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RAG & Retrieval Lab · Intermediate

RAG Playground

Step through a full Retrieval-Augmented Generation pipeline — ingest, chunk, embed, retrieve, generate.

Full RAG workflow Similarity search Grounded answers

Try RAG in Action

1
Document Ingestion
Load and prepare documents from various sources (PDFs, web pages, databases)
Click "Load Documents" to see document ingestion in action

How it works:

RAG systems start by ingesting documents from various sources. These can be PDFs, web pages, databases, or any text content. The system processes these documents and prepares them for the next step.

2
Text Chunking
Split documents into smaller, manageable chunks for better processing
Documents will be chunked here after ingestion

Why chunking matters:

Large documents are split into smaller chunks to fit within token limits and improve retrieval accuracy. Chunk size affects both retrieval quality and computational efficiency.

3
Embedding Generation
Convert text chunks into numerical vectors using embedding models
Embeddings will be generated from chunks

Embeddings explained:

Embeddings convert text into numerical vectors that capture semantic meaning. Similar texts have similar vectors, enabling mathematical similarity comparisons.

4
Vector Database
Store embeddings in a vector database for efficient similarity search
Click "Store Vectors" to populate the vector database

Vector databases:

Vector databases are optimized for storing and querying high-dimensional vectors. They use specialized indexes like HNSW or IVF for fast similarity search.

5
Query Processing
Convert user queries into embeddings for similarity search
User query will be processed here

Query processing:

User queries are converted to embeddings using the same model used for documents. This ensures queries and documents exist in the same vector space for accurate similarity matching.

6
Similarity Search
Find the most relevant chunks using cosine similarity or other metrics
Similarity search results will appear here

Similarity metrics:

Cosine similarity measures the angle between vectors, euclidean distance measures spatial distance, and dot product considers both angle and magnitude. Each has different use cases.

7
Context Retrieval
Retrieve and prepare the most relevant context for the LLM
Retrieved context will be assembled here

Context preparation:

Retrieved chunks are combined and formatted as context for the LLM. The order and formatting of context can significantly impact response quality.

8
LLM Generation
Generate responses using retrieved context and the user query
LLM response will be generated here

Generation process:

The LLM receives both the user query and retrieved context in a structured prompt. It generates responses based on the provided context, reducing hallucinations and improving accuracy.

Key RAG Concepts

Why RAG?

Reduces Hallucinations: Provides factual context from your documents

Current Information: Works with up-to-date data beyond training cutoff

Domain Specific: Tailored responses using your knowledge base

Transparent: Responses can cite sources and show reasoning

Key Components

Embedding Model: Converts text to vectors (e.g., OpenAI, Sentence-BERT)

Vector Database: Stores and searches embeddings (Pinecone, ChromaDB)

Retriever: Finds relevant documents using similarity search

Generator: LLM that creates responses with retrieved context

Best Practices

Chunk Size: Balance between context and specificity (512-1024 tokens)

Embedding Quality: Choose embeddings that match your domain

Retrieval Strategy: Experiment with different similarity metrics

Context Length: Don't exceed LLM context limits