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150251fd49
Added missed provider pages and links. Fixed inconsistent formatting. Added arxiv references to docstirngs. --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
79 lines
2.6 KiB
Markdown
79 lines
2.6 KiB
Markdown
# RAG - AWS Bedrock, FAISS
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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 [these notebooks](https://github.com/aws-samples/amazon-bedrock-workshop/tree/main/02_KnowledgeBases_and_RAG).
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See [The FAISS Library](https://arxiv.org/pdf/2401.08281) paper for more details.
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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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``` |