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# `langchain_community.cache`.SQLiteCache

class langchain_community.cache.SQLiteCache(database_path: str = '.langchain.db')

  Cache that uses SQLite as a backend.

  Initialize by creating the engine and all tables.

  Methods

  `__init__`([database_path]) | Initialize by creating the engine and 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

    database_path (str) –

  __init__(database_path: str = '.langchain.db')

    Initialize by creating the engine and all tables.

    Parameters

      database_path (str) –

  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

      - 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

    Clear cache.

    Parameters

      kwargs (Any) –

    Return type

      None

  lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]]

    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

    Update based on prompt and llm_string.

    Parameters

      - prompt (str) –

      - llm_string (str) –

      - return_val (Sequence[Generation]) –

    Return type

      None

# Examples using SQLiteCache

- <!– ruff: noqa: F821 –>

- From LangChain, import standard modules for prompting.

- To make the caching really obvious, lets use a slower model.