You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
langchain/templates/rag-aws-bedrock
Bagatur 2424fff3f1
notebook fmt (#12498)
8 months ago
..
rag_aws_bedrock notebook fmt (#12498) 8 months ago
tests AWS Bedrock RAG template (#12450) 8 months ago
LICENSE AWS Bedrock RAG template (#12450) 8 months ago
README.md Updated the Bedrock rag template (#12462) 8 months ago
main.py notebook fmt (#12498) 8 months ago
poetry.lock Added a rag template for Kendra (#12470) 8 months ago
pyproject.toml Added a rag template for Kendra (#12470) 8 months ago
rag_aws_bedrock.ipynb notebook fmt (#12498) 8 months ago

README.md

RAG AWS Bedrock

AWS Bedrock is a managed serve that offers a set of foundation models.

Here we will use Anthropic Claude for text generation and Amazon Titan for text embedding.

We will use FAISS as our vectorstore.

(See this notebook for additional context on the RAG pipeline.)

Code here uses the boto3 library to connect with the Bedrock service. See this page for setting up and configuring boto3 to work with an AWS account.

FAISS

You need to install the faiss-cpu package to work with the FAISS vector store.

pip install faiss-cpu

LLM and Embeddings

The code assumes that you are working with the default AWS profile and us-east-1 region. If not, specify these environment variables to reflect the correct region and AWS profile.

  • AWS_DEFAULT_REGION
  • AWS_PROFILE