# Google

## Document Loader
### Google BigQuery

>[Google BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
`BigQuery` is a part of the `Google Cloud Platform`.

First, you need to install `google-cloud-bigquery` python package.

```bash
pip install google-cloud-bigquery
```

See a [usage example](/docs/integrations/document_loaders/google_bigquery).

```python
from langchain.document_loaders import BigQueryLoader
```

### Google Cloud Storage

>[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.

First, you need to install `google-cloud-storage` python package.

```bash
pip install google-cloud-storage
```

There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.

See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_directory).

```python
from langchain.document_loaders import GCSDirectoryLoader
```
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).

```python
from langchain.document_loaders import GCSFileLoader
```

### Google Drive

>[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.

Currently, only `Google Docs` are supported.

First, you need to install several python package.

```bash
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
```

See a [usage example and authorizing instructions](/docs/integrations/document_loaders/google_drive.html).

```python
from langchain.document_loaders import GoogleDriveLoader
```

## Vector Store
### Google Vertex AI MatchingEngine

> [Google Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides
> the industry's leading high-scale low latency vector database. These vector databases are commonly
> referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.

We need to install several python packages.

```bash
pip install tensorflow google-cloud-aiplatform tensorflow-hub tensorflow-text
```

See a [usage example](/docs/integrations/vectorstores/matchingengine).

```python
from langchain.vectorstores import MatchingEngine
```

## Tools
### Google Search

- Install requirements with `pip install google-api-python-client`
- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively

There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:

```python
from langchain.utilities import GoogleSearchAPIWrapper
```
For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search.html).

You can easily load this wrapper as a Tool (to use with an Agent). You can do this with:
```python
from langchain.agents import load_tools
tools = load_tools(["google-search"])
```