Package Methods (0.10.0)

Summary of entries of Methods for langchain-google-cloud-sql-pg.

langchain_google_cloud_sql_pg.engine._get_iam_principal_email

_get_iam_principal_email(credentials: google.auth.credentials.Credentials) -> str

Get email address associated with current authenticated IAM principal.

See more: langchain_google_cloud_sql_pg.engine._get_iam_principal_email

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

PostgresChatMessageHistory( key: object, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, history: langchain_google_cloud_sql_pg.async_chat_message_history.AsyncPostgresChatMessageHistory, )

PostgresChatMessageHistory constructor.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_message

aadd_message(message: langchain_core.messages.base.BaseMessage) -> None

Append the message to the record in PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_message

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_messages

aadd_messages( messages: typing.Sequence[langchain_core.messages.base.BaseMessage], ) -> None

Append a list of messages to the record in PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_messages

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aclear

aclear() -> None

Clear session memory from PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aclear

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.add_message

add_message(message: langchain_core.messages.base.BaseMessage) -> None

Append the message to the record in PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.add_message

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.add_messages

add_messages( messages: typing.Sequence[langchain_core.messages.base.BaseMessage], ) -> None

Append a list of messages to the record in PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.add_messages

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.clear

clear() -> None

Clear session memory from PostgreSQL.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.clear

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.create

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, schema_name: str = "public", ) -> langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

Create a new PostgresChatMessageHistory instance.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.create

langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.create_sync

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, session_id: str, table_name: str, schema_name: str = "public", ) -> langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory

Create a new PostgresChatMessageHistory instance.

See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.create_sync

langchain_google_cloud_sql_pg.engine.Column.__post_init__

__post_init__()

Check if initialization parameters are valid.

See more: langchain_google_cloud_sql_pg.engine.Column.post_init

langchain_google_cloud_sql_pg.engine.PostgresEngine

PostgresEngine( key: object, pool: sqlalchemy.ext.asyncio.engine.AsyncEngine, loop: typing.Optional[asyncio.events.AbstractEventLoop], thread: typing.Optional[threading.Thread], )

PostgresEngine constructor.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine

langchain_google_cloud_sql_pg.engine.PostgresEngine._ainit_chat_history_table

_ainit_chat_history_table(table_name: str, schema_name: str = "public") -> None

Create a Cloud SQL table to store chat history.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._ainit_chat_history_table

langchain_google_cloud_sql_pg.engine.PostgresEngine._ainit_vectorstore_table

_ainit_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

Create a table for saving of vectors to be used with PostgresVectorStore.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._ainit_vectorstore_table

langchain_google_cloud_sql_pg.engine.PostgresEngine._aload_table_schema

_aload_table_schema( table_name: str, schema_name: str = "public" ) -> sqlalchemy.sql.schema.Table

Load table schema from existing table in PgSQL database.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._aload_table_schema

langchain_google_cloud_sql_pg.engine.PostgresEngine._create

_create( project_id: str, region: str, instance: str, database: str, ip_type: typing.Union[str, google.cloud.sql.connector.enums.IPTypes], user: typing.Optional[str] = None, password: typing.Optional[str] = None, loop: typing.Optional[asyncio.events.AbstractEventLoop] = None, thread: typing.Optional[threading.Thread] = None, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

Create a PostgresEngine instance.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._create

langchain_google_cloud_sql_pg.engine.PostgresEngine._run_as_async

_run_as_async( coro: typing.Awaitable[langchain_google_cloud_sql_pg.engine.T], ) -> langchain_google_cloud_sql_pg.engine.T

Run an async coroutine asynchronously.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._run_as_async

langchain_google_cloud_sql_pg.engine.PostgresEngine._run_as_sync

_run_as_sync( coro: typing.Awaitable[langchain_google_cloud_sql_pg.engine.T], ) -> langchain_google_cloud_sql_pg.engine.T

Run an async coroutine synchronously.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._run_as_sync

langchain_google_cloud_sql_pg.engine.PostgresEngine.afrom_instance

afrom_instance( project_id: str, region: str, instance: str, database: str, user: typing.Optional[str] = None, password: typing.Optional[str] = None, ip_type: typing.Union[ str, google.cloud.sql.connector.enums.IPTypes ] = IPTypes.PUBLIC, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

Create a PostgresEngine from a Postgres instance.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.afrom_instance

langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_chat_history_table

ainit_chat_history_table(table_name: str, schema_name: str = "public") -> None

Create a Cloud SQL table to store chat history.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_chat_history_table

langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_document_table

ainit_document_table( table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", store_metadata: bool = True, ) -> None

Create a table for saving of langchain documents.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_document_table

langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_vectorstore_table

ainit_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

Create a table for saving of vectors to be used with PostgresVectorStore.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.ainit_vectorstore_table

langchain_google_cloud_sql_pg.engine.PostgresEngine.close

close() -> None

Dispose of connection pool.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.close

langchain_google_cloud_sql_pg.engine.PostgresEngine.from_engine

from_engine( engine: sqlalchemy.ext.asyncio.engine.AsyncEngine, loop: typing.Optional[asyncio.events.AbstractEventLoop] = None, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

Create an PostgresEngine instance from an AsyncEngine.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.from_engine

langchain_google_cloud_sql_pg.engine.PostgresEngine.from_engine_args

from_engine_args( url: typing.Union[str, sqlalchemy.engine.url.URL], **kwargs: typing.Any ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

Create an PostgresEngine instance from arguments.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.from_engine_args

langchain_google_cloud_sql_pg.engine.PostgresEngine.from_instance

from_instance( project_id: str, region: str, instance: str, database: str, user: typing.Optional[str] = None, password: typing.Optional[str] = None, ip_type: typing.Union[ str, google.cloud.sql.connector.enums.IPTypes ] = IPTypes.PUBLIC, quota_project: typing.Optional[str] = None, iam_account_email: typing.Optional[str] = None, ) -> langchain_google_cloud_sql_pg.engine.PostgresEngine

Create a PostgresEngine from a Postgres instance.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.from_instance

langchain_google_cloud_sql_pg.engine.PostgresEngine.init_chat_history_table

init_chat_history_table(table_name: str, schema_name: str = "public") -> None

Create a Cloud SQL table to store chat history.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.init_chat_history_table

langchain_google_cloud_sql_pg.engine.PostgresEngine.init_document_table

init_document_table( table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", store_metadata: bool = True, ) -> None

Create a table for saving of langchain documents.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.init_document_table

langchain_google_cloud_sql_pg.engine.PostgresEngine.init_vectorstore_table

init_vectorstore_table( table_name: str, vector_size: int, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[langchain_google_cloud_sql_pg.engine.Column] = [], metadata_json_column: str = "langchain_metadata", id_column: typing.Union[ str, langchain_google_cloud_sql_pg.engine.Column ] = "langchain_id", overwrite_existing: bool = False, store_metadata: bool = True, ) -> None

Create a table for saving of vectors to be used with PostgresVectorStore.

See more: langchain_google_cloud_sql_pg.engine.PostgresEngine.init_vectorstore_table

langchain_google_cloud_sql_pg.indexes.BaseIndex.index_options

index_options() -> str

Set index query options for vector store initialization.

See more: langchain_google_cloud_sql_pg.indexes.BaseIndex.index_options

langchain_google_cloud_sql_pg.indexes.DistanceStrategy._generate_next_value_

_generate_next_value_(start, count, last_values)

Generate the next value when not given.

See more: langchain_google_cloud_sql_pg.indexes.DistanceStrategy.generate_next_value

langchain_google_cloud_sql_pg.indexes.HNSWIndex.index_options

index_options() -> str

Set index query options for vector store initialization.

See more: langchain_google_cloud_sql_pg.indexes.HNSWIndex.index_options

langchain_google_cloud_sql_pg.indexes.HNSWQueryOptions.to_string

to_string()

Convert index attributes to string.

See more: langchain_google_cloud_sql_pg.indexes.HNSWQueryOptions.to_string

langchain_google_cloud_sql_pg.indexes.IVFFlatIndex.index_options

index_options() -> str

Set index query options for vector store initialization.

See more: langchain_google_cloud_sql_pg.indexes.IVFFlatIndex.index_options

langchain_google_cloud_sql_pg.indexes.IVFFlatQueryOptions.to_string

to_string()

Convert index attributes to string.

See more: langchain_google_cloud_sql_pg.indexes.IVFFlatQueryOptions.to_string

langchain_google_cloud_sql_pg.indexes.QueryOptions.to_string

to_string() -> str

Convert index attributes to string.

See more: langchain_google_cloud_sql_pg.indexes.QueryOptions.to_string

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver

PostgresDocumentSaver( key: object, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, saver: langchain_google_cloud_sql_pg.async_loader.AsyncPostgresDocumentSaver, )

PostgresDocumentSaver constructor.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.aadd_documents

aadd_documents(docs: typing.List[langchain_core.documents.base.Document]) -> None

Save documents in the DocumentSaver table.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.aadd_documents

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.add_documents

add_documents(docs: typing.List[langchain_core.documents.base.Document]) -> None

Save documents in the DocumentSaver table.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.add_documents

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.adelete

adelete(docs: typing.List[langchain_core.documents.base.Document]) -> None

Delete all instances of a document from the DocumentSaver table by matching the entire Document object.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.adelete

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.create

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: typing.List[str] = [], metadata_json_column: typing.Optional[str] = "langchain_metadata", ) -> langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver

Create an PostgresDocumentSaver instance.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.create

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.create_sync

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", content_column: str = "page_content", metadata_columns: typing.List[str] = [], metadata_json_column: str = "langchain_metadata", ) -> langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver

Create an PostgresDocumentSaver instance.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.create_sync

langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.delete

delete(docs: typing.List[langchain_core.documents.base.Document]) -> None

Delete all instances of a document from the DocumentSaver table by matching the entire Document object.

See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.delete

langchain_google_cloud_sql_pg.loader.PostgresLoader

PostgresLoader( key: object, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, loader: langchain_google_cloud_sql_pg.async_loader.AsyncPostgresLoader, )

PostgresLoader constructor.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader

langchain_google_cloud_sql_pg.loader.PostgresLoader.alazy_load

alazy_load() -> typing.AsyncIterator[langchain_core.documents.base.Document]

Load PostgreSQL data into Document objects lazily.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.alazy_load

langchain_google_cloud_sql_pg.loader.PostgresLoader.aload

aload() -> typing.List[langchain_core.documents.base.Document]

Load PostgreSQL data into Document objects.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.aload

langchain_google_cloud_sql_pg.loader.PostgresLoader.create

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, query: typing.Optional[str] = None, table_name: typing.Optional[str] = None, schema_name: str = "public", content_columns: typing.Optional[typing.List[str]] = None, metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, format: typing.Optional[str] = None, formatter: typing.Optional[typing.Callable] = None, ) -> langchain_google_cloud_sql_pg.loader.PostgresLoader

Create a new PostgresLoader instance.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.create

langchain_google_cloud_sql_pg.loader.PostgresLoader.create_sync

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, query: typing.Optional[str] = None, table_name: typing.Optional[str] = None, schema_name: str = "public", content_columns: typing.Optional[typing.List[str]] = None, metadata_columns: typing.Optional[typing.List[str]] = None, metadata_json_column: typing.Optional[str] = None, format: typing.Optional[str] = None, formatter: typing.Optional[typing.Callable] = None, ) -> langchain_google_cloud_sql_pg.loader.PostgresLoader

Create a new PostgresLoader instance.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.create_sync

langchain_google_cloud_sql_pg.loader.PostgresLoader.lazy_load

lazy_load() -> typing.Iterator[langchain_core.documents.base.Document]

Load PostgreSQL data into Document objects lazily.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.lazy_load

langchain_google_cloud_sql_pg.loader.PostgresLoader.load

load() -> typing.List[langchain_core.documents.base.Document]

Load PostgreSQL data into Document objects.

See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.load

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

PostgresVectorStore( key: object, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, vs: langchain_google_cloud_sql_pg.async_vectorstore.AsyncPostgresVectorStore, )

PostgresVectorStore constructor.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore._select_relevance_score_fn

_select_relevance_score_fn() -> typing.Callable[[float], float]

Select a relevance function based on distance strategy.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore._select_relevance_score_fn

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aadd_documents

aadd_documents( documents: typing.List[langchain_core.documents.base.Document], ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.List[str]

Embed documents and add to the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aadd_documents

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aadd_texts

aadd_texts( texts: typing.Iterable[str], metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.List[str]

Embed texts and add to the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aadd_texts

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aapply_vector_index

aapply_vector_index( index: langchain_google_cloud_sql_pg.indexes.BaseIndex, name: typing.Optional[str] = None, concurrently: bool = False, ) -> None

Create an index on the vector store table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aapply_vector_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.add_documents

add_documents( documents: typing.List[langchain_core.documents.base.Document], ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.List[str]

Embed documents and add to the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.add_documents

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.add_texts

add_texts( texts: typing.Iterable[str], metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.List[str]

Embed texts and add to the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.add_texts

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.adelete

adelete( ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.Optional[bool]

Delete records from the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.adelete

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.adrop_vector_index

adrop_vector_index(index_name: typing.Optional[str] = None) -> None

Drop the vector index.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.adrop_vector_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.afrom_documents

afrom_documents( documents: typing.List[langchain_core.documents.base.Document], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", ids: typing.Optional[typing.List] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create an PostgresVectorStore instance from documents.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.afrom_documents

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.afrom_texts

afrom_texts( texts: typing.List[str], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create an PostgresVectorStore instance from texts.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.afrom_texts

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.ais_valid_index

ais_valid_index(index_name: typing.Optional[str] = None) -> bool

Check if index exists in the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.ais_valid_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search

amax_marginal_relevance_search( query: str, k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search_by_vector

amax_marginal_relevance_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search_with_score_by_vector

amax_marginal_relevance_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search_with_score_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.apply_vector_index

apply_vector_index( index: langchain_google_cloud_sql_pg.indexes.BaseIndex, name: typing.Optional[str] = None, concurrently: bool = False, ) -> None

Create an index on the vector store table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.apply_vector_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.areindex

areindex(index_name: typing.Optional[str] = None) -> None

Re-index the vector store table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.areindex

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search

asimilarity_search( query: str, k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by similarity search on query.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_by_vector

asimilarity_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by vector similarity search.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_with_score

asimilarity_search_with_score( query: str, k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by similarity search on query.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_with_score

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_with_score_by_vector

asimilarity_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by vector similarity search.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_with_score_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.create

create( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, embedding_service: langchain_core.embeddings.embeddings.Embeddings, table_name: str, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create a new PostgresVectorStore instance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.create

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.create_sync

create_sync( engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, embedding_service: langchain_core.embeddings.embeddings.Embeddings, table_name: str, schema_name: str = "public", content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create a new PostgresVectorStore instance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.create_sync

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.delete

delete( ids: typing.Optional[typing.List] = None, **kwargs: typing.Any ) -> typing.Optional[bool]

Delete records from the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.delete

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.drop_vector_index

drop_vector_index(index_name: typing.Optional[str] = None) -> None

Drop the vector index.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.drop_vector_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.from_documents

from_documents( documents: typing.List[langchain_core.documents.base.Document], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", ids: typing.Optional[typing.List] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create an PostgresVectorStore instance from documents.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.from_documents

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.from_texts

from_texts( texts: typing.List[str], embedding: langchain_core.embeddings.embeddings.Embeddings, engine: langchain_google_cloud_sql_pg.engine.PostgresEngine, table_name: str, schema_name: str = "public", metadatas: typing.Optional[typing.List[dict]] = None, ids: typing.Optional[typing.List] = None, content_column: str = "content", embedding_column: str = "embedding", metadata_columns: typing.List[str] = [], ignore_metadata_columns: typing.Optional[typing.List[str]] = None, id_column: str = "langchain_id", metadata_json_column: 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, ) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore

Create an PostgresVectorStore instance from texts.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.from_texts

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.is_valid_index

is_valid_index(index_name: typing.Optional[str] = None) -> bool

Check if index exists in the table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.is_valid_index

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search

max_marginal_relevance_search( query: str, k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search_by_vector

max_marginal_relevance_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search_with_score_by_vector

max_marginal_relevance_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, fetch_k: typing.Optional[int] = None, lambda_mult: typing.Optional[float] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected using the maximal marginal relevance.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search_with_score_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.reindex

reindex(index_name: typing.Optional[str] = None) -> None

Re-index the vector store table.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.reindex

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search

similarity_search( query: str, k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by similarity search on query.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_by_vector

similarity_search_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[langchain_core.documents.base.Document]

Return docs selected by vector similarity search.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_by_vector

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_with_score

similarity_search_with_score( query: str, k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by similarity search on query.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_with_score

langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_with_score_by_vector

similarity_search_with_score_by_vector( embedding: typing.List[float], k: typing.Optional[int] = None, filter: typing.Optional[str] = None, **kwargs: typing.Any ) -> typing.List[typing.Tuple[langchain_core.documents.base.Document, float]]

Return docs and distance scores selected by similarity search on vector.

See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_with_score_by_vector