mirror of
https://github.com/hwchase17/langchain
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78 lines
2.5 KiB
Markdown
78 lines
2.5 KiB
Markdown
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# rag-aws-bedrock
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This template is designed to connect with the AWS Bedrock service, a managed server that offers a set of foundation models.
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It primarily uses the `Anthropic Claude` for text generation and `Amazon Titan` for text embedding, and utilizes FAISS as the vectorstore.
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For additional context on the RAG pipeline, refer to [this notebook](https://github.com/aws-samples/amazon-bedrock-workshop/blob/main/03_QuestionAnswering/01_qa_w_rag_claude.ipynb).
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## Environment Setup
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Before you can use this package, ensure that you have configured `boto3` to work with your AWS account.
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For details on how to set up and configure `boto3`, visit [this page](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration).
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In addition, you need to install the `faiss-cpu` package to work with the FAISS vector store:
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```bash
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pip install faiss-cpu
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```
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You should also set the following environment variables to reflect your AWS profile and region (if you're not using the `default` AWS profile and `us-east-1` region):
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* `AWS_DEFAULT_REGION`
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* `AWS_PROFILE`
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## Usage
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First, install the LangChain CLI:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package:
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```shell
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langchain app new my-app --package rag-aws-bedrock
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```
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To add this package to an existing project:
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```shell
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langchain app add rag-aws-bedrock
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```
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Then add the following code to your `server.py` file:
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```python
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from rag_aws_bedrock import chain as rag_aws_bedrock_chain
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add_routes(app, rag_aws_bedrock_chain, path="/rag-aws-bedrock")
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```
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(Optional) If you have access to LangSmith, you can configure it to trace, monitor, and debug LangChain applications. If you don't have access, you can skip this section.
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server running locally at [http://localhost:8000](http://localhost:8000)
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You can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) and access the playground at [http://127.0.0.1:8000/rag-aws-bedrock/playground](http://127.0.0.1:8000/rag-aws-bedrock/playground).
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You can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-aws-bedrock")
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``` |