langchain/docs/extras/integrations/providers/bedrock.mdx

# 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](/docs/integrations/llms/bedrock).

```python
from langchain.llms.bedrock import Bedrock
```

## Text Embedding Models

See a [usage example](/docs/integrations/text_embedding/bedrock).
```python
from langchain.embeddings import BedrockEmbeddings
```
Doc refactor (#6300) Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> 2023-06-16 18:52:56 +00:00			`# 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`

Update Integrations links (#8206) 2023-07-25 04:20:32 +00:00			`See a [usage example](/docs/integrations/llms/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
			```python
Fixed inaccurate import in integrations:providers:bedrock documentation (#8915) Description: Fixed inaccurate import in integrations:providers:bedrock documentation In the current version of the bedrock documentation, page https://python.langchain.com/docs/integrations/providers/bedrock it states that the import is from langchain import Bedrock This has been changed to from langchain.llms.bedrock import Bedrock as stated in https://python.langchain.com/docs/integrations/llms/bedrock Issue: Not applicable Dependencies No dependencies required Tag maintainer @baskaryan Twitter handle: Not applicable 2023-08-08 14:24:36 +00:00			`from langchain.llms.bedrock import 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			```

			`## Text Embedding Models`

Update Integrations links (#8206) 2023-07-25 04:20:32 +00:00			`See a [usage example](/docs/integrations/text_embedding/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			```python
			`from langchain.embeddings import BedrockEmbeddings`
			```