Package Methods (0.5.0)
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Summary of entries of Methods for langchain-google-cloud-sql-pg.
langchain_google_cloud_sql_pg.chat_message_history._aget_messages
_aget_messages(
engine: langchain_google_cloud_sql_pg.engine.PostgresEngine,
session_id: str,
table_name: str,
) -> typing.List[langchain_core.messages.base.BaseMessage]Retrieve the messages from PostgreSQL.
See more: langchain_google_cloud_sql_pg.chat_message_history._aget_messages
langchain_google_cloud_sql_pg.engine._get_iam_principal_email
_get_iam_principal_email(credentials: google.auth.credentials.Credentials) -> strGet 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.vectorstore.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.ndarrayRow-wise cosine similarity between two equal-width matrices.
See more: langchain_google_cloud_sql_pg.vectorstore.cosine_similarity
langchain_google_cloud_sql_pg.vectorstore.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.
See more: langchain_google_cloud_sql_pg.vectorstore.maximal_marginal_relevance
langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_message
aadd_message(message: langchain_core.messages.base.BaseMessage) -> NoneAppend 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],
) -> NoneAdd a list of messages.
See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aadd_messages
langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.aclear
aclear() -> NoneClear 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) -> NoneAdd a Message object to the store.
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],
) -> NoneAdd a list of messages.
See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.add_messages
langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.clear
clear() -> NoneRemove all messages from the store.
See more: langchain_google_cloud_sql_pg.chat_message_history.PostgresChatMessageHistory.clear
langchain_google_cloud_sql_pg.engine.PostgresEngine._aexecute
_aexecute(query: str, params: typing.Optional[dict] = None)Execute a SQL query.
See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._aexecute
langchain_google_cloud_sql_pg.engine.PostgresEngine._aexecute_outside_tx
_aexecute_outside_tx(query: str)Execute a SQL query.
See more: langchain_google_cloud_sql_pg.engine.PostgresEngine._aexecute_outside_tx
langchain_google_cloud_sql_pg.engine.PostgresEngine._aload_table_schema
_aload_table_schema(table_name: str) -> sqlalchemy.sql.schema.TableLoad 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.ainit_document_table
ainit_document_table(
table_name: str,
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,
) -> NoneCreate 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,
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: str = "langchain_id",
overwrite_existing: bool = False,
store_metadata: bool = True,
) -> NoneCreate 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.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.loader.PostgresDocumentSaver._aload_table_schema
_aload_table_schema() -> sqlalchemy.sql.schema.TableLoad table schema from existing table in PgSQL database.
See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver._aload_table_schema
langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.aadd_documents
aadd_documents(docs: typing.List[langchain_core.documents.base.Document]) -> NoneSave documents in the DocumentSaver table.
See more: langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.aadd_documents
langchain_google_cloud_sql_pg.loader.PostgresDocumentSaver.adelete
adelete(docs: typing.List[langchain_core.documents.base.Document]) -> NoneDelete 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.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,
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,
)Constructor for PostgresLoader .
See more: langchain_google_cloud_sql_pg.loader.PostgresLoader.create
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.aadd_documents
aadd_documents(
documents: typing.List[langchain_core.documents.base.Document],
ids: typing.Optional[typing.List[str]] = None,
**kwargs: typing.Any
) -> typing.List[str]Run more documents through the embeddings and add to the vectorstore.
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[str]] = None,
**kwargs: typing.Any
) -> typing.List[str]Run more texts through the embeddings and add to the vectorstore.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.aadd_texts
langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.add_documents
add_documents(
documents: typing.List[langchain_core.documents.base.Document],
ids: typing.Optional[typing.List[str]] = None,
**kwargs: typing.Any
) -> typing.List[str]Run more documents through the embeddings and add to the vectorstore.
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[str]] = None,
**kwargs: typing.Any
) -> typing.List[str]Run more texts through the embeddings and add to the vectorstore.
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[str]] = None, **kwargs: typing.Any
) -> typing.Optional[bool]Delete by vector ID or other criteria.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.adelete
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,
ids: typing.Optional[typing.List[str]] = 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",
**kwargs: typing.Any
) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStoreReturn VectorStore initialized from documents and embeddings.
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,
metadatas: typing.Optional[typing.List[dict]] = None,
ids: typing.Optional[typing.List[str]] = 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",
**kwargs: typing.Any
) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStoreReturn VectorStore initialized from texts and embeddings.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.afrom_texts
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.
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 most similar to 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 most similar to embedding vector.
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]]Run similarity search with distance asynchronously.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.asimilarity_search_with_score
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,
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,
)Constructor for PostgresVectorStore.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.create
langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.delete
delete(
ids: typing.Optional[typing.List[str]] = None, **kwargs: typing.Any
) -> typing.Optional[bool]Delete by vector ID or other criteria.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.delete
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,
ids: typing.Optional[typing.List[str]] = 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",
**kwargs: typing.Any
) -> langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStoreReturn VectorStore initialized from documents and embeddings.
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,
metadatas: typing.Optional[typing.List[dict]] = None,
ids: typing.Optional[typing.List[str]] = 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",
**kwargs: typing.Any
)Return VectorStore initialized from texts and embeddings.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.from_texts
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.
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 most similar to 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 most similar to embedding vector.
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]]Run similarity search with distance.
See more: langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.similarity_search_with_score