Table of Contents
Introduction to RAG Architecture
Retrieval-Augmented Generation (RAG) combines the strengths of retrieval systems and generative models to produce accurate and context-aware responses. Various RAG architectures have been developed to address different challenges and use cases. This guide presents a comprehensive table listing different types of RAG architectures, their specific use cases, and key differences.
Table: RAG Architecture Types
| RAG Architecture Type | Description | Specific Use Cases | Implementation Details | Key Differences |
|---|---|---|---|---|
| Basic RAG | Standard RAG architecture where the model retrieves relevant documents and generates responses conditioned on these documents. | – General Question Answering: Ideal for creating chatbots or virtual assistants that answer FAQs or general queries. – Customer Support: Can be deployed to handle common customer inquiries by retrieving relevant support articles. – Knowledge Base Querying: Facilitates access to organizational knowledge bases or documentation. Example: A customer service bot that retrieves answers from a company’s FAQ page to assist users in real-time. | – Chunking: Use simple chunking with chunks of about 500 tokens each to balance context and computational efficiency. – Embedding Model: Utilize models like Sentence-BERT ( all-MiniLM-L6-v2) for effective semantic understanding.– Document Types: Best suited for text documents, FAQs, simple PDFs, and other straightforward textual formats. – Suitable LLMs: Models like GPT-3.5, GPT-4, or BERT work well due to their strong language understanding capabilities. | Baseline model combining retrieval and generation. |
| RAG-Sequence | Generates the entire response conditioned on retrieved documents in one pass. | – Single-Turn Question Answering: Effective for scenarios where each query is independent, such as search engines or informational kiosks. – Document Summarization: Can summarize long documents by retrieving relevant sections and generating a cohesive summary. Example: A summarization tool that ingests research papers and provides concise summaries for quick understanding. | – Chunking: Employ overlapping chunks of 512 tokens with an overlap of 128 tokens to preserve context between chunks. – Embedding Model: Models like RoBERTa-base help capture nuanced contextual relationships. – Document Types: Works well with articles, blogs, long PDFs, and other extended texts. – Suitable LLMs: Models like GPT-3.5, GPT-4, or T5 are suitable due to their strong sequence-to-sequence generation capabilities. | Retrieves documents once and generates the response sequentially. |
| RAG-Token | Performs retrieval at each token generation step, conditioning each token on retrieved documents. | – Code Completion: Ideal for IDE plugins that provide real-time code suggestions by retrieving relevant code snippets. – Complex Reasoning Tasks: Suitable for mathematical problem-solving or logical reasoning that requires step-by-step information retrieval. – Real-Time Data Retrieval: Useful in applications where the latest information is critical, such as stock market analysis. Example: An AI assistant that helps programmers by suggesting code snippets and functions as they type, retrieving the most relevant examples from extensive code repositories. | – Chunking: Use small chunks of 256 tokens to allow for rapid retrieval and integration during token generation. – Embedding Model: OpenAI Ada Embeddings are effective for capturing fine-grained semantic relationships. – Document Types: Best with code files, technical manuals, dynamic content like APIs. – Suitable LLMs: Models like GPT-4, Codex, or GPT-Neo that can handle token-level retrieval and generation. | Retrieval occurs at each token generation step for finer context adaptation. |
| Graph RAG | Integrates knowledge graphs into the RAG framework to leverage relationships between entities and concepts. | – Semantic Search: Enhances search engines by understanding entity relationships, providing more accurate results. – Explainable AI: Offers transparency by explaining how conclusions are reached via the knowledge graph. – Biomedical Data Analysis: Assists in drug discovery by mapping relationships between compounds, genes, and diseases. Example: A medical assistant that uses a biomedical knowledge graph to provide detailed explanations of how certain symptoms might be related to potential diagnoses. | – Chunking: Use entity-based chunks with variable sizes to focus on specific nodes and their connections. – Embedding Model: Models like TransE or ComplEx are designed for knowledge graph embeddings. – Document Types: Works best with structured data, RDFs, knowledge bases, and other graph-based data. – Suitable LLMs: GPT-4 or Transformer-XL that can handle complex relational data. | Incorporates graph structures to capture relational data between entities. |
| Funnel RAG | Utilizes a multi-stage retrieval and generation process, narrowing down information through each stage like a funnel. | – Large Document Summarization: Efficiently summarizes books or lengthy reports by progressively filtering relevant content. – Detailed Information Extraction: Extracts specific details from massive datasets, such as extracting financial metrics from annual reports. Example: A tool that summarizes a 500-page legal document by first identifying relevant sections and then distilling those into key points for legal professionals. | – Chunking: Start with large chunks of 1,024 tokens and progressively refine to smaller chunks of 256 tokens in subsequent stages. – Embedding Model: Universal Sentence Encoder for initial broad retrieval, followed by more specific models in later stages. – Document Types: Ideal for long reports, research papers, books, and other extensive documents. – Suitable LLMs: Models like GPT-3.5, T5, or BART that can handle multi-stage processing. | Multi-stage approach refining retrieval and generation iteratively. |
| Iterative RAG | Performs multiple rounds of retrieval and generation, refining the output based on previous iterations and feedback. | – Interactive Chatbots: Engages users in multi-turn conversations, refining responses based on user feedback. – Clarification Question Answering: Asks follow-up questions to better understand ambiguous queries. Example: A customer support bot that iteratively asks the user for more details to diagnose and solve technical issues effectively. | – Chunking: Use adaptive chunking, adjusting chunk sizes based on each iteration’s needs. – Embedding Model: Models like DistilBERT or SBERT for efficient processing. – Document Types: Suited for dialogue transcripts, customer interaction logs, and conversational data. – Suitable LLMs: GPT-3.5, DialoGPT, or BlenderBot that are optimized for conversational AI. | Introduces a feedback loop for continuous refinement of responses. |
| Hybrid RAG | Combines vector-based (semantic) and keyword-based (lexical) retrieval methods to enhance the retrieval process. | – Legal Document Search: Retrieves documents that not only contain exact legal terms but also semantically related information. – Patent Search: Finds patents that are relevant both in terms of specific terminology and underlying concepts. – Code Search: Helps developers find code snippets by matching exact function names and understanding code semantics. Example: A legal research tool that helps lawyers find relevant case laws by matching specific legal phrases and understanding the broader legal principles involved. | – Chunking: Use mixed chunking of 512 tokens with highlighted keywords to balance semantic meaning and exact matches. – Embedding Model: Combine BM25 for keyword matching and Sentence-BERT for semantic similarity. – Document Types: Best with legal texts, patents, code repositories, where both exact terms and semantics are crucial. – Suitable LLMs: GPT-4, LEGAL-BERT, or CodeBERT for domain-specific understanding. | Integrates multiple retrieval methods for comprehensive document retrieval. |
| Hierarchical RAG | Employs a hierarchical retrieval mechanism, retrieving documents, then sections, and then specific paragraphs or sentences. | – Document-Level Question Answering: Answers questions about specific sections within large documents like manuals or textbooks. – Topic-Specific Information Retrieval: Allows users to drill down from broad topics to specific details. Example: An educational platform where students can ask questions about a textbook, and the system provides answers by retrieving the relevant chapter, section, and paragraph. | – Chunking: Use hierarchical chunks organized as document > section > paragraph, with sizes varying at each level. – Embedding Model: Utilize Hierarchical Embeddings that capture context at multiple levels. – Document Types: Suitable for large documents, technical manuals, books, where structure is essential. – Suitable LLMs: Models like GPT-3.5, Longformer, or LED that can handle long-range dependencies. | Hierarchical structure in retrieval to maintain context at different granularities. |
| Knowledge Graph RAG | Leverages knowledge graphs to enhance retrieval and reasoning, focusing on structured data. | – Medical Diagnosis Assistance: Helps healthcare professionals by connecting symptoms, diseases, and treatments using a medical knowledge graph. – Legal Reasoning: Assists in building legal arguments by mapping precedents and statutes. – Scientific Research Assistance: Aids researchers by linking related studies, findings, and theories. Example: A research assistant that helps scientists discover new connections between genes and diseases by navigating through biomedical knowledge graphs. | – Chunking: Use entity and relation chunks with variable sizes to capture complex structures. – Embedding Model: Models like GraphSAGE or R-GCN designed for graph data. – Document Types: Best with knowledge graphs, ontologies, databases, where structured relationships are key. – Suitable LLMs: GPT-4 or specialized Graph Neural Networks that can process structured data effectively. | Uses structured knowledge bases for retrieval and reasoning. |
| Multi-hop RAG | Capable of reasoning over multiple documents by performing multi-hop retrieval to connect disparate pieces of information. | – Complex Question Answering: Answers questions that require synthesizing information from multiple sources, such as “What are the impacts of climate change on marine biodiversity in the Pacific Ocean?” – Problem-Solving Tasks: Assists in tasks that need step-by-step reasoning, like mathematical proofs or strategic planning. Example: An investigative journalism tool that gathers and connects information from various reports to provide a comprehensive story on a complex issue. | – Chunking: Use linked chunks of 256 tokens to facilitate connections between documents. – Embedding Model: Employ DPR (Dense Passage Retrieval) for effective multi-hop retrieval. – Document Types: Works with interlinked articles, web pages, research papers where information is distributed. – Suitable LLMs: Models like GPT-3.5, GPT-4, or T5 that can handle complex reasoning. | Multi-hop retrieval and reasoning to synthesize information from multiple sources. |
| Context-aware RAG | Incorporates conversation history or additional user context into retrieval and generation to produce more relevant responses. | – Conversational AI: Powers chatbots that remember previous interactions, providing a more natural and coherent conversation flow. – Personalized Assistants: Delivers tailored responses based on user preferences and history. – Customer Service Bots: Understands the customer’s issue in context, improving support quality. Example: A virtual assistant that not only answers questions but also remembers past interactions, such as previous bookings or preferences, to provide personalized recommendations. | – Chunking: Use session-based chunks of 512 tokens that include relevant conversation history. – Embedding Model: Universal Sentence Encoder (USE) with contextual augmentation to capture conversation nuances. – Document Types: Suited for chat logs, personalized user data, and any context-rich content. – Suitable LLMs: Models like GPT-3.5, BlenderBot, or DialoGPT optimized for context retention. | Utilizes context from previous interactions to inform current retrieval and generation. |
| Advanced RAG with Re-ranking | Enhances retrieval by re-ranking documents using advanced models before feeding them into the generator. | – High-Precision Information Retrieval: Required in legal, medical, or financial domains where accuracy is critical. – Compliance-Related Queries: Helps organizations retrieve documents that meet specific regulatory requirements. Example: A compliance tool that retrieves and ranks policy documents to ensure that a company’s procedures align with new regulations, highlighting the most relevant sections for review. | – Chunking: Start with initial chunks of 512 tokens and re-chunk top results into smaller chunks of 256 tokens for detailed analysis. – Embedding Model: Use Cross-Encoders like those trained on the MS MARCO dataset for effective re-ranking. – Document Types: Ideal for legal documents, compliance reports, and other sensitive materials. – Suitable LLMs: Models like GPT-4, RoBERTa-large, or T5 that can handle complex language understanding. | Implements re-ranking to improve the quality and relevance of retrieved documents. |
| RAG with Reinforcement Learning (RL) | Incorporates reinforcement learning to optimize retrieval and generation strategies based on feedback or predefined rewards. | – Adaptive Learning Systems: Personalizes educational content by adapting to student performance over time. – Personalized Recommendation Engines: Improves suggestions by learning from user interactions and feedback. Example: An e-learning platform that adapts the difficulty and style of content based on student interactions, maximizing engagement and learning outcomes. | – Chunking: Use dynamic chunking where chunk size adjusts based on RL policies to optimize performance. – Embedding Model: Utilize RL-based Embeddings that can learn and adapt from rewards. – Document Types: Works with user-generated content, dynamic databases, where data changes frequently. – Suitable LLMs: Models like GPT-3.5 or GPT-4 fine-tuned with reinforcement learning techniques. | Uses reinforcement learning to improve performance iteratively based on rewards or feedback. |
| RAG with External Memory | Utilizes an external memory component to store and update information over time, allowing dynamic knowledge access. | – Stateful Interactions: Enables chatbots to maintain long-term context over extended conversations or sessions. – Updating Knowledge Bases: Allows systems to learn new information without retraining the entire model. – Learning New Information: Useful in environments where data changes rapidly, such as news or stock markets. Example: A customer service bot that remembers previous issues reported by a user and references them in future interactions to provide better support. | – Chunking: Use memory chunks of 256 tokens per entry for efficient retrieval and storage. – Embedding Model: Implement Memory Networks to manage external memory effectively. – Document Types: Suited for logs, real-time data feeds, knowledge bases that require frequent updates. – Suitable LLMs: Models like GPT-4, MemN2N, or Transformer-XL that support external memory mechanisms. | Incorporates external memory mechanisms for dynamic knowledge access and updates. |
| Domain-specific RAG | Tailors the RAG architecture to a specific domain by fine-tuning on domain-specific data and adjusting retrieval components accordingly. | – Medical Assistants: Provide accurate medical information by retrieving from specialized medical texts and databases. – Legal Advisors: Assist lawyers by retrieving relevant case laws and statutes. – Financial Analysis Tools: Offer investment insights by analyzing financial reports and market data. – Educational Tutors: Help students by providing explanations and answers from educational materials. Example: A medical assistant that helps doctors by retrieving and summarizing the latest research articles related to a specific condition or treatment. | – Chunking: Use domain-optimized chunks with sizes varying based on the complexity of the content. – Embedding Model: Employ models like BioBERT for medical texts or FinBERT for financial documents. – Document Types: Best suited for domain-specific texts, medical records, financial reports, and other specialized documents. – Suitable LLMs: GPT-4 or domain-specific LLMs fine-tuned on relevant data. | Customized retrieval and generation components optimized for a particular domain. |
| RAG with Prompt Engineering | Employs advanced prompting strategies to guide the model’s generation process more effectively. | – Content Creation: Assists writers by generating creative content based on specific prompts or themes. – Instructional Content: Generates step-by-step guides or tutorials. – Style Transfer: Adapts content to match a particular writing style or tone. Example: A marketing tool that generates product descriptions or ad copy tailored to a specific audience or brand voice based on carefully crafted prompts. | – Chunking: Use prompt-aligned chunks of 512 tokens to ensure the model has sufficient context. – Embedding Model: Utilize Prompt-based Embeddings that are sensitive to the structure and content of prompts. – Document Types: Works well with creative writing prompts, templates, and other guided content formats. – Suitable LLMs: Models like GPT-3.5, GPT-4, or T5 known for their generative capabilities. | Focuses on designing prompts to steer the model’s outputs in desired directions. |
| Personalized RAG | Adjusts retrieval and generation based on user profiles, preferences, or past interactions to deliver personalized responses. | – Personalized Shopping Assistants: Recommend products based on user preferences and browsing history. – Individualized Learning Platforms: Provide custom educational content that adapts to the learner’s pace and interests. – Custom Content Delivery: Tailor news feeds or articles to match user interests. Example: A news app that curates articles based on a user’s reading habits, providing more content on topics they engage with and less on those they skip. | – Chunking: Use user-specific chunks of 256 tokens to focus on personalized content. – Embedding Model: Employ User Profile Embeddings that capture individual preferences and behaviors. – Document Types: Works with user histories, preference data, and personalized datasets. – Suitable LLMs: GPT-3.5 or GPT-4 with personalization capabilities. | Personalizes both retrieval and generation components to individual users. |
| Fusion-in-Decoder RAG (FiD-RAG) | Fuses retrieved documents within the decoder module rather than the encoder, allowing simultaneous attention over the query and retrieved documents. | – Detailed Reporting: Generates comprehensive reports by integrating multiple data sources during generation. – Complex Content Generation: Produces content that requires deep integration of retrieved information, such as technical documentation. Example: An automated analyst that generates financial reports by simultaneously considering market data, company performance metrics, and economic indicators during the generation process. | – Chunking: Use parallel chunks of 512 tokens each to feed into the decoder simultaneously. – Embedding Model: Utilize Transformer-based Embeddings that are compatible with decoder fusion. – Document Types: Ideal for reports, analytical documents, and content requiring synthesis of multiple sources. – Suitable LLMs: Models like T5, BART, or GPT-3.5 that support fusion in the decoder. | Fusion strategy occurs in the decoder, potentially improving integration of retrieved knowledge during generation. |
| Cross-modal RAG | Extends the RAG framework to handle multiple data modalities in both retrieval and generation phases. | – Multimodal Assistants: Assist users by processing and generating content across text, images, and audio. – Image Captioning with Retrieval Support: Generates image descriptions by retrieving related textual information. – Video Analysis: Summarizes or explains video content by integrating visual and textual data. Example: A virtual assistant that can answer questions about images uploaded by the user, such as identifying objects or describing scenes, by retrieving and integrating relevant information from both text and image databases. | – Chunking: Use modality-specific chunks, e.g., text chunks of 256 tokens and image features of fixed sizes. – Embedding Model: Utilize CLIP Embeddings that can handle both text and image data. – Document Types: Works with images, audio transcripts, videos, and other multimodal content. – Suitable LLMs: GPT-4 (Multimodal), BLIP, or DALL·E capable of processing multiple data types. | Handles and integrates multiple data modalities within the RAG architecture. |
| Ensemble RAG | Combines multiple RAG models or retrieval components to improve robustness and performance through ensemble methods. | – High-Reliability Applications: Essential in medical diagnosis or critical decision support systems where accuracy is paramount. – Diverse Content Generation: Produces richer content by combining the strengths of different models. Example: A medical diagnostic tool that aggregates suggestions from multiple specialized RAG models to provide a comprehensive assessment, reducing the risk of errors from any single model. | – Chunking: Use model-specific chunks with sizes varying based on each model’s requirements. – Embedding Model: Employ Multiple Embeddings to capture diverse semantic representations. – Document Types: Handles diverse document types depending on the models used, offering flexibility. – Suitable LLMs: GPT-4, or an ensemble of LLMs like T5, BERT, and GPT-3.5. | Uses ensemble techniques to aggregate outputs from multiple models or retrieval strategies. |
| Secure RAG | Incorporates security and privacy features into the RAG architecture, such as data encryption and access control. | – Handling Sensitive Information: Critical in domains like healthcare, finance, or legal, where data privacy is essential. – Compliance with Regulations: Ensures that data processing complies with laws like GDPR or HIPAA. Example: A financial advisor tool that securely processes client data to provide investment recommendations without exposing sensitive information, ensuring compliance with financial regulations. | – Chunking: Use encrypted chunks of 512 tokens to secure data during processing. – Embedding Model: Utilize Encrypted Embeddings that preserve privacy. – Document Types: Best with sensitive documents like medical records, legal files, and financial statements. – Suitable LLMs: GPT-4 with secure deployment practices, possibly on-premises. | Emphasizes security, privacy, and regulatory compliance within the RAG system. |
| Low-resource RAG | Optimizes the RAG architecture for environments with limited computational resources. | – Edge Device Deployment: Enables AI functionalities on IoT devices, smartphones, or other hardware with limited capabilities. – Mobile Applications: Provides AI features in apps without heavy computational demands. – Low-Bandwidth Environments: Useful in remote areas with limited internet connectivity. Example: A mobile app that provides offline language translation by retrieving and generating text using compact models suitable for on-device processing. | – Chunking: Use compressed chunks of 128 tokens to reduce memory usage. – Embedding Model: Utilize lightweight models like TinyBERT or MobileBERT. – Document Types: Best with short texts, summaries, lightweight data to keep processing minimal. – Suitable LLMs: DistilGPT-2, TinyGPT, or other compact models. | Focuses on resource efficiency without significantly compromising performance. |
| Federated RAG | Implements federated learning principles within the RAG framework, allowing models to be trained across decentralized data sources. | – Data Privacy Applications: Critical in healthcare or finance, where data cannot be centralized. – Collaborative Corporate Tools: Enables multiple branches of a company to contribute to a shared model without sharing sensitive data. Example: A medical research platform where hospitals contribute to a shared diagnostic model by training on local patient data, improving the model collectively without exposing individual patient records. | – Chunking: Use local chunks with sizes varying per device to accommodate different data distributions. – Embedding Model: Employ Federated Embeddings that can be trained in a decentralized manner. – Document Types: Works with decentralized data, user-owned documents, maintaining data privacy. – Suitable LLMs: GPT-3.5 or custom LLMs adapted for federated learning. | Distributes training across multiple devices or servers to enhance privacy and security. |
| Multi-lingual RAG | Extends the RAG architecture to support multiple languages in both retrieval and generation. | – Global Customer Support: Provides assistance in multiple languages, catering to an international user base. – Multi-lingual Chatbots: Engages users worldwide by understanding and generating responses in their native languages. – International Knowledge Bases: Facilitates access to information across different languages. Example: A global Q&A platform that allows users to ask questions in one language and receive answers synthesized from documents in multiple languages, breaking down language barriers. | – Chunking: Use language-specific chunks of 512 tokens to handle linguistic nuances. – Embedding Model: Utilize LaBSE (Language-agnostic BERT Sentence Embedding) for cross-lingual capabilities. – Document Types: Works with multilingual texts, translations, and cross-language datasets. – Suitable LLMs: mBERT, XLM-RoBERTa, or GPT-3.5 with multilingual support. | Handles multi-lingual data, enabling cross-language retrieval and generation. |
Conclusion
Next Steps:
Assess Your Application Needs: Identify the specific requirements of your use case, including data types, performance needs, and user expectations.
Choose the Right Architecture: Use this guide to select a RAG architecture that aligns with your goals.
Implement Thoughtfully: Pay attention to the implementation details, such as chunking strategies and model selection, to optimize performance.
Iterate and Optimize: Continuously test and refine your system based on feedback and performance metrics.
Stay Updated: Keep abreast of the latest developments in RAG technologies to leverage new advancements and best practices.
