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docs/docs/integrations/providers/aws.mdx
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@ -28,7 +28,7 @@ pip install langchain-community boto3
> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy
> generative AI capabilities into your applications using the AWS services you are already familiar with.
See a [usage example](/docs/integrations/chat/bedrock).
See a [usage example](/docs/docs/integrations/chat/bedrock.ipynb).
```python
from langchain_aws import ChatBedrock
@ -56,7 +56,7 @@ from langchain_aws import ChatBedrockConverse
### Bedrock
See a [usage example](/docs/integrations/llms/bedrock).
See a [usage example](docs/docs/integrations/llms/bedrock.ipynb).
```python
from langchain_aws import BedrockLLM
@ -76,7 +76,7 @@ from langchain_aws import BedrockLLM
> You pay for the API calls you receive and the amount of data transferred out and, with the `API Gateway`
> tiered pricing model, you can reduce your cost as your API usage scales.
See a [usage example](/docs/integrations/llms/amazon_api_gateway).
See a [usage example](/docs/docs/integrations/llms/amazon_api_gateway.ipynb).
```python
from langchain_community.llms import AmazonAPIGateway
@ -89,7 +89,7 @@ from langchain_community.llms import AmazonAPIGateway
We use `SageMaker` to host our model and expose it as the `SageMaker Endpoint`.
See a [usage example](/docs/integrations/llms/sagemaker).
See a [usage example](/docs/docs/integrations/llms/sagemaker.ipynb).
```python
from langchain_aws import SagemakerEndpoint
@ -99,14 +99,14 @@ from langchain_aws import SagemakerEndpoint
### Bedrock
See a [usage example](/docs/integrations/text_embedding/bedrock).
See a [usage example](/docs/docs/integrations/text_embedding/bedrock.ipynb).
```python
from langchain_aws import BedrockEmbeddings
```
### SageMaker Endpoint
See a [usage example](/docs/integrations/text_embedding/sagemaker-endpoint).
See a [usage example](/docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb).
```python
from langchain_community.embeddings import SagemakerEndpointEmbeddings
from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase
@ -121,9 +121,9 @@ from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase
>[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)
See a [usage example for S3DirectoryLoader](/docs/integrations/document_loaders/aws_s3_directory).
See a [usage example for S3DirectoryLoader](/docs/docs/integrations/document_loaders/aws_s3_directory.ipynb).
See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s3_file).
See a [usage example for S3FileLoader](/docs/docs/integrations/document_loaders/aws_s3_file.ipynb).
```python
from langchain_community.document_loaders import S3DirectoryLoader, S3FileLoader
@ -134,7 +134,7 @@ from langchain_community.document_loaders import S3DirectoryLoader, S3FileLoader
>[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine
> learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.
See a [usage example](/docs/integrations/document_loaders/amazon_textract).
See a [usage example](/docs/docs/integrations/document_loaders/amazon_textract.ipynb).
```python
from langchain_community.document_loaders import AmazonTextractPDFLoader
@ -145,7 +145,7 @@ from langchain_community.document_loaders import AmazonTextractPDFLoader
>[Amazon Athena](https://aws.amazon.com/athena/) is a serverless, interactive analytics service built
>on open-source frameworks, supporting open-table and file formats.
See a [usage example](/docs/integrations/document_loaders/athena).
See a [usage example](/docs/docs/integrations/document_loaders/athena.ipynb).
```python
from langchain_community.document_loaders.athena import AthenaLoader
@ -159,7 +159,7 @@ from langchain_community.document_loaders.athena import AthenaLoader
> enabling various AWS services and your applications to query and connect
> to the data they need efficiently.
See a [usage example](/docs/integrations/document_loaders/glue_catalog).
See a [usage example](/docs/docs/integrations/document_loaders/glue_catalog.ipynb).
```python
from langchain_community.document_loaders.glue_catalog import GlueCatalogLoader
@ -182,7 +182,7 @@ We need to install several python libraries.
pip install boto3 requests requests-aws4auth
```
See a [usage example](/docs/integrations/vectorstores/opensearch#using-aos-amazon-opensearch-service).
See a [usage example](/docs/docs/integrations/vectorstores/opensearch.ipynb).
```python
from langchain_community.vectorstores import OpenSearchVectorSearch
@ -196,7 +196,7 @@ from langchain_community.vectorstores import OpenSearchVectorSearch
#### Installation and Setup
See [detail configuration instructions](/docs/integrations/vectorstores/documentdb).
See [detail configuration instructions](/docs/docs/integrations/vectorstores/documentdb.ipynb).
We need to install the `pymongo` python package.
@ -210,7 +210,7 @@ pip install pymongo
AWS offers services for computing, databases, storage, analytics, and other functionality. For an overview of all AWS services, see [Cloud Computing with Amazon Web Services](https://aws.amazon.com/what-is-aws/).
See a [usage example](/docs/integrations/vectorstores/documentdb).
See a [usage example](/docs/docs/integrations/vectorstores/documentdb.ipynb).
```python
from langchain_community.vectorstores import DocumentDBVectorSearch
@ -232,7 +232,7 @@ vds = InMemoryVectorStore.from_documents(
index_name=INDEX_NAME,
)
```
See a [usage example](/docs/integrations/vectorstores/memorydb).
See a [usage example](/docs/docs/integrations/vectorstores/memorydb.ipynb).
## Retrievers
@ -254,7 +254,7 @@ We need to install the `langchain-aws` library.
pip install langchain-aws
```
See a [usage example](/docs/integrations/retrievers/amazon_kendra_retriever).
See a [usage example](/docs/docs/integrations/retrievers/amazon_kendra_retriever.ipynb).
```python
from langchain_aws import AmazonKendraRetriever
@ -272,7 +272,7 @@ We need to install the `langchain-aws` library.
pip install langchain-aws
```
See a [usage example](/docs/integrations/retrievers/bedrock).
See a [usage example](/docs/docs/integrations/retrievers/bedrock.ipynb).
```python
from langchain_aws import AmazonKnowledgeBasesRetriever
@ -294,7 +294,7 @@ We need to install `boto3` python library.
pip install boto3
```
See a [usage example](/docs/integrations/tools/awslambda).
See a [usage example](/docs/docs/integrations/tools/awslambda.ipynb).
## Memory
@ -311,7 +311,7 @@ We need to install the `boto3` library.
pip install boto3
```
See a [usage example](/docs/integrations/memory/aws_dynamodb).
See a [usage example](/docs/docs/integrations/memory/aws_dynamodb.ipynb).
```python
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
@ -332,7 +332,7 @@ pip install langchain-aws
### Amazon Neptune with Cypher
See a [usage example](/docs/integrations/graphs/amazon_neptune_open_cypher).
See a [usage example](/docs/docs/integrations/graphs/amazon_neptune_open_cypher.ipynb).
```python
from langchain_aws.graphs import NeptuneGraph
@ -342,7 +342,7 @@ from langchain_aws.chains import create_neptune_opencypher_qa_chain
### Amazon Neptune with SPARQL
See a [usage example](/docs/integrations/graphs/amazon_neptune_sparql).
See a [usage example](/docs/docs/integrations/graphs/amazon_neptune_sparql.ipynb).
```python
from langchain_aws.graphs import NeptuneRdfGraph
@ -374,7 +374,7 @@ We need to install several python libraries.
pip install google-search-results sagemaker
```
See a [usage example](/docs/integrations/callbacks/sagemaker_tracking).
See a [usage example](/docs/docs/integrations/callbacks/sagemaker_tracking.ipynb).
```python
from langchain_community.callbacks import SageMakerCallbackHandler