llama-index-packs-corrective-rag 0.1.2

llama-index packs corrective_rag paper implementation

Corrective Retrieval Augmented Generation Llama Pack

This LlamaPack implements the Corrective Retrieval Augmented Generation (CRAG) paper

Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation.

This LlamaPack uses Tavily AI API for web-searches. So, we recommend you to get the api-key before proceeding further.

Installation

pip install llama-index llama-index-tools-tavily-research

CLI Usage

You can download llamapacks directly using llamaindex-cli, which comes installed with the llama-index python package:

llamaindex-cli download-llamapack CorrectiveRAGPack --download-dir ./corrective_rag_pack

You can then inspect the files at ./corrective_rag_pack and use them as a template for your own project.

Code Usage

You can download the pack to a the ./corrective_rag_pack directory:

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
CorrectiveRAGPack = download_llama_pack(
    "CorrectiveRAGPack", "./corrective_rag_pack"
)

# You can use any llama-hub loader to get documents!
corrective_rag = CorrectiveRAGPack(documents, tavily_ai_api_key)

From here, you can use the pack, or inspect and modify the pack in ./corrective_rag_pack.

The run() function contains around logic behind Corrective Retrieval Augmented Generation - CRAG paper.

response = corrective_rag.run("<query>", similarity_top_k=2)