## LLM Caching integrations

This notebook covers how to cache results of individual LLM calls using different caches.

```
from langchain.globals import set_llm_cachefrom langchain_openai import OpenAI# To make the caching really obvious, lets use a slower model.llm = OpenAI(model_name="gpt-3.5-turbo-instruct", n=2, best_of=2)
```

## `In Memory` Cache[​](#in-memory-cache "Direct link to in-memory-cache")

```
from langchain.cache import InMemoryCacheset_llm_cache(InMemoryCache())
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 52.2 ms, sys: 15.2 ms, total: 67.4 msWall time: 1.19 s
```

```
"\n\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!"
```

```
%%time# The second time it is, so it goes fasterllm("Tell me a joke")
```

```
CPU times: user 191 µs, sys: 11 µs, total: 202 µsWall time: 205 µs
```

```
"\n\nWhy couldn't the bicycle stand up by itself? Because it was...two tired!"
```

## `SQLite` Cache[​](#sqlite-cache "Direct link to sqlite-cache")

```
# We can do the same thing with a SQLite cachefrom langchain.cache import SQLiteCacheset_llm_cache(SQLiteCache(database_path=".langchain.db"))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 33.2 ms, sys: 18.1 ms, total: 51.2 msWall time: 667 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
```

```
%%time# The second time it is, so it goes fasterllm("Tell me a joke")
```

```
CPU times: user 4.86 ms, sys: 1.97 ms, total: 6.83 msWall time: 5.79 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
```

## `Upstash Redis` Cache[​](#upstash-redis-cache "Direct link to upstash-redis-cache")

### Standard Cache[​](#standard-cache "Direct link to Standard Cache")

Use [Upstash Redis](https://upstash.com/) to cache prompts and responses with a serverless HTTP API.

```
import langchainfrom langchain.cache import UpstashRedisCachefrom upstash_redis import RedisURL = "<UPSTASH_REDIS_REST_URL>"TOKEN = "<UPSTASH_REDIS_REST_TOKEN>"langchain.llm_cache = UpstashRedisCache(redis_=Redis(url=URL, token=TOKEN))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 7.56 ms, sys: 2.98 ms, total: 10.5 msWall time: 1.14 s
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

```
%%time# The second time it is, so it goes fasterllm("Tell me a joke")
```

```
CPU times: user 2.78 ms, sys: 1.95 ms, total: 4.73 msWall time: 82.9 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

## `Redis` Cache[​](#redis-cache "Direct link to redis-cache")

### Standard Cache[​](#standard-cache-1 "Direct link to Standard Cache")

Use [Redis](https://python.langchain.com/docs/integrations/providers/redis/) to cache prompts and responses.

```
# We can do the same thing with a Redis cache# (make sure your local Redis instance is running first before running this example)from langchain.cache import RedisCachefrom redis import Redisset_llm_cache(RedisCache(redis_=Redis()))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 6.88 ms, sys: 8.75 ms, total: 15.6 msWall time: 1.04 s
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

```
%%time# The second time it is, so it goes fasterllm("Tell me a joke")
```

```
CPU times: user 1.59 ms, sys: 610 µs, total: 2.2 msWall time: 5.58 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

### Semantic Cache[​](#semantic-cache "Direct link to Semantic Cache")

Use [Redis](https://python.langchain.com/docs/integrations/providers/redis/) to cache prompts and responses and evaluate hits based on semantic similarity.

```
from langchain.cache import RedisSemanticCachefrom langchain_openai import OpenAIEmbeddingsset_llm_cache(    RedisSemanticCache(redis_url="redis://localhost:6379", embedding=OpenAIEmbeddings()))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 351 ms, sys: 156 ms, total: 507 msWall time: 3.37 s
```

```
"\n\nWhy don't scientists trust atoms?\nBecause they make up everything."
```

```
%%time# The second time, while not a direct hit, the question is semantically similar to the original question,# so it uses the cached result!llm("Tell me one joke")
```

```
CPU times: user 6.25 ms, sys: 2.72 ms, total: 8.97 msWall time: 262 ms
```

```
"\n\nWhy don't scientists trust atoms?\nBecause they make up everything."
```

## `GPTCache`[​](#gptcache "Direct link to gptcache")

We can use [GPTCache](https://github.com/zilliztech/GPTCache) for exact match caching OR to cache results based on semantic similarity

Let’s first start with an example of exact match

```
import hashlibfrom gptcache import Cachefrom gptcache.manager.factory import manager_factoryfrom gptcache.processor.pre import get_promptfrom langchain.cache import GPTCachedef get_hashed_name(name):    return hashlib.sha256(name.encode()).hexdigest()def init_gptcache(cache_obj: Cache, llm: str):    hashed_llm = get_hashed_name(llm)    cache_obj.init(        pre_embedding_func=get_prompt,        data_manager=manager_factory(manager="map", data_dir=f"map_cache_{hashed_llm}"),    )set_llm_cache(GPTCache(init_gptcache))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 21.5 ms, sys: 21.3 ms, total: 42.8 msWall time: 6.2 s
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

```
%%time# The second time it is, so it goes fasterllm("Tell me a joke")
```

```
CPU times: user 571 µs, sys: 43 µs, total: 614 µsWall time: 635 µs
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

Let’s now show an example of similarity caching

```
import hashlibfrom gptcache import Cachefrom gptcache.adapter.api import init_similar_cachefrom langchain.cache import GPTCachedef get_hashed_name(name):    return hashlib.sha256(name.encode()).hexdigest()def init_gptcache(cache_obj: Cache, llm: str):    hashed_llm = get_hashed_name(llm)    init_similar_cache(cache_obj=cache_obj, data_dir=f"similar_cache_{hashed_llm}")set_llm_cache(GPTCache(init_gptcache))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 1.42 s, sys: 279 ms, total: 1.7 sWall time: 8.44 s
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
```

```
%%time# This is an exact match, so it finds it in the cachellm("Tell me a joke")
```

```
CPU times: user 866 ms, sys: 20 ms, total: 886 msWall time: 226 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
```

```
%%time# This is not an exact match, but semantically within distance so it hits!llm("Tell me joke")
```

```
CPU times: user 853 ms, sys: 14.8 ms, total: 868 msWall time: 224 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side.'
```

## `Momento` Cache[​](#momento-cache "Direct link to momento-cache")

Use [Momento](https://python.langchain.com/docs/integrations/providers/momento/) to cache prompts and responses.

Requires momento to use, uncomment below to install:

```
%pip install --upgrade --quiet  momento
```

You’ll need to get a Momento auth token to use this class. This can either be passed in to a momento.CacheClient if you’d like to instantiate that directly, as a named parameter `auth_token` to `MomentoChatMessageHistory.from_client_params`, or can just be set as an environment variable `MOMENTO_AUTH_TOKEN`.

```
from datetime import timedeltafrom langchain.cache import MomentoCachecache_name = "langchain"ttl = timedelta(days=1)set_llm_cache(MomentoCache.from_client_params(cache_name, ttl))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 40.7 ms, sys: 16.5 ms, total: 57.2 msWall time: 1.73 s
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

```
%%time# The second time it is, so it goes faster# When run in the same region as the cache, latencies are single digit msllm("Tell me a joke")
```

```
CPU times: user 3.16 ms, sys: 2.98 ms, total: 6.14 msWall time: 57.9 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

## `SQLAlchemy` Cache[​](#sqlalchemy-cache "Direct link to sqlalchemy-cache")

You can use `SQLAlchemyCache` to cache with any SQL database supported by `SQLAlchemy`.

```
# from langchain.cache import SQLAlchemyCache# from sqlalchemy import create_engine# engine = create_engine("postgresql://postgres:postgres@localhost:5432/postgres")# set_llm_cache(SQLAlchemyCache(engine))
```

### Custom SQLAlchemy Schemas[​](#custom-sqlalchemy-schemas "Direct link to Custom SQLAlchemy Schemas")

```
# You can define your own declarative SQLAlchemyCache child class to customize the schema used for caching. For example, to support high-speed fulltext prompt indexing with Postgres, use:from langchain.cache import SQLAlchemyCachefrom sqlalchemy import Column, Computed, Index, Integer, Sequence, String, create_enginefrom sqlalchemy.ext.declarative import declarative_basefrom sqlalchemy_utils import TSVectorTypeBase = declarative_base()class FulltextLLMCache(Base):  # type: ignore    """Postgres table for fulltext-indexed LLM Cache"""    __tablename__ = "llm_cache_fulltext"    id = Column(Integer, Sequence("cache_id"), primary_key=True)    prompt = Column(String, nullable=False)    llm = Column(String, nullable=False)    idx = Column(Integer)    response = Column(String)    prompt_tsv = Column(        TSVectorType(),        Computed("to_tsvector('english', llm || ' ' || prompt)", persisted=True),    )    __table_args__ = (        Index("idx_fulltext_prompt_tsv", prompt_tsv, postgresql_using="gin"),    )engine = create_engine("postgresql://postgres:postgres@localhost:5432/postgres")set_llm_cache(SQLAlchemyCache(engine, FulltextLLMCache))
```

## `Cassandra` caches[​](#cassandra-caches "Direct link to cassandra-caches")

You can use Cassandra / Astra DB through CQL for caching LLM responses, choosing from the exact-match `CassandraCache` or the (vector-similarity-based) `CassandraSemanticCache`.

Let’s see both in action in the following cells.

#### Connect to the DB[​](#connect-to-the-db "Direct link to Connect to the DB")

First you need to establish a `Session` to the DB and to specify a _keyspace_ for the cache table(s). The following gets you connected to Astra DB through CQL (see e.g. [here](https://cassio.org/start_here/#vector-database) for more backends and connection options).

```
import getpasskeyspace = input("\nKeyspace name? ")ASTRA_DB_APPLICATION_TOKEN = getpass.getpass('\nAstra DB Token ("AstraCS:...") ')ASTRA_DB_SECURE_BUNDLE_PATH = input("Full path to your Secure Connect Bundle? ")
```

```
Keyspace name? my_keyspaceAstra DB Token ("AstraCS:...") ········Full path to your Secure Connect Bundle? /path/to/secure-connect-databasename.zip
```

```
from cassandra.auth import PlainTextAuthProviderfrom cassandra.cluster import Clustercluster = Cluster(    cloud={        "secure_connect_bundle": ASTRA_DB_SECURE_BUNDLE_PATH,    },    auth_provider=PlainTextAuthProvider("token", ASTRA_DB_APPLICATION_TOKEN),)session = cluster.connect()
```

### Exact cache[​](#exact-cache "Direct link to Exact cache")

This will avoid invoking the LLM when the supplied prompt is _exactly_ the same as one encountered already:

```
from langchain.cache import CassandraCachefrom langchain.globals import set_llm_cacheset_llm_cache(CassandraCache(session=session, keyspace=keyspace))
```

```
%%timeprint(llm("Why is the Moon always showing the same side?"))
```

```
The Moon always shows the same side because it is tidally locked to Earth.CPU times: user 41.7 ms, sys: 153 µs, total: 41.8 msWall time: 1.96 s
```

```
%%timeprint(llm("Why is the Moon always showing the same side?"))
```

```
The Moon always shows the same side because it is tidally locked to Earth.CPU times: user 4.09 ms, sys: 0 ns, total: 4.09 msWall time: 119 ms
```

### Semantic cache[​](#semantic-cache-1 "Direct link to Semantic cache")

This cache will do a semantic similarity search and return a hit if it finds a cached entry that is similar enough, For this, you need to provide an `Embeddings` instance of your choice.

```
from langchain_openai import OpenAIEmbeddingsembedding = OpenAIEmbeddings()
```

```
from langchain.cache import CassandraSemanticCacheset_llm_cache(    CassandraSemanticCache(        session=session,        keyspace=keyspace,        embedding=embedding,        table_name="cass_sem_cache",    ))
```

```
%%timeprint(llm("Why is the Moon always showing the same side?"))
```

```
The Moon always shows the same side because it is tidally locked with Earth. This means that the same side of the Moon always faces Earth.CPU times: user 21.3 ms, sys: 177 µs, total: 21.4 msWall time: 3.09 s
```

```
%%timeprint(llm("How come we always see one face of the moon?"))
```

```
The Moon always shows the same side because it is tidally locked with Earth. This means that the same side of the Moon always faces Earth.CPU times: user 10.9 ms, sys: 17 µs, total: 10.9 msWall time: 461 ms
```

#### Attribution statement[​](#attribution-statement "Direct link to Attribution statement")

> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries.

## `Astra DB` Caches[​](#astra-db-caches "Direct link to astra-db-caches")

You can easily use [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) as an LLM cache, with either the “exact” or the “semantic-based” cache.

Make sure you have a running database (it must be a Vector-enabled database to use the Semantic cache) and get the required credentials on your Astra dashboard:

*   the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`
*   the Token looks like `AstraCS:6gBhNmsk135....`

```
import getpassASTRA_DB_API_ENDPOINT = input("ASTRA_DB_API_ENDPOINT = ")ASTRA_DB_APPLICATION_TOKEN = getpass.getpass("ASTRA_DB_APPLICATION_TOKEN = ")
```

```
ASTRA_DB_API_ENDPOINT =  https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.comASTRA_DB_APPLICATION_TOKEN =  ········
```

### Astra DB exact LLM cache[​](#astra-db-exact-llm-cache "Direct link to Astra DB exact LLM cache")

This will avoid invoking the LLM when the supplied prompt is _exactly_ the same as one encountered already:

```
from langchain.cache import AstraDBCachefrom langchain.globals import set_llm_cacheset_llm_cache(    AstraDBCache(        api_endpoint=ASTRA_DB_API_ENDPOINT,        token=ASTRA_DB_APPLICATION_TOKEN,    ))
```

```
%%timeprint(llm("Is a true fakery the same as a fake truth?"))
```

```
There is no definitive answer to this question as it depends on the interpretation of the terms "true fakery" and "fake truth". However, one possible interpretation is that a true fakery is a counterfeit or imitation that is intended to deceive, whereas a fake truth is a false statement that is presented as if it were true.CPU times: user 70.8 ms, sys: 4.13 ms, total: 74.9 msWall time: 2.06 s
```

```
%%timeprint(llm("Is a true fakery the same as a fake truth?"))
```

```
There is no definitive answer to this question as it depends on the interpretation of the terms "true fakery" and "fake truth". However, one possible interpretation is that a true fakery is a counterfeit or imitation that is intended to deceive, whereas a fake truth is a false statement that is presented as if it were true.CPU times: user 15.1 ms, sys: 3.7 ms, total: 18.8 msWall time: 531 ms
```

### Astra DB Semantic cache[​](#astra-db-semantic-cache "Direct link to Astra DB Semantic cache")

This cache will do a semantic similarity search and return a hit if it finds a cached entry that is similar enough, For this, you need to provide an `Embeddings` instance of your choice.

```
from langchain_openai import OpenAIEmbeddingsembedding = OpenAIEmbeddings()
```

```
from langchain.cache import AstraDBSemanticCacheset_llm_cache(    AstraDBSemanticCache(        api_endpoint=ASTRA_DB_API_ENDPOINT,        token=ASTRA_DB_APPLICATION_TOKEN,        embedding=embedding,        collection_name="demo_semantic_cache",    ))
```

```
%%timeprint(llm("Are there truths that are false?"))
```

```
There is no definitive answer to this question since it presupposes a great deal about the nature of truth itself, which is a matter of considerable philosophical debate. It is possible, however, to construct scenarios in which something could be considered true despite being false, such as if someone sincerely believes something to be true even though it is not.CPU times: user 65.6 ms, sys: 15.3 ms, total: 80.9 msWall time: 2.72 s
```

```
%%timeprint(llm("Is is possible that something false can be also true?"))
```

```
There is no definitive answer to this question since it presupposes a great deal about the nature of truth itself, which is a matter of considerable philosophical debate. It is possible, however, to construct scenarios in which something could be considered true despite being false, such as if someone sincerely believes something to be true even though it is not.CPU times: user 29.3 ms, sys: 6.21 ms, total: 35.5 msWall time: 1.03 s
```

## Azure Cosmos DB Semantic Cache[​](#azure-cosmos-db-semantic-cache "Direct link to Azure Cosmos DB Semantic Cache")

You can use this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) for caching.

```
from langchain_community.cache import AzureCosmosDBSemanticCachefrom langchain_community.vectorstores.azure_cosmos_db import (    CosmosDBSimilarityType,    CosmosDBVectorSearchType,)from langchain_openai import OpenAIEmbeddings# Read more about Azure CosmosDB Mongo vCore vector search here https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-searchNAMESPACE = "langchain_test_db.langchain_test_collection"CONNECTION_STRING = (    "Please provide your azure cosmos mongo vCore vector db connection string")DB_NAME, COLLECTION_NAME = NAMESPACE.split(".")# Default value for these paramsnum_lists = 3dimensions = 1536similarity_algorithm = CosmosDBSimilarityType.COSkind = CosmosDBVectorSearchType.VECTOR_IVFm = 16ef_construction = 64ef_search = 40score_threshold = 0.9application_name = "LANGCHAIN_CACHING_PYTHON"set_llm_cache(    AzureCosmosDBSemanticCache(        cosmosdb_connection_string=CONNECTION_STRING,        cosmosdb_client=None,        embedding=OpenAIEmbeddings(),        database_name=DB_NAME,        collection_name=COLLECTION_NAME,        num_lists=num_lists,        similarity=similarity_algorithm,        kind=kind,        dimensions=dimensions,        m=m,        ef_construction=ef_construction,        ef_search=ef_search,        score_threshold=score_threshold,        application_name=application_name,    ))
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 45.6 ms, sys: 19.7 ms, total: 65.3 msWall time: 2.29 s
```

```
'\n\nWhy was the math book sad? Because it had too many problems.'
```

```
%%time# The first time, it is not yet in cache, so it should take longerllm("Tell me a joke")
```

```
CPU times: user 9.61 ms, sys: 3.42 ms, total: 13 msWall time: 474 ms
```

```
'\n\nWhy was the math book sad? Because it had too many problems.'
```

## Optional Caching[​](#optional-caching "Direct link to Optional Caching")

You can also turn off caching for specific LLMs should you choose. In the example below, even though global caching is enabled, we turn it off for a specific LLM

```
llm = OpenAI(model_name="gpt-3.5-turbo-instruct", n=2, best_of=2, cache=False)
```

```
%%timellm("Tell me a joke")
```

```
CPU times: user 5.8 ms, sys: 2.71 ms, total: 8.51 msWall time: 745 ms
```

```
'\n\nWhy did the chicken cross the road?\n\nTo get to the other side!'
```

```
%%timellm("Tell me a joke")
```

```
CPU times: user 4.91 ms, sys: 2.64 ms, total: 7.55 msWall time: 623 ms
```

```
'\n\nTwo guys stole a calendar. They got six months each.'
```

## Optional Caching in Chains[​](#optional-caching-in-chains "Direct link to Optional Caching in Chains")

You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards.

As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step.

```
llm = OpenAI(model_name="gpt-3.5-turbo-instruct")no_cache_llm = OpenAI(model_name="gpt-3.5-turbo-instruct", cache=False)
```

```
from langchain_text_splitters import CharacterTextSplittertext_splitter = CharacterTextSplitter()
```

```
with open("../../modules/state_of_the_union.txt") as f:    state_of_the_union = f.read()texts = text_splitter.split_text(state_of_the_union)
```

```
from langchain_community.docstore.document import Documentdocs = [Document(page_content=t) for t in texts[:3]]from langchain.chains.summarize import load_summarize_chain
```

```
chain = load_summarize_chain(llm, chain_type="map_reduce", reduce_llm=no_cache_llm)
```

```
CPU times: user 452 ms, sys: 60.3 ms, total: 512 msWall time: 5.09 s
```

```
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure. In response to Russian aggression in Ukraine, the United States is joining with European allies to impose sanctions and isolate Russia. American forces are being mobilized to protect NATO countries in the event that Putin decides to keep moving west. The Ukrainians are bravely fighting back, but the next few weeks will be hard for them. Putin will pay a high price for his actions in the long run. Americans should not be alarmed, as the United States is taking action to protect its interests and allies.'
```

When we run it again, we see that it runs substantially faster but the final answer is different. This is due to caching at the map steps, but not at the reduce step.

```
CPU times: user 11.5 ms, sys: 4.33 ms, total: 15.8 msWall time: 1.04 s
```

```
'\n\nPresident Biden is discussing the American Rescue Plan and the Bipartisan Infrastructure Law, which will create jobs and help Americans. He also talks about his vision for America, which includes investing in education and infrastructure.'
```

```
!rm .langchain.db sqlite.db
```