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community[patch]: Improve Cassandra VectorStore docsctrings (#21620)

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pull/18427/head^2
Christophe Bornet 4 weeks ago committed by GitHub
parent 40aff1eacc
commit 66a4da8ad0
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1 changed files with 133 additions and 33 deletions
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166
libs/community/langchain_community/vectorstores/cassandra.py
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@ -33,35 +33,6 @@ CVST = TypeVar("CVST", bound="Cassandra")
class Cassandra(VectorStore):
"""Apache Cassandra(R) for vector-store workloads.
To use it, you need a recent installation of the `cassio` library
and a Cassandra cluster / Astra DB instance supporting vector capabilities.
Visit the cassio.org website for extensive quickstarts and code examples.
Example:
.. code-block:: python
from langchain_community.vectorstores import Cassandra
from langchain_community.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
session = ... # create your Cassandra session object
keyspace = 'my_keyspace' # the keyspace should exist already
table_name = 'my_vector_store'
vectorstore = Cassandra(embeddings, session, keyspace, table_name)
Args:
embedding: Embedding function to use.
session: Cassandra driver session. If not provided, it is resolved from cassio.
keyspace: Cassandra key space. If not provided, it is resolved from cassio.
table_name: Cassandra table (required).
ttl_seconds: Optional time-to-live for the added texts.
body_index_options: Optional options used to create the body index.
Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
"""
_embedding_dimension: Union[int, None]
def _get_embedding_dimension(self) -> int:
@ -89,6 +60,37 @@ class Cassandra(VectorStore):
body_index_options: Optional[List[Tuple[str, Any]]] = None,
setup_mode: SetupMode = SetupMode.SYNC,
) -> None:
"""Apache Cassandra(R) for vector-store workloads.
To use it, you need a recent installation of the `cassio` library
and a Cassandra cluster / Astra DB instance supporting vector capabilities.
Visit the cassio.org website for extensive quickstarts and code examples.
Example:
.. code-block:: python
from langchain_community.vectorstores import Cassandra
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
session = ... # create your Cassandra session object
keyspace = 'my_keyspace' # the keyspace should exist already
table_name = 'my_vector_store'
vectorstore = Cassandra(embeddings, session, keyspace, table_name)
Args:
embedding: Embedding function to use.
session: Cassandra driver session. If not provided, it is resolved from
cassio.
keyspace: Cassandra key space. If not provided, it is resolved from cassio.
table_name: Cassandra table (required).
ttl_seconds: Optional time-to-live for the added texts.
body_index_options: Optional options used to create the body index.
Eg. body_index_options = [cassio.table.cql.STANDARD_ANALYZER]
setup_mode: mode used to create the Cassandra table (SYNC,
ASYNC or OFF).
"""
try:
from cassio.table import MetadataVectorCassandraTable
except (ImportError, ModuleNotFoundError):
@ -164,9 +166,19 @@ class Cassandra(VectorStore):
await self.table.aclear()
def delete_by_document_id(self, document_id: str) -> None:
"""Delete by document ID.
Args:
document_id: the document ID to delete.
"""
return self.table.delete(row_id=document_id)
async def adelete_by_document_id(self, document_id: str) -> None:
"""Delete by document ID.
Args:
document_id: the document ID to delete.
"""
return await self.table.adelete(row_id=document_id)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
@ -370,8 +382,8 @@ class Cassandra(VectorStore):
"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
@ -399,6 +411,17 @@ class Cassandra(VectorStore):
filter: Optional[Dict[str, str]] = None,
body_search: Optional[Union[str, List[str]]] = None,
) -> List[Tuple[Document, float, str]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of (Document, score, id), the most similar to the query vector.
"""
embedding_vector = self.embedding.embed_query(query)
return self.similarity_search_with_score_id_by_vector(
embedding=embedding_vector,
@ -414,6 +437,17 @@ class Cassandra(VectorStore):
filter: Optional[Dict[str, str]] = None,
body_search: Optional[Union[str, List[str]]] = None,
) -> List[Tuple[Document, float, str]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of (Document, score, id), the most similar to the query vector.
"""
embedding_vector = await self.embedding.aembed_query(query)
return await self.asimilarity_search_with_score_id_by_vector(
embedding=embedding_vector,
@ -461,8 +495,8 @@ class Cassandra(VectorStore):
"""Return docs most similar to embedding vector.
Args:
embedding (str): Embedding to look up documents similar to.
k (int): Number of Documents to return. Defaults to 4.
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
@ -491,6 +525,17 @@ class Cassandra(VectorStore):
body_search: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of Document, the most similar to the query vector.
"""
embedding_vector = self.embedding.embed_query(query)
return self.similarity_search_by_vector(
embedding_vector,
@ -507,6 +552,17 @@ class Cassandra(VectorStore):
body_search: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of Document, the most similar to the query vector.
"""
embedding_vector = await self.embedding.aembed_query(query)
return await self.asimilarity_search_by_vector(
embedding_vector,
@ -523,6 +579,17 @@ class Cassandra(VectorStore):
body_search: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of Document, the most similar to the query vector.
"""
return [
doc
for doc, _ in self.similarity_search_with_score_by_vector(
@ -541,6 +608,17 @@ class Cassandra(VectorStore):
body_search: Optional[Union[str, List[str]]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of Document, the most similar to the query vector.
"""
return [
doc
for doc, _ in await self.asimilarity_search_with_score_by_vector(
@ -558,6 +636,17 @@ class Cassandra(VectorStore):
filter: Optional[Dict[str, str]] = None,
body_search: Optional[Union[str, List[str]]] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of (Document, score), the most similar to the query vector.
"""
embedding_vector = self.embedding.embed_query(query)
return self.similarity_search_with_score_by_vector(
embedding_vector,
@ -573,6 +662,17 @@ class Cassandra(VectorStore):
filter: Optional[Dict[str, str]] = None,
body_search: Optional[Union[str, List[str]]] = None,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter on the metadata to apply.
body_search: Document textual search terms to apply.
Only supported by Astra DB at the moment.
Returns:
List of (Document, score), the most similar to the query vector.
"""
embedding_vector = await self.embedding.aembed_query(query)
return await self.asimilarity_search_with_score_by_vector(
embedding_vector,

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