Back

title = “Evaluating PDF-to-RAG Applications” date = 2025-02-12T16:15:00+05:30 draft = false description = “Retrieval-Augmented Generation (RAG) models have transformed how information is extracted and generated from PDFs. In this article, we evaluate multiple RAG frameworks based on efficiency, accuracy, and performance.” image = “blog-img.webp” categories = [“TECH”] author = “Tanisha” authorImg = “author_avatar.jpg”

Introduction

Retrieval-Augmented Generation (RAG) models have transformed how information is extracted and generated from PDFs. These models combine retrieval-based search with generative AI to improve context-aware responses. This article evaluates different RAG models for PDF processing based on various performance metrics.

RAG models represent a transformative approach in natural language processing (NLP), merging the capabilities of generative models with external information retrieval systems to enhance the accuracy and contextuality of generated responses [1]. RAG has gained attention in various sectors, including healthcare, education, and legal research, where precise and relevant information retrieval is crucial for informed decision-making [2].

By leveraging both generative and retrieval mechanisms, RAG models enable more accurate outputs, enriching responses with factual content that reflects real-world complexities [3]. These systems have revolutionized the way organizations handle large volumes of unstructured data, allowing for more efficient information extraction and summarization [4].

What Is Retrieval-Augmented Generation (RAG)?

RAG is an AI technique that enhances generative models by integrating external knowledge retrieval. This approach is particularly useful for document analysis, enabling more informed and contextually accurate responses [5].

The architecture of RAG models consists of two primary components:

  • Retriever: Fetches relevant data from external sources based on user queries. Retrieval methods include Sparse Retrieval (TF-IDF, BM25) and Dense Retrieval (BERT, RoBERTa) [6]. Sparse retrieval methods rely on exact keyword matching, while dense retrieval employs semantic search techniques to find the most contextually relevant results [7].
  • Generator: Uses retrieved data to generate coherent responses, synthesizing external knowledge with its internal language model. This ensures that generated responses maintain a high level of contextual awareness and factual accuracy [8].

The RAG process involves three critical stages:

  1. Data Ingestion: Preprocessing documents and creating vector embeddings for retrieval.
  2. Retrieval: Searching and selecting the most relevant content.
  3. Generation: Using the retrieved information to produce a coherent and well-informed response.

Applications of RAG include:

  • Customer Support: Enhancing chatbot accuracy with knowledge bases [9].
  • Legal Research: Summarizing legal documents for efficient analysis [10].
  • Financial Analysis: Extracting insights from large datasets [11].
  • Healthcare: Assisting in medical documentation and research by summarizing key findings from extensive medical literature [12].
  • Academic Research: Helping researchers find and synthesize relevant papers efficiently [13].

Evaluation Criteria

To assess different RAG models for PDF processing, we used the following parameters:

Latency (Processing Time)

  • Measures the time taken to process and generate responses from PDFs. Faster processing time is essential for real-time applications and scalability [14].

Retrieval Accuracy

  • Evaluates how effectively relevant content is extracted from documents. A high retrieval accuracy ensures that the system selects the most relevant information for response generation [15].

Context Retention

  • Determines how well the model preserves document structure and meaning. Maintaining contextual integrity is critical, especially for legal and academic documents [16].

Relevance Score

  • Assesses the quality of generated answers compared to the ground truth. The relevance score ensures that generated responses align with expected answers, minimizing hallucinations [17].

Token Usage Efficiency

  • Analyzes token consumption for cost efficiency. Optimized token usage reduces computational costs while maintaining response quality [18].

Traditional NLP metrics like BLEU and ROUGE may not fully capture RAG effectiveness. Instead, Retrieval-Augmented Generation Assessment Suite (RAGAS) provides a more comprehensive evaluation by measuring relevance, accuracy, and fidelity [19].

Key metrics for evaluation:

  • Retrieval Metrics: Recall@k (measures fraction of relevant instances retrieved).
  • Rank-Based Metrics: Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP) (assess ranking quality of retrieved documents).
  • Generation Metrics: BLEU (text similarity) and F1 Score (balance of precision and recall).
  • Human Evaluation: Ensures context relevance beyond automated metrics.
  • Context Relevance: Measures how well the retrieved content supports generated responses, ensuring factual consistency [20].

Open-Source Contribution

Our public repository is open for contributions. You can:

How to Contribute

  1. Fork the Repository: Click the ‘Fork’ button on GitHub to create a copy in your account.
  2. Clone the Repository: Use git clone <repository_url> to download the repository.
  3. Create a Branch: Use git checkout -b feature-branch-name to create a new branch.
  4. Make Your Changes: Edit the code, documentation, or add new features.
  5. Commit and Push: Use git commit -m "Your commit message" and git push origin feature-branch-name.
  6. Submit a Pull Request: Navigate to GitHub and submit a pull request for review.
  7. Engage with the Community: Discuss and refine contributions with maintainers.

GitHub Repository: Evaluation RAG for PDFs

References

  1. OpenRAG Documentation [https://github.com/openrag-project/openrag]
  2. LangChain Documentation [https://github.com/hwchase17/langchain]
  3. GPT-Index (LlamaIndex) Documentation [https://github.com/jerryjliu/llama_index]
  4. Pinecone RAG Implementation [https://github.com/pinecone-io/pinecone]
  5. Retrieval-Augmented Generation Research [https://arxiv.org/abs/2005.11401]
  6. BERT for Dense Retrieval [https://arxiv.org/abs/1907.11692]
  7. Sparse Retrieval Methods (BM25) [https://en.wikipedia.org/wiki/Okapi_BM25]
  8. Generative AI in RAG [https://arxiv.org/abs/2103.07285]
  9. Chatbot Integration with RAG [https://arxiv.org/abs/2201.05387]
  10. Legal Research Using RAG [https://arxiv.org/abs/2104.08691]
  11. Financial Analysis with AI [https://arxiv.org/abs/2210.08069]
  12. Healthcare Applications of RAG [https://arxiv.org/abs/2012.07143]
  13. Academic Research and AI [https://arxiv.org/abs/2006.14236]
  14. Performance Benchmarking in RAG [https://arxiv.org/abs/2301.04773]
  15. Evaluating Retrieval Accuracy [https://arxiv.org/abs/2007.08998]
  16. Context Preservation in AI Models [https://arxiv.org/abs/2105.03457]
  17. Hallucination Reduction in AI [https://arxiv.org/abs/2303.08152]
  18. Token Efficiency in RAG Models [https://arxiv.org/abs/2211.09242]
  19. RAGAS: Evaluating RAG Effectiveness [https://arxiv.org/abs/2305.14772]
  20. Human Evaluation in AI Models [https://arxiv.org/abs/2209.05436]

Share this post