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
:
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
,
engine_args
:
typing
.
Mapping
=
{},
)
-
> 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
,
engine_args
:
typing
.
Mapping
=
{},
)
-
> 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
:
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
:
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
:
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
,
engine_args
:
typing
.
Mapping
=
{},
)
-
> 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
:
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
:
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
:
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
:
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
:
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
:
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
:
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
:
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
()
-
> 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
[
list
[
str
]]
=
None
,
metadata_columns
:
typing
.
Optional
[
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
[
list
[
str
]]
=
None
,
metadata_columns
:
typing
.
Optional
[
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
()
-
> 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
:
list
[
langchain_core
.
documents
.
base
.
Document
],
ids
:
typing
.
Optional
[
list
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
[
list
[
dict
]]
=
None
,
ids
:
typing
.
Optional
[
list
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
:
list
[
langchain_core
.
documents
.
base
.
Document
],
ids
:
typing
.
Optional
[
list
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
[
list
[
dict
]]
=
None
,
ids
:
typing
.
Optional
[
list
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
[
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
:
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
[
list
]
=
None
,
content_column
:
str
=
"content"
,
embedding_column
:
str
=
"embedding"
,
metadata_columns
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
:
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
[
list
[
dict
]]
=
None
,
ids
:
typing
.
Optional
[
list
]
=
None
,
content_column
:
str
=
"content"
,
embedding_column
:
str
=
"embedding"
,
metadata_columns
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
)
-
> 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
:
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
)
-
> list
[
langchain_core
.
documents
.
base
.
Document
]
Return docs selected using the maximal marginal relevance.
langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.amax_marginal_relevance_search_with_score_by_vector
amax_marginal_relevance_search_with_score_by_vector
(
embedding
:
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
)
-
> list
[
tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Return docs and distance scores selected using the maximal marginal relevance.
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
)
-
> 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
:
list
[
float
],
k
:
typing
.
Optional
[
int
]
=
None
,
filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
)
-
> list
[
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
:
list
[
float
],
k
:
typing
.
Optional
[
int
]
=
None
,
filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> list
[
tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Return docs and distance scores selected by vector similarity search.
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
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
[
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
:
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
[
list
]
=
None
,
content_column
:
str
=
"content"
,
embedding_column
:
str
=
"embedding"
,
metadata_columns
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
:
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
[
list
[
dict
]]
=
None
,
ids
:
typing
.
Optional
[
list
]
=
None
,
content_column
:
str
=
"content"
,
embedding_column
:
str
=
"embedding"
,
metadata_columns
:
list
[
str
]
=
[],
ignore_metadata_columns
:
typing
.
Optional
[
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
)
-
> 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
:
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
)
-
> list
[
langchain_core
.
documents
.
base
.
Document
]
Return docs selected using the maximal marginal relevance.
langchain_google_cloud_sql_pg.vectorstore.PostgresVectorStore.max_marginal_relevance_search_with_score_by_vector
max_marginal_relevance_search_with_score_by_vector
(
embedding
:
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
)
-
> list
[
tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Return docs and distance scores selected using the maximal marginal relevance.
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
)
-
> 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
:
list
[
float
],
k
:
typing
.
Optional
[
int
]
=
None
,
filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> 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
)
-
> list
[
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
:
list
[
float
],
k
:
typing
.
Optional
[
int
]
=
None
,
filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> list
[
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
