Evaluate RAG

Google Colab

colab.research.google.com

In this tutorial we will look into building a RAG pipeline and evaluating it with Phoenix Evals. It has the the following sections:

Understanding Retrieval Augmented Generation (RAG).
Building RAG (with the help of a framework such as LlamaIndex).
Evaluating RAG with Phoenix Evals.

Retrieval Augmented Generation (RAG)

LLMs are trained on vast amounts of data, but these will not include your specific data (things like company knowledge bases and documentation). Retrieval-Augmented Generation (RAG) addresses this by dynamically incorporating your data as context during the generation process. This is done not by altering the training data of the LLMs but by allowing the model to access and utilize your data in real-time to provide more tailored and contextually relevant responses. In RAG, your data is loaded and prepared for queries. This process is called indexing. User queries act on this index, which filters your data down to the most relevant context. This context and your query then are sent to the LLM along with a prompt, and the LLM provides a response. RAG is a critical component for building applications such a chatbots or agents and you will want to know RAG techniques on how to get data into your application.

Stages within RAG

There are five key stages within RAG, which will in turn be a part of any larger RAG application.

Loading: This refers to getting your data from where it lives - whether it’s text files, PDFs, another website, a database or an API - into your pipeline.
Indexing: This means creating a data structure that allows for querying the data. For LLMs this nearly always means creating vector embeddings, numerical representations of the meaning of your data, as well as numerous other metadata strategies to make it easy to accurately find contextually relevant data.
Storing: Once your data is indexed, you will want to store your index, along with any other metadata, to avoid the need to re-index it.
Querying: For any given indexing strategy there are many ways you can utilize LLMs and data structures to query, including sub-queries, multi-step queries, and hybrid strategies.
Evaluation: A critical step in any pipeline is checking how effective it is relative to other strategies, or when you make changes. Evaluation provides objective measures on how accurate, faithful, and fast your responses to queries are.

Build a RAG system

Now that we have understood the stages of RAG, let’s build a pipeline. We will use LlamaIndex for RAG and Phoenix Evals for evaluation.

!pip install -qq "arize-phoenix[experimental,llama-index]>=2.0"

# The nest_asyncio module enables the nesting of asynchronous functions within an already running async loop.
# This is necessary because Jupyter notebooks inherently operate in an asynchronous loop.
# By applying nest_asyncio, we can run additional async functions within this existing loop without conflicts.
import nest_asyncio

nest_asyncio.apply()

import os
from getpass import getpass

import pandas as pd
from phoenix.client import Client
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, set_global_handler
from llama_index.llms.openai import OpenAI
from llama_index.core.node_parser import SimpleNodeParser

During this tutorial, we will capture all the data we need to evaluate our RAG pipeline using Phoenix Tracing. To enable this, simply start the phoenix application and instrument LlamaIndex.

import phoenix as px
client = Client()
px.launch_app()

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)

For this tutorial we will be using OpenAI for creating synthetic data as well as for evaluation.

if not (openai_api_key := os.getenv("OPENAI_API_KEY")):
    openai_api_key = getpass("🔑 Enter your OpenAI API key: ")
os.environ["OPENAI_API_KEY"] = openai_api_key

Let’s use an essay by Paul Graham to build our RAG pipeline.

!mkdir -p 'data/paul_graham/'
!curl 'https://raw.githubusercontent.com/Arize-ai/phoenix-assets/main/data/paul_graham/paul_graham_essay.txt' -o 'data/paul_graham/paul_graham_essay.txt'

Load Data and Build an Index

documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

# Define an LLM
llm = OpenAI(model="gpt-4")

# Build index with a chunk_size of 512
node_parser = SimpleNodeParser.from_defaults(chunk_size=512)
nodes = node_parser.get_nodes_from_documents(documents)
vector_index = VectorStoreIndex(nodes)

Build a QueryEngine and start querying.

query_engine = vector_index.as_query_engine()

response_vector = query_engine.query("What did the author do growing up?")

Check the response that you get from the query.

response_vector.response

'The author wrote short stories and worked on programming, specifically on an IBM 1401 computer in 9th grade.'

By default LlamaIndex retrieves two similar nodes/ chunks. You can modify that in vector_index.as_query_engine(similarity_top_k=k). Let’s check the text in each of these retrieved nodes.

# First retrieved node
response_vector.source_nodes[0].get_text()

'What I Worked On\n\nFebruary 2021\n\nBefore college the two main things I worked on, outside of school, were writing and programming. I didn\'t write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n\nThe first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district\'s 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain\'s lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n\nThe language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\n\nI was puzzled by the 1401. I couldn\'t figure out what to do with it. And in retrospect there\'s not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn\'t have any data stored on punched cards. The only other option was to do things that didn\'t rely on any input, like calculate approximations of pi, but I didn\'t know enough math to do anything interesting of that type. So I\'m not surprised I can\'t remember any programs I wrote, because they can\'t have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn\'t. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager\'s expression made clear.\n\nWith microcomputers, everything changed.'

# Second retrieved node
response_vector.source_nodes[1].get_text()

"It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\n\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\n\nIn the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code.\n\nNow that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\n\n\n\n\n\n\n\n\n\nNotes\n\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\n\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\n\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco."

Remember that we are using Phoenix Tracing to capture all the data we need to evaluate our RAG pipeline. You can view the traces in the phoenix application.

print("phoenix URL", px.active_session().url)

We can access the traces by directly pulling the spans from the phoenix session.

from phoenix.client import Client

client = Client()
spans_df = client.spans.get_spans_dataframe()

spans_df[["name", "span_kind", "attributes.input.value", "attributes.retrieval.documents"]].head()

context.span_id	name	span_kind	attributes.input.value	attributes.retrieval.documents
6aba9eee-91c9-4ee2-81e9-1bdae2eb435d	llm	LLM	NaN	NaN
cc9feb6a-30ba-4f32-af8d-8c62dd1b1b23	synthesize	CHAIN	What did the author do growing up?	NaN
8202dbe5-d17e-4939-abd8-153cad08bdca	embedding	EMBEDDING	NaN	NaN
aeadad73-485f-400b-bd9d-842abfaa460b	retrieve	RETRIEVER	What did the author do growing up?	[{‘document.content’: ‘What I Worked OnFebru…
9e25c528-5e2f-4719-899a-8248bab290ec	query	CHAIN	What did the author do growing up?	NaN

Note that the traces have captured the documents that were retrieved by the query engine. This is nice because it means we can introspect the documents without having to keep track of them ourselves.

spans_with_docs_df = spans_df[spans_df["attributes.retrieval.documents"].notnull()]

spans_with_docs_df[["attributes.input.value", "attributes.retrieval.documents"]].head()

context.span_id	attributes.input.value	attributes.retrieval.documents
aeadad73-485f-400b-bd9d-842abfaa460b	What did the author do growing up?	[{‘document.content’: ‘What I Worked OnFebru…

We have built a RAG pipeline and also have instrumented it using Phoenix Tracing. We now need to evaluate it’s performance. We can assess our RAG system/query engine using Phoenix’s LLM Evals. Let’s examine how to leverage these tools to quantify the quality of our retrieval-augmented generation system.

Evaluation

Evaluation should serve as the primary metric for assessing your RAG application. It determines whether the pipeline will produce accurate responses based on the data sources and range of queries. While it’s beneficial to examine individual queries and responses, this approach is impractical as the volume of edge-cases and failures increases. Instead, it’s more effective to establish a suite of metrics and automated evaluations. These tools can provide insights into overall system performance and can identify specific areas that may require scrutiny. In a RAG system, evaluation focuses on two critical aspects:

Retrieval Evaluation: To assess the accuracy and relevance of the documents that were retrieved
Response Evaluation: Measure the appropriateness of the response generated by the system when the context was provided.

Generate Question Context Pairs

For the evaluation of a RAG system, it’s essential to have queries that can fetch the correct context and subsequently generate an appropriate response. For this tutorial, let’s use Phoenix’s LLM to help us create the question-context pairs. First, let’s create a dataframe of all the document chunks that we have indexed.

# Let's construct a dataframe of just the documents that are in our index
document_chunks_df = pd.DataFrame({"text": [node.get_text() for node in nodes]})
document_chunks_df.head()

	text
0	What I Worked On\n\nFebruary 2021\n\nBefore co…
1	I was puzzled by the 1401. I couldn’t figure o…
2	I remember vividly how impressed and envious I…
3	I couldn’t have put this into words when I was…
4	This was more like it; this was what I had exp…

Now that we have the document chunks, let’s prompt an LLM to generate us 3 questions per chunk. Note that you could manually solicit questions from your team or customers, but this is a quick and easy way to generate a large number of questions.

generate_questions_template = """\
Context information is below.

---------------------
{text}
---------------------

Given the context information and not prior knowledge.
generate only questions based on the below query.

You are a Teacher/ Professor. Your task is to setup \
3 questions for an upcoming \
quiz/examination. The questions should be diverse in nature \
across the document. Restrict the questions to the \
context information provided."

Output the questions in JSON format with the keys question_1, question_2, question_3.
"""

import json

from phoenix.evals import LLM
from phoenix.evals.executors import AsyncExecutor
from phoenix.evals.utils import default_tqdm_progress_bar_formatter


llm = LLM(provider="openai", model="gpt-4o")


def output_parser(response: str):
    try:
        return json.loads(response)
    except json.JSONDecodeError as e:
        return {"__error__": str(e)}


async def generate_questions(row):
    prompt = generate_questions_template.format(text=row["text"])
    response = await llm.async_generate_text(prompt=prompt)
    return output_parser(response)


executor = AsyncExecutor(
    generation_fn=generate_questions,
    concurrency=10,
    tqdm_bar_format=default_tqdm_progress_bar_formatter("Generating questions"),
)
results, details = await executor.execute(
    [row.to_dict() for _, row in document_chunks_df.iterrows()]
)

questions_df = document_chunks_df.copy()
for i, parsed in enumerate(results):
    if isinstance(parsed, dict):
        for col in ["question_1", "question_2", "question_3"]:
            questions_df.at[i, col] = parsed.get(col)

questions_df.head()

	question_1	question_2	question_3
0	What were the two main things the author worked…	What was the language the author used to write…	What was the author’s clearest memory regarding…
1	What were the limitations of the 1401 computer…	How did microcomputers change the author’s exp…	Why did the author’s father buy a TRS-80 compu…
2	What was the author’s first experience with co…	Why did the author decide to switch from study…	What were the two things that influenced the a…
3	What were the two things that inspired the aut…	What programming language did the author learn…	What was the author’s undergraduate thesis about?
4	What was the author’s undergraduate thesis about?	Which three grad schools did the author apply to?	What realization did the author have during th…

# Construct a dataframe of the questions and the document chunks
questions_with_document_chunk_df = pd.concat([questions_df, document_chunks_df], axis=1)
questions_with_document_chunk_df = questions_with_document_chunk_df.melt(
    id_vars=["text"], value_name="question"
).drop("variable", axis=1)
# If the above step was interrupted, there might be questions missing. Let's run this to clean up the dataframe.
questions_with_document_chunk_df = questions_with_document_chunk_df[
    questions_with_document_chunk_df["question"].notnull()
]

The LLM has generated three questions per chunk. Let’s take a quick look.

questions_with_document_chunk_df.head(10)

	text	question
0	What I Worked On\n\nFebruary 2021\n\nBefore co…	What were the two main things the author worked…
1	I was puzzled by the 1401. I couldn’t figure o…	What were the limitations of the 1401 computer…
2	I remember vividly how impressed and envious I…	What was the author’s first experience with co…
3	I couldn’t have put this into words when I was…	What were the two things that inspired the aut…
4	This was more like it; this was what I had exp…	What was the author’s undergraduate thesis about?
5	Only Harvard accepted me, so that was where I …	What realization did the author have during th…
6	So I decided to focus on Lisp. In fact, I deci…	What motivated the author to write a book about…
7	Anyone who wanted one to play around with could…	What realization did the author have while vis…
8	I knew intellectually that people made art — t…	What was the author’s initial perception of pe…
9	Then one day in April 1990 a crack appeared in…	What was the author’s initial plan for their d…

Retrieval Evaluation

We are now prepared to perform our retrieval evaluations. We will execute the queries we generated in the previous step and verify whether or not that the correct context is retrieved.

# First things first, let's reset phoenix
px.close_app()
px.launch_app()

# loop over the questions and generate the answers
for _, row in questions_with_document_chunk_df.iterrows():
    question = row["question"]
    response_vector = query_engine.query(question)
    print(f"Question: {question}\nAnswer: {response_vector.response}\n")

Now that we have executed the queries, we can start validating whether or not the RAG system was able to retrieve the correct context. Let’s extract all the retrieved documents from the traces logged to phoenix. (For an in-depth explanation of how to export trace data from the phoenix runtime, consult the docs).

from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
retrieved_documents_df = client.spans.get_spans_dataframe(
    query=SpanQuery()
    .where("span_kind == 'RETRIEVER'")
    .select("input.value")
    .explode("retrieval.documents", reference="document.content", document_score="document.score")
)
retrieved_documents_df = retrieved_documents_df.rename(columns={"input.value": "input"})
retrieved_documents_df

		context.trace_id	input	reference	document_score
context.span_id	document_position
b375be95-8e5e-4817-a29f-e18f7aaa3e98	0	20e0f915-e089-4e8e-8314-b68ffdffd7d1	How does leaving YC affect the author’s relati…	On one of them I realized I was ready to hand …	0.820411
1	20e0f915-e089-4e8e-8314-b68ffdffd7d1	How does leaving YC affect the author’s relati…	That was what it took for Rtm to offer unsolic…	0.815969
e4e68b51-dbc9-4154-85a4-5cc69382050d	0	4ad14fd2-0950-4b3f-9613-e1be5e51b5a4	Why did YC become a fund for a couple of years…	For example, one thing Julian had done for us …	0.860981
1	4ad14fd2-0950-4b3f-9613-e1be5e51b5a4	Why did YC become a fund for a couple of years…	They were an impressive group. That first batc…	0.849695
27ba6b6f-828b-4732-bfcc-3262775cd71f	0	d62fb8e8-4247-40ac-8808-818861bfb059	Why did the author choose the name ‘Y Combinat…	Screw the VCs who were taking so long to make …	0.868981
…	…	…	…	…	…
353f152c-44ce-4f3e-a323-0caa90f4c078	1	6b7bebf6-bed3-45fd-828a-0730d8f358ba	What was the author’s first experience with co…	What I Worked On\n\nFebruary 2021\n\nBefore co…	0.877719
16de2060-dd9b-4622-92a1-9be080564a40	0	6ce5800d-7186-414e-a1cf-1efb8d39c8d4	What were the limitations of the 1401 computer…	I was puzzled by the 1401. I couldn’t figure o…	0.847688
1	6ce5800d-7186-414e-a1cf-1efb8d39c8d4	What were the limitations of the 1401 computer…	I remember vividly how impressed and envious I…	0.836979
e996c90f-4ea9-4f7c-b145-cf461de7d09b	0	a328a85a-aadd-44f5-b49a-2748d0bd4d2f	What were the two main things the author worked…	What I Worked On\n\nFebruary 2021\n\nBefore co…	0.843280
1	a328a85a-aadd-44f5-b49a-2748d0bd4d2f	What were the two main things the author worked…	Then one day in April 1990 a crack appeared in…	0.822055

Let’s now use Phoenix’s LLM Evals to evaluate the relevance of the retrieved documents with regards to the query. Note, we’ve turned on explanations which prompts the LLM to explain it’s reasoning. This can be useful for debugging and for figuring out potential corrective actions.

from phoenix.evals import LLM, async_evaluate_dataframe
from phoenix.evals.metrics import DocumentRelevanceEvaluator

llm = LLM(provider="openai", model="gpt-4-turbo-preview")
relevance_evaluator = DocumentRelevanceEvaluator(llm=llm)

retrieved_documents_relevance_df = await async_evaluate_dataframe(
    dataframe=retrieved_documents_df,
    evaluators=[relevance_evaluator],
    concurrency=10,
)

retrieved_documents_relevance_df.head()

We can now combine the documents with the relevance evaluations to compute retrieval metrics. These metrics will help us understand how well the RAG system is performing.

documents_with_relevance_df = pd.concat(
    [retrieved_documents_df, retrieved_documents_relevance_df.add_prefix("eval_")], axis=1
)
documents_with_relevance_df

Let’s compute Normalized Discounted Cumulative Gain NCDG at 2 for all our retrieval steps. In information retrieval, this metric is often used to measure effectiveness of search engine algorithms and related applications.

import numpy as np
from sklearn.metrics import ndcg_score


def _compute_ndcg(df: pd.DataFrame, k: int):
    """Compute NDCG@k in the presence of missing values"""
    n = max(2, len(df))
    eval_scores = np.zeros(n)
    doc_scores = np.zeros(n)
    eval_scores[: len(df)] = df.eval_score
    doc_scores[: len(df)] = df.document_score
    try:
        return ndcg_score([eval_scores], [doc_scores], k=k)
    except ValueError:
        return np.nan


ndcg_at_2 = pd.DataFrame(
    {"score": documents_with_relevance_df.groupby("context.span_id").apply(_compute_ndcg, k=2)}
)

ndcg_at_2

Let’s also compute precision at 2 for all our retrieval steps.

precision_at_2 = pd.DataFrame(
    {
        "score": documents_with_relevance_df.groupby("context.span_id").apply(
            lambda x: x.eval_score[:2].sum(skipna=False) / 2
        )
    }
)

precision_at_2

Lastly, let’s compute whether or not a correct document was retrieved at all for each query (e.g. a hit)

hit = pd.DataFrame(
    {
        "hit": documents_with_relevance_df.groupby("context.span_id").apply(
            lambda x: x.eval_score[:2].sum(skipna=False) > 0
        )
    }
)

Let’s now view the results in a combined dataframe.

retrievals_df = client.spans.get_spans_dataframe(
    query=SpanQuery().where("span_kind == 'RETRIEVER'").select("input.value")
)
retrievals_df = retrievals_df.rename(columns={"input.value": "input"})
rag_evaluation_dataframe = pd.concat(
    [
        retrievals_df["attributes.input.value"],
        ndcg_at_2.add_prefix("ncdg@2_"),
        precision_at_2.add_prefix("precision@2_"),
        hit,
    ],
    axis=1,
)
rag_evaluation_dataframe

Observations

Let’s now take our results and aggregate them to get a sense of how well our RAG system is performing.

# Aggregate the scores across the retrievals
results = rag_evaluation_dataframe.mean(numeric_only=True)
results

ncdg@2_score         0.913450
precision@2_score    0.804598
hit                  0.936782
dtype: float64

As we can see from the above numbers, our RAG system is not perfect, there are times when it fails to retrieve the correct context within the first two documents. At other times the correct context is included in the top 2 results but non-relevant information is also included in the context. This is an indication that we need to improve our retrieval strategy. One possible solution could be to increase the number of documents retrieved and then use a more sophisticated ranking strategy (such as a reranker) to select the correct context. We have now evaluated our RAG system’s retrieval performance. Let’s send these evaluations to Phoenix for visualization. By sending the evaluations to Phoenix, you will be able to view the evaluations alongside the traces that were captured earlier.

from phoenix.client import Client

px_client = Client()
px_client.spans.log_span_annotations_dataframe(
    dataframe=ndcg_at_2,
    annotation_name="ndcg@2",
    annotator_kind="LLM",
)
px_client.spans.log_span_annotations_dataframe(
    dataframe=precision_at_2,
    annotation_name="precision@2",
    annotator_kind="LLM",
)
px_client.spans.log_document_annotations_dataframe(
    dataframe=retrieved_documents_relevance_df,
    annotation_name="relevance",
    annotator_kind="LLM",
)

Response Evaluation

The retrieval evaluations demonstrates that our RAG system is not perfect. However, it’s possible that the LLM is able to generate the correct response even when the context is incorrect. Let’s evaluate the responses generated by the LLM.

import pandas as pd
from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
root_spans_df = client.spans.get_spans_dataframe(
    query=SpanQuery().where("parent_id is None").select("input.value", "output.value")
)
root_spans_df = root_spans_df.rename(columns={"input.value": "input", "output.value": "output"})
retriever_df = client.spans.get_spans_dataframe(
    query=SpanQuery()
    .where("span_kind == 'RETRIEVER'")
    .select("parent_id")
    .concat("retrieval.documents", reference="document.content")
)
retriever_df = retriever_df.rename(columns={"parent_id": "span_id"})
qa_with_reference_df = pd.concat([root_spans_df, retriever_df], axis=1, join="inner")
qa_with_reference_df

Now that we have a dataset of the question, context, and response (input, reference, and output), we now can measure how well the LLM is responding to the queries. For details on the QA correctness evaluation, see the LLM Evals documentation.

from phoenix.evals import LLM, async_evaluate_dataframe
from phoenix.evals.metrics import CorrectnessEvaluator, FaithfulnessEvaluator

llm = LLM(provider="openai", model="gpt-4-turbo-preview")
qa_evaluator = CorrectnessEvaluator(llm=llm)
hallucination_evaluator = FaithfulnessEvaluator(llm=llm)

evals_df = await async_evaluate_dataframe(
    dataframe=qa_with_reference_df,
    evaluators=[qa_evaluator, hallucination_evaluator],
    concurrency=10,
)
qa_correctness_eval_df = pd.concat(
    [
        evals_df[["context.span_id"]],
        pd.DataFrame(evals_df["correctness_score"].tolist(), index=evals_df.index),
    ],
    axis=1,
)
hallucination_eval_df = pd.concat(
    [
        evals_df[["context.span_id"]],
        pd.DataFrame(evals_df["faithfulness_score"].tolist(), index=evals_df.index),
    ],
    axis=1,
)

qa_correctness_eval_df.head()

hallucination_eval_df.head()

Observations

Let’s now take our results and aggregate them to get a sense of how well the LLM is answering the questions given the context.

qa_correctness_eval_df.mean(numeric_only=True)

score    0.931034
dtype: float64

hallucination_eval_df.mean(numeric_only=True)

score    0.051724
dtype: float64

Our QA Correctness score of 0.91 and a Hallucinations score 0.05 signifies that the generated answers are correct ~91% of the time and that the responses contain hallucinations 5% of the time - there is room for improvement. This could be due to the retrieval strategy or the LLM itself. We will need to investigate further to determine the root cause. Since we have evaluated our RAG system’s QA performance and Hallucinations performance, let’s send these evaluations to Phoenix for visualization.

from phoenix.client.resources.spans import SpanAnnotationData

qac_rows = qa_correctness_eval_df.reset_index()[
    ["context.span_id", "score", "label", "explanation"]
].dropna(how="all", subset=["score", "label", "explanation"])
hall_rows = hallucination_eval_df.reset_index()[
    ["context.span_id", "score", "label", "explanation"]
].dropna(how="all", subset=["score", "label", "explanation"])

# Construct annotations in the same style as retrieval metrics
qa_annotations: list[SpanAnnotationData] = [
    SpanAnnotationData(
        name="Q&A Correctness",
        span_id=str(row["context.span_id"]),
        annotator_kind="LLM",
        result={
            **({"score": float(row["score"])} if pd.notna(row["score"]) else {}),
            **({"label": str(row["label"])} if pd.notna(row["label"]) else {}),
            **({"explanation": str(row["explanation"])} if pd.notna(row["explanation"]) else {}),
        },
    )
    for _, row in qac_rows.iterrows()
]

hall_annotations: list[SpanAnnotationData] = [
    SpanAnnotationData(
        name="Hallucination",
        span_id=str(row["context.span_id"]),
        annotator_kind="LLM",
        result={
            **({"score": float(row["score"])} if pd.notna(row["score"]) else {}),
            **({"label": str(row["label"])} if pd.notna(row["label"]) else {}),
            **({"explanation": str(row["explanation"])} if pd.notna(row["explanation"]) else {}),
        },
    )
    for _, row in hall_rows.iterrows()
]

client.spans.log_span_annotations(span_annotations=qa_annotations, sync=False)
client.spans.log_span_annotations(span_annotations=hall_annotations, sync=False)

We now have sent all our evaluations to Phoenix. Let’s go to the Phoenix application and view the results! Since we’ve sent all the evals to Phoenix, we can analyze the results together to make a determination on whether or not poor retrieval or irrelevant context has an effect on the LLM’s ability to generate the correct response.

print("phoenix URL", px.active_session().url)

phoenix URL http://localhost:6006/

Conclusion

We have explored how to build and evaluate a RAG pipeline using LlamaIndex and Phoenix, with a specific focus on evaluating the retrieval system and generated responses within the pipelines. Phoenix offers a variety of other evaluations that can be used to assess the performance of your LLM Application. For more details, see the LLM Evals documentation.

AI Engineering Workflows

Tracing

Human-in-the-Loop Workflows (Annotations)

Prompts

Evaluation

Guardrails & Safety

Datasets & Experiments

Google Colab

Retrieval Augmented Generation (RAG)

Stages within RAG

Build a RAG system

Load Data and Build an Index

Evaluation

Generate Question Context Pairs

Retrieval Evaluation

Observations

Response Evaluation

Observations

Conclusion

Google Colab

​Retrieval Augmented Generation (RAG)

​Stages within RAG

​Build a RAG system

​Load Data and Build an Index

​Evaluation

​Generate Question Context Pairs

​Retrieval Evaluation

​Observations

​Response Evaluation

​Observations

​Conclusion

Retrieval Augmented Generation (RAG)

Stages within RAG

Build a RAG system

Load Data and Build an Index

Evaluation

Generate Question Context Pairs

Retrieval Evaluation

Observations

Response Evaluation

Observations

Conclusion