MongoDBCache#

class langchain_mongodb.cache.MongoDBCache(connection_string: str, collection_name: str = 'default', database_name: str = 'default', **kwargs: Dict[str, Any])[source]#

MongoDB Atlas cache

A cache that uses MongoDB Atlas as a backend

Initialize Atlas Cache. Creates collection on instantiation

Parameters:

  • collection_name (str) – Name of collection for cache to live. Defaults to “default”.

  • connection_string (str) – Connection URI to MongoDB Atlas. Defaults to “default”.

  • database_name (str) – Name of database for cache to live. Defaults to “default”.

  • kwargs (Dict[str, Any]) –

Attributes

LLM

PROMPT

RETURN_VAL

collection

Returns the collection used to store cache values.

database

Returns the database used to store cache values.

Methods

__init__(connection_string[, ...])

Initialize Atlas Cache.

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 cache that can take additional keyword arguments.

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__(connection_string: str, collection_name: str = 'default', database_name: str = 'default', **kwargs: Dict[str, Any]) → None[source]#

Initialize Atlas Cache. Creates collection on instantiation

Parameters:

  • collection_name (str) – Name of collection for cache to live. Defaults to “default”.

  • connection_string (str) – Connection URI to MongoDB Atlas. Defaults to “default”.

  • database_name (str) – Name of database for cache to live. Defaults to “default”.

  • kwargs (Dict[str, Any]) –

Return type:

None

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 cache that can take additional keyword arguments. Any additional arguments will propagate as filtration criteria for what gets deleted.

E.g.

# Delete only entries that have llm_string as “fake-model” self.clear(llm_string=”fake-model”)

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

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