Tony20100/langchain_full huggingface dataset:
langchain_community.cache.CassandraCache¶
class langchain_community.cache.CassandraCache(session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = 'langchain_llm_cache', ttl_seconds: Optional[int] = None, skip_provisioning: bool = False, setup_mode: CassandraSetupMode = SetupMode.SYNC)[source]¶
Cache that uses Cassandra / Astra DB as a backend.
Example
import cassio
from langchain_community.cache import CassandraCache
from langchain_core.globals import set_llm_cache
cassio.init(auto=True)  # Requires env. variables, see CassIO docs
set_llm_cache(CassandraCache())
It uses a single Cassandra table.
The lookup keys (which get to form the primary key) are:
prompt, a string
llm_string, a deterministic str representation of the model parameters.
(needed to prevent same-prompt-different-model collisions)
Parameters
session (Optional[CassandraSession]) – an open Cassandra session.
Leave unspecified to use the global cassio init (see below)
keyspace (Optional[str]) – the keyspace to use for storing the cache.
Leave unspecified to use the global cassio init (see below)
table_name (str) – name of the Cassandra table to use as cache
ttl_seconds (Optional[int]) – time-to-live for cache entries
(default: None, i.e. forever)
setup_mode (CassandraSetupMode) – a value in langchain_community.utilities.cassandra.SetupMode.
Choose between SYNC, ASYNC and OFF - the latter if the Cassandra
table is guaranteed to exist already, for a faster initialization.
skip_provisioning (bool) – 
Note
The session and keyspace parameters, when left out (or passed as None),
fall back to the globally-available cassio settings if any are available.
fall back to the globally-available cassio settings if any are available.
In other words, if a previously-run ‘cassio.init(…)’ has been
executed previously anywhere in the code, Cassandra-based objects
need not specify the connection parameters at all.
Methods
__init__([session, keyspace, table_name, ...])
aclear(**kwargs)
Clear cache.
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.
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__(session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = 'langchain_llm_cache', ttl_seconds: Optional[int] = None, skip_provisioning: bool = False, setup_mode: CassandraSetupMode = SetupMode.SYNC)[source]¶
Parameters
session (Optional[CassandraSession]) – 
keyspace (Optional[str]) – 
table_name (str) – 
ttl_seconds (Optional[int]) – 
skip_provisioning (bool) – 
setup_mode (CassandraSetupMode) – 
async aclear(**kwargs: Any) → None[source]¶
Clear cache. This is for all LLMs at once.
Parameters
kwargs (Any) – 
Return type
Parameters
kwargs (Any) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
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[source]¶
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.
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. This is for all LLMs at once.
Parameters
kwargs (Any) – 
Return type
None
delete(prompt: str, llm_string: str) → None[source]¶
Evict from cache if there’s an entry.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
None
delete_through_llm(prompt: str, llm: LLM, stop: Optional[List[str]] = None) → None[source]¶
A wrapper around delete with the LLM being passed.
In case the llm.invoke(prompt) calls have a stop param, you should
pass it here
Parameters
prompt (str) – 
llm (LLM) – 
stop (Optional[List[str]]) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
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
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]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
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
Examples using CassandraCache¶
Cassandra
Model caches
langchain_community.cache.GPTCache¶
class langchain_community.cache.GPTCache(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)[source]¶
Cache that uses GPTCache as a backend.
Initialize by passing in init function (default: None).
Parameters
init_func (Optional[Callable[[Any], None]]) – init GPTCache function
(default – None)
Example:
.. code-block:: python
# Initialize GPTCache with a custom init function
import gptcache
from gptcache.processor.pre import get_prompt
from gptcache.manager.factory import get_data_manager
from langchain_community.globals import set_llm_cache
# Avoid multiple caches using the same file,
causing different llm model caches to affect each other
def init_gptcache(cache_obj: gptcache.Cache, llm str):
cache_obj.init(pre_embedding_func=get_prompt,
data_manager=manager_factory(
manager=”map”,
data_dir=f”map_cache_{llm}”
),
)
set_llm_cache(GPTCache(init_gptcache))
Methods
__init__([init_func])
Initialize by passing in init function (default: None).
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 the cache data.
update(prompt, llm_string, return_val)
Update cache.
update(prompt, llm_string, return_val)
Update cache.
__init__(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)[source]¶
Initialize by passing in init function (default: None).
Parameters
init_func (Optional[Callable[[Any], None]]) – init GPTCache function
(default – None)
Example:
.. code-block:: python
# Initialize GPTCache with a custom init function
import gptcache
from gptcache.processor.pre import get_prompt
from gptcache.manager.factory import get_data_manager
from langchain_community.globals import set_llm_cache
# Avoid multiple caches using the same file,
causing different llm model caches to affect each other
def init_gptcache(cache_obj: gptcache.Cache, llm str):
cache_obj.init(pre_embedding_func=get_prompt,
data_manager=manager_factory(
manager=”map”,
data_dir=f”map_cache_{llm}”
),
)
set_llm_cache(GPTCache(init_gptcache))
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.
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
kwargs (Any) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up the cache data.
First, retrieve the corresponding cache object using the llm_string parameter,
and then retrieve the data from the cache based on the prompt.
Parameters
prompt (str) –
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.
First, retrieve the corresponding cache object using the llm_string parameter,
and then store the prompt and return_val in the cache object.
Parameters
prompt (str) – 
llm_string (str) – 
return_val (Sequence[Generation]) – 
Return type
None
Examples using GPTCache¶
Model caches
langchain_astradb.cache.AstraDBCache¶
class langchain_astradb.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: SetupMode = SetupMode.SYNC)[source]¶
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. If not provided, the environment
variable ASTRA_DB_APPLICATION_TOKEN is inspected.
api_endpoint (Optional[str]) – full URL to the API endpoint, such as
https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided,
the environment variable ASTRA_DB_API_ENDPOINT is inspected.
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.
If not provided, the environment variable ASTRA_DB_KEYSPACE is
If not provided, the environment variable ASTRA_DB_KEYSPACE is
inspected. Defaults to the database’s “default namespace”.
setup_mode (SetupMode) – 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.
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: SetupMode = SetupMode.SYNC)[source]¶
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. If not provided, the environment
variable ASTRA_DB_APPLICATION_TOKEN is inspected.
api_endpoint (Optional[str]) – full URL to the API endpoint, such as
https://<DB-ID>-us-east1.apps.astra.datastax.com. If not provided,
the environment variable ASTRA_DB_API_ENDPOINT is inspected.
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.
If not provided, the environment variable ASTRA_DB_KEYSPACE is
inspected. Defaults to the database’s “default namespace”.
inspected. Defaults to the database’s “default namespace”.
setup_mode (SetupMode) – 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
async adelete_through_llm(prompt: str, llm: LLM, stop: Optional[List[str]] = None) → None[source]¶
A wrapper around adelete with the LLM being passed.
In case the llm(prompt) calls have a stop param, you should pass it here
Parameters
prompt (str) – 
llm (LLM) – 
stop (Optional[List[str]]) – 
Return type
None
async alookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
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.
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[source]¶
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 that can take additional keyword arguments.
Parameters
kwargs (Any) – 
Return type
None
delete(prompt: str, llm_string: str) → None[source]¶
Evict from cache if there’s an entry.
Parameters
prompt (str) – 
llm_string (str) – 
Return type
None
llm_string (str) – 
Return type
None
delete_through_llm(prompt: str, llm: LLM, stop: Optional[List[str]] = None) → None[source]¶
A wrapper around delete with the LLM being passed.
In case the llm(prompt) calls have a stop param, you should pass it here
Parameters
prompt (str) – 
llm (LLM) – 
stop (Optional[List[str]]) – 
Return type
None
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
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]]
update(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None[source]¶
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.
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
langchain_community.cache.AstraDBSemanticCache¶
class langchain_community.cache.AstraDBSemanticCache(*, collection_name: str = 'langchain_astradb_semantic_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, setup_mode: AstraSetupMode = SetupMode.SYNC, pre_delete_collection: bool = False, embedding: Embeddings, metric: Optional[str] = None, similarity_threshold: float = 0.85)[source]¶
[Deprecated]
Notes
Deprecated since version 0.0.28.
Cache that uses Astra DB as a vector-store backend for semantic
(i.e. similarity-based) lookup.
It uses a single (vector) collection and can store
cached values from several LLMs, so the LLM’s ‘llm_string’ is stored
in the document metadata.
You can choose the preferred similarity (or use the API default).
The default score threshold is tuned to the default metric.
Tune it carefully yourself if switching to another distance metric.
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,