langchain/docs/integrations/amazon_bedrock.md

# Amazon Bedrock

>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

## Installation and Setup

```bash
pip install boto3
```

## LLM

See a [usage example](../modules/models/llms/integrations/bedrock.ipynb).

```python
from langchain import Bedrock
```

## Text Embedding Models

See a [usage example](../modules/models/text_embedding/examples/amazon_bedrock.ipynb).
```python
from langchain.embeddings import BedrockEmbeddings
```
docs: `ecosystem/integrations` update 5 (#5752) - added missed integration to `docs/ecosystem/integrations/` - updated notebooks to consistent format: changed titles, file names; added descriptions #### Who can review? @hwchase17 @dev2049 2023-06-05 23:08:55 +00:00			`# Amazon Bedrock`
Bedrock llm and embeddings (#5464) # Bedrock LLM and Embeddings This PR adds a new LLM and an Embeddings class for the [Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes example notebooks for using the LLM class in a conversation chain and embeddings usage in creating an embedding for a query and document. Note: AWS is doing a private release of the Bedrock service on 05/31/2023; users need to request access and added to an allowlist in order to start using the Bedrock models and embeddings. Please use the [Bedrock Home Page](https://aws.amazon.com/bedrock) to request access and to learn more about the models available in Bedrock. <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> 2023-05-31 14:17:01 +00:00
			`>[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.`

			`## Installation and Setup`

			```bash
			`pip install boto3`
			```

			`## LLM`

			`See a [usage example](../modules/models/llms/integrations/bedrock.ipynb).`

			```python
			`from langchain import Bedrock`
			```

			`## Text Embedding Models`

docs: `ecosystem/integrations` update 5 (#5752) - added missed integration to `docs/ecosystem/integrations/` - updated notebooks to consistent format: changed titles, file names; added descriptions #### Who can review? @hwchase17 @dev2049 2023-06-05 23:08:55 +00:00			`See a [usage example](../modules/models/text_embedding/examples/amazon_bedrock.ipynb).`
Bedrock llm and embeddings (#5464) # Bedrock LLM and Embeddings This PR adds a new LLM and an Embeddings class for the [Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes example notebooks for using the LLM class in a conversation chain and embeddings usage in creating an embedding for a query and document. Note: AWS is doing a private release of the Bedrock service on 05/31/2023; users need to request access and added to an allowlist in order to start using the Bedrock models and embeddings. Please use the [Bedrock Home Page](https://aws.amazon.com/bedrock) to request access and to learn more about the models available in Bedrock. <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> 2023-05-31 14:17:01 +00:00			```python
			`from langchain.embeddings import BedrockEmbeddings`
			```