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# 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 -->
25 lines
627 B
Markdown
25 lines
627 B
Markdown
# Amazon Bedrock
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>[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.
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## Installation and Setup
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```bash
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pip install boto3
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```
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## LLM
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See a [usage example](../modules/models/llms/integrations/bedrock.ipynb).
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```python
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from langchain import Bedrock
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```
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## Text Embedding Models
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See a [usage example](../modules/models/text_embedding/examples/bedrock.ipynb).
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```python
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from langchain.embeddings import BedrockEmbeddings
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```
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