llama-index-packs-rag-evaluator 0.4.1

llama-index packs rag_evaluator integration

Retrieval-Augmented Generation (RAG) Evaluation Pack

Get benchmark scores on your own RAG pipeline (i.e. QueryEngine) on a RAG dataset (i.e., LabelledRagDataset). Specifically this pack takes in as input a query engine and a LabelledRagDataset, which can also be downloaded from llama-hub.

CLI Usage

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

llamaindex-cli download-llamapack RagEvaluatorPack --download-dir ./rag_evaluator_pack

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

Code Usage

You can download the pack to the ./rag_evaluator_pack directory through python code as well. The sample script below demonstrates how to construct RagEvaluatorPack using a LabelledRagDataset downloaded from llama-hub and a simple RAG pipeline built off of its source documents.

from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex

# download a LabelledRagDataset from llama-hub
rag_dataset, documents = download_llama_dataset(
    "PaulGrahamEssayDataset", "./paul_graham"
)

# build a basic RAG pipeline off of the source documents
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()

# Time to benchmark/evaluate this RAG pipeline
# Download and install dependencies
RagEvaluatorPack = download_llama_pack(
    "RagEvaluatorPack", "./rag_evaluator_pack"
)

# construction requires a query_engine, a rag_dataset, and optionally a judge_llm
rag_evaluator_pack = RagEvaluatorPack(
    query_engine=query_engine, rag_dataset=rag_dataset
)

# PERFORM EVALUATION
benchmark_df = rag_evaluator_pack.run()  # async arun() also supported
print(benchmark_df)

Output:

rag                            base_rag
metrics
mean_correctness_score         4.511364
mean_relevancy_score           0.931818
mean_faithfulness_score        1.000000
mean_context_similarity_score  0.945952

Note that rag_evaluator_pack.run() will also save two files in the same directory in which the pack was invoked:

.
├── benchmark.csv (CSV format of the benchmark scores)
└── _evaluations.json (raw evaluation results for all examples & predictions)