the system prompt and context.
|
|
|
|
Args:
|
|
system_prompt: The system prompt string
|
|
main_context: The main context string
|
|
|
|
Returns:
|
|
int: Total number of tokens
|
|
"""
|
|
encoding = tiktoken.encoding_for_model(self._model)
|
|
return len(encoding.encode(system_prompt)) + len(encoding.encode(main_context))
|
|
|
|
def token_limit(self) -> int:
|
|
return self._token_limit |