Exploratory Data Analysis For Rag

Exploratory Data Analysis (EDA) on Token Length for Retriever-Augmented Generation (RAG) Pipelines

Introduction Exploratory Data Analysis (EDA) is an essential step in the machine learning pipeline. It allows us to understand the nature of the data without making any assumptions. In the context of a Retriever-Augmented Generation (RAG) system, understanding the token length distribution across documents can provide valuable insights for indexing, retrieval efficiency, and ultimately, the performance of the generation module.

Step 1: Data Loading and Initial Exploration We start by loading our dataset into a Pandas DataFrame, which includes various fields such as document_id, main_content, and derived metrics like content_length and word_count.

import pandas as pd

# Load the dataset
df = pd.read_json('/path/to/your/dataset.json')

Step 2: Descriptive Statistics The first step in our EDA is to calculate descriptive statistics to understand the central tendency and dispersion of word counts across our documents.

# Descriptive statistics for word counts
word_count_descriptive_stats = df['word_count'].describe()

This includes metrics such as mean, median, standard deviation, minimum and maximum values, which help us grasp the general size of text data the model will handle.

Step 3: Distribution Analysis Understanding the distribution of token lengths is crucial. We visualize this using a Kernel Density Estimation (KDE) plot, which helps in seeing the shape of the distribution of word counts.

import seaborn as sns
import matplotlib.pyplot as plt

# Plotting the frequency distribution of word counts
plt.figure(figsize=(10, 6))
sns.kdeplot(df['word_count'], fill=True, color="blue", alpha=0.5)
plt.title('Frequency Distribution of Word Counts in Main Content')
plt.xlabel('Word Count')
plt.ylabel('Density')
plt.grid(True)
plt.show()

Step 4: Outlier Detection Identifying outliers is a significant step in preparing our data for the indexing phase. Outliers can skew the performance of the retriever. We calculate and visualize how many documents exceed typical word counts, setting thresholds based on our distribution analysis.

# Counting entries with unusually high word counts
high_word_count = df[df['word_count'] > 4000].shape[0]
print(f"Documents with word count > 4000: {high_word_count}")

Step 5: Impact Analysis We analyze how the token length impacts the retrieval phase. Documents with too few or too many words might affect the retriever’s efficiency and accuracy, leading to poorer results during the generation phase.

Conclusion Token length analysis is a foundational aspect of EDA for RAG systems. It informs decisions in subsequent pipeline stages, such as adjusting the retriever’s parameters or preprocessing steps to normalize document lengths. Our goal is to ensure that the retriever can work efficiently with the indexed data, balancing between comprehensiveness and retrieval speed.