Tony20100/langchain_full huggingface dataset:
langchain_community.cache.SQLAlchemyCache¶
class langchain_community.cache.SQLAlchemyCache(engine: ~sqlalchemy.engine.base.Engine, cache_schema: ~typing.Type[~langchain_community.cache.FullLLMCache] = <class 'langchain_community.cache.FullLLMCache'>)[source]¶
Cache that uses SQAlchemy as a backend.
Initialize by creating all tables.
Methods
__init__(engine[, cache_schema])
Initialize by creating all tables.
aclear(**kwargs)
Clear cache that can take additional keyword arguments.
alookup(prompt, llm_string)
Look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
clear(**kwargs)
Clear cache.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update based on prompt and llm_string.
Parameters
engine (Engine) – 
cache_schema (Type[FullLLMCache]) – 
__init__(engine: ~sqlalchemy.engine.base.Engine, cache_schema: ~typing.Type[~langchain_community.cache.FullLLMCache] = <class 'langchain_community.cache.FullLLMCache'>)[source]¶
Initialize by creating all tables.
Parameters
engine (Engine) – 
cache_schema (Type[FullLLMCache]) – 
async aclear(**kwargs: Any) → None¶
Clear cache that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶
Look up based on prompt and llm_string.
A cache implementation is expected to generate a key from the 2-tuple
A cache implementation is expected to generate a key from the 2-tuple
of prompt and llm_string (e.g., by concatenating them with a delimiter).
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
Returns
On a cache miss, return None. On a cache hit, return the cached value.
The cached value is a list of Generations (or subclasses).
Return type
Optional[Sequence[Generation]]
async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶
Update cache based on prompt and llm_string.
The prompt and llm_string are used to generate a key for the cache.
The key should match that of the look up method.
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations
(or subclasses).
Return type
None
clear(**kwargs: Any) → None[source]¶
Clear cache.
Parameters
None
clear(**kwargs: Any) → None[source]¶
Clear cache.
Parameters
kwargs (Any) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
Optional[Sequence[Generation]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
Update based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
None
Examples using SQLAlchemyCache¶
Model caches
langchain_community.cache.UpstashRedisCache¶
class langchain_community.cache.UpstashRedisCache(redis_: Any, *, ttl: Optional[int] = None)[source]¶
Cache that uses Upstash Redis as a backend.
Initialize an instance of UpstashRedisCache.
This method initializes an object with Upstash Redis caching capabilities.
It takes a redis_ parameter, which should be an instance of an Upstash Redis
client class, allowing the object to interact with Upstash Redis
server for caching purposes.
Parameters
redis – An instance of Upstash Redis client class
(e.g., Redis) used for caching.
This allows the object to communicate with
Redis server for caching operations on.
ttl (int, optional) – Time-to-live (TTL) for cached items in seconds.
If provided, it sets the time duration for how long cached
items will remain valid. If not provided, cached items will not
have an automatic expiration.
redis_ (Any) – 
Methods
__init__(redis_, *[, ttl])
Initialize an instance of UpstashRedisCache.
aclear(**kwargs)
Clear cache that can take additional keyword arguments.
alookup(prompt, llm_string)
Look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
clear(**kwargs)
Clear cache.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__(redis_: Any, *, ttl: Optional[int] = None)[source]¶
Initialize an instance of UpstashRedisCache.
This method initializes an object with Upstash Redis caching capabilities.
This method initializes an object with Upstash Redis caching capabilities.
It takes a redis_ parameter, which should be an instance of an Upstash Redis
client class, allowing the object to interact with Upstash Redis
server for caching purposes.
Parameters
redis – An instance of Upstash Redis client class
(e.g., Redis) used for caching.
This allows the object to communicate with
Redis server for caching operations on.
ttl (int, optional) – Time-to-live (TTL) for cached items in seconds.
If provided, it sets the time duration for how long cached
items will remain valid. If not provided, cached items will not
have an automatic expiration.
redis_ (Any) – 
async aclear(**kwargs: Any) → None¶
Clear cache that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶
Look up based on prompt and llm_string.
A cache implementation is expected to generate a key from the 2-tuple
of prompt and llm_string (e.g., by concatenating them with a delimiter).
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
Returns
On a cache miss, return None. On a cache hit, return the cached value.
The cached value is a list of Generations (or subclasses).
Return type
Optional[Sequence[Generation]]
Return type
Optional[Sequence[Generation]]
async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶
Update cache based on prompt and llm_string.
The prompt and llm_string are used to generate a key for the cache.
The key should match that of the look up method.
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations
(or subclasses).
Return type
None
clear(**kwargs: Any) → None[source]¶
Clear cache. If asynchronous is True, flush asynchronously.
This flushes the whole db.
Parameters
kwargs (Any) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
Optional[Sequence[Generation]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
Update cache based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
None
Examples using UpstashRedisCache¶
Model caches
Upstash Vector
langchain_elasticsearch.cache.ElasticsearchCache¶
class langchain_elasticsearch.cache.ElasticsearchCache(index_name: str, store_input: bool = True, store_input_params: bool = True, metadata: Optional[Dict[str, Any]] = None, *, es_url: Optional[str] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_api_key: Optional[str] = None, es_password: Optional[str] = None, es_params: Optional[Dict[str, Any]] = None)[source]¶
An Elasticsearch cache integration for LLMs.
Initialize the Elasticsearch cache store by specifying the index/alias
to use and determining which additional information (like input, input
parameters, and any other metadata) should be stored in the cache.
Parameters
index_name (str) – The name of the index or the alias to use for the cache.
If they do not exist an index is created,
according to the default mapping defined by the mapping property.
store_input (bool) – Whether to store the LLM input in the cache, i.e.,
the input prompt. Default to True.
store_input_params (bool) – Whether to store the input parameters in the
cache, i.e., the LLM parameters used to generate the LLM response.
Default to True.
metadata (Optional[dict]) – Additional metadata to store in the cache,
for filtering purposes. This must be JSON serializable in an
Elasticsearch document. Default to None.
es_url (Optional[str]) – URL of the Elasticsearch instance to connect to.
es_cloud_id (Optional[str]) – Cloud ID of the Elasticsearch instance to connect to.
es_user (Optional[str]) – Username to use when connecting to Elasticsearch.
es_password (Optional[str]) – Password to use when connecting to Elasticsearch.
es_password (Optional[str]) – Password to use when connecting to Elasticsearch.
es_api_key (Optional[str]) – API key to use when connecting to Elasticsearch.
es_params (Optional[Dict[str, Any]]) – Other parameters for the Elasticsearch client.
Attributes
mapping
Get the default mapping for the index.
Methods
__init__(index_name[, store_input, ...])
Initialize the Elasticsearch cache store by specifying the index/alias to use and determining which additional information (like input, input parameters, and any other metadata) should be stored in the cache.
aclear(**kwargs)
Clear cache that can take additional keyword arguments.
alookup(prompt, llm_string)
Look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
build_document(prompt, llm_string, return_val)
Build the Elasticsearch document for storing a single LLM interaction
clear(**kwargs)
Clear cache.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update based on prompt and llm_string.
__init__(index_name: str, store_input: bool = True, store_input_params: bool = True, metadata: Optional[Dict[str, Any]] = None, *, es_url: Optional[str] = None, es_cloud_id: Optional[str] = None, es_user: Optional[str] = None, es_api_key: Optional[str] = None, es_password: Optional[str] = None, es_params: Optional[Dict[str, Any]] = None)[source]¶
Initialize the Elasticsearch cache store by specifying the index/alias
to use and determining which additional information (like input, input
to use and determining which additional information (like input, input
parameters, and any other metadata) should be stored in the cache.
Parameters
index_name (str) – The name of the index or the alias to use for the cache.
If they do not exist an index is created,
according to the default mapping defined by the mapping property.
store_input (bool) – Whether to store the LLM input in the cache, i.e.,
the input prompt. Default to True.
store_input_params (bool) – Whether to store the input parameters in the
cache, i.e., the LLM parameters used to generate the LLM response.
Default to True.
metadata (Optional[dict]) – Additional metadata to store in the cache,
for filtering purposes. This must be JSON serializable in an
Elasticsearch document. Default to None.
es_url (Optional[str]) – URL of the Elasticsearch instance to connect to.
es_cloud_id (Optional[str]) – Cloud ID of the Elasticsearch instance to connect to.
es_user (Optional[str]) – Username to use when connecting to Elasticsearch.
es_password (Optional[str]) – Password to use when connecting to Elasticsearch.
es_api_key (Optional[str]) – API key to use when connecting to Elasticsearch.
es_params (Optional[Dict[str, Any]]) – Other parameters for the Elasticsearch client.
async aclear(**kwargs: Any) → None¶
Clear cache that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶
Look up based on prompt and llm_string.
A cache implementation is expected to generate a key from the 2-tuple
of prompt and llm_string (e.g., by concatenating them with a delimiter).
Parameters
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
Returns
On a cache miss, return None. On a cache hit, return the cached value.
The cached value is a list of Generations (or subclasses).
Return type
Optional[Sequence[Generation]]
async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶
Update cache based on prompt and llm_string.
The prompt and llm_string are used to generate a key for the cache.
The key should match that of the look up method.
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations
(or subclasses).
Return type
None
build_document(prompt: str, llm_string: str, return_val: Sequence[Generation]) → Dict[str, Any][source]¶
Build the Elasticsearch document for storing a single LLM interaction
Parameters
prompt (str) – 
llm_string (str) –
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
Dict[str, Any]
clear(**kwargs: Any) → None[source]¶
Clear cache.
Parameters
kwargs (Any) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
Optional[Sequence[Generation]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
Update based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
None
langchain_community.cache.OpenSearchSemanticCache¶
class langchain_community.cache.OpenSearchSemanticCache(opensearch_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]¶
Cache that uses OpenSearch vector store backend
Parameters
opensearch_url (str) – URL to connect to OpenSearch.
embedding (Embedding) – Embedding provider for semantic encoding and search.
score_threshold (float, 0.2) – 
Example:
.. code-block:: python
import langchain
from langchain.cache import OpenSearchSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = OpenSearchSemanticCache(
opensearch_url=”http//localhost:9200”,
embedding=OpenAIEmbeddings()
)
Methods
__init__(opensearch_url, embedding[, ...])
param opensearch_url
URL to connect to OpenSearch.
aclear(**kwargs)
Clear cache that can take additional keyword arguments.
alookup(prompt, llm_string)
Look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
clear(**kwargs)
Clear semantic cache for a given llm_string.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__(opensearch_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]¶
Parameters
opensearch_url (str) – URL to connect to OpenSearch.
embedding (Embedding) – Embedding provider for semantic encoding and search.
score_threshold (float, 0.2) –
score_threshold (float, 0.2) – 
Example:
.. code-block:: python
import langchain
from langchain.cache import OpenSearchSemanticCache
from langchain.embeddings import OpenAIEmbeddings
langchain.llm_cache = OpenSearchSemanticCache(
opensearch_url=”http//localhost:9200”,
embedding=OpenAIEmbeddings()
)
async aclear(**kwargs: Any) → None¶
Clear cache that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]¶
Look up based on prompt and llm_string.
A cache implementation is expected to generate a key from the 2-tuple
of prompt and llm_string (e.g., by concatenating them with a delimiter).
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
Returns
On a cache miss, return None. On a cache hit, return the cached value.
The cached value is a list of Generations (or subclasses).
Return type
Optional[Sequence[Generation]]
async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None¶
Update cache based on prompt and llm_string.
The prompt and llm_string are used to generate a key for the cache.
The key should match that of the look up method.
Parameters
The key should match that of the look up method.
Parameters
prompt (str) – a string representation of the prompt.
In the case of a Chat model, the prompt is a non-trivial
serialization of the prompt into the language model.
llm_string (str) – A string representation of the LLM configuration.
This is used to capture the invocation parameters of the LLM
(e.g., model name, temperature, stop tokens, max tokens, etc.).
These invocation parameters are serialized into a string
representation.
return_val (Sequence[Generation]) – The value to be cached. The value is a list of Generations
(or subclasses).
Return type
None
clear(**kwargs: Any) → None[source]¶
Clear semantic cache for a given llm_string.
Parameters
kwargs (Any) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
Optional[Sequence[Generation]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
Update cache based on prompt and llm_string.
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
None
Examples using OpenSearchSemanticCache¶
Model caches
langchain_community.cache.AstraDBCache¶
class langchain_community.cache.AstraDBCache(*, collection_name: str = 'langchain_astradb_cache', token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, pre_delete_collection: bool = False, setup_mode: AstraSetupMode = SetupMode.SYNC)[source]¶
[Deprecated]
Notes
Deprecated since version 0.0.28.
Cache that uses Astra DB as a backend.
It uses a single collection as a kv store
The lookup keys, combined in the _id of the documents, are:
prompt, a string
llm_string, a deterministic str representation of the model parameters.
(needed to prevent same-prompt-different-model collisions)
Parameters
collection_name (str) – name of the Astra DB collection to create/use.
token (Optional[str]) – API token for Astra DB usage.
api_endpoint (Optional[str]) – full URL to the API endpoint,
such as https://<DB-ID>-us-east1.apps.astra.datastax.com.
astra_db_client (Optional[AstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AstraDB’ instance.
async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.
namespace (Optional[str]) – namespace (aka keyspace) where the
collection is created. Defaults to the database’s “default namespace”.
setup_mode (AstraSetupMode) – mode used to create the Astra DB collection (SYNC, ASYNC or
OFF).
pre_delete_collection (bool) – whether to delete the collection
before creating it. If False and the collection already exists,
the collection will be used as is.
Methods
__init__(*[, collection_name, token, ...])
Cache that uses Astra DB as a backend.
aclear(**kwargs)
Clear cache that can take additional keyword arguments.
adelete(prompt, llm_string)
Evict from cache if there's an entry.
adelete_through_llm(prompt, llm[, stop])
A wrapper around adelete with the LLM being passed.
alookup(prompt, llm_string)
Look up based on prompt and llm_string.
aupdate(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
clear(**kwargs)
Clear cache that can take additional keyword arguments.
delete(prompt, llm_string)
Evict from cache if there's an entry.
delete_through_llm(prompt, llm[, stop])
A wrapper around delete with the LLM being passed.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__(*, collection_name: str = 'langchain_astradb_cache', token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, pre_delete_collection: bool = False, setup_mode: AstraSetupMode = SetupMode.SYNC)[source]¶
Cache that uses Astra DB as a backend.
It uses a single collection as a kv store
It uses a single collection as a kv store
The lookup keys, combined in the _id of the documents, are:
prompt, a string
llm_string, a deterministic str representation of the model parameters.
(needed to prevent same-prompt-different-model collisions)
Parameters
collection_name (str) – name of the Astra DB collection to create/use.
token (Optional[str]) – API token for Astra DB usage.
api_endpoint (Optional[str]) – full URL to the API endpoint,
such as https://<DB-ID>-us-east1.apps.astra.datastax.com.
astra_db_client (Optional[AstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AstraDB’ instance.
async_astra_db_client (Optional[AsyncAstraDB]) – alternative to token+api_endpoint,
you can pass an already-created ‘astrapy.db.AsyncAstraDB’ instance.
namespace (Optional[str]) – namespace (aka keyspace) where the
collection is created. Defaults to the database’s “default namespace”.
setup_mode (AstraSetupMode) – mode used to create the Astra DB collection (SYNC, ASYNC or
OFF).
pre_delete_collection (bool) – whether to delete the collection
before creating it. If False and the collection already exists,
the collection will be used as is.
async aclear(**kwargs: Any) → None[source]¶
Clear cache that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
async adelete(prompt: str, llm_string: str) → None[source]¶
Evict from cache if there’s an entry.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
None