Part1 : How to create your own first RAG ?

Retrieval-Augmented Generation (RAG) is an AI technique that enhances generative models by integrating a retrieval system to fetch relevant external information before generating responses. This improves the accuracy, relevance, and factual correctness of AI-generated content.

1. Definition of RAG

RAG combines retrieval-based and generation-based AI models. Instead of relying only on pre-trained knowledge, a RAG system retrieves relevant documents from an external knowledge base and incorporates them into the response generation process.

Main Components of a RAG System

1. Query Understanding:
   • The system processes and refines the input query for retrieval.
2. Retrieval System:
   • Finds relevant documents using embeddings and similarity search.
3. Context Processing:
   • Extracts useful content from retrieved documents.
4. Generation Model:
   • Uses the retrieved content to generate a response.
5. Post-Processing & Response Validation:
• Ensures coherence, factuality, and relevance.

2. Required Technologies for RAG Implementation

The technology stack for building a RAG system depends on the database size, frontend requirements, and use case.

(a) Database for Storing Knowledge

• Small to Medium Scale (100k - 1M documents):
  • Vector Database: FAISS, ChromaDB, Weaviate
  • Text Database: PostgreSQL, SQLite
• Large Scale (1M+ documents):
  • Distributed Vector Stores: Milvus, Pinecone
  • NoSQL Databases: Elasticsearch, MongoDB

(b) Frontend Technologies

• Web Applications: React.js, Vue.js, Next.js
• Mobile Applications: Flutter, React Native
• Chatbot Integration: Streamlit, Gradio for quick prototyping

(c) Backend & Model Deployment

• Retrieval & Processing: Python (FastAPI, Flask)
• Embedding Generation: Sentence Transformers, OpenAI embeddings
• LLM Inference: Open-source models (Llama, Mistral, Phi), OpenAI API, Hugging Face models
• Infrastructure: Docker, Kubernetes, AWS, GCP

3. Applications of RAG Systems

RAG is used in various fields for improving AI-generated responses with real-time or domain-specific knowledge.

1. Enterprise Search & Knowledge Management
   • Automates document retrieval in companies.
2. Customer Support Chatbots
   • Enhances chatbot responses using company documentation.
3. Legal & Medical AI Assistants
   • Provides accurate information by retrieving case law or medical research papers.
4. Academic Research & Literature Review
   • Helps in summarizing papers and finding related works.
5. Coding Assistants & Debugging Tools
   • Retrieves documentation and best practices from codebases.
6. E-commerce & Product Recommendation
   • Enhances search engines with contextual retrieval.

4. Different Approaches to RAG

(a) Classic RAG

• Uses a retrieval model (BM25, dense embeddings) followed by a language model to generate responses.

(b) Multi-Hop RAG

• Retrieves multiple documents iteratively to answer complex queries.

(c) Hybrid RAG

• Combines semantic search (vector-based) and keyword search (BM25) to improve retrieval accuracy.

(d) Graph-Based RAG

• Uses a knowledge graph instead of a vector database to retrieve related concepts and facts.

(e) Agent-Based RAG

• Uses LLM agents to dynamically choose retrieval strategies, improving response flexibility.

Conclusion

Building your first RAG system requires:

• A retrieval component (vector search or hybrid search).
• A language model to process retrieved data.
• A database optimized for scalability.
• A frontend/backend for integration with applications.

In next the article, we explore together tips to implement a basic RAG.