langchain/docs/docs/integrations/platforms/google.mdx

# Google

All functionality related to [Google Cloud Platform](https://cloud.google.com/)

## LLMs

### Vertex AI

Access PaLM LLMs like `text-bison` and `code-bison` via Google Cloud.

```python
from langchain.llms import VertexAI
```

### Model Garden

Access PaLM and hundreds of OSS models via Vertex AI Model Garden.

```python
from langchain.llms import VertexAIModelGarden
```

## Chat models

### Vertex AI

Access PaLM chat models like `chat-bison` and `codechat-bison` via Google Cloud.

```python
from langchain.chat_models import ChatVertexAI
```


## 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, we 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, we 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, we 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
```

### Google ScaNN

>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
> (Scalable Nearest Neighbors) is a python package.
> 
>`ScaNN` is a method for efficient vector similarity search at scale.

>`ScaNN` includes search space pruning and quantization for Maximum Inner
> Product Search and also supports other distance functions such as
> Euclidean distance. The implementation is optimized for x86 processors
> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
> for more details.

We need to install `scann` python package.

```bash
pip install scann
```

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

```python
from langchain.vectorstores import ScaNN
```

## Retrievers
### Vertex AI Search

> [Google Cloud Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/introduction)
> allows developers to quickly build generative AI powered search engines for customers and employees.

First, you need to install the `google-cloud-discoveryengine` Python package.

```bash
pip install google-cloud-discoveryengine
```

See a [usage example](/docs/integrations/retrievers/google_vertex_ai_search).

```python
from langchain.retrievers import GoogleVertexAISearchRetriever
```

### Document AI Warehouse
> [Google Cloud Document AI Warehouse](https://cloud.google.com/document-ai-warehouse)
> allows enterprises to search, store, govern, and manage documents and their AI-extracted 
> data and metadata in a single platform. Documents should be uploaded outside of Langchain,
> 

```python
from langchain.retrievers import GoogleDocumentAIWarehouseRetriever
docai_wh_retriever = GoogleDocumentAIWarehouseRetriever(
    project_number=...
)
query = ...
documents = docai_wh_retriever.get_relevant_documents(
    query, user_ldap=...
)
```

## 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).

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

## Document Transformer
### Google Document AI

>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform` 
> service to transform unstructured data from documents into structured data, making it easier 
> to understand, analyze, and consume.  



We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)  
The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`) 
and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
We can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
tab in the Google Cloud Console.

```bash
pip install google-cloud-documentai
pip install google-cloud-documentai-toolbox
```


See a [usage example](/docs/integrations/document_transformers/docai).

```python
from langchain.document_loaders.blob_loaders import Blob
from langchain.document_loaders.parsers import DocAIParser
```