Module vectorstore (0.8.0)
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API documentation for vectorstore
module.
Classes
PostgresVectorStore(
key: object,
engine: langchain_google_cloud_sql_pg.engine.PostgresEngine,
embedding_service: langchain_core.embeddings.embeddings.Embeddings,
table_name: str,
content_column: str = "content",
embedding_column: str = "embedding",
metadata_columns: typing.List[str] = [],
id_column: str = "langchain_id",
metadata_json_column: typing.Optional[str] = "langchain_metadata",
distance_strategy: langchain_google_cloud_sql_pg.indexes.DistanceStrategy = DistanceStrategy.COSINE_DISTANCE,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
index_query_options: typing.Optional[
langchain_google_cloud_sql_pg.indexes.QueryOptions
] = None,
)
Google Cloud SQL for PostgreSQL Vector Store class
Modules
Functions
cosine_similarity
cosine_similarity(
X: typing.Union[
typing.List[typing.List[float]], typing.List[numpy.ndarray], numpy.ndarray
],
Y: typing.Union[
typing.List[typing.List[float]], typing.List[numpy.ndarray], numpy.ndarray
],
) -> numpy.ndarray
Row-wise cosine similarity between two equal-width matrices.
maximal_marginal_relevance
maximal_marginal_relevance(
query_embedding: numpy.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> typing.List[int]
Calculate maximal marginal relevance.
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Last updated 2024-09-24 UTC.
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{"lastModified": "Last updated 2024-09-24 UTC."}
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