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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
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Leonid Ganeline
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@ -1,6 +1,6 @@
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# AWS
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All functionality related to AWS platform
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All functionality related to [Amazon AWS](https://aws.amazon.com/) platform
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## LLMs
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@ -70,7 +70,7 @@ from langchain.llms.sagemaker_endpoint import ContentHandlerBase
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## Document loaders
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### AWS S3 Directory
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### AWS S3 Directory and File
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>[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
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>[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
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>[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
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@ -82,3 +82,24 @@ See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s
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```python
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from langchain.document_loaders import S3DirectoryLoader, S3FileLoader
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```
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## Memory
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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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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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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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@ -1,6 +1,6 @@
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# Google
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All functionality related to Google Platform
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All functionality related to [Google Cloud Platform](https://cloud.google.com/)
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## LLMs
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@ -37,7 +37,7 @@ from langchain.chat_models import ChatVertexAI
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>[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.
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`BigQuery` is a part of the `Google Cloud Platform`.
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First, you need to install `google-cloud-bigquery` python package.
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First, we need to install `google-cloud-bigquery` python package.
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```bash
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pip install google-cloud-bigquery
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@ -53,7 +53,7 @@ from langchain.document_loaders import BigQueryLoader
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>[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
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First, you need to install `google-cloud-storage` python package.
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First, we need to install `google-cloud-storage` python package.
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```bash
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pip install google-cloud-storage
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@ -78,7 +78,7 @@ from langchain.document_loaders import GCSFileLoader
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Currently, only `Google Docs` are supported.
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First, you need to install several python package.
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First, we need to install several python package.
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```bash
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pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
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@ -109,6 +109,32 @@ See a [usage example](/docs/integrations/vectorstores/matchingengine).
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from langchain.vectorstores import MatchingEngine
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```
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### Google 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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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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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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## Tools
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### Google Search
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@ -123,8 +149,36 @@ from langchain.utilities import GoogleSearchAPIWrapper
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```
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For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search.html).
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You can easily load this wrapper as a Tool (to use with an Agent). You can do this with:
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We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
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```python
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from langchain.agents import load_tools
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tools = load_tools(["google-search"])
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```
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## Document Transformer
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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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We 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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We 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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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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@ -1,23 +0,0 @@
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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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@ -1,28 +0,0 @@
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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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@ -1,29 +0,0 @@
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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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