RAG Architecture Patterns

1. Basic/Naive RAG

• Description: Simple retrieval-then-generate approach

• Flow: Query → Retrieve relevant documents → Generate response using retrieved context

• Components: Single vector database, basic embedding model, LLM for generation

• Use Case: Simple Q&A applications, proof of concepts

• Limitations: No query optimization, basic relevance matching

2. Advanced RAG

• Description: Enhanced retrieval with pre/post-processing

• Features: Query rewriting, document re-ranking, result filtering

• Components: Query optimizer, multiple retrieval strategies, re-ranking models

• Improvements: Better relevance, reduced hallucination, contextual understanding

• Use Case: Production applications requiring higher accuracy

3. Modular RAG

• Description: Flexible, component-based architecture

• Features: Interchangeable modules (retrieval, generation, reasoning)

• Components: Pluggable retrievers, generators, evaluators, orchestrators

• Benefits: Customizable pipelines, easier testing and optimization

• Use Case: Complex applications with varying requirements

4. Agentic RAG

• Description: Agent-driven retrieval with dynamic decision-making

• Features: Multi-step reasoning, adaptive retrieval strategies, tool usage

• Components: Planning agents, retrieval agents, synthesis agents

• Capabilities: Complex query decomposition, iterative refinement

• Use Case: Research assistance, complex problem-solving

5. Hierarchical RAG

• Description: Multi-level retrieval with document hierarchy

• Structure: Document → Section → Paragraph → Sentence levels

• Features: Coarse-to-fine retrieval, contextual preservation

• Benefits: Better long-document understanding, improved context relevance

• Use Case: Technical documentation, legal documents, research papers

6. Fusion RAG (Hybrid RAG)

• Description: Combines multiple retrieval methods

• Approaches: Dense + sparse retrieval, multiple embedding models

• Techniques: Reciprocal rank fusion, weighted combination

• Benefits: Improved recall and precision, robust retrieval

• Use Case: Diverse content types, comprehensive search requirements

7. Self-RAG

• Description: Self-reflective retrieval with quality assessment

• Features: Retrieval necessity prediction, relevance scoring, response verification

• Components: Reflection tokens, quality critics, adaptive triggering

• Benefits: Reduced unnecessary retrievals, improved factual accuracy

• Use Case: High-stakes applications requiring reliability

8. Corrective RAG (CRAG)

• Description: Error-correcting retrieval with web fallback

• Features: Retrieval quality assessment, web search integration, knowledge correction

• Flow: Assess retrieval → Correct if needed → Generate with verified knowledge

• Benefits: Handles knowledge gaps, improves factual accuracy

• Use Case: Dynamic knowledge domains, fact-checking applications

9. Adaptive RAG

• Description: Dynamic strategy selection based on query complexity

• Features: Query classification, strategy routing, adaptive processing

• Strategies: No retrieval, single-step, multi-step, iterative

• Benefits: Optimized processing, cost efficiency, improved performance

• Use Case: Mixed query types, production optimization

10. GraphRAG

• Description: Graph-based knowledge representation and retrieval

• Features: Entity relationships, graph traversal, community detection

• Components: Knowledge graphs, graph databases, relationship reasoning

• Benefits: Complex relationship understanding, multi-hop reasoning

• Use Case: Knowledge-intensive domains, relationship-heavy queries

11. Multi-Modal RAG

• Description: Retrieval across text, images, audio, video

• Features: Cross-modal embeddings, multi-modal fusion, diverse content types

• Components: Vision encoders, audio processors, multi-modal LLMs

• Use Case: Rich media applications, comprehensive content search

12. Conversational RAG

• Description: Context-aware retrieval for multi-turn conversations

• Features: Conversation history integration, context tracking, query refinement

• Components: Memory management, context windows, dialogue state tracking

• Use Case: Chatbots, virtual assistants, interactive applications

13. Temporal RAG

• Description: Time-aware retrieval considering temporal relevance

• Features: Temporal embeddings, time-based filtering, recency weighting

• Components: Time-aware indexing, temporal ranking, freshness scoring

• Use Case: News applications, time-sensitive information, evolving knowledge

14. Federated RAG

• Description: Retrieval across multiple distributed knowledge sources

• Features: Cross-source querying, source-specific optimization, result aggregation

• Components: Multiple vector stores, federation layer, source routing

• Use Case: Multi-tenant systems, organizational silos, diverse data sources

15. Streaming RAG

• Description: Real-time retrieval and generation for continuous data

• Features: Incremental updates, real-time indexing, streaming responses

• Components: Stream processing, dynamic indexing, real-time APIs

• Use Case: Live data feeds, real-time monitoring, continuous learning

Selection Criteria:

• Query Complexity: Simple → Basic RAG; Complex → Agentic RAG

• Accuracy Requirements: High → Self-RAG, CRAG; Standard → Advanced RAG

• Data Types: Text → Basic RAG; Mixed → Multi-Modal RAG

• Scale: Small → Naive RAG; Enterprise → Modular/Federated RAG

• Real-time Needs: Static → Basic RAG; Dynamic → Streaming/Adaptive RAG

• Relationship Importance: Entity-heavy → GraphRAG; Document-focused → Hierarchical RAG

Each pattern addresses specific use cases and requirements, and modern implementations often combine multiple patterns for optimal performance.

RAG Patterns Supported on AWS/Bedrock

Fully Supported on AWS Bedrock

1. Basic/Naive RAG

• AWS Services: Bedrock Knowledge Bases, OpenSearch, Kendra

• Implementation: Direct integration with foundation models

• Status: Production-ready, well-documented

2. Advanced RAG

• AWS Services: Bedrock Knowledge Bases with chunking strategies, Kendra intelligent ranking

• Features: Query preprocessing, result re-ranking, metadata filtering

• Status: Supported through configuration options

3. Agentic RAG

• AWS Services: Bedrock Agents (dedicated service)

• Capabilities: Function calling, tool orchestration, multi-step reasoning

• Integration: Lambda functions, API Gateway, other AWS services

• Status: Native support, actively developed

4. Multi-Modal RAG

• AWS Services: Bedrock (Claude 3, GPT-4V), Rekognition, Textract

• Support: Text + image retrieval and generation

• Status: Supported through multimodal foundation models

5. Conversational RAG

• AWS Services: Bedrock with conversation memory, DynamoDB for session storage

• Features: Context preservation, multi-turn conversations

• Status: Supported through application design patterns

 Partially Supported (Requires Custom Implementation)

6. Self-RAG

• Implementation: Custom logic using Bedrock APIs + Lambda

• Components: Retrieval quality assessment, response verification

• Status: Possible but requires significant custom development

7. Corrective RAG (CRAG)

• Implementation: Bedrock + custom orchestration + web search APIs

• Components: Quality assessment, fallback to web search

• Status: Achievable through multi-service architecture

8. GraphRAG

• AWS Services: Neptune (graph database) + Bedrock

• Implementation: Custom integration between graph queries and LLM generation

• Status: Technically possible, limited native support

9. Fusion/Hybrid RAG

• AWS Services: Kendra (keyword) + OpenSearch (semantic) + custom ranking

• Implementation: Multi-retriever setup with result fusion

• Status: Achievable through architecture design

10. Hierarchical RAG

• Implementation: Custom chunking strategies in Bedrock Knowledge Bases

• Features: Document structure preservation, multi-level retrieval

• Status: Limited native support, mostly custom implementation

Custom Implementation

11. Modular RAG

• Status: Framework concept, not a specific AWS service feature

• Implementation: Achievable through microservices architecture

12. Adaptive RAG

• Implementation: Custom routing logic + multiple Bedrock configurations

• Status: Requires significant custom orchestration

13. Federated RAG

• Implementation: Cross-account/cross-region custom setup

• Status: Architecturally possible but complex

14. Temporal RAG

• Implementation: Custom time-aware indexing + metadata filtering

• Status: Limited native temporal awareness

15. Streaming RAG

• AWS Services: Kinesis + Lambda + Bedrock for real-time processing

• Status: Possible through event-driven architecture

AWS Bedrock-Specific Capabilities:

Bedrock Knowledge Bases

• Vector storage (OpenSearch, Pinecone, Redis)

• Automatic chunking and embedding

• Metadata filtering and hybrid search

• Integration with S3, SharePoint, Confluence, Salesforce

Bedrock Agents

• Function Calling: Lambda integration

• Action Groups: Custom tool definitions

• Memory: Conversation persistence

• Orchestration: Multi-step reasoning and planning

• Guardrails: Safety and content filtering

Supported Foundation Models

• Anthropic Claude: Text and multimodal

• Amazon Titan: Text embeddings and generation

• Cohere: Text generation and embeddings

• Meta Llama: Text generation

• Stability AI: Image generation

Recommended Architecture Patterns on AWS:

Production-Ready (Native Support):

1. Basic RAG: Bedrock Knowledge Bases + Foundation Models

2. Agentic RAG: Bedrock Agents + Lambda functions

3. Conversational RAG: Bedrock + DynamoDB + API Gateway

Advanced (Custom Implementation):

1. Hybrid RAG: Kendra + OpenSearch + custom fusion logic

2. Multi-Modal RAG: Bedrock multimodal models + S3 + Rekognition

3. Self-RAG: Custom orchestration with quality assessment

Academically Established RAG Patterns (Peer-Reviewed):

1. Basic/Naive RAG

• Paper: "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020, NeurIPS)

• Status: Foundational paper, highly cited (~3000+ citations)

2. Self-RAG

• Paper: "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection" (Asai et al., 2023, ICLR 2024)

• Status: Peer-reviewed, significant impact

3. Corrective RAG (CRAG)

• Paper: "Corrective Retrieval Augmented Generation" (Yan et al., 2024, ICLR 2024)

• Status: Recently accepted at top-tier venue

4. GraphRAG

• Papers: Multiple works including "Graph-RAG" papers and Microsoft's GraphRAG implementation

• Status: Mixed - concept is established, specific implementations vary

5. Fusion/Hybrid RAG

• Papers: "Precise Zero-Shot Dense Retrieval without Relevance Labels" (Gao et al., 2023) and related fusion techniques

• Status: Rank fusion methods well-established in IR literature

Industry/Practitioner Patterns 

6. Modular RAG - More of an engineering pattern from LangChain, LlamaIndex
7. Agentic RAG - Emerging from agent frameworks, not formalized academically yet
8. Hierarchical RAG - Implementation pattern, limited formal research
9. Adaptive RAG - Engineering optimization, some academic work emerging
10. Multi-Modal RAG - Active research area but fragmented approaches
Others - Mostly industry terminology or implementation patterns

Academic Research Sources:

• ACL, EMNLP, NAACL - Main NLP venues

• ICLR, NeurIPS, ICML - ML conferences with RAG research

• SIGIR, ECIR - Information retrieval conferences

• arXiv - Preprints (not peer-reviewed but influential)