Caching

LangChain provides an optional caching layer for chat models. This is useful for two reasons:

It can save you money by reducing the number of API calls you make to the LLM provider, if you’re often requesting the same completion multiple times. It can speed up your application by reducing the number of API calls you make to the LLM provider.

OpenAI

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-3.5-turbo-0125")

Anthropic

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-sonnet-20240229")

Google

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-pro")

Cohere

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")

FireworksAI

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/mixtral-8x7b-instruct")

MistralAI

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")

TogetherAI

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",)

# <!-- ruff: noqa: F821 -->
from langchain.globals import set_llm_cache

API Reference:

• set_llm_cache

In Memory Cache

%%time
from langchain.cache import InMemoryCache

set_llm_cache(InMemoryCache())

# The first time, it is not yet in cache, so it should take longer
llm.predict("Tell me a joke")

API Reference:

• InMemoryCache

CPU times: user 17.7 ms, sys: 9.35 ms, total: 27.1 ms
Wall time: 801 ms

"Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"

%%time
# The second time it is, so it goes faster
llm.predict("Tell me a joke")

CPU times: user 1.42 ms, sys: 419 µs, total: 1.83 ms
Wall time: 1.83 ms

"Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"

SQLite Cache

!rm .langchain.db

# We can do the same thing with a SQLite cache
from langchain.cache import SQLiteCache

set_llm_cache(SQLiteCache(database_path=".langchain.db"))

API Reference:

• SQLiteCache

%%time
# The first time, it is not yet in cache, so it should take longer
llm.predict("Tell me a joke")

CPU times: user 23.2 ms, sys: 17.8 ms, total: 40.9 ms
Wall time: 592 ms

"Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"

%%time
# The second time it is, so it goes faster
llm.predict("Tell me a joke")

CPU times: user 5.61 ms, sys: 22.5 ms, total: 28.1 ms
Wall time: 47.5 ms

"Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"