AWS Bedrock x AutoRAG¶
Setting Up the AWS profile¶
First, you need to have an AWS account.
And you need to set up the AWS CLI with your AWS account.
You can find detailed information about the AWS CLI configuration at the following link
Using AWS Bedrock with AutoRAG¶
For using AWS Bedrock, you can use Llama Index LLm’s bedrock
at the AutoRAG config YAML file without any further configuration.
Writing the Config YAML File¶
Here’s the modified YAML configuration using Bedrock
:
nodes:
- node_line_name: node_line_1
nodes:
- node_type: generator
modules:
- module_type: llama_index_llm
llm: bedrock
model: amazon.titan-text-express-v1
profile_name: your_profile_name # Plz replace this with your profile name
You can find the model ID at the following link
multiple models can be used in the same way.
nodes:
- node_line_name: node_line_1
nodes:
- node_type: generator
modules:
- module_type: llama_index_llm
llm: bedrock
model: [amazon.titan-text-express-v1, Claude 3.5 Sonnet, Llama 3.2 90B Instruct]
profile_name: your_profile_name # Plz replace this with your profile name
For full YAML files, please see the sample_config folder in the AutoRAG repo at here.
Running AutoRAG¶
Before running AutoRAG, make sure you have your QA dataset and corpus dataset ready. If you want to know how to make it, visit here.
Run AutoRAG with the following command:
autorag evaluate \
- qa_data_path ./path/to/qa.parquet \
- corpus_data_path ./path/to/corpus.parquet \
- project_dir ./path/to/project_dir \
- config ./path/to/bedrock_config.yaml
AutoRAG will automatically experiment and optimize RAG.