CouchbaseCache#

class langchain_couchbase.cache.CouchbaseCache(cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, **kwargs: Dict[str, Any])[source]#

Couchbase LLM Cache LLM Cache that uses Couchbase as the backend

Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store

documents in.

Parameters:

  • cluster (Cluster) –

  • bucket_name (str) –

  • scope_name (str) –

  • collection_name (str) –

  • kwargs (Dict[str, Any]) –

Attributes

LLM

PROMPT

RETURN_VAL

Methods

__init__(cluster, bucket_name, scope_name, ...)

Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store documents in. :type collection_name: str.

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 the cache.

lookup(prompt, llm_string)

Look up from cache based on prompt and llm_string.

update(prompt, llm_string, return_val)

Update cache based on prompt and llm_string.
__init__(cluster: Cluster, bucket_name: str, scope_name: str, collection_name: str, **kwargs: Dict[str, Any]) → None[source]#

Initialize the Couchbase LLM Cache :param cluster: couchbase cluster object with active connection. :type cluster: Cluster :param bucket_name: name of the bucket to store documents in. :type bucket_name: str :param scope_name: name of the scope in bucket to store documents in. :type scope_name: str :param collection_name: name of the collection in the scope to store

documents in.

Parameters:

  • cluster (Cluster) –

  • bucket_name (str) –

  • scope_name (str) –

  • collection_name (str) –

  • 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 the cache. This will delete all documents in the collection. This requires an index on the collection.

Parameters:

kwargs (Any) –

Return type:

None

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

Look up from cache 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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