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docs: Update Google Provider documentation (#17970)

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**Description:** Clean up Google product names and fix document loader
section
**Issue:** NA
**Dependencies:** None

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Co-authored-by: Bagatur <baskaryan@gmail.com>
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Averi Kitsch 7 months ago committed by GitHub
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@ -4,7 +4,7 @@ All functionality related to [Google Cloud Platform](https://cloud.google.com/)
## Chat models
### Google AI
### Google Generative AI
Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `ChatGoogleGenerativeAI` class.
@ -25,14 +25,14 @@ llm = ChatGoogleGenerativeAI(model="gemini-pro")
llm.invoke("Sing a ballad of LangChain.")
```
Gemini vision model supports image inputs when providing a single chat message. Example:
Gemini vision model supports image inputs when providing a single chat message.
```python
from langchain_core.messages import HumanMessage
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI(model="gemini-pro-vision")
# example
message = HumanMessage(
content=[
{
@ -69,29 +69,27 @@ See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
from langchain_google_vertexai import ChatVertexAI
```
## Document Loaders
### Google BigQuery
## LLMs
> [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`.
### Google Generative AI
We need to install `google-cloud-bigquery` python package.
Access GoogleAI `Gemini` models such as `gemini-pro` and `gemini-pro-vision` through the `GoogleGenerativeAI` class.
Install python package.
```bash
pip install google-cloud-bigquery
pip install langchain-google-genai
```
See a [usage example](/docs/integrations/document_loaders/google_bigquery).
See a [usage example](/docs/integrations/llms/google_ai).
```python
from langchain_community.document_loaders import BigQueryLoader
from langchain_google_genai import GoogleGenerativeAI
```
## LLMs
### Vertex AI
Access to `Gemini` and `PaLM` LLMs (like `text-bison` and `code-bison`) via `Google Vertex AI`.
Access to `Gemini` and `PaLM` LLMs (like `text-bison` and `code-bison`) via `Vertex AI` on Google Cloud.
We need to install `langchain-google-vertexai` python package.
@ -107,7 +105,7 @@ from langchain_google_vertexai import VertexAI
### Model Garden
Access PaLM and hundreds of OSS models via `Vertex AI Model Garden`.
Access PaLM and hundreds of OSS models via `Vertex AI Model Garden` on Google Cloud.
We need to install `langchain-google-vertexai` python package.
@ -121,71 +119,11 @@ See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model
from langchain_google_vertexai import VertexAIModelGarden
```
### Google Cloud Storage
>[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
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_community.document_loaders import GCSDirectoryLoader
```
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
```python
from langchain_community.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.
We need to install several python packages.
```bash
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
```python
from langchain_community.document_loaders import GoogleDriveLoader
```
### Speech-to-Text
> [Google Cloud Speech-to-Text](https://cloud.google.com/speech-to-text) is an audio transcription API powered by Google's speech recognition models.
This document loader transcribes audio files and outputs the text results as Documents.
First, we need to install the python package.
```bash
pip install google-cloud-speech
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
```python
from langchain_community.document_loaders import GoogleSpeechToTextLoader
```
## Vector Stores
### Google Vertex AI Vector Search
### Vertex AI Vector Search
> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview),
> [Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) from Google Cloud,
> formerly known as `Vertex AI Matching Engine`, 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.
@ -202,12 +140,12 @@ See a [usage example](/docs/integrations/vectorstores/google_vertex_ai_vector_se
from langchain_community.vectorstores import MatchingEngine
```
### Google BigQuery Vector Search
### BigQuery
> [Google BigQuery](https://cloud.google.com/bigquery),
> [BigQuery](https://cloud.google.com/bigquery),
> BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
>
> [Google BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
> [BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
> BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
> It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
@ -265,11 +203,10 @@ See a [usage example and authorization instructions](/docs/integrations/retrieve
from langchain_googledrive.retrievers import GoogleDriveRetriever
```
### 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.
> [Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/introduction)
> from Google Cloud allows developers to quickly build generative AI powered search engines for customers and employees.
We need to install the `google-cloud-discoveryengine` python package.
@ -284,10 +221,10 @@ 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
> [Document AI Warehouse](https://cloud.google.com/document-ai-warehouse)
> from Google Cloud allows enterprises to search, store, govern, and manage documents and their AI-extracted
> data and metadata in a single platform.
>
```python
from langchain.retrievers import GoogleDocumentAIWarehouseRetriever
@ -300,11 +237,136 @@ documents = docai_wh_retriever.get_relevant_documents(
)
```
## Document Loaders
### BigQuery
> [BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data in Google Cloud.
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_community.document_loaders import BigQueryLoader
```
### Cloud Storage
>[Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data in Google Cloud.
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_community.document_loaders import GCSDirectoryLoader
```
See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
```python
from langchain_community.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.
We need to install several python packages.
```bash
pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
```python
from langchain_community.document_loaders import GoogleDriveLoader
```
### Speech-to-Text
> [Speech-to-Text](https://cloud.google.com/speech-to-text) is an audio transcription API powered by Google's speech recognition models in Google Cloud.
This document loader transcribes audio files and outputs the text results as Documents.
First, we need to install the python package.
```bash
pip install google-cloud-speech
```
See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
```python
from langchain_community.document_loaders import GoogleSpeechToTextLoader
```
## Document Transformers
### Document AI
>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a Google Cloud
> service that transforms 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_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers import DocAIParser
```
### Google Translate
> [Google Translate](https://translate.google.com/) is a multilingual neural machine
> translation service developed by Google to translate text, documents and websites
> from one language into another.
The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
First, we need to install the python package.
```bash
pip install google-cloud-translate
```
See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
```python
from langchain_community.document_transformers import GoogleTranslateTransformer
```
## Tools
### Google Cloud Text-to-Speech
### Text-to-Speech
>[Google Cloud Text-to-Speech](https://cloud.google.com/text-to-speech) enables developers to
>[Text-to-Speech](https://cloud.google.com/text-to-speech) is a Google Cloud service that enables developers to
> synthesize natural-sounding speech with 100+ voices, available in multiple languages and variants.
> It applies DeepMind’s groundbreaking research in WaveNet and Google’s powerful neural networks
> to deliver the highest fidelity possible.
@ -321,7 +383,6 @@ See a [usage example and authorization instructions](/docs/integrations/tools/go
from langchain.tools import GoogleCloudTextToSpeechTool
```
### Google Drive
We need to install several python packages.
@ -439,55 +500,6 @@ from langchain_community.tools.google_trends import GoogleTrendsQueryRun
from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper
```
## Document Transformers
### Google Document AI
>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform`
> service that transforms 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_community.document_loaders.blob_loaders import Blob
from langchain_community.document_loaders.parsers import DocAIParser
```
### Google Translate
> [Google Translate](https://translate.google.com/) is a multilingual neural machine
> translation service developed by Google to translate text, documents and websites
> from one language into another.
The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
First, we need to install the python package.
```bash
pip install google-cloud-translate
```
See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
```python
from langchain_community.document_transformers import GoogleTranslateTransformer
```
## Toolkits
### GMail
@ -509,9 +521,9 @@ from langchain_community.agent_toolkits import GmailToolkit
## Memory
### Cloud Firestore
### Firestore
> [`Cloud Firestore`](https://cloud.google.com/firestore) is a NoSQL document database built for automatic scaling, high performance, and ease of application development.
> [`Firestore`](https://cloud.google.com/firestore) is a NoSQL document database built for automatic scaling, high performance, and ease of application development in Google Cloud.
First, we need to install the python package.
@ -556,7 +568,7 @@ See [usage examples and authorization instructions](/docs/integrations/tools/sea
from langchain_community.utilities import SearchApiAPIWrapper
```
### SerpAPI
### SerpApi
>[SerpApi](https://serpapi.com/) provides a 3rd-party API to access Google search results.
@ -627,4 +639,4 @@ See a [usage example](/docs/integrations/document_loaders/youtube_transcript).
```python
from langchain_community.document_loaders import YoutubeLoader
```
```

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