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RAG (Retrieval-Augmented Generation) applications combine the power of large language models with external knowledge bases to provide accurate, contextual responses grounded in your own documents and data.

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Why RAG?

Factual Accuracy

Ground responses in actual documents instead of relying on model training

Up-to-Date Information

Query live data and recent documents without retraining models

Source Attribution

Provide citations and references for all information

Domain Specificity

Use your own proprietary data and documents

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All RAG Application Projects

Agentic RAG

Intelligent RAG with Agno and GPT-4o combining web URL indexing, semantic search, and LanceDB vector storage.

Chat with Code

Natural language code exploration and documentation with semantic code search and analysis.

PDF RAG Analyser

Specialized PDF analysis with vector search for contracts, reports, and technical documents.

Resume Optimizer

Job-specific resume enhancement using RAG to match job descriptions with candidate experience.

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RAG Architecture

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Standard RAG Pipeline

# 1. Document Loading
documents = load_documents(["doc1.pdf", "doc2.txt"])

# 2. Chunking
chunks = split_documents(documents, chunk_size=500)

# 3. Embedding
embeddings = generate_embeddings(chunks)

# 4. Vector Storage
vector_db.store(embeddings)

# 5. Query & Retrieval
query_embedding = generate_embedding(user_query)
relevant_chunks = vector_db.search(query_embedding, top_k=5)

# 6. Generation
response = llm.generate(
    context=relevant_chunks,
    query=user_query
)

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Key Components

Supported Formats:
  • PDF documents
  • Text files
  • Web pages (HTML)
  • Images (with OCR)
  • Code repositories
Processing Steps:
  • Text extraction
  • Chunking strategies
  • Metadata preservation
  • Quality filtering
Popular Options:
  • LanceDB - Serverless vector database
  • Qdrant - Vector similarity search engine
  • Pinecone - Managed vector database
  • Weaviate - Open-source vector database
Features:
  • Semantic similarity search
  • Hybrid search (vector + keyword)
  • Filtering and metadata queries
  • Scalable storage
Common Choices:
  • OpenAI text-embedding-3-small
  • OpenAI text-embedding-3-large
  • Sentence Transformers
  • Custom fine-tuned models
Considerations:
  • Embedding dimension
  • Domain specificity
  • Performance vs. accuracy
  • Cost per embedding

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Use Case Categories

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Document Q&A

  • PDF RAG Analyser - Contract and report analysis
  • Chat with Code - Codebase exploration
  • LlamaIndex Starter - General document Q&A

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Specialized Processing

  • Gemma OCR - Image and scan processing
  • Nvidia OCR - High-performance OCR
  • Contextual AI RAG - Advanced retrieval

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Application-Specific

  • Resume Optimizer - Job matching
  • WFGY LLM Debugger - Code debugging
  • Agentic RAG with Web Search - Real-time information

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Advanced RAG Techniques

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Agentic RAG

Combines agents with RAG for intelligent retrieval:
  • Query planning - Break down complex queries
  • Multi-source retrieval - Search multiple knowledge bases
  • Iterative refinement - Refine search based on results
  • Self-correction - Verify and improve responses

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Contextual Retrieval

  • Contextual embeddings - Include document context in chunks
  • Hierarchical chunking - Parent-child document relationships
  • Metadata filtering - Pre-filter before semantic search
  • Re-ranking - Score and sort results by relevance
  • Vector search - Semantic similarity
  • Keyword search - Exact matches
  • Combined scoring - Weighted fusion of both methods

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Getting Started

1

Choose a RAG Project

Select based on your document types and use case
2

Prepare Your Documents

Gather documents you want to query (PDFs, text files, etc.)
3

Set Up Vector Database

Install and configure your chosen vector database
4

Index Documents

Load and embed your documents into the vector database
5

Query and Test

Ask questions and verify responses are accurate
6

Tune Performance

Adjust chunk sizes, retrieval parameters, and prompts

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Prerequisites

RAG applications typically require:
  • Python 3.10+ for frameworks
  • LLM API keys (OpenAI, Nebius, etc.)
  • Vector database setup
  • Document processing libraries (PyPDF, OCR tools)
  • Sufficient storage for vector embeddings

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Performance Optimization

Chunking Strategy

Optimize chunk size and overlap for your documents

Embedding Selection

Choose embeddings that match your domain

Retrieval Tuning

Adjust top-k and similarity thresholds

Prompt Engineering

Craft prompts that guide the model to use context

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Next Steps

Add Memory

Combine RAG with memory for context-aware retrieval

Integrate Tools

Use MCP to access external data sources

Build Complex Systems

Create multi-agent RAG workflows

Build docs developers (and LLMs) love

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