mirror of https://github.com/hwchase17/langchain
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 pagespull/11420/head
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# AWS DynamoDB
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>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html)
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> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
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## Installation and Setup
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We have to configur the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
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We need to install the `boto3` library.
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```bash
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pip install boto3
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```
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## Memory
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See a [usage example](/docs/integrations/memory/aws_dynamodb).
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```python
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from langchain.memory import DynamoDBChatMessageHistory
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```
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# Google Document AI
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>[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform`
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> service to transform unstructured data from documents into structured data, making it easier
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> to understand, analyze, and consume.
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## Installation and Setup
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You need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)
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The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`)
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and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
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You can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
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tab in the Google Cloud Console.
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```bash
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pip install google-cloud-documentai
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pip install google-cloud-documentai-toolbox
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```
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## Document Transformer
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See a [usage example](/docs/integrations/document_transformers/docai).
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```python
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from langchain.document_loaders.blob_loaders import Blob
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from langchain.document_loaders.parsers import DocAIParser
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```
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# ScaNN
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>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
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> (Scalable Nearest Neighbors) is a python package.
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>
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>`ScaNN` is a method for efficient vector similarity search at scale.
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>ScaNN includes search space pruning and quantization for Maximum Inner
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> Product Search and also supports other distance functions such as
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> Euclidean distance. The implementation is optimized for x86 processors
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> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
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> for more details.
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## Installation and Setup
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We need to install `scann` python package.
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```bash
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pip install scann
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```
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## Vector Store
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See a [usage example](/docs/integrations/vectorstores/scann).
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```python
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from langchain.vectorstores import ScaNN
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```
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