Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG)

Overview

Retrieval Augmented Generation (RAG) combines the generative capabilities of large language models with precision information retrieval systems. This pattern provides LLMs with access to current, domain-specific, or private information through efficient document indexing and semantic search.

Use this pattern when building:

• Knowledge bases and document search systems
• Customer support chatbots with company-specific information
• Research assistants requiring factual accuracy
• Question-answering systems over private datasets
• Content recommendation systems with contextual relevance
• Educational systems with curriculum-specific content

Architecture Diagram

flowchart TB
    User[User Query]
    ServiceEntry[Service Entry Point]
    SmartBucket[SmartBucket]

    User --> ServiceEntry
    ServiceEntry --> SmartBucket

    subgraph Ingestion ["Data Ingestion"]
        Documents[Documents/Data]
        APIs[External APIs]
        Files[Files/PDFs/Text]

        Documents --> SmartBucket
        APIs --> SmartBucket
        Files --> SmartBucket
    end

    subgraph Processing ["SmartBucket Processing"]
        Indexing[Vector Indexing]
        TextSearch[Text Search]
        Metadata[Metadata Filtering]

        SmartBucket --> Indexing
        SmartBucket --> TextSearch
        SmartBucket --> Metadata
    end

    subgraph Generation ["Response Generation"]
        Context[Retrieved Context]
        AI[AI Model]
        GeneratedResponse[Generated Response]

        SmartBucket --> Context
        Context --> AI
        AI --> GeneratedResponse
    end

    GeneratedResponse --> ServiceEntry
    ServiceEntry --> User

Components

• SmartBucket - Core component handling data storage, indexing, and retrieval with automatic vector embeddings
• Service (Optional) - Entry point for user interactions, handling routing, authentication, and response formatting
• AI (Optional) - Language model for response generation using retrieved context

Logical Flow

1. Data Ingestion - Documents and structured data uploaded to SmartBucket through various input methods

2. Automatic Processing - SmartBucket extracts text, generates embeddings, creates search indices, and stores metadata

3. Query Processing - SmartBucket analyzes queries to determine optimal search strategy (semantic, keyword, or hybrid)

4. Context Retrieval - SmartBucket searches indexed content and returns most relevant chunks ranked by relevance scores

5. Response Generation - Retrieved context optionally combined with query and sent to AI model for generation

6. Result Delivery - Final response enriched with retrieved information returned with optional citations and references

Implementation

1. Create SmartBucket - Deploy SmartBucket component configured for your data types and search requirements

2. Configure Indexing - Set up chunking strategies and embedding models based on content type and use case

3. Load Initial Data - Populate SmartBucket with initial dataset through batch upload or API ingestion

4. Implement Query Interface - Create query endpoints accepting user questions and returning contextual responses

5. Production Setup - Add authentication, rate limiting, monitoring, and external data source integration for updates

raindrop.manifest

Basic RAG

raindrop.manifest

application "rag_system" {

  smartBucket "knowledge_base" {
  }

}

RAG with Service

raindrop.manifest

application "rag_application" {

  service "api" {
  }

  smartBucket "knowledge_base" {
  }

  ai "generator" {
  }

}

Best Practices

• Structure your content - Organize documents with clear metadata for better filtering and categorization
• Optimize chunk size - Balance context preservation and search precision (typically 500-1500 tokens)
• Clean your data - Remove noise, ensure consistent formatting, and validate content quality before indexing
• Use hybrid search - Combine semantic and keyword search for best results across different query types
• Implement reranking - Use confidence thresholds to filter low-quality matches from search results
• Test with real queries - Validate search quality with actual user questions from your domain
• Monitor indexing speed - Large datasets may require batch processing strategies for optimal performance
• Cache frequent queries - Implement result caching for commonly asked questions to improve response times
• Scale incrementally - Start with smaller datasets and scale based on usage patterns and performance metrics