Introduction

Welcome to the Complete RAG repository! This repository contains a comprehensive guide and implementation of Retrieval-Augmented Generation (RAG) from scratch. RAG is a powerful framework that combines the strengths of retrieval-based and generation-based models to improve natural language generation (NLG) tasks by incorporating external knowledge through retrieval mechanisms.

This guide walks you through each stage of building a RAG system, from data collection and preprocessing to training and evaluation. Whether you’re new to RAG or looking to refine your implementation, this repository provides all the tools, concepts, and code necessary.

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Retrieval-Augmented Generation (RAG) Process

Retrieval-Augmented Generation (RAG) is a framework that enhances language models by retrieving relevant information from external knowledge bases before generating responses. This improves accuracy, relevance, and contextual understanding. The process involves multiple stages, including query construction, translation, routing, retrieval, indexing, and generation.

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Table of Contents

• Introduction
• understanding Knowledge Bases
• Query Construction
• Query Translation
• Routing
• Retrieval
• Indexing
• Generation
• Advanced Rag

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Query Construction

The first step in RAG is formulating the query for retrieval. Depending on the type of database, different query construction methods are used:

1. Relational Databases (Relational DBs)

• Uses Text-to-SQL conversion.
• Converts natural language queries into SQL queries with optional vector-based search using PGVector.

2. Graph Databases (GraphDBs)

• Uses Text-to-Cypher conversion.
• Converts natural language queries into Cypher queries for Graph Databases.

3. Vector Databases (VectorDBs)

• Uses Self-query retriever.
• Auto-generates metadata filters from the query to refine search results.

Here is a complete code Notebook for query construction and translation.

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Query Translation

Before retrieving documents, the input query is refined and transformed into a better-suited format using various techniques:

• Multi-query: Reformulates a single question into multiple related queries.
• RAG-Fusion: Combines results from multiple queries to enhance retrieval.
• Decomposition: Breaks down complex queries into simpler sub-queries.
• Step-back: Generalizes the query to retrieve broader relevant information.
• HyDE (Hypothetical Document Embeddings): Generates hypothetical documents to enhance retrieval quality.

Here is a complete code Notebook for query construction and translation.

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Routing

Routing determines which database should be queried and how.

1. Logical Routing

• The LLM selects the appropriate database (Relational DB, GraphDB, or VectorDB) based on the query.

2. Semantic Routing

• Uses embedding-based similarity matching to route the query to the most relevant prompt.

Here is complete process of RAG Routing through code in this Notebook

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Retrieval

Once a query is routed, the system retrieves relevant documents. This step consists of:

1. Ranking

• Filters and ranks retrieved documents using methods like Re-Rank, RankGPT, and RAG-Fusion.

2. Refinement

• CRAG (Compression-RAG) further filters and compresses documents to improve relevance.

3. Active Retrieval

• If retrieved documents are not relevant, the system re-retrieves data from alternative sources (e.g., web search).

Here is complete process of RAG Retrivel through code in this Notebook

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Indexing

Indexing optimizes document retrieval by structuring stored data efficiently.

1. Chunk Optimization

• Semantic Splitter: Splits text into meaningful chunks based on semantic delimiters for better embedding.

2. Multi-Representation Indexing

• Parent Document, Dense X: Converts documents into compact retrieval units, such as summaries.

3. Specialized Embeddings

• Uses advanced embedding models like Fine-tuning, CoLBERT for better retrieval.

4. Hierarchical Indexing

• RAPTOR organizes documents in a tree-like structure with summaries at different abstraction levels.

Here is complete process of RAG Indexing and Generation through code in this Notebook

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Generation

The final step is generating responses based on retrieved documents.

1. Active Retrieval

• Uses document generation quality to determine whether additional re-retrieval is needed.

2. Self-RAG & RRR (Re-Retrieve & Rewrite)

• Enhances response quality by re-writing the query or re-retrieving better documents.

Here is complete process of RAG Indexing and Generation through code in this Notebook

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Advanced Rag

This project implements a sophisticated Retrieval-Augmented Generation (RAG) architecture by combining three complementary modules:

Notebook	Role / Purpose
HyDe_Hypothetical_document_embedding.ipynb	Builds dense embeddings for documents using hybrid / hypothetical embedding strategies to maximize retrieval relevance
contextual_chunk_headers.ipynb	Splits documents into contextual chunks and associates header-level metadata to each chunk for enhanced retrieval context
corrective_RAG.ipynb	Wraps the retrieval + generation loop with corrective feedback to iteratively refine output and reduce hallucinations

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Conclusion

The RAG pipeline efficiently combines retrieval and generation techniques to improve the accuracy of language models. By integrating structured and unstructured data sources, it enhances the quality of generated responses, making it a powerful framework for AI-driven applications.

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