diff --git a/docs/extras/integrations/platforms/aws.mdx b/docs/extras/integrations/platforms/aws.mdx index fedd14181c0..6570935e800 100644 --- a/docs/extras/integrations/platforms/aws.mdx +++ b/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 +``` \ No newline at end of file diff --git a/docs/extras/integrations/platforms/google.mdx b/docs/extras/integrations/platforms/google.mdx index 7f7ab56cfb6..68b80544eeb 100644 --- a/docs/extras/integrations/platforms/google.mdx +++ b/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 +``` diff --git a/docs/extras/integrations/providers/aws_dynamodb.mdx b/docs/extras/integrations/providers/aws_dynamodb.mdx deleted file mode 100644 index 70c16295f07..00000000000 --- a/docs/extras/integrations/providers/aws_dynamodb.mdx +++ /dev/null @@ -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 -``` diff --git a/docs/extras/integrations/providers/google_document_ai.mdx b/docs/extras/integrations/providers/google_document_ai.mdx deleted file mode 100644 index 431cc4ac5f8..00000000000 --- a/docs/extras/integrations/providers/google_document_ai.mdx +++ /dev/null @@ -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 -``` diff --git a/docs/extras/integrations/providers/scann.mdx b/docs/extras/integrations/providers/scann.mdx deleted file mode 100644 index f801207b821..00000000000 --- a/docs/extras/integrations/providers/scann.mdx +++ /dev/null @@ -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 -``` -