Summary of entries of Methods for langchain-google-spanner.
langchain_google_spanner.loader._load_doc_to_row
_load_doc_to_row
(
table_fields
:
typing
.
List
[
str
],
doc
:
langchain_core
.
documents
.
base
.
Document
,
content_column
:
str
,
metadata_json_column
:
str
,
parse_json
:
bool
=
True
,
)
-
> tuple
Load document to row.
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory._verify_schema
_verify_schema
()
-
> None
Verify table exists with required schema for SpannerChatMessageHistory class.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory._verify_schema
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.add_message
add_message
(
message
:
langchain_core
.
messages
.
base
.
BaseMessage
)
-
> None
Append the message to the record in Cloud Spanner.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.add_message
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.clear
clear
()
-
> None
Clear session memory from Cloud Spanner.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.clear
langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.create_chat_history_table
create_chat_history_table
(
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
)
-
> None
Create a chat history table in a Cloud Spanner database.
See more: langchain_google_spanner.chat_message_history.SpannerChatMessageHistory.create_chat_history_table
langchain_google_spanner.loader.SpannerDocumentSaver
SpannerDocumentSaver
(
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
content_column
:
str
=
"page_content"
,
metadata_columns
:
typing
.
List
[
str
]
=
[],
metadata_json_column
:
str
=
"langchain_metadata"
,
primary_key
:
typing
.
Optional
[
str
]
=
None
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
)
Initialize Spanner document saver.
See more: langchain_google_spanner.loader.SpannerDocumentSaver
langchain_google_spanner.loader.SpannerDocumentSaver.add_documents
add_documents
(
documents
:
typing
.
List
[
langchain_core
.
documents
.
base
.
Document
])
Add documents to the Spanner table.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.add_documents
langchain_google_spanner.loader.SpannerDocumentSaver.create_table
create_table
(
client
:
google
.
cloud
.
spanner_v1
.
client
.
Client
,
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
primary_key
:
str
,
metadata_json_column
:
str
,
content_column
:
str
,
metadata_columns
:
typing
.
List
[
langchain_google_spanner
.
loader
.
Column
],
)
Create a new table in Spanner database.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.create_table
langchain_google_spanner.loader.SpannerDocumentSaver.delete
delete
(
documents
:
typing
.
List
[
langchain_core
.
documents
.
base
.
Document
])
Delete documents from the table.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.delete
langchain_google_spanner.loader.SpannerDocumentSaver.init_document_table
init_document_table
(
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
content_column
:
str
=
"page_content"
,
metadata_columns
:
typing
.
List
[
langchain_google_spanner
.
loader
.
Column
]
=
[],
primary_key
:
str
=
""
,
store_metadata
:
bool
=
True
,
metadata_json_column
:
str
=
"langchain_metadata"
,
)
Create a new table to store docs with a custom schema.
See more: langchain_google_spanner.loader.SpannerDocumentSaver.init_document_table
langchain_google_spanner.loader.SpannerLoader
SpannerLoader
(
instance_id
:
str
,
database_id
:
str
,
query
:
str
,
content_columns
:
typing
.
List
[
str
]
=
[],
metadata_columns
:
typing
.
List
[
str
]
=
[],
format
:
str
=
"text"
,
databoost
:
bool
=
False
,
metadata_json_column
:
str
=
"langchain_metadata"
,
staleness
:
typing
.
Union
[
float
,
datetime
.
datetime
]
=
0.0
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
)
Initialize Spanner document loader.
langchain_google_spanner.loader.SpannerLoader.lazy_load
lazy_load
()
-
> typing
.
Iterator
[
langchain_core
.
documents
.
base
.
Document
]
A lazy loader for langchain documents from a Spanner database.
See more: langchain_google_spanner.loader.SpannerLoader.lazy_load
langchain_google_spanner.loader.SpannerLoader.load
load
()
-
> typing
.
List
[
langchain_core
.
documents
.
base
.
Document
]
Load langchain documents from a Spanner database.
See more: langchain_google_spanner.loader.SpannerLoader.load
langchain_google_spanner.vector_store.DialectSemantics.getDistanceFunction
getDistanceFunction
(
distance_strategy
=
DistanceStrategy
.
EUCLIDEIAN
)
-
> str
Abstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.DialectSemantics.getDistanceFunction
langchain_google_spanner.vector_store.GoogleSqlSemnatics.getDistanceFunction
getDistanceFunction
(
distance_strategy
=
DistanceStrategy
.
EUCLIDEIAN
)
-
> str
Abstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.GoogleSqlSemnatics.getDistanceFunction
langchain_google_spanner.vector_store.PGSqlSemnatics.getDistanceFunction
getDistanceFunction
(
distance_strategy
=
DistanceStrategy
.
EUCLIDEIAN
)
-
> str
Abstract method to get the distance function based on the provided distance strategy.
See more: langchain_google_spanner.vector_store.PGSqlSemnatics.getDistanceFunction
langchain_google_spanner.vector_store.QueryParameters
QueryParameters
(
algorithm
=
NearestNeighborsAlgorithm
.
EXACT_NEAREST_NEIGHBOR
,
distance_strategy
=
DistanceStrategy
.
EUCLIDEIAN
,
read_timestamp
:
typing
.
Optional
[
datetime
.
datetime
]
=
None
,
min_read_timestamp
:
typing
.
Optional
[
datetime
.
datetime
]
=
None
,
max_staleness
:
typing
.
Optional
[
datetime
.
timedelta
]
=
None
,
exact_staleness
:
typing
.
Optional
[
datetime
.
timedelta
]
=
None
,
)
Initialize query parameters.
See more: langchain_google_spanner.vector_store.QueryParameters
langchain_google_spanner.vector_store.SpannerVectorStore._generate_sql
_generate_sql
(
dialect
,
table_name
,
id_column
,
content_column
,
embedding_column
,
column_configs
,
primary_key
,
secondary_indexes
:
typing
.
Optional
[
typing
.
List
[
langchain_google_spanner
.
vector_store
.
SecondaryIndex
]
]
=
None
,
)
Generate SQL for creating the vector store table.
See more: langchain_google_spanner.vector_store.SpannerVectorStore._generate_sql
langchain_google_spanner.vector_store.SpannerVectorStore._select_relevance_score_fn
_select_relevance_score_fn
()
-
> typing
.
Callable
[[
float
],
float
]
The 'correct' relevance function may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed.
See more: langchain_google_spanner.vector_store.SpannerVectorStore._select_relevance_score_fn
langchain_google_spanner.vector_store.SpannerVectorStore.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
]
Add documents to the vector store.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.add_documents
langchain_google_spanner.vector_store.SpannerVectorStore.add_texts
add_texts
(
texts
:
typing
.
Iterable
[
str
],
metadatas
:
typing
.
Optional
[
typing
.
List
[
dict
]]
=
None
,
ids
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
batch_size
:
int
=
5000
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
List
[
str
]
Add texts to the vector store index.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.add_texts
langchain_google_spanner.vector_store.SpannerVectorStore.delete
delete
(
ids
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
documents
:
typing
.
Optional
[
typing
.
List
[
langchain_core
.
documents
.
base
.
Document
]
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
Optional
[
bool
]
Delete records from the vector store.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.delete
langchain_google_spanner.vector_store.SpannerVectorStore.from_documents
from_documents
(
documents
:
typing
.
List
[
langchain_core
.
documents
.
base
.
Document
],
embedding
:
langchain_core
.
embeddings
.
embeddings
.
Embeddings
,
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
id_column
:
str
=
'langchain_id'
,
content_column
:
str
=
'content'
,
embedding_column
:
str
=
'embedding'
,
ids
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
metadata_columns
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
ignore_metadata_columns
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
metadata_json_column
:
typing
.
Optional
[
str
]
=
None
,
query_parameter
:
langchain_google_spanner
.
vector_store
.
QueryParameters
=
Initialize SpannerVectorStore from a list of documents.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.from_documents
langchain_google_spanner.vector_store.SpannerVectorStore.from_texts
from_texts
(
texts
:
typing
.
List
[
str
],
embedding
:
langchain_core
.
embeddings
.
embeddings
.
Embeddings
,
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
metadatas
:
typing
.
Optional
[
typing
.
List
[
dict
]]
=
None
,
id_column
:
str
=
'langchain_id'
,
content_column
:
str
=
'content'
,
embedding_column
:
str
=
'embedding'
,
ids
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
metadata_columns
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
ignore_metadata_columns
:
typing
.
Optional
[
typing
.
List
[
str
]]
=
None
,
metadata_json_column
:
typing
.
Optional
[
str
]
=
None
,
query_parameter
:
langchain_google_spanner
.
vector_store
.
QueryParameters
=
Initialize SpannerVectorStore from a list of texts.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.from_texts
langchain_google_spanner.vector_store.SpannerVectorStore.init_vector_store_table
init_vector_store_table
(
instance_id
:
str
,
database_id
:
str
,
table_name
:
str
,
client
:
typing
.
Optional
[
google
.
cloud
.
spanner_v1
.
client
.
Client
]
=
None
,
id_column
:
typing
.
Union
[
str
,
langchain_google_spanner
.
vector_store
.
TableColumn
]
=
"langchain_id"
,
content_column
:
str
=
"content"
,
embedding_column
:
str
=
"embedding"
,
metadata_columns
:
typing
.
Optional
[
typing
.
List
[
langchain_google_spanner
.
vector_store
.
TableColumn
]
]
=
None
,
primary_key
:
typing
.
Optional
[
str
]
=
None
,
vector_size
:
typing
.
Optional
[
int
]
=
None
,
secondary_indexes
:
typing
.
Optional
[
typing
.
List
[
langchain_google_spanner
.
vector_store
.
SecondaryIndex
]
]
=
None
,
)
-
> bool
Initialize the vector store new table in Google Cloud Spanner.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.init_vector_store_table
langchain_google_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search
max_marginal_relevance_search
(
query
:
str
,
k
:
int
=
4
,
fetch_k
:
int
=
20
,
lambda_mult
:
float
=
0.5
,
pre_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_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search
langchain_google_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search_by_vector
max_marginal_relevance_search_by_vector
(
embedding
:
typing
.
List
[
float
],
k
:
int
=
4
,
fetch_k
:
int
=
20
,
lambda_mult
:
float
=
0.5
,
pre_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_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search_by_vector
langchain_google_spanner.vector_store.SpannerVectorStore.max_marginal_relevance_search_with_score_by_vector
max_marginal_relevance_search_with_score_by_vector
(
embedding
:
typing
.
List
[
float
],
k
:
int
=
4
,
fetch_k
:
int
=
20
,
lambda_mult
:
float
=
0.5
,
pre_filter
:
typing
.
Optional
[
str
]
=
None
,
)
-
> typing
.
List
[
typing
.
Tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Return docs and their similarity scores selected using the maximal marginal relevance.
langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search
similarity_search
(
query
:
str
,
k
:
int
=
4
,
pre_filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
List
[
langchain_core
.
documents
.
base
.
Document
]
Perform similarity search for a given query.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search
langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_by_vector
similarity_search_by_vector
(
embedding
:
typing
.
List
[
float
],
k
:
int
=
4
,
pre_filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
List
[
langchain_core
.
documents
.
base
.
Document
]
Perform similarity search by vector.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_by_vector
langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score
similarity_search_with_score
(
query
:
str
,
k
:
int
=
4
,
pre_filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
List
[
typing
.
Tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Perform similarity search for a given query with scores.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score
langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score_by_vector
similarity_search_with_score_by_vector
(
embedding
:
typing
.
List
[
float
],
k
:
int
=
4
,
pre_filter
:
typing
.
Optional
[
str
]
=
None
,
**
kwargs
:
typing
.
Any
)
-
> typing
.
List
[
typing
.
Tuple
[
langchain_core
.
documents
.
base
.
Document
,
float
]]
Perform similarity search for a given query.
See more: langchain_google_spanner.vector_store.SpannerVectorStore.similarity_search_with_score_by_vector
