> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Google spanner integration
> Integrate with the Google spanner vector store using LangChain Python.
> [Spanner](https://cloud.google.com/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` for Vector Search with `SpannerVectorStore` class.
Learn more about the package on [GitHub](https://github.com/googleapis/langchain-google-spanner-python/).
[](https://colab.research.google.com/github/googleapis/langchain-google-spanner-python/blob/main/docs/vector_store.ipynb)
## Before you begin
To run this notebook, you will need to do the following:
* [Create a Google Cloud Project](https://developers.google.com/workspace/guides/create-project)
* [Enable the Cloud Spanner API](https://console.cloud.google.com/flows/enableapi?apiid=spanner.googleapis.com)
* [Create a Spanner instance](https://cloud.google.com/spanner/docs/create-manage-instances)
* [Create a Spanner database](https://cloud.google.com/spanner/docs/create-manage-databases)
### 🦜🔗 Library installation
The integration lives in its own `langchain-google-spanner` package, so we need to install it.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pip install -qU langchain-google-spanner langchain-google-vertexai
```
**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.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# # 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)
```
### 🔐 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 [Vertex AI Workbench setup instructions](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env).
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from google.colab import auth
auth.authenticate_user()
```
### ☁ 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:
* Run `gcloud config list`.
* Run `gcloud projects list`.
* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113).
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# @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}
%env GOOGLE_CLOUD_PROJECT={PROJECT_ID}
```
### 💡 API enablement
The `langchain-google-spanner` package requires that you [enable the Spanner API](https://console.cloud.google.com/flows/enableapi?apiid=spanner.googleapis.com) in your Google Cloud Project.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# enable Spanner API
!gcloud services enable spanner.googleapis.com
```
## Basic usage
### Set spanner database values
Find your database values, in the [Spanner Instances page](https://console.cloud.google.com/spanner?_ga=2.223735448.2062268965.1707700487-2088871159.1707257687).
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# @title Set Your Values Here { display-mode: "form" }
INSTANCE = "my-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vectors_search_data" # @param {type: "string"}
```
### Initialize a table
The `SpannerVectorStore` class instance requires a database table with id, content and embeddings columns.
The helper method `init_vector_store_table()` that can be used to create a table with the proper schema for you.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_spanner import SecondaryIndex, SpannerVectorStore, TableColumn
SpannerVectorStore.init_vector_store_table(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
# Customize the table creation
# id_column="row_id",
# content_column="content_column",
# metadata_columns=[
# TableColumn(name="metadata", type="JSON", is_null=True),
# TableColumn(name="title", type="STRING(MAX)", is_null=False),
# ],
# secondary_indexes=[
# SecondaryIndex(index_name="row_id_and_title", columns=["row_id", "title"])
# ],
)
```
### Create an embedding class instance
You can use any [LangChain embeddings model](/oss/python/integrations/embeddings/).
You may need to enable Vertex AI API to use `VertexAIEmbeddings`. We recommend setting the embedding model's version for production, learn more about the [Text embeddings models](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-embeddings).
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
# enable Vertex AI API
!gcloud services enable aiplatform.googleapis.com
```
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
from langchain_google_vertexai import VertexAIEmbeddings
embeddings = VertexAIEmbeddings(
model_name="textembedding-gecko@latest", project=PROJECT_ID
)
```
### SpannerVectorStore
To initialize the `SpannerVectorStore` class you need to provide 4 required arguments and other arguments are optional and only need to pass if it's different from default ones
1. `instance_id` - The name of the Spanner instance
2. `database_id` - The name of the Spanner database
3. `table_name` - The name of the table within the database to store the documents & their embeddings.
4. `embedding_service` - The Embeddings implementation which is used to generate the embeddings.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
db = SpannerVectorStore(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
embedding_service=embeddings,
# Connect to a custom vector store table
# id_column="row_id",
# content_column="content",
# metadata_columns=["metadata", "title"],
)
```
#### Add documents
To add documents in the vector store.
The `langchain-community` package is no longer maintained. Examples that import from `langchain_community` may be outdated or broken. Use with caution.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import uuid
from langchain_community.document_loaders import HNLoader
loader = HNLoader("https://news.ycombinator.com/item?id=34817881")
documents = loader.load()
ids = [str(uuid.uuid4()) for _ in range(len(documents))]
db.add_documents(documents, ids)
```
#### Search documents
To search documents in the vector store with similarity search.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
db.similarity_search(query="Explain me vector store?", k=3)
```
#### Search documents
To search documents in the vector store with max marginal relevance search.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
db.max_marginal_relevance_search("Testing the langchain integration with spanner", k=3)
```
#### Delete documents
To remove documents from the vector store, use the IDs that correspond to the values in the \`row\_id\`\` column when initializing the VectorStore.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
db.delete(ids=["id1", "id2"])
```
#### Delete documents
To remove documents from the vector store, you can utilize the documents themselves. The content column and metadata columns provided during VectorStore initialization will be used to find out the rows corresponding to the documents. Any matching rows will then be deleted.
```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
db.delete(documents=[documents[0], documents[1]])
```
***
[Connect these docs](/use-these-docs) to Claude, VSCode, and more via MCP for real-time answers.
[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/python/integrations/vectorstores/google_spanner.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).