Caching strategies for cost reduction in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to enable simple in-memory caching in LangChain.

LangChain

from langchain.cache import [1]

cache = [1]()

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ADiskCache

BInMemoryCache

CRedisCache

DNoCache

Attempts:

3 left

💡 Hint

Common Mistakes

Using DiskCache or RedisCache without setup causes errors.

Using NoCache disables caching.

InMemoryCache is the built-in simple cache for LangChain that stores data in memory to reduce repeated calls.

2fill in blank

medium

Complete the code to set the cache for LangChain's global cache system.

LangChain

from langchain.cache import cache

cache.[1] = InMemoryCache()

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Acache

Bset_cache

Cenabled

Dclear

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to call a method like set_cache instead of assigning.

Using clear which removes cache data.

The global cache instance is set by assigning to cache.cache.

3fill in blank

hard

Fix the error in the code to use RedisCache properly for caching.

LangChain

from langchain.cache import RedisCache

cache = RedisCache(redis_url='[1]')

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Aredis://localhost:6379

Bhttp://localhost:6379

Clocalhost:6379

Dredis://127.0.0.1

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting the 'redis://' prefix causes connection errors.

Using 'http://' instead of 'redis://'.

RedisCache expects a Redis URL starting with 'redis://', including host and port.

4fill in blank

hard

Fill both blanks to create a cache key function that uses the input text length and a prefix.

LangChain

def cache_key_function(input_text):
    return f"[1]_[2]"

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A"prefix"

Blen(input_text)

Cinput_text

D"cachekey"

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole input text as a key can cause very long keys.

Omitting the prefix reduces key uniqueness.

The function returns a string combining a prefix and the length of the input text to create a cache key.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension caching only results with cost below a threshold.

LangChain

costs = {'a': 10, 'b': 5, 'c': 20}
cached = {k: v for k, v in costs.items() if v [1] [2] and k != [3]

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A<

B15

C'c'

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' changes the filter logic.

Not excluding the key 'c' causes unwanted caching.

This comprehension caches items with cost less than 15 and excludes key 'c'.