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 use Spanner to save, load and delete langchain documents with SpannerLoader and SpannerDocumentSaver. Learn more about the package on GitHub. Open In Colab

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Before You Begin

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

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🦜🔗 Library Installation

The integration lives in its own langchain-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)

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☁ 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: 
# @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 config set project {PROJECT_ID}

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🔐 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 instructions here.
from google.colab import auth

auth.authenticate_user()

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Basic Usage

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Save documents

Save langchain documents with SpannerDocumentSaver.add_documents(<documents>). To initialize SpannerDocumentSaver 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.documents import Document
from langchain_google_spanner import 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)

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Querying for Documents from Spanner

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

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Load documents from table

Load langchain documents with SpannerLoader.load() or SpannerLoader.lazy_load(). lazy_load returns a generator that only queries database during the iteration. To initialize SpannerLoader 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_spanner import SpannerLoader

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

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

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Delete documents

Delete a list of langchain documents from the table with SpannerDocumentSaver.delete(<documents>). 
docs = loader.load()
print("Documents before delete:", docs)

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

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Advanced Usage

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Custom client

The client created by default is the default client. To pass in credentials and project explicitly, a custom client can be passed to the constructor. 
from google.cloud import spanner
from google.oauth2 import 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,
)

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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.

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Customize page content format

The SpannerLoader assumes there is a column called page_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 to text (space-separated string concatenation). There are other format that user can specify, including text, JSON, YAML, CSV.

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Customize metadata format

The SpannerLoader assumes there is a metadata column called langchain_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"]
)

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Customize JSON metadata column name

By default, the loader uses langchain_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"
)

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Custom staleness

The default staleness 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,
)

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Turn on data boost

By default, the loader will not use data 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,
)

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Custom client

The client created by default is the default client. To pass in credentials and project explicitly, a custom client can be passed to the constructor. 
from google.cloud import spanner

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

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

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Initialize custom schema for Spanner

The SpannerDocumentSaver will have a init_document_table method to create a new table to store docs with custom schema. 
from langchain_google_spanner import 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),
    ],
)