Building a Powerful RAG System for Academic Publications

Introduction

Retrieval Augmented Generation (RAG) has revolutionized how we interact with large document collections. In this publication, we'll explore how to build a robust RAG system specifically designed for academic publications, similar to the one implemented in this project.

What is RAG?

RAG combines the power of retrieval systems with generative AI to provide accurate, contextually relevant responses to user queries. Unlike traditional LLMs that rely solely on their training data, RAG systems:

1. Retrieve relevant documents from a knowledge base
2. Augment the prompt with this retrieved context
3. Generate responses based on both the query and retrieved information

def answer_query(user_question, top_k=5):
    # Step 1: Retrieve relevant documents
    query_embedding = generate_embeddings(user_question)
    relevant_chunks = retrieve_relevant_chunks(query_embedding, top_k=top_k)
    
    # Step 2: Augment prompt with retrieved context
    combined_context = "\n\n".join(relevant_chunks)
    augmented_prompt = f"Context:\n{combined_context}\n\nQuestion: {user_question}\nAnswer:"
    
    # Step 3: Generate response
    answer = generate_answer(augmented_prompt)
    return answer

Key Components of Our RAG System

1. Vector Database

At the heart of our system is a vector database (Qdrant) that stores embeddings of publication chunks. This enables semantic search beyond simple keyword matching.

2. Embedding Generation

We use Jina AI embeddings to convert text into high-dimensional vectors that capture semantic meaning:

def generate_embeddings(text):
    # Convert text to vector representation
    return jina_embedding_model.encode(text)

3. Retrieval Mechanism

When a query arrives, we:

1. Convert it to an embedding
2. Find the most similar document chunks in our vector database
3. Return these chunks as context

def retrieve_relevant_chunks(query_embedding, top_k=5):
    results = vector_db.search(
        collection_name="publications",
        query_vector=query_embedding,
        limit=top_k
    )
    return [result.payload["content"] for result in results]

4. Memory Management

Our system maintains conversation history to provide contextual responses:

def modify_question_with_memory(new_question, past_questions):
    if not past_questions:
        return new_question
        
    # Use LLM to create standalone question incorporating context
    system_prompt = f"Chat history: {' '.join(past_questions)}\nLatest question: {new_question}"
    standalone_question = llm.invoke(system_prompt)
    return standalone_question

5. User Interface

We've implemented both Flask and Streamlit interfaces:

The Streamlit interface provides:

• Real-time chat with typing indicators
• Chat history panel
• Ability to reload previous questions

Performance Optimization

To ensure fast response times:

1. Chunk Size Optimization: We've found 512-token chunks provide the best balance between context and retrieval precision
2. Top-k Tuning: Retrieving 5 chunks offers sufficient context without overwhelming the LLM
3. Embedding Caching: We cache embeddings to avoid regenerating them for repeated queries

Real-World Applications

This RAG system can be applied to:

1. Research Assistance: Help researchers quickly find relevant papers
2. Literature Review: Summarize key findings across multiple publications
3. Academic Q&A: Answer specific questions about research methodologies or findings
4. Citation Analysis: Identify the most influential papers in a field

Conclusion

Building a RAG system for academic publications combines the best of information retrieval and generative AI. By following the architecture outlined in this publication, you can create powerful tools that make navigating the vast landscape of academic literature more efficient and insightful.

References

1. Lewis, P., et al. (2020). "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"
2. Gao, L., et al. (2023). "Precise Zero-Shot Dense Retrieval without Relevance Labels"
3. Izacard, G., & Grave, E. (2021). "Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering"