Stuff Chain

This cookbook demonstrates how to use the stuff chain for BasicRAG. For more information, refer to RAG-PIPELINES.

Illustration of stuff chain (Source: LangChain)

Note that this cookbook assumes that you already have the Llama-2-13b-chat LLM ready, for more details on how to quantize and run an LLM locally, refer to the LLM section under Getting Started.

from grag.components.multivec_retriever import Retriever
from grag.components.vectordb.deeplake_client import DeepLakeClient
from grag.rag.basic_rag import BasicRAG

client = DeepLakeClient(collection_name="grag")
retriever = Retriever(vectordb=client)

rag = BasicRAG(model_name="Llama-2-13b-chat", retriever=retriever)
# Note that doc_chain='stuff' is the default hence not passed to the class explicitly.


if __name__ == "__main__":
    while True:
        query = input("Query:")
        rag(query)