RAG Knowledge Base Engine — Reaves Labs & Learning

Document Ingestion Simulator

Paste text and configure chunking to see how documents are split for retrieval.

Paste Document Text

Retrieval-Augmented Generation (RAG) is a technique that combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems fetch relevant documents from a knowledge base at inference time.

The RAG pipeline consists of several stages. First, documents are ingested and split into smaller chunks. These chunks are then embedded into vector representations using an embedding model. The vectors are stored in a vector database for efficient similarity search.

When a user asks a question, the query is also embedded into a vector. The system then searches the vector database for the most similar document chunks. These retrieved chunks are inserted into the prompt as context, allowing the language model to generate an answer grounded in the retrieved information.

Chunking strategy is critical to RAG performance. If chunks are too large, they may contain irrelevant information that dilutes the signal. If chunks are too small, they may lack sufficient context. Common strategies include fixed-size chunking, semantic chunking based on topic boundaries, paragraph-based chunking, and sentence-level chunking.

Overlap between chunks helps preserve context at boundaries. A typical overlap of 10-20% of the chunk size ensures that information at chunk boundaries is not lost. This is especially important for fixed-size chunking where splits may occur mid-sentence or mid-thought.

Vector databases like Pinecone, Weaviate, Chroma, and Qdrant provide efficient approximate nearest neighbor search. Each has different tradeoffs in terms of scalability, pricing, and feature set. Choosing the right vector database depends on your scale, budget, and whether you need managed or self-hosted solutions.

Chunking Strategy Chunk Size (characters) 500 chars Overlap (characters) 50 chars Estimated Token Multiplier

Chunk Results

Chunk Detail

Embedding Space Visualizer

Chunks mapped to 2D vector space (simulated t-SNE projection). Click any point to find similar chunks.

● Chunks ● Query ● Top-K Results Lines = similarity

Similarity Metric Top-K Results Query Text

Retrieval Pipeline Builder

Configure every stage of your RAG retrieval pipeline.

Embed

ada-002

➔

Vector DB

Pinecone

➔

Chunk

512 tok

➔

Retrieve

Top-5

➔

Rerank

None

➔

Generate

GPT-4o

Embedding Configuration

Embedding Model Vector Database

Retrieval Configuration

Chunk Size (tokens) Chunk Overlap (tokens) Top-K Similarity Threshold 0.70

Search Strategy

Search Type Retrieval Pattern

Post-Retrieval

Reranking Model Generation Model Max Context Window (tokens)

Prompt Template Designer

Build and preview the assembled RAG prompt that gets sent to the LLM.

System Prompt Context Template User Query Template Chunk Separator Number of Context Chunks Simulated Query

Assembled Prompt Preview

Click "Assemble Prompt" to see the full prompt...

Built-In Knowledge Base

Searchable RAG architecture reference with benchmarks, comparisons, and best practices.

Architecture Export

Export your complete RAG pipeline configuration as JSON.

Export Options

Include ingestion config Include retrieval config Include prompt template Include cost estimates Include metadata

Configuration JSON

Click "Generate JSON Config" to export your RAG architecture...