Spanner is a highly scalable database that combines unlimited scalability with relational semantics, such as secondary indexes, strong consistency, schemas, and SQL providing 99.999% availability in one easy solution.

This notebook goes over how to useSpanner tosave, load and delete langchain documents withSpannerLoader andSpannerDocumentSaver.

Learn more about the package onGitHub.

Before You Begin​

To run this notebook, you will need to do the following:

• Create a Google Cloud Project
• Enable the Cloud Spanner API
• Create a Spanner instance
• Create a Spanner database
• Create a Spanner table

After confirmed access to database in the runtime environment of this notebook, filling the following values and run the cell before running example scripts.

# @markdown Please specify an instance id, a database, and a table for demo purpose.
INSTANCE_ID="test_instance"# @param {type:"string"}
DATABASE_ID="test_database"# @param {type:"string"}
TABLE_NAME="test_table"# @param {type:"string"}

🦜🔗 Library Installation​

The integration lives in its ownlangchain-google-spanner package, so we need to install it.

%pip install-upgrade--quiet langchain-google-spanner langchain

Colab only: Uncomment the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.

# # Automatically restart kernel after installs so that your environment can access the new packages
# import IPython

# app = IPython.Application.instance()
# app.kernel.do_shutdown(True)

☁ Set Your Google Cloud Project​

Set your Google Cloud project so that you can leverage Google Cloud resources within this notebook.

If you don't know your project ID, try the following:

• Rungcloud config list.
• Rungcloud projects list.
• See the support page:Locate the project ID.

# @markdown Please fill in the value below with your Google Cloud project ID and then run the cell.

PROJECT_ID="my-project-id"# @param {type:"string"}

# Set the project id
!gcloud configset project{PROJECT_ID}

🔐 Authentication​

Authenticate to Google Cloud as the IAM user logged into this notebook in order to access your Google Cloud Project.

• If you are using Colab to run this notebook, use the cell below and continue.
• If you are using Vertex AI Workbench, check out the setup instructionshere.

from google.colabimport auth

auth.authenticate_user()

Basic Usage​

Save documents​

Save langchain documents withSpannerDocumentSaver.add_documents(<documents>). To initializeSpannerDocumentSaver class you need to provide 3 things:

1. instance_id - An instance of Spanner to load data from.
2. database_id - An instance of Spanner database to load data from.
3. table_name - The name of the table within the Spanner database to store langchain documents.

from langchain_core.documentsimport Document
from langchain_google_spannerimport SpannerDocumentSaver

test_docs=[
    Document(
        page_content="Apple Granny Smith 150 0.99 1",
        metadata={"fruit_id":1},
),
    Document(
        page_content="Banana Cavendish 200 0.59 0",
        metadata={"fruit_id":2},
),
    Document(
        page_content="Orange Navel 80 1.29 1",
        metadata={"fruit_id":3},
),
]

saver= SpannerDocumentSaver(
    instance_id=INSTANCE_ID,
    database_id=DATABASE_ID,
    table_name=TABLE_NAME,
)
saver.add_documents(test_docs)

Querying for Documents from Spanner​

For more details on connecting to a Spanner table, please check thePython SDK documentation.

Load documents from table​

Load langchain documents withSpannerLoader.load() orSpannerLoader.lazy_load().lazy_load returns a generator that only queries database during the iteration. To initializeSpannerLoader class you need to provide:

1. instance_id - An instance of Spanner to load data from.
2. database_id - An instance of Spanner database to load data from.
3. query - A query of the database dialect.

from langchain_google_spannerimport SpannerLoader

query=f"SELECT * from{TABLE_NAME}"
loader= SpannerLoader(
    instance_id=INSTANCE_ID,
    database_id=DATABASE_ID,
    query=query,
)

for docin loader.lazy_load():
print(doc)
break

Delete documents​

Delete a list of langchain documents from the table withSpannerDocumentSaver.delete(<documents>).

docs= loader.load()
print("Documents before delete:", docs)

doc= test_docs[0]
saver.delete([doc])
print("Documents after delete:", loader.load())

Advanced Usage​

Custom client​

The client created by default is the default client. To pass incredentials andproject explicitly, a custom client can be passed to the constructor.

from google.cloudimport spanner
from google.oauth2import service_account

creds= service_account.Credentials.from_service_account_file("/path/to/key.json")
custom_client= spanner.Client(project="my-project", credentials=creds)
loader= SpannerLoader(
    INSTANCE_ID,
    DATABASE_ID,
    query,
    client=custom_client,
)

Customize Document Page Content & Metadata​

The loader will returns a list of Documents with page content from a specific data columns. All other data columns will be added to metadata. Each row becomes a document.

Customize page content format​

The SpannerLoader assumes there is a column calledpage_content. These defaults can be changed like so:

custom_content_loader= SpannerLoader(
    INSTANCE_ID, DATABASE_ID, query, content_columns=["custom_content"]
)

If multiple columns are specified, the page content's string format will default totext (space-separated string concatenation). There are other format that user can specify, includingtext,JSON,YAML,CSV.

Customize metadata format​

The SpannerLoader assumes there is a metadata column calledlangchain_metadata that store JSON data. The metadata column will be used as the base dictionary. By default, all other column data will be added and may overwrite the original value. These defaults can be changed like so:

custom_metadata_loader= SpannerLoader(
    INSTANCE_ID, DATABASE_ID, query, metadata_columns=["column1","column2"]
)

Customize JSON metadata column name​

By default, the loader useslangchain_metadata as the base dictionary. This can be customized to select a JSON column to use as base dictionary for the Document's metadata.

custom_metadata_json_loader= SpannerLoader(
    INSTANCE_ID, DATABASE_ID, query, metadata_json_column="another-json-column"
)

Custom staleness​

The defaultstaleness is 15s. This can be customized by specifying a weaker bound (which can either be to perform all reads as of a given timestamp), or as of a given duration in the past.

import datetime

timestamp= datetime.datetime.utcnow()
custom_timestamp_loader= SpannerLoader(
    INSTANCE_ID,
    DATABASE_ID,
    query,
    staleness=timestamp,
)

duration=20.0
custom_duration_loader= SpannerLoader(
    INSTANCE_ID,
    DATABASE_ID,
    query,
    staleness=duration,
)

Turn on data boost​

By default, the loader will not usedata boost since it has additional costs associated, and require additional IAM permissions. However, user can choose to turn it on.

custom_databoost_loader= SpannerLoader(
    INSTANCE_ID,
    DATABASE_ID,
    query,
    databoost=True,
)

Custom client​

The client created by default is the default client. To pass incredentials andproject explicitly, a custom client can be passed to the constructor.

from google.cloudimport spanner

custom_client= spanner.Client(project="my-project", credentials=creds)
saver= SpannerDocumentSaver(
    INSTANCE_ID,
    DATABASE_ID,
    TABLE_NAME,
    client=custom_client,
)

Custom initialization for SpannerDocumentSaver​

The SpannerDocumentSaver allows custom initialization. This allows user to specify how the Document is saved into the table.

content_column: This will be used as the column name for the Document's page content. Defaulted topage_content.

metadata_columns: These metadata will be saved into specific columns if the key exists in the Document's metadata.

metadata_json_column: This will be the column name for the spcial JSON column. Defaulted tolangchain_metadata.

custom_saver= SpannerDocumentSaver(
    INSTANCE_ID,
    DATABASE_ID,
    TABLE_NAME,
    content_column="my-content",
    metadata_columns=["foo"],
    metadata_json_column="my-special-json-column",
)

Initialize custom schema for Spanner​

The SpannerDocumentSaver will have ainit_document_table method to create a new table to store docs with custom schema.

from langchain_google_spannerimport Column

new_table_name="my_new_table"

SpannerDocumentSaver.init_document_table(
    INSTANCE_ID,
    DATABASE_ID,
    new_table_name,
    content_column="my-page-content",
    metadata_columns=[
        Column("category","STRING(36)",True),
        Column("price","FLOAT64",False),
],
)

Related​

• Document loaderconceptual guide
• Document loaderhow-to guides