InMemorySemanticCache#

class langchain_aws.vectorstores.inmemorydb.cache.InMemorySemanticCache(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]#

Cache that uses MemoryDB as a vector-store backend.

Initialize by passing in the init GPTCache func

Parameters:

  • redis_url (str) – URL to connect to MemoryDB.

  • embedding (Embedding) – Embedding provider for semantic encoding and search.

  • score_threshold (float, 0.2)

Example:

from langchain_core.globals import set_llm_cache

from langchain_aws.cache import InMemorySemanticCache

set_llm_cache(InMemorySemanticCache(
    redis_url="redis://localhost:6379",
    embedding=OpenAIEmbeddings()
))

Attributes

DEFAULT_SCHEMA

Methods

__init__(redis_url, embedding[, score_threshold])

Initialize by passing in the init GPTCache func

aclear(**kwargs)

Async clear cache that can take additional keyword arguments.

alookup(prompt, llm_string)

Async look up based on prompt and llm_string.

aupdate(prompt, llm_string, return_val)

Async 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__(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]#

Initialize by passing in the init GPTCache func

Parameters:

  • redis_url (str) – URL to connect to MemoryDB.

  • embedding (Embedding) – Embedding provider for semantic encoding and search.

  • score_threshold (float, 0.2)

Example:

from langchain_core.globals import set_llm_cache

from langchain_aws.cache import InMemorySemanticCache

set_llm_cache(InMemorySemanticCache(
    redis_url="redis://localhost:6379",
    embedding=OpenAIEmbeddings()
))
async aclear(**kwargs: Any) → None#

Async clear cache that can take additional keyword arguments.

Parameters:

kwargs (Any)

Return type:

None

async alookup(prompt: str, llm_string: str) → Sequence[Generation] | None#

Async 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:

Sequence[Generation] | None

async aupdate(prompt: str, llm_string: str, return_val: Sequence[Generation]) → None#

Async 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 semantic cache for a given llm_string.

Parameters:

kwargs (Any)

Return type:

None

lookup(prompt: str, llm_string: str) → Sequence[Generation] | None[source]#

Look up based on prompt and llm_string.

Parameters:

  • prompt (str)

  • llm_string (str)

Return type:

Sequence[Generation] | None

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