happyer1. Preface
Generative Artificial Intelligence (GenAI), such as ChatGPT and Midjourney, has demonstrated exceptional performance in tasks like text generation and text-to-image generation. However, these models also have limitations, such as a tendency to produce hallucinations, weak mathematical abilities, and a lack of explainability. Research into Retrieval-Augmented Generation (RAG) technology aims to improve the factualness and reasonableness of generated content by enabling models to interact with the external world and acquire knowledge.
2. Introduction to RAG Technology
RAG technology provides large language models (LLMs) with retrieved information as a basis for generating answers. It typically includes two stages: retrieving contextually relevant information and using the retrieved knowledge to guide the generation process. RAG is a popular system architecture based on LLMs, widely used in question-answering services and applications that interact with data.
3. Basic RAG Technology
The basic RAG process includes chunking text, embedding chunks using an encoding model, placing vectors into an index, and finally creating prompts for the LLM to guide the model in answering user queries. At runtime, the user query is vectorized using the same encoder model, a search for the query vector is performed to find the most relevant results, and these are input as context into the LLM prompt.
4. Advanced RAG Techniques
4.1. Chunking and Vectorization
Chunking involves dividing a document into chunks of a certain size to better represent its semantics. Vectorization is the selection of a model to embed our chunks, such as the bge-large or E5 embedding series.
4.2. Search Index
The search index is a key part of the RAG process, used to store vectorized content. Approximate nearest neighbor searches can be implemented using Faiss, nmslib, annoy, or managed solutions like OpenSearch or ElasticSearch.
4.3. Re-ranking and Filtering
Retrieved results can be optimized through filtering, re-ranking, or transformation. Models such as LLMs, sentence-transformer cross-encoders, Cohere re-ranking endpoints, etc., can be used to re-rank results.
4.4. Query Transformation
Query transformation uses LLMs to modify user input to improve retrieval quality, including breaking down complex queries into subqueries, back-off prompts, query rewriting, etc.
4.5. Chat Engine
The chat engine takes into account the conversational context, supporting subsequent questions and user commands related to the previous conversation context.
4.6. Query Routing
Query routing is a decision step supported by LLMs that decides the next action based on the user query, such as summarizing, performing a search, or trying multiple routes.
4.7. Agents in RAG
Agents are LLMs capable of reasoning and providing a set of tools and tasks. Agents can include deterministic functions, external APIs, or other Agents.
4.8. Response Synthesizer
The response synthesizer is the final step in the RAG pipeline, generating answers based on all retrieved contexts and the initial user query.
5. Architecture of RAG
The RAG model architecture contains two core components: the retriever and the generator.
Retriever: Typically a BERT-based neural network model that encodes the input query into a vector and compares it with pre-encoded document vectors in the database to retrieve the most relevant document fragments.
Generator: A pre-trained sequence-to-sequence model that receives document fragments provided by the retriever and the original query, fuses this information through an attention mechanism, and generates coherent and relevant text output.
5.1. Retrieval System
5.1.1. Indexed Database
The core of the retrieval system is an indexed database that stores a large number of documents or information fragments. These documents can be text files, web pages, books, or any other form of text data.
5.1.2. Retrieval Algorithm
The retrieval algorithm is responsible for finding the most relevant information from the indexed database based on the user's query. This typically involves techniques such as keyword matching, semantic search, and ranking algorithms.
5.1.3. Retrieval Efficiency
To improve retrieval efficiency, retrieval systems often use techniques such as inverted indexing and vector search to quickly locate the most relevant documents or information fragments.
5.2. Generation Model
5.2.1. Pre-trained Language Model
The generation model is usually a pre-trained language model, such as GPT, BERT, or other variants. These models have been trained on a large amount of text data and have the ability to understand and generate natural language.
5.2.2. Context Fusion
In RAG, the generation model must not only process the user's original query but also fuse the relevant context information provided by the retrieval system. This requires the model to understand and integrate information from different sources.
5.2.3. Generation Strategy
The generation model generates answers or text based on the fused context information. This may involve conditional generation, sequence-to-sequence transformation, and other techniques.
5.3. RAG Workflow
5.3.1. Receive Query
The user poses a question or query, which is the starting point of the RAG workflow.
5.3.2. Retrieve Information
The retrieval system retrieves the most relevant information from the indexed database based on the user's query.
5.3.3. Information Fusion
The retrieved information is passed to the generation model to be fused with the user's original query.
5.3.4. Generate Output
The generation model combines the retrieved information and the user's query to generate an answer or text.
5.3.5. Output Results
The generated text is returned to the user as the final output.
6. Advantages of RAG
6.1. Context Understanding
RAG can use retrieved context information to improve understanding of user queries, thereby generating more accurate answers.
6.2. Information Richness
By retrieving a large amount of data, RAG can provide richer and more detailed answers, surpassing the limitations of traditional language models.
6.3. Flexibility
RAG can be applied to various tasks, including question-answering systems, text summarization, content creation, etc., and is highly flexible.
7. Fine-tuning RAG System Models
The RAG system involves multiple models, including encoder models, LLMs, and sorters, all of which can be fine-tuned to improve performance.
8. Evaluating RAG Systems
Evaluating the performance of RAG systems can use multiple frameworks, focusing on metrics such as answer relevance, fundamentality, fidelity, and the relevance of retrieved context.
9. Codia AI's products
Codia AI has rich experience in multimodal, image processing, and AI.
1.Codia AI DesignGen: Prompt to UI for Website, Landing Page, Blog
2.Codia AI Design: Screenshot to Editable Figma Design
3.Codia AI VectorMagic: Image to Full-Color Vector/PNG to SVG
4.Codia AI Figma to code:HTML, CSS, React, Vue, iOS, Android, Flutter, Tailwind, Web, Native,...
10. Conclusion
RAG (Retrieval-Augmented Generation) is a hybrid model that combines retrieval and generation capabilities, capable of providing more accurate and coherent answers. It has a wide range of applications across various fields, demonstrating its powerful potential. As technology continues to evolve, RAG models are expected to achieve even greater breakthroughs and applications in the future.
