当前行为

我得到了一个堆栈跟踪

False
True
Traceback (most recent call last):
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/development/scripts/chatbot-postgres-test.py", line 129, in <module>
    execution_time = timeit.timeit(lambda: llm.invoke("Tell me a joke"), number=1)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/timeit.py", line 237, in timeit
    return Timer(stmt, setup, timer, globals).timeit(number)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3.11/timeit.py", line 180, in timeit
    timing = self.inner(it, self.timer)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<timeit-src>", line 6, in inner
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/development/scripts/chatbot-postgres-test.py", line 129, in <lambda>
    execution_time = timeit.timeit(lambda: llm.invoke("Tell me a joke"), number=1)
                                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 153, in invoke
    self.generate_prompt(
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 546, in generate_prompt
    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 407, in generate
    raise e
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 397, in generate
    self._generate_with_cache(
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 579, in _generate_with_cache
    cache_val = llm_cache.lookup(prompt, llm_string)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/langchain_community/cache.py", line 813, in lookup
    res = get(prompt, cache_obj=_gptcache)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/gptcache/adapter/api.py", line 124, in get
    res = adapt(
          ^^^^^^
  File "/home/theinhumaneme/Documents/NebuLogic/conversation-bot/venv/lib/python3.11/site-packages/gptcache/adapter/adapter.py", line 33, in adapt
    raise NotInitError()
gptcache.utils.error.NotInitError: The cache should be inited before using

预期行为

我应该能够正常使用缓存

重现步骤

import os
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAI
os.environ["OPENAI_API_KEY"] = ""
CONNECTION_STRING = "postgresql+psycopg2://postgres:postgres@localhost:5432/postgres"

import hashlib
import timeit
from gptcache import Cache, cache
from gptcache.adapter.api import init_similar_cache
from langchain.globals import set_llm_cache
from langchain_community.cache import GPTCache, SQLiteCache
from langchain_openai import OpenAIEmbeddings
from gptcache.manager import get_data_manager, CacheBase, VectorBase
from langchain.globals import set_llm_cache
from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation

def get_content_func(data, **_):
    return data.get("prompt").split("Question")[-1]

openai_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
cache_base = CacheBase(
    "postgresql",
    sql_url="postgresql+psycopg2://postgres:postgres@127.0.0.1:5432/postgres",
)
vector_base = VectorBase(
    "pgvector",
    host="127.0.0.1",
    port="5432",
    user="postgres",
    password="postgres",
    dimension=1536,
)
data_manager = get_data_manager(cache_base, vector_base)

# cache.init(
#     pre_embedding_func=get_content_func,
#     embedding_func=OpenAIEmbeddings(model="text-embedding-3-small").embed_query,
#     data_manager=data_manager,
#     similarity_evaluation=SearchDistanceEvaluation(),
# )
def init_gptcache(cache_obj: Cache, llm: str):
    print(cache.has_init)
    cache.init(
        pre_embedding_func=get_content_func,
        embedding_func=OpenAIEmbeddings(model="text-embedding-3-small").embed_query,
        data_manager=data_manager,
        similarity_evaluation=SearchDistanceEvaluation(),
    )
    print(cache.has_init)

llm_model = "gpt-3.5-turbo-0125"
llm = ChatOpenAI(temperature=0, model_name=llm_model)

set_llm_cache(GPTCache(init_gptcache))

execution_time = timeit.timeit(lambda: llm.invoke("Tell me a joke"), number=1)
print(f"Execution time: {execution_time} seconds")

execution_time = timeit.timeit(lambda: llm.invoke("Tell me a joke"), number=1)
print(f"Execution time: {execution_time} seconds")

环境

• 无响应*

其他信息？

当我使用 set_llm_cache() 从 langchain 时，我得到了这个错误。当我正常使用它时，它是有效的，即初始化，但当我尝试使用 openai 嵌入文本时失败了。当我将函数中的代码更改为 embed_query 时，我得到了意外的 extra_params 传递。谢谢 :)