如何缓存聊天模型的响应
LangChain 为聊天模型提供了一个可选的缓存层。这主要有两个好处:
- 如果您经常请求相同的完成,这可以通过减少您对大模型供应商的 API 调用次数来节省您的费用。这在应用开发期间尤其有用。
- 通过减少您对大模型供应商的 API 调用次数,它可以加快您的应用程序速度。
本指南将引导您如何在您的应用中启用此功能。
- OpenAI
- Anthropic
- Azure
- Cohere
- NVIDIA
- FireworksAI
- Groq
- MistralAI
- TogetherAI
pip install -qU langchain-openai
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
pip install -qU langchain-anthropic
import getpass
import os
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
pip install -qU langchain-openai
import getpass
import os
os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)
pip install -qU langchain-google-vertexai
import getpass
import os
os.environ["GOOGLE_API_KEY"] = getpass.getpass()
from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model="gemini-1.5-flash")
pip install -qU langchain-cohere
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass()
from langchain_cohere import ChatCohere
llm = ChatCohere(model="command-r-plus")
pip install -qU langchain-nvidia-ai-endpoints
import getpass
import os
os.environ["NVIDIA_API_KEY"] = getpass.getpass()
from langchain import ChatNVIDIA
llm = ChatNVIDIA(model="meta/llama3-70b-instruct")
pip install -qU langchain-fireworks
import getpass
import os
os.environ["FIREWORKS_API_KEY"] = getpass.getpass()
from langchain_fireworks import ChatFireworks
llm = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")
pip install -qU langchain-groq
import getpass
import os
os.environ["GROQ_API_KEY"] = getpass.getpass()
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama3-8b-8192")
pip install -qU langchain-mistralai
import getpass
import os
os.environ["MISTRAL_API_KEY"] = getpass.getpass()
from langchain_mistralai import ChatMistralAI
llm = ChatMistralAI(model="mistral-large-latest")
pip install -qU langchain-openai
import getpass
import os
os.environ["TOGETHER_API_KEY"] = getpass.getpass()
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="https://api.together.xyz/v1",
api_key=os.environ["TOGETHER_API_KEY"],
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)
<!--IMPORTS:[{"imported": "set_llm_cache", "source": "langchain_core.globals", "docs": "https://python.langchain.com/api_reference/core/globals/langchain_core.globals.set_llm_cache.html", "title": "How to cache chat model responses"}]-->
# <!-- ruff: noqa: F821 -->
from langchain_core.globals import set_llm_cache
内存缓存
这是一个临时缓存,用于在内存中存储模型调用。当您的环境重启时,它将被清除,并且在进程之间不共享。
<!--IMPORTS:[{"imported": "InMemoryCache", "source": "langchain_core.caches", "docs": "https://python.langchain.com/api_reference/core/caches/langchain_core.caches.InMemoryCache.html", "title": "How to cache chat model responses"}]-->
%%time
from langchain_core.caches import InMemoryCache
set_llm_cache(InMemoryCache())
# The first time, it is not yet in cache, so it should take longer
llm.invoke("Tell me a joke")
CPU times: user 645 ms, sys: 214 ms, total: 859 ms
Wall time: 829 ms
AIMessage(content="Why don't scientists trust atoms?\n\nBecause they make up everything!", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 11, 'total_tokens': 24}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-b6836bdd-8c30-436b-828f-0ac5fc9ab50e-0')
%%time
# The second time it is, so it goes faster
llm.invoke("Tell me a joke")
CPU times: user 822 µs, sys: 288 µs, total: 1.11 ms
Wall time: 1.06 ms
AIMessage(content="Why don't scientists trust atoms?\n\nBecause they make up everything!", response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 11, 'total_tokens': 24}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-b6836bdd-8c30-436b-828f-0ac5fc9ab50e-0')
SQLite 缓存
此缓存实现使用 SQLite
数据库来存储响应,并且在进程重启时仍然有效。
!rm .langchain.db
<!--IMPORTS:[{"imported": "SQLiteCache", "source": "langchain_community.cache", "docs": "https://python.langchain.com/api_reference/community/cache/langchain_community.cache.SQLiteCache.html", "title": "How to cache chat model responses"}]-->
# We can do the same thing with a SQLite cache
from langchain_community.cache import SQLiteCache
set_llm_cache(SQLiteCache(database_path=".langchain.db"))
%%time
# The first time, it is not yet in cache, so it should take longer
llm.invoke("Tell me a joke")
CPU times: user 9.91 ms, sys: 7.68 ms, total: 17.6 ms
Wall time: 657 ms
AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field!', response_metadata={'token_usage': {'completion_tokens': 17, 'prompt_tokens': 11, 'total_tokens': 28}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': 'fp_c2295e73ad', 'finish_reason': 'stop', 'logprobs': None}, id='run-39d9e1e8-7766-4970-b1d8-f50213fd94c5-0')
%%time
# The second time it is, so it goes faster
llm.invoke("Tell me a joke")
CPU times: user 52.2 ms, sys: 60.5 ms, total: 113 ms
Wall time: 127 ms
AIMessage(content='Why did the scarecrow win an award? Because he was outstanding in his field!', id='run-39d9e1e8-7766-4970-b1d8-f50213fd94c5-0')
下一步
您现在已经学习了如何缓存模型响应以节省时间和金钱。
接下来,请查看本节中其他关于聊天模型的使用指南,例如 如何让模型返回结构化输出 或 如何创建您自己的自定义聊天模型。