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#测试GPTCache,进行精确匹配缓存或基于语义相似性缓存结果 | |
import langchain | |
import gptcache | |
import time | |
from langchain.llms import OpenAI | |
from gptcache.processor.pre import get_prompt | |
from gptcache.manager.factory import get_data_manager | |
from langchain.cache import GPTCache | |
llm = OpenAI(model_name="text-davinci-003", n=2, best_of=2) | |
# Avoid multiple caches using the same file, causing different llm model caches to affect each other | |
i = 0 | |
file_prefix = "data_map" | |
def init_gptcache_map(cache_obj: gptcache.Cache): | |
global i | |
cache_path = f'{file_prefix}_{i}.txt' | |
cache_obj.init( | |
pre_embedding_func=get_prompt, | |
data_manager=get_data_manager(data_path=cache_path), | |
) | |
i += 1 | |
langchain.llm_cache = GPTCache(init_gptcache_map) | |
for i in range(20): | |
start = time.perf_counter() | |
prompt = "男生有2人,女生有{:d}人,一共多少人?".format(i) | |
print("男生有2人,女生有{:d}人, {:s}。 suspend: {:0.4f}".format(i, llm(prompt), time.perf_counter() - start)) |