Retrieval-Augmented Generation (RAG)

A production-grade implementation of a domain-aware RAG workflow designed for factual, verifiable, and context-grounded AI responses.

Overview

This project focuses on building an end-to-end Retrieval-Augmented Generation (RAG) system capable of extracting high-quality insights from complex, domain-specific documents. The goal was to create a pipeline that delivers accurate, citation-backed responses by combining advanced document indexing with powerful LLM reasoning—avoiding common hallucination issues seen in standalone models.

Problem Statement

Most LLMs struggle with accuracy when answering questions tied to proprietary, recent, or detailed domain knowledge. They lack source traceability and often fabricate information. This RAG system solves that by integrating an external vector-based memory layer which ensures that all answers are grounded in retrieved evidence.

Indexing Pipeline

        1. Document Ingestion: Extracted structured text from PDFs using LangChain’s PyPDFLoader.

        2. Smart Chunking: Applied RecursiveCharacterTextSplitter with optimized chunk size & overlap to retain context boundaries.

        3. Embedding Generation: Converted text chunks into dense vectors using OpenAI embeddings or open-source alternatives like BGE/GTE.

        4. Vector Storage: Indexed embeddings in Pinecone (production) or ChromaDB (local) for fast semantic search.

Retrieval & Answer Generation

Query Encoding: User questions are embedded using the same embedding model.
Semantic Search: Vector DB retrieves the top-K most relevant document chunks.
Context Formation: Retrieved passages are structured into a clean prompt.
LLM Synthesis: GPT-4 / Llama models produce a final answer grounded strictly in the retrieved evidence.

Tech Stack

Frameworks: LangChain, LlamaIndex
Vector DB: Pinecone / ChromaDB
LLMs: OpenAI GPT-4, Llama 3
Deployment: Streamlit / FastAPI

Key Outcomes

✔ Significant reduction in hallucination due to evidence-based answering
✔ Automatic citations using metadata from chunks
✔ High retrieval accuracy across domain-specific queries
✔ Modular architecture supporting re-ranking & hybrid search

Conclusion

This project demonstrates the capability to design and implement a modern RAG system from scratch—covering document processing, embedding workflows, vector search optimization, and LLM reasoning orchestration. The result is a scalable, reliable AI system capable of delivering precise, evidence-grounded knowledge for real-world enterprise use cases.

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