Evaluate RAG Pipeline using RAGAS

RAG Evaluation Pipeline

RAG(Retrieval Augmented Generation)

Retrieval Augmented Generation (RAG) is a very popular framework or class of LLM Application. The basic principle of RAG is to leverage external data sources to give LLMs contextual reference. Any RAG implementation has two aspects:

• Generation : Generation is performed by the LLM, which generates the answer by using the retrieved information.
• Retrieval : Retriever retrieves the relevant information.

Pitfalls in LLM Assisted Evaluation

• Positional Bias: LLMs may give higher scores to responses placed in a specific position within a text, potentially leading to unfair evaluations. For example, they might favor answers located at the beginning or end of a paragraph.
• Preference for Integers: LLMs’ inclination to assign higher scores to responses containing integers can skew evaluations in favor of numerical answers, even if they are not necessarily more accurate or appropriate.
• Bias Towards Model-Generated Responses: LLMs’ tendency to prefer their own style of responses can result in a biased assessment, where model-generated answers are favored over human-generated ones, even if the latter are more suitable or accurate.
• Numerical Preference: LLMs’ preference for certain numbers can introduce subjectivity into the evaluation process, as their choice of favored numbers may not always align with the context or content being assessed.
• Stochastic Nature: The stochastic nature of LLMs means that their evaluations can vary each time they are invoked, even with the same input. This randomness can make it challenging to establish consistent evaluation criteria and may lead to different outcomes for similar responses in different evaluation sessions.

Evaluating RAG

When evaluating a RAG pipeline, both of generator and retriever need to be evaluated separately and together to get an overall score as well as the individual scores to pinpoint the aspects to improve.

Why Evaluate RAG Pipelines?

• Objective Measurement: Objectively measuring RAG (Retrieval-Augmented Generation) pipelines provides an unbiased view of how well they are functioning. This helps in making informed decisions for enhancement.
• Performance Enhancement: Through evaluation, weaknesses or bottlenecks in the pipeline can be identified and addressed, leading to performance improvements, which are essential for achieving desired results efficiently.
• Cost Reduction: By assessing the pipeline’s performance, organizations can pinpoint inefficiencies or redundancies that may be inflating operational costs. Addressing these issues can lead to cost savings.
• Quality Assurance: It’s essential to ensure the quality of the pipeline’s output before deploying it in a production environment. Evaluation helps detect and rectify any issues that could compromise the quality of the final results, maintaining the integrity of the pipeline’s outcomes.

What is RAGAS ?

ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard. This is where ragas (RAG Assessment) comes in.

ragas provides you with the tools based on the latest research for evaluating LLM-generated text to give you insights about your RAG pipeline. ragas can be integrated with your CI/CD to provide continuous checks to ensure performance.

Ragas references the following data:

• Question: These are the questions your RAG pipeline will be evaluated on.
• Answer: The answer generated from the RAG pipeline and presented to the user.
• Contexts: The contexts passed into the LLM to answer the question.
• Ground Truths: The ground truth answer to the questions.

From Source

• The harmonic mean of these 4 aspects gives you the ragas score which is a single measure of the performance of your QA system across all the important aspects.

The following output is produced by Ragas:

1. Retrieval: context_relevancy and context_recall which represents the measure of the performance of your retrieval system.

2. Generation : faithfulness which measures hallucinations and answer_relevancy which measures the answers to question relevance.

Explanation:

Faithfulness : how factually accurate is the generated answer

• Aims to quantify hallucinations in generated answers: This point indicates that the evaluation process has the primary goal of measuring and quantifying hallucinations in the generated answers. Hallucinations refer to instances where the language model produces information or claims that are not accurate or supported by the input context. The evaluation seeks to identify and count these instances to gauge the model’s reliability and truthfulness.
• Formulated as NLI problem: The evaluation approach is structured as a Natural Language Inference (NLI) problem. NLI involves determining the logical relationship between two statements: a premise and a hypothesis. In this context, the premise could be the context or input provided to the language model, while the hypothesis is the generated answer. By framing the evaluation in this way, it aims to assess whether the generated answer logically follows from the given context.
• Identifies and verifies statements from the generated answer against the context: This point highlights that the evaluation process involves identifying individual statements within the generated answer and then verifying whether each statement is supported or corroborated by the context provided. This step helps assess the factual accuracy of the generated content and its alignment with the input context.
• Final Score is then computed as a ratio of the number of statements that can be inferred to the total number of statements: To arrive at a final evaluation score, the process counts the number of statements within the generated answer that can be logically inferred or verified based on the input context. This count is then divided by the total number of statements in the answer. The resulting ratio provides a quantitative measure of the model’s ability to produce contextually relevant and accurate information. A higher ratio suggests better performance in terms of providing contextually relevant information.

Answer Relevancy : How relevant is the generated answer to the question

• Aims to quantify presence of partial or redundant information in answer: This point refers to the goal of a particular evaluation or assessment process. When evaluating answers or responses generated by a system, one of the objectives is to determine the extent to which the response contains information that is partially repetitive or redundant. In other words, it seeks to measure whether the answer includes unnecessary or duplicated information that may affect its quality or effectiveness. By quantifying such redundancies or partial information, evaluators can provide feedback for improvement and ensure that responses are concise and informative.
• Formulation of a QG problem: This point suggests the initiation of a Question Generation (QG) problem. In natural language processing, QG involves the creation of questions from given content or statements. Formulating a QG problem means defining the task of generating questions based on a specific context or text. This can be a crucial step in various applications, such as education, information retrieval, or content creation, as it enables the generation of questions that test comprehension, engage users, or aid in knowledge extraction.
• Paradigm measures the similarity between generated question and the actual question: This point refers to an evaluation paradigm or method that assesses the similarity between questions generated by a system and questions that serve as a reference or ground truth (the “actual question”). In this context, the goal is to determine how closely the generated questions align with the desired or correct questions. Similarity measures can include various metrics, such as cosine similarity, Jaccard similarity, or more advanced natural language processing techniques. Assessing this similarity helps gauge the quality and relevance of the generated questions and provides insights into the performance of question generation systems.

Context Relevancy :

• Aims to quantify precision of retrieved context : The evaluation process focuses on measuring how accurately the retrieved context aligns with the information needed to answer a question, providing a numerical representation of this precision.
• Helps to optimize chunk size : This indicates that through evaluation, one can determine the ideal size or granularity of text chunks to be retrieved, ensuring that they are neither too large nor too small for effective information extraction.
• Identifies and extracts sentences from given context taht is necessary to answer given question. : It refers to the evaluation’s role in pinpointing and extracting the specific sentences from the provided context that are relevant and essential for answering a given question accurately.
• Final score :It is the ratio of number of extracted sentences to total number of sentences in the given context. The he evaluation’s ultimate metric is a ratio, quantifying the proportion of sentences correctly extracted from the context in relation to the total number of sentences in that context, offering a measure of the extraction’s completeness and accuracy.

Context Recall : Retrieving all the relevant context to answer the question

• Requires annotated answer. This serves as a reference for assessing the system’s performance.
• Formulated as a combination of candidate sentence extraction and NLI. This approach allows the system to estimate both the data points it correctly captured (True Positives, TP) and those it failed to capture (False Negatives, FN).The paradigm estimates the data points that were captured(TP) asnd also missed(FN)
• Final Score = TP /(TP +FN). This score provides a quantitative measure of the system’s performance in capturing relevant information.

Implementing Ragas Evaluation to a medical question answering system

Here we have use the following :

• A vectorstore to the embeddings (Qdrant)
• An embedding model to vectorize the document and user queries(sentence-transformers/all-mpnet-base-v2)
• A vectorstore retriever to retrieve documents.
• A response generator. This uses a ChatPromptTemplate to combine query and documents(gpt-3.5–turbo-16k)

Install required packages

! pip install -qU openai langchain  transformers tiktoken  sentence-transformers qdrant-client
! pip install ragas

Import required packages

from qdrant_client import models, QdrantClient
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores.qdrant import Qdrant
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate
#
from tqdm.auto import tqdm
from uuid import uuid4
from transformers import pipeline
from sentence_transformers import SentenceTransformer, util
import numpy as np
import pandas as pd
from time import time,sleep
import openai
import tiktoken
#
import os
import json
#
import io

Set the openai key

from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass("Enter Openai key:")

Download the input data

loader = CSVLoader(file_path='/content/Medical_QA.csv',source_column="link")
data = loader.load()

Embeddings

embeddings = HuggingFaceEmbeddings(model_kwargs = {'device': 'cpu'},
                                    encode_kwargs = {'normalize_embeddings': False})

Instantiate the LLM

llm=ChatOpenAI(model_name='gpt-3.5-turbo-16k',
               openai_api_key=os.environ["OPENAI_API_KEY"],
               temperature=0)

Prepare Metadata and content to be stored in vectorstore

metadatas = []
texts = []
for row in data:
  metadatas.append(row.metadata)
  texts.append(row.page_content)
print(len(metadatas),len(texts))

Build the vectorestore and store the text vector along with metadata

doc_store = Qdrant.from_texts(texts,
                              metadatas=metadatas,
                              embedding=embeddings,
                              location=":memory:",
                              prefer_grpc=True,
                              collection="medical_qa_search")

Prepare the prompt template to enable Q&A

#query vector store
prompt_template = """Use the following pieces of context to answer the question enclosed within  3 backticks at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
Please provide an answer which is factually correct and based on the information retrieved from the vector store.
Please also mention any quotes supporting the answer if any present in the context supplied within two double quotes "" .

{context}

QUESTION:```{question}```
ANSWER:
"""
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context","question"]
  )
#
chain_type_kwargs = {"prompt": PROMPT}

Query the Vectorstore and Provide answers using the RetrievalQA method of Langchain

qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(model_name='gpt-3.5-turbo-16k',
                                                  openai_api_key=os.environ["OPENAI_API_KEY"],
                                                  temperature=0),
                                  chain_type="stuff",
                                  chain_type_kwargs={"prompt": PROMPT},
                                  retriever=doc_store.as_retriever(search_kwargs={"k": 5}),
                                  return_source_documents=True
                                  )
#
questions = input("Please provide the symptoms here :")
print(questions)
result = qa(questions)
#
print(result.keys())

{"question":"Please provide the symptoms here :My 12 year old son has Poor coordination Unsteady walk and a tendency to stumble while walking and poor coordination between two hands.What might be the possible cuase?
My 12 year old son has Poor coordination Unsteady walk and a tendency to stumble while walking and poor coordination between two hands.What might be the possible cuase?"}

dict_keys(['query', 'result', 'source_documents'])

print(result['query'])
#My 12 year old son has Poor coordination Unsteady walk and a tendency to stumble while walking and poor coordination between two hands.What might be the possible cuase?
print(result['result'])
#The possible cause of your son's symptoms could be cerebral palsy. Cerebral palsy is a group of disorders that affect movement and muscle tone or posture. It is caused by damage that occurs to the immature brain as it develops, most often before birth. Symptoms of cerebral palsy include poor coordination, unsteady walk, and difficulty with fine motor tasks. It is important to consult with a doctor for a proper diagnosis and to rule out other possible causes.
print(result['source_documents'][0].page_content)
"""
Disease: Palsy, cerebral (See: Palsy, cerebral, also known asCerebral palsy)
Symptoms: Signs and symptoms can vary greatly. Movement and coordination problems associated with cerebral palsy include: Cerebral palsy can affect the whole body or it might be limited primarily to one limb or one side of the body. The brain disorder causing cerebral palsy doesnt change with time so the symptoms usually dont worsen with age. However as the child gets older some symptoms might become more or less apparent. And muscle shortening and muscle rigidity can worsen if not treated aggressively. Brain abnormalities associated with cerebral palsy might also contribute to other neurological problems including: Its important to get a prompt diagnosis for a movement disorder or delays in your childs development. See your childs doctor if you have concerns about episodes of loss of awareness of surroundings or of abnormal bodily movements abnormal muscle tone impaired coordination swallowing difficulties eye muscle imbalance or other developmental issues. Variations in muscle tone such as being either too stiff or too floppy Stiff muscles and exaggerated reflexes (spasticity) Stiff muscles with normal reflexes (rigidity) Lack of balance and muscle coordination (ataxia) Tremors or involuntary movements Slow writhing movements Delays in reaching motor skills milestones such as pushing up on arms sitting up or crawling Favoring one side of the body such as reaching with one hand or dragging a leg while crawling Difficulty walking such as walking on toes a crouched gait a scissors-like gait with knees crossing a wide gait or an asymmetrical gait Excessive drooling or problems with swallowing Difficulty with sucking or eating Delays in speech development or difficulty speaking Learning difficulties Difficulty with fine motor skills such as buttoning clothes or picking up utensils Seizures Difficulty seeing and hearing Intellectual disabilities Seizures Abnormal touch or pain perceptions Oral diseases Mental health conditions Urinary incontinence
Causes: Cerebral palsy is caused by an abnormality or disruption in brain development most often before a child is born. In many cases the cause isnt known. Factors that can lead to problems with brain development include: Gene mutations that lead to abnormal development Maternal infections that affect the developing fetus Fetal stroke a disruption of blood supply to the developing brain Bleeding into the brain in the womb or as a newborn Infant infections that cause inflammation in or around the brain Traumatic head injury to an infant from a motor vehicle accident or fall Lack of oxygen to the brain related to difficult labor or delivery although birth-related asphyxia is much less commonly a cause than historically thought
diagnosis: Signs and symptoms of cerebral palsy can become more apparent over time so a diagnosis might not be made until a few months after birth. If your family doctor or pediatrician suspects your child has cerebral palsy he or she will evaluate your childs signs and symptoms monitor growth and development review your childs medical history and conduct a physical exam. Your doctor might refer you to a specialist trained in treating children with brain and nervous system conditions (pediatric neurologist pediatric physical medicine and rehabilitation specialist or child developmental specialist). Your doctor might also order a series of tests to make a diagnosis and rule out other possible causes. Brain-imaging technologies can reveal areas of damage or abnormal development in the brain. These tests might include the following: MRI. An MRI scan uses radio waves and a magnetic field to produce detailed 3D or cross-sectional images of your childs brain. An MRI can often identify lesions or abnormalities in your childs brain. This test is painless but its noisy and can take up to an hour to complete. Your child will likely receive a sedative or light general anesthesia beforehand. If your child is suspected of having seizures an EEG can evaluate the condition further. Seizures can develop in a child with epilepsy. In an EEG test a series of electrodes are attached to your childs scalp. The EEG records the electrical activity of your childs brain. Its common for there to be changes in normal brain wave patterns in epilepsy. Tests on the blood urine or skin might be used to screen for genetic or metabolic problems. If your child is diagnosed with cerebral palsy youll likely be referred to specialists to test your child for other conditions often associated with the disorder. These tests can identify problems with: \n    MRI. An MRI scan uses radio waves and a magnetic field to produce detailed 3D or cross-sectional images of your childs brain. An MRI can often identify lesions or abnormalities in your childs brain.\n    This test is painless but its noisy and can take up to an hour to complete. Your child will likely receive a sedative or light general anesthesia beforehand.\n     Cranial ultrasound. This can be performed during infancy. A cranial ultrasound uses high-frequency sound waves to produce images of the brain. An ultrasound doesnt produce a detailed image but it may be used because its quick and inexpensive and it can provide a valuable preliminary assessment of the brain. Vision Hearing Speech Intellect Development Movement Cerebral palsy care at Mayo Clinic CT scan EEG (electroencephalogram) Genetic testing MRI Ultrasound MRI. Cranial ultrasound.
Overview: Cerebral palsy is a group of disorders that affect movement and muscle tone or posture. Its caused by damage that occurs to the immature brain as it develops most often before birth. Signs and symptoms appear during infancy or preschool years. In general cerebral palsy causes impaired movement associated with abnormal reflexes floppiness or rigidity of the limbs and trunk abnormal posture involuntary movements unsteady walking or some combination of these. People with cerebral palsy can have problems swallowing and commonly have eye muscle imbalance in which the eyes dont focus on the same object. They also might have reduced range of motion at various joints of their bodies due to muscle stiffness. Cerebral palsys effect on function varies greatly. Some affected people can walk; others need assistance. Some people show normal or near-normal intellect but others have intellectual disabilities. Epilepsy blindness or deafness also might be present. Book: Mayo Clinic Family Health Book 5th Edition Newsletter: Mayo Clinic Health Letter — Digital Edition
link: https://www.mayoclinic.org/diseases-conditions/cerebral-palsy/symptoms-causes/syc-20353999
"""

Evaluate the Q&A RAG pipeline using RAGAS

In order to evaluate the qa system we have generated a few relevant questions To work with ragas all you need are the following data

• question: list[str] - These are the questions you RAG pipeline will be evaluated on.
• answer: list[str] - The answer generated from the RAG pipeline and give to the user.
• contexts: list[list[str]] - The contexts which where passed into the LLM to answer the question.
• ground_truths: list[list[str]] - The ground truth answer to the questions.

RAGAS Metrics

Ragas provides you with a few metrics to evaluate the different aspects of your RAG systems namely

1. metrics to evaluate retrieval: offers context_relevancy and context_recall which give you the measure of the performance of your retrieval system.
2. metrics to evaluate generation: offers faithfulness which measures hallucinations and answer_relevancy which measures how to the point the answers are to the question.

The harmonic mean of these 4 aspects gives you the ragas score which is a single measure of the performance of your QA system across all the important aspects.

RagasEvaluatorChain

RagasEvaluatorChain creates a wrapper around the metrics ragas provides (documented here), making it easier to run these evaluation with LangChain and Langsmith.

The evaluator chain has the following APIs

• call(): call the RagasEvaluatorChain directly on the result of a QA chain.
• evaluate(): evaluate on a list of examples (with the input queries) and predictions (outputs from the QA chain).
• evaluate_run(): method implemented that is called by langsmith evaluators to evaluate langsmith datasets.

#In order to evaluate the qa system we generated a few relevant questions and answers
eval_questions = [
    "I have persistent back pain since 4 weeks,I workouut but havent had any sports injury.What might be the cause of the back pain?",
    "I have shortness of breath and frequently feel nauseated and tired.What can be the possible cause?",
    "My 12 year old son has Poor coordination Unsteady walk and a tendency to stumble while walking and poor coordination between two hands.What might be the possible cuase?",
    "What is Baby acne ?",
    "What is Botulism ?",
]

eval_answers = [
    "From the symptoms mentioned you might have a disloacted disk",  # incorrect answer
    "You might have asthama.",  # incorrect answer
    " Movement and coordination problems associated with cerebral palsy.Please consult a doctor for better diagnosis.",
    "Baby acne is small, inflamed bumps on a baby's face, neck, back or chest.",
    "Botulism is a rare and potentially fatal illness caused by a toxin produced by the bacterium Clostridium botulinum.",
]

examples = [
    {"query": q, "ground_truths": [eval_answers[i]]}
    for i, q in enumerate(eval_questions)
print(examples)

"""
[{'query': 'I have persistent back pain since 4 weeks,I workouut but havent had any sports injury.What might be the cause of the back pain?',
  'ground_truths': ['From the symptoms mentioned you might have a disloacted disk']},
 {'query': 'I have shortness of breath and frequently feel nauseated and tired.What can be the possible cause?',
  'ground_truths': ['You might have asthama.']},
 {'query': 'My 12 year old son has Poor coordination Unsteady walk and a tendency to stumble while walking and poor coordination between two hands.What might be the possible cuase?',
  'ground_truths': [' Movement and coordination problems associated with cerebral palsy.Please consult a doctor for better diagnosis.']},
 {'query': 'What is Baby acne ?',
  'ground_truths': ["Baby acne is small, inflamed bumps on a baby's face, neck, back or chest."]},
 {'query': 'What is Botulism ?',
  'ground_truths': ['Botulism is a rare and potentially fatal illness caused by a toxin produced by the bacterium Clostridium botulinum.']}]
"""

Import RAGAS Metrics

from ragas.langchain.evalchain import RagasEvaluatorChain
from ragas.metrics import (
    faithfulness,
    answer_relevancy,
    context_relevancy,
    context_recall,
)

# create evaluation chains
faithfulness_chain = RagasEvaluatorChain(metric=faithfulness)
answer_rel_chain = RagasEvaluatorChain(metric=answer_relevancy)
context_rel_chain = RagasEvaluatorChain(metric=context_relevancy)
context_recall_chain = RagasEvaluatorChain(metric=context_recall)

call(): call the RagasEvaluatorChain directly on the result of a QA chain.

result = qa(examples[4])
print(result["result"])
"""
Botulism is a rare but serious condition caused by toxins from bacteria called Clostridium botulinum. It can be classified into three common forms: foodborne botulism, wound botulism, and infant botulism.
Foodborne botulism occurs when the bacteria grow and produce toxins in low-acid home-canned foods or other contaminated foods.
Wound botulism occurs when the bacteria enter a wound and produce toxins.
Infant botulism occurs when infants consume spores of the bacteria, which then grow and produce toxins in their intestinal tracts. 
Botulism can cause symptoms such as difficulty swallowing or speaking, dry mouth, facial weakness, blurred or double vision, drooping eyelids, trouble breathing, nausea, vomiting, abdominal cramps, and paralysis. 
Seek urgent medical care if botulism is suspected, as early treatment increases the chances of survival and reduces the risk of complications. 
Botulism is not contagious from person to person.
"""

Faithfulness Score

eval_result = faithfulness_chain(result)
print(eval_result["faithfulness_score"])
#1.0

Context Recall Score

eval_result = context_recall_chain(result)
print(eval_result["context_recall_score"])
#0.6

Answer relevancy score

eval_result = answer_rel_chain(result)
print(eval_result['answer_relevancy_score'])
#0.8688623215992995

Context relevancy score

eval_result = context_rel_chain(result)
print(eval_result['context_ relevancy_score'])
#0.00709349482598966

Evaluate on a list of examples (with the input queries) and predictions (outputs from the QA chain)

Evaluate a list of inputs/queries and the outputs/predictions from the QA chain.

# run the queries as a batch for efficiency
predictions = qa.batch(examples)

# evaluate faitfulness
print("evaluating...")
r = faithfulness_chain.evaluate(examples, predictions)
r

"""
evaluating...
100%|██████████| 1/1 [00:56<00:00, 56.71s/it]
[{'faithfulness_score': 1.0},
 {'faithfulness_score': 0.5},
 {'faithfulness_score': 0.33333333333333337},
 {'faithfulness_score': 1.0},
 {'faithfulness_score': 1.0}]
"""

# evaluate context recall
print("evaluating...")
r = context_recall_chain.evaluate(examples, predictions)
r

""" 
evaluating...
100%|██████████| 1/1 [00:16<00:00, 16.24s/it]
[{'context_recall_score': 1.0},
 {'context_recall_score': 0.0},
 {'context_recall_score': 0.75},
 {'context_recall_score': 1.0},
 {'context_recall_score': 0.6}]
"""

# evaluate answer relevancy
print("evaluating...")
r = answer_rel_chain.evaluate(examples, predictions)
r

"""
evaluating...
100%|██████████| 1/1 [00:06<00:00,  6.92s/it]
[{'answer_relevancy_score': 0.8704144307657256},
 {'answer_relevancy_score': 0.9434430750939785},
 {'answer_relevancy_score': 0.9708515024033316},
 {'answer_relevancy_score': 0.9238230494841894},
 {'answer_relevancy_score': 0.8688623215992995}]
"""

# evaluate context relevancy
print("evaluating...")
r = context_rel_chain.evaluate(examples, predictions)
r

"""
evaluating...
100%|██████████| 1/1 [02:41<00:00, 161.69s/it]
[{'context_ relevancy_score': 0.0047178951281945675},
 {'context_ relevancy_score': 0.013274134345393363},
 {'context_ relevancy_score': 0.10438636639960726},
 {'context_ relevancy_score': 0.009622637588198823},
 {'context_ relevancy_score': 0.00709349482598966}]
"""

Conclusion:

Evaluating language models and pipelines, particularly those with retrieval-augmented generation, is essential for objective measurement and performance enhancement. It helps organizations reduce costs, ensure quality before deployment, and maintain the integrity of their systems. Additionally, the specific evaluation paradigm discussed requires annotated answers, employs a combination of sentence extraction and NLI, and calculates a final score based on True Positives and False Negatives, offering a structured approach to assessing system performance. These considerations underscore the importance of systematic evaluation in the ever-evolving field of natural language processing and artificial intelligence.

References:

ragas/README.md at main · explodinggradients/ragas

🦜️🔗 LangChain | 🦜️🔗 LangChain

Connect with me