merge pages into google and AWS pages (#11312)

There are several pages in `integrations/providers/more` that belongs to
Google and AWS `integrations/providers`.
- moved content of these pages into the Google and AWS
`integrations/providers` pages
- removed these individual pages

docs/extras/integrations/platforms/aws.mdx

@@ -1,6 +1,6 @@
 # AWS
 
-All functionality related to AWS platform
+All functionality related to [Amazon AWS](https://aws.amazon.com/) platform
 
 ## LLMs
 
@@ -70,7 +70,7 @@ from langchain.llms.sagemaker_endpoint import ContentHandlerBase
 
 ## Document loaders
 
-### AWS S3 Directory
+### AWS S3 Directory and File
 >[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
 >[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
 >[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
@@ -82,3 +82,24 @@ See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s
 ```python
 from langchain.document_loaders import S3DirectoryLoader, S3FileLoader
 ```
+
+## Memory
+
+### AWS DynamoDB
+
+>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) 
+> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
+ 
+We have to configur the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). 
+
+We need to install the `boto3` library.
+
+```bash
+pip install boto3
+```
+
+See a [usage example](/docs/integrations/memory/aws_dynamodb).
+
+```python
+from langchain.memory import DynamoDBChatMessageHistory
+```

docs/extras/integrations/platforms/google.mdx

@@ -1,6 +1,6 @@
 # Google
 
-All functionality related to Google Platform
+All functionality related to [Google Cloud Platform](https://cloud.google.com/)
 
 ## LLMs
 
@@ -37,7 +37,7 @@ from langchain.chat_models import ChatVertexAI
 >[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.
+First, we need to install `google-cloud-bigquery` python package.
 
 ```bash
 pip install google-cloud-bigquery
@@ -53,7 +53,7 @@ from langchain.document_loaders import BigQueryLoader
 
 >[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.
+First, we need to install `google-cloud-storage` python package.
 
 ```bash
 pip install google-cloud-storage
@@ -78,7 +78,7 @@ from langchain.document_loaders import GCSFileLoader
 
 Currently, only `Google Docs` are supported.
 
-First, you need to install several python package.
+First, we need to install several python package.
 
 ```bash
 pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
@@ -109,6 +109,32 @@ See a [usage example](/docs/integrations/vectorstores/matchingengine).
 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
+```
+
+
 ## Tools
 ### Google Search
 
@@ -123,8 +149,36 @@ 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:
+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
+```

docs/extras/integrations/providers/aws_dynamodb.mdx

@@ -1,23 +0,0 @@
-# AWS DynamoDB
-
->[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) 
-> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
- 
-## Installation and Setup
-
-We have to configur the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). 
-
-We need to install the `boto3` library.
-
-```bash
-pip install boto3
-```
-
-
-## Memory
-
-See a [usage example](/docs/integrations/memory/aws_dynamodb).
-
-```python
-from langchain.memory import DynamoDBChatMessageHistory
-```

docs/extras/integrations/providers/google_document_ai.mdx

@@ -1,28 +0,0 @@
-# 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.  
-
-
-## Installation and Setup
-
-You 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`.
-You 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
-```
-
-## Document Transformer
-
-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
-```

docs/extras/integrations/providers/scann.mdx

@@ -1,29 +0,0 @@
-# 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.
-
-## Installation and Setup
-
-We need to install `scann` python package.
-
-```bash
-pip install scann
-```
-
-## Vector Store
-
-See a [usage example](/docs/integrations/vectorstores/scann).
-
-```python
-from langchain.vectorstores import ScaNN
-```
-