Vertex AI¶

Vertex AI offers everything you need to build and use generative AI—from AI solutions, to Search and Conversation, to 100+ foundation models, to a unified AI platform. You get access to models like PaLM 2 which can be used to score your RAG responses and pipelines with Ragas instead of the default OpenAI.

This tutorial will show you can you can use PaLM 2 with Ragas for evaluation.

Note

this guide is for folks who are using Google VertexAI endpoints. Check the evaluation guide if your using OpenAI endpoints.

Load Sample Dataset¶

# data
from datasets import load_dataset

amnesty_qa = load_dataset("explodinggradients/amnesty_qa", "english_v2")
amnesty_qa

Found cached dataset amnesty_qa (/home/jjmachan/.cache/huggingface/datasets/explodinggradients___amnesty_qa/english_v2/2.0.0/d0ed9800191a31943ee52a5c22ee4305e28a33f5edcd9a323802112cff07cc24)

DatasetDict({
    eval: Dataset({
        features: ['question', 'ground_truth', 'answer', 'contexts'],
        num_rows: 20
    })
})

Now lets import the metrics we are going to use:

from ragas.metrics import (
    context_precision,
    answer_relevancy,
    faithfulness,
    context_recall,
    answer_similarity,
    answer_correctness,
)
from ragas.metrics.critique import harmfulness

# list of metrics we're going to use
metrics = [
    faithfulness,
    answer_relevancy,
    context_recall,
    context_precision,
    harmfulness,
    answer_similarity,
    answer_correctness,
]

By default Ragas uses ChatOpenAI for evaluations, lets swap that out with ChatVertexAI. We’ll wrap ChatVertexAI with Ragas’ LangchainLLMWrapper object to work with the langchain-google-vertexai package. We also need to change the embeddings used for evaluations for OpenAIEmbeddings to VertexAIEmbeddings for metrices that need it, which in our case is answer_relevancy.

import google.auth
from langchain_google_vertexai import ChatVertexAI, VertexAIEmbeddings
from ragas.llms import LangchainLLMWrapper

config = {
    "project_id": "<your-project-id>",
    "chat_model_id": "gemini-1.0-pro-002",
    "embedding_model_id": "textembedding-gecko",
}

# authenticate to GCP
creds, _ = google.auth.default(quota_project_id=config["project_id"])

# create Langchain LLM and Embeddings
vertextai_llm = ChatVertexAI(
    credentials=creds,
    model_name=config["chat_model_id"],
)
vertextai_embeddings = VertexAIEmbeddings(
    credentials=creds, model_name=config["embedding_model_id"]
)

Evaluation¶

Running the evalutation is as simple as calling evaluate on the Dataset with the metrics of your choice.

from ragas import evaluate

result = evaluate(
    amnesty_qa["eval"].select(range(1)),  # using 1 as example due to quota constrains
    metrics=metrics,
    llm=vertextai_llm,
    embeddings=vertextai_embeddings,
)

result

{'faithfulness': 0.9583, 'answer_relevancy': 0.8608, 'context_recall': 1.0000, 'context_precision': 1.0000, 'harmfulness': 1.0000, 'answer_similarity': 0.9405, 'answer_correctness': 0.3757}

and there you have the it, all the scores you need.

now if we want to dig into the results and figure out examples where your pipeline performed worse or really good you can easily convert it into a pandas array and use your standard analytics tools too!

df = result.to_pandas()
df.head()

	question	ground_truth	answer	contexts	faithfulness	answer_relevancy	context_recall	context_precision	harmfulness	answer_similarity	answer_correctness
0	What are the global implications of the USA Su...	The global implications of the USA Supreme Cou...	The global implications of the USA Supreme Cou...	[- In 2022, the USA Supreme Court handed down ...	0.958333	0.86077	1.0	1.0	1	0.940453	0.375738

And thats it!

if you have any suggestion/feedbacks/things your not happy about, please do share it in the issue section. We love hearing from you 😁