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A Comprehensive Guide to RAG Implementations
Retrieval-Augmented Generation (RAG) is a rapidly evolving concept in AI-driven applications. As a freelancer specializing in building RAG applications, I've seen firsthand how versatile and powerful this architecture can be. This blog post will delve into various types of RAG implementations, providing insights into their structures, use cases, and advantages.
Today, Iโll cover the following:
Understanding RAG
Simple RAG
Simple RAG with Memory
Branched RAG
HyDe (Hypothetical Document Embedding)
Adaptive RAG
Corrective RAG (CRAG)
Self-RAG
Agentic RAG
Integrating Vector Stores
Letโs dive in ๐คฟ
1. Understanding RAG
RAG isn't a machine learning algorithm; it's a pattern of software architecture. It leverages a generative AI system (such as a Large Language Model, LLM) and a data source to power AI applications. The primary goal is to enhance the output's relevance and quality, reduce hallucinations, and utilize proprietary data without training custom models.
Diagram of How RAG Works
I wrote a guide with the basic concepts of RAG a few months ago, you can access it here:
2. Simple RAG
Workflow:
Input Reception: The application receives user input.
Data Retrieval: The input is used to fetch relevant data from a database.
Prompt Generation: The retrieved data is injected into a prompt for the LLM.
Response Generation: The LLM generates a response, which is returned to the user.
Use Case: Ideal for straightforward applications where user queries directly relate to stored data.
3. Simple RAG with Memory
Workflow:
Input Reception and Memory Check: The application receives user input and checks previous conversations.
Query Transformation: The query is transformed based on conversation memory to include relevant context.
Data Retrieval and Prompt Generation: Similar to Simple RAG, with added context for better relevance.
Response Generation: The LLM generates a contextually aware response.
Use Case: Suitable for applications where maintaining context over extended interactions is crucial, such as customer support.
4. Branched RAG
Workflow:
Input Reception: The application receives user input.
Source Determination: Determines which data source(s) should be queried based on the input.
Data Retrieval and Prompt Generation: Fetches data from the selected source and generates a prompt for the LLM.
Response Generation: The LLM generates a response based on the specific data source.
Use Case: Effective in applications requiring data from multiple distinct sources, such as research or multi-domain knowledge systems.
5. HyDe (Hypothetical Document Embedding)
Workflow:
Input Reception: The application receives user input.
Hypothetical Answer Generation: The LLM generates a hypothetical answer to the query.
Data Retrieval: Uses the hypothetical answer to fetch relevant documents from a similarity-based system, like a vector store.
Prompt Generation and Response: Generates a prompt with the fetched documents and returns the LLMโs response.
Use Case: Useful when the query itself isn't sufficient for effective data retrieval, enhancing the relevance of retrieved information.
Advanced RAG Strategies
6. Adaptive RAG
Concept: Adaptive RAG combines query analysis with active/self-corrective RAG. The system routes queries through different strategies based on their nature.
Implementation:
Query Analysis: Determines the appropriate retrieval strategy (e.g., no retrieval, single-shot RAG, iterative RAG).
Strategy Execution: Executes the determined strategy, adjusting as needed for optimal relevance and accuracy.
Use Case: Suitable for dynamic environments where queries vary widely, such as search engines or AI assistants.
Adaptive RAG
7. Corrective RAG (CRAG)
Concept: CRAG incorporates self-reflection and self-grading to enhance retrieval accuracy.
Workflow:
Initial Retrieval: Retrieves documents based on the input query.
Knowledge Refinement: Partitions documents into "knowledge strips" and grades each for relevance.
Supplementary Retrieval: If necessary, performs additional retrieval using web search or other sources.
Prompt Generation and Response: Uses refined knowledge for prompt generation and response.
Use Case: Effective in high-stakes environments where accuracy is critical, such as legal or medical applications.
Corrective RAG diagram
8. Self-RAG
Concept: Self-RAG includes self-reflection and self-grading on both retrieved documents and generated responses.
Workflow:
Decision to Retrieve: Determines if retrieval is necessary based on the input query and previous generations.
Relevance Check: Assesses the relevance of retrieved passages.
Generation Verification: Verifies that the LLM's generation is supported by the retrieved documents.
Response Utility: Ensures the generated response is useful and relevant.
Use Case: Ideal for applications requiring high reliability and minimal hallucination, such as automated research assistants or knowledge base systems.
Self-RAG Diagram
9. Agentic RAG
Agentic RAG is an advanced, agent-based approach to question answering over multiple documents in a coordinated manner. It involves comparing different documents, summarizing specific documents, or comparing various summaries. Agentic RAG is a flexible framework that supports complex tasks requiring planning, multi-step reasoning, tool use, and learning over time.
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- Document Agents: Each document is assigned a dedicated agent capable of answering questions and summarizing within its own document.
- Meta-Agent: A top-level agent manages all the document agents, orchestrating their interactions and integrating their outputs to generate a coherent and comprehensive response.
Agentic RAG Diagram
10. Integrating Vector Stores
Vector stores are commonly used in RAG implementations. They transform text into embeddings (N-dimensional vectors), allowing cosine similarity to assess semantic closeness. This method significantly improves the relevance of retrieved information.
Learn more about Vector Databases with this guide:
Conclusion
RAG implementations offer a versatile and robust framework for building AI-driven applications. Each pattern serves unique needs and use cases, from simple retrieval and generation to advanced self-corrective strategies. Developers can create more effective, accurate, and reliable generative AI systems by understanding these patterns and their applications. Whether you're a freelancer like me or part of a larger organization, leveraging RAG can enhance the capabilities and performance of your AI solutions.
Hereโs a recap and a quick summary:
RAG Technique | Simple Definition | When to Use |
Simple RAG | Retrieves relevant documents based on the query and uses them to generate an answer | Basic question-answering tasks where context is needed |
Simple RAG with Memory | Extends Simple RAG by maintaining context from previous interactions | Conversational AI where continuity between queries is important |
Branched RAG | Performs multiple retrieval steps, refining the search based on intermediate results | Complex queries requiring multi-step reasoning or information synthesis |
HyDE (Hypothetical Document Embedding) | Generates a hypothetical ideal document before retrieval to improve search relevance | When dealing with queries that might not have exact matches in the knowledge base |
Adaptive RAG | Dynamically adjusts retrieval and generation strategies based on the query type or difficulty | Varied query types or when dealing with a diverse knowledge base |
Corrective RAG (CRAG) | Iteratively refines generated responses by fact-checking against retrieved information | High-stakes scenarios requiring increased accuracy and fact verification |
Self-RAG | The model critiques and improves its own responses using self-reflection and retrieval | Tasks requiring high accuracy and when there's time for multiple refinement steps |
Agentic RAG | Combines RAG with agentic behavior, allowing for more complex, multi-step problem-solving | Complex tasks requiring planning, decision-making, and external tool use |
Enjoy your Sunday, folks! Summer is here! โ๏ธ
Armand
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