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community[patch]: Fix AstraDBCache docstrings (#17802)
This commit is contained in:
Christophe Bornet
GitHub
parent
865cabff05
commit
b13e52b6ac
@ -1366,16 +1366,6 @@ ASTRA_DB_CACHE_DEFAULT_COLLECTION_NAME = "langchain_astradb_cache"
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class AstraDBCache(BaseCache):
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"""
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Cache that uses Astra DB as a backend.
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It uses a single collection as a kv store
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The lookup keys, combined in the _id of the documents, are:
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- prompt, a string
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- llm_string, a deterministic str representation of the model parameters.
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(needed to prevent same-prompt-different-model collisions)
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"""
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@staticmethod
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def _make_id(prompt: str, llm_string: str) -> str:
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return f"{_hash(prompt)}#{_hash(llm_string)}"
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@ -1393,25 +1383,30 @@ class AstraDBCache(BaseCache):
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setup_mode: SetupMode = SetupMode.SYNC,
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):
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"""
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Create an AstraDB cache using a collection for storage.
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Cache that uses Astra DB as a backend.
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Args (only keyword-arguments accepted):
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collection_name (str): name of the Astra DB collection to create/use.
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token (Optional[str]): API token for Astra DB usage.
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api_endpoint (Optional[str]): full URL to the API endpoint,
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such as "https://<DB-ID>-us-east1.apps.astra.datastax.com".
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astra_db_client (Optional[AstraDB]):
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*alternative to token+api_endpoint*,
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It uses a single collection as a kv store
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The lookup keys, combined in the _id of the documents, are:
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- prompt, a string
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- llm_string, a deterministic str representation of the model parameters.
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(needed to prevent same-prompt-different-model collisions)
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Args:
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collection_name: name of the Astra DB collection to create/use.
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token: API token for Astra DB usage.
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api_endpoint: full URL to the API endpoint,
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such as `https://<DB-ID>-us-east1.apps.astra.datastax.com`.
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astra_db_client: *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AstraDB' instance.
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async_astra_db_client (Optional[AsyncAstraDB]):
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*alternative to token+api_endpoint*,
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async_astra_db_client: *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AsyncAstraDB' instance.
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namespace (Optional[str]): namespace (aka keyspace) where the
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namespace: namespace (aka keyspace) where the
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collection is created. Defaults to the database's "default namespace".
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pre_delete_collection (bool): whether to delete and re-create the
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collection. Defaults to False.
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async_setup (bool): whether to create the collection asynchronously.
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Enable only if there is a running asyncio event loop. Defaults to False.
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setup_mode: mode used to create the Astra DB collection (SYNC, ASYNC or
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OFF).
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pre_delete_collection: whether to delete the collection
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before creating it. If False and the collection already exists,
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the collection will be used as is.
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"""
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self.astra_env = _AstraDBCollectionEnvironment(
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collection_name=collection_name,
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@ -1427,7 +1422,6 @@ class AstraDBCache(BaseCache):
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self.async_collection = self.astra_env.async_collection
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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self.astra_env.ensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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item = self.collection.find_one(
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@ -1441,7 +1435,6 @@ class AstraDBCache(BaseCache):
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return _loads_generations(item["body_blob"]) if item is not None else None
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async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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await self.astra_env.aensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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item = (
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@ -1457,7 +1450,6 @@ class AstraDBCache(BaseCache):
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return _loads_generations(item["body_blob"]) if item is not None else None
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache based on prompt and llm_string."""
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self.astra_env.ensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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blob = _dumps_generations(return_val)
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@ -1471,7 +1463,6 @@ class AstraDBCache(BaseCache):
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async def aupdate(
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self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
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) -> None:
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"""Update cache based on prompt and llm_string."""
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await self.astra_env.aensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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blob = _dumps_generations(return_val)
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@ -1523,12 +1514,10 @@ class AstraDBCache(BaseCache):
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await self.async_collection.delete_one(doc_id)
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def clear(self, **kwargs: Any) -> None:
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"""Clear cache. This is for all LLMs at once."""
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self.astra_env.ensure_db_setup()
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self.collection.clear()
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async def aclear(self, **kwargs: Any) -> None:
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"""Clear cache. This is for all LLMs at once."""
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await self.astra_env.aensure_db_setup()
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await self.async_collection.clear()
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@ -1575,18 +1564,6 @@ def _async_lru_cache(maxsize: int = 128, typed: bool = False) -> Callable:
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class AstraDBSemanticCache(BaseCache):
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"""
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Cache that uses Astra DB as a vector-store backend for semantic
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(i.e. similarity-based) lookup.
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It uses a single (vector) collection and can store
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cached values from several LLMs, so the LLM's 'llm_string' is stored
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in the document metadata.
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You can choose the preferred similarity (or use the API default) --
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remember the threshold might require metric-dependent tuning.
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"""
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def __init__(
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self,
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*,
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@ -1603,33 +1580,38 @@ class AstraDBSemanticCache(BaseCache):
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similarity_threshold: float = ASTRA_DB_SEMANTIC_CACHE_DEFAULT_THRESHOLD,
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):
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"""
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Initialize the cache with all relevant parameters.
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Args:
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Cache that uses Astra DB as a vector-store backend for semantic
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(i.e. similarity-based) lookup.
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collection_name (str): name of the Astra DB collection to create/use.
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token (Optional[str]): API token for Astra DB usage.
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api_endpoint (Optional[str]): full URL to the API endpoint,
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such as "https://<DB-ID>-us-east1.apps.astra.datastax.com".
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astra_db_client (Optional[AstraDB]): *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AstraDB' instance.
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async_astra_db_client (Optional[AsyncAstraDB]):
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*alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AsyncAstraDB' instance.
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namespace (Optional[str]): namespace (aka keyspace) where the
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collection is created. Defaults to the database's "default namespace".
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setup_mode (SetupMode): mode used to create the collection in the DB
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(SYNC, ASYNC or OFF). Defaults to SYNC.
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pre_delete_collection (bool): whether to delete and re-create the
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collection. Defaults to False.
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embedding (Embedding): Embedding provider for semantic
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encoding and search.
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metric: the function to use for evaluating similarity of text embeddings.
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Defaults to 'cosine' (alternatives: 'euclidean', 'dot_product')
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similarity_threshold (float, optional): the minimum similarity
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for accepting a (semantic-search) match.
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It uses a single (vector) collection and can store
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cached values from several LLMs, so the LLM's 'llm_string' is stored
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in the document metadata.
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You can choose the preferred similarity (or use the API default).
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The default score threshold is tuned to the default metric.
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Tune it carefully yourself if switching to another distance metric.
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Args:
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collection_name: name of the Astra DB collection to create/use.
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token: API token for Astra DB usage.
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api_endpoint: full URL to the API endpoint,
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such as `https://<DB-ID>-us-east1.apps.astra.datastax.com`.
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astra_db_client: *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AstraDB' instance.
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async_astra_db_client: *alternative to token+api_endpoint*,
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you can pass an already-created 'astrapy.db.AsyncAstraDB' instance.
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namespace: namespace (aka keyspace) where the
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collection is created. Defaults to the database's "default namespace".
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setup_mode: mode used to create the Astra DB collection (SYNC, ASYNC or
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OFF).
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pre_delete_collection: whether to delete the collection
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before creating it. If False and the collection already exists,
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the collection will be used as is.
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embedding: Embedding provider for semantic encoding and search.
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metric: the function to use for evaluating similarity of text embeddings.
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Defaults to 'cosine' (alternatives: 'euclidean', 'dot_product')
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similarity_threshold: the minimum similarity for accepting a
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(semantic-search) match.
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"""
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self.embedding = embedding
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self.metric = metric
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@ -1685,7 +1667,6 @@ class AstraDBSemanticCache(BaseCache):
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return f"{_hash(prompt)}#{_hash(llm_string)}"
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def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
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"""Update cache based on prompt and llm_string."""
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self.astra_env.ensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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llm_string_hash = _hash(llm_string)
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@ -1704,7 +1685,6 @@ class AstraDBSemanticCache(BaseCache):
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async def aupdate(
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self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE
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) -> None:
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"""Update cache based on prompt and llm_string."""
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await self.astra_env.aensure_db_setup()
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doc_id = self._make_id(prompt, llm_string)
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llm_string_hash = _hash(llm_string)
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@ -1721,7 +1701,6 @@ class AstraDBSemanticCache(BaseCache):
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)
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def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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hit_with_id = self.lookup_with_id(prompt, llm_string)
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if hit_with_id is not None:
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return hit_with_id[1]
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@ -1729,7 +1708,6 @@ class AstraDBSemanticCache(BaseCache):
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return None
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async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
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"""Look up based on prompt and llm_string."""
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hit_with_id = await self.alookup_with_id(prompt, llm_string)
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if hit_with_id is not None:
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return hit_with_id[1]
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@ -1835,11 +1813,9 @@ class AstraDBSemanticCache(BaseCache):
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await self.async_collection.delete_one(document_id)
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def clear(self, **kwargs: Any) -> None:
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"""Clear the *whole* semantic cache."""
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self.astra_env.ensure_db_setup()
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self.collection.clear()
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async def aclear(self, **kwargs: Any) -> None:
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"""Clear the *whole* semantic cache."""
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await self.astra_env.aensure_db_setup()
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await self.async_collection.clear()
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