RAG stands for Retrieval-Augmented Generation. RAG is a technique used in natural language processing (NLP) that aims to improve the quality of generated text by incorporating external information which the system retrieves from a large corpus aka documents or databases. Let’s take a closer look at how RAG works.
As we said, RAG stands for Retrieval-Augmented Generation, so we have three components that are closely connected and form the technique – retrieval, augmentation and generation 🙂
The Retrieval Component works by searching and fetching relevant documents or more commonly pieces of information from a large collection of data and documents. The search is of course based on a given input query or prompt. Commonly the retrieval component uses advanced search algorithms or pre-trained models to find the most relevant information that can enhance the generated response – in the process the search employs several techniques to find documents based on semantic similarity rather than keyword matching.
Augmentation Process: starts once relevant information is retrieved, and involves combining the retrieved information with the input query to create a more informed and contextually appropriate response.
Generation Component is usually presented by a language model, such as GPT (Generative Pre-trained Transformer), which generates the final output text. By integrating the retrieved information, the language model can produce more accurate, informative, and contextually relevant responses.
Imagine you have a huge library of books, and you’re asked a question that you need to answer. First, you go into the library and pick out a few books that you think have the best information to help with the question. This is like the the computer quickly finds and gathers relevant information from a vast pool of data. Once the computer has these “books” (data), it doesn’t just copy the information verbatim; instead, it uses this information to create a new, coherent, and contextually appropriate response. This step is where the augmentation and generation happens — the basic ability of the computer to generate answers is enhanced (or augmented) by the specific information it has retrieved.
So, in essence, RAG is a technique where a computer first finds the right information and then uses that information to generate a knowledgeable and precise answer to a question. The computer’s response is “augmented” or improved by adding detailed, relevant information from its search to the final answer it generates. This makes the answer not just a generic response, but one that is informed and tailored to the specific question, enhancing the quality and relevance of the communication, this is exactly why RAG is so powerful.
Naive RAG
In the context of Naive RAG, the retrieval step typically involves fetching relevant documents or data without highly sophisticated processing or optimization that more advanced versions might employ. The main advantages are
simplicity – naive RAG models are generally simpler to implement and understand, they require fewer customizations and less tuning compared to more complex RAG setups, making them more accessible for initial experiments or when resources are limited. They are better than “nothing” (by nothing I mean plain models) because even a basic implementation of RAG usually outperforms traditional generative models that do not use external information.
Naive’s simplicity still leaves us with quite a few drawbacks. The most prominent one is the inability to fully optimize the retrieval process. It might retrieve less relevant documents, impacting the overall quality and relevance of the generated responses. Another important problem is the scalability issue – as the data size or complexity of queries increases, Naive RAG might struggle to maintain performance without more sophisticated retrieval mechanisms or optimizations. Consequently, more straightforward implementations might not handle nuances, ambiguities, and complex query contexts as effectively as advanced models. This can lead to less accurate or contextually inappropriate responses.
Advanced RAG
Advanced RAG served as an improvement of Naive RAG and its main advantage implement various pre-retrieval and post-retrieval improvements to enhance their performance and accuracy. These improvements address limitations in naive RAG models by optimizing the retrieval process, improving the integration of retrieved information, and refining the generated outputs. Here are some key techniques used:
Pre-Retrieval Improvements
The aim of this process is to enhance the quality of the content being indexed. This involves refining the text from data sources by eliminating irrelevant information, resolving ambiguities and inaccuracies, preserving context, and updating outdated documents. Adding appropriate metadata to each data chunk significantly boosts the quality of the retrieved documents. In some cases, the user’s original query may not be optimal for the language model. Therefore, query rewriting techniques are employed to adjust the query based on the model’s characteristics, thereby improving the quality of the generated output. Below you’ll find the most common methods that can improve the pre-retrieval process:
Query Reformulation:
Before retrieving documents, the system can reformulate the input query to improve retrieval quality. Techniques like query expansion, synonym generation, and context addition help ensure that the query captures the user’s intent more accurately.
Context-Aware Retrieval:
By incorporating additional context from the conversation or previous interactions, the retrieval system can better understand what information is relevant, improving the relevance of retrieved documents.
Advanced Indexing:
Utilizing more sophisticated indexing techniques, such as semantic indexing with embeddings, ensures that the retrieval system can quickly and accurately locate relevant documents based on their semantic content, not just keywords.
Filtering and Preprocessing:
Before retrieval, the system can filter out irrelevant documents or preprocess the data to ensure that only the most relevant information is considered. This can include removing duplicates, outdated information, or overly general content.
Post-Retrieval Improvements
After retrieving high-quality chunks, how these are integrated with the user query to form the prompt significantly impacts the quality of the generated response. Simply appending chunks to the query can exceed the context window limit, introduce noise, and degrade response quality. Therefore, additional techniques like re-ranking and prompt compression are employed to address these challenges.
Re-ranking involves reordering the retrieved chunks based on their contextual similarity to the user query, rather than relying solely on vector similarity. Prompt compression reduces noise by compressing irrelevant information, highlighting important passages, and shortening the context length. Below you’ll find the most common methods that can improve the post-retrieval process:
Re-Ranking:
After the initial retrieval, the system can re-rank the retrieved documents using more sophisticated models, such as neural re-rankers. These models consider deeper semantic similarities and contextual relevance to prioritize the most useful documents.
Document Summarization:
Instead of using entire documents, advanced RAG systems can summarize retrieved documents to extract the most relevant information. This reduces the amount of text the generative model needs to process and helps focus on key points.
Context Integration:
Enhancing how the retrieved information is integrated into the generative model is crucial. Techniques like attention mechanisms or hierarchical models can help the system focus on the most relevant parts of the retrieved documents when generating responses.
Feedback Loops:
Implementing feedback mechanisms where the system learns from user interactions can refine both retrieval and generation processes over time. This can involve user feedback on response relevance or automated evaluation metrics.
Ensemble Methods:
Combining multiple retrieval and generative models can leverage their strengths and mitigate weaknesses. For instance, an ensemble of different retrievers can provide a diverse set of documents, which can be combined to enhance the generative model’s output.
Fine-Tuning with Retrieved Data:
Fine-tuning the generative model on datasets that include retrieved documents as part of the training data can help the model learn to better incorporate external information into its responses.
Modular RAG
Modular Retrieval-Augmented Generation (RAG) and advanced RAG both aim to improve the performance of language models by integrating retrieval mechanisms. However, Modular RAG is designed with a highly flexible and interchangeable architecture, where different components of the system can be independently developed, replaced, or improved – it consists of distinct, well-defined modules for retrieval, re-ranking, compression, and generation, and each module performs a specific task and communicates with other modules through standardized interfaces.
One of it main advantages is flexibility, because modular design allows for easy experimentation with different retrieval algorithms, ranking methods, or generative models. Developers can plug in different modules without altering the entire system, facilitating iterative improvements and customization. For example, in modular RAG we can integrate a search module for similarity retrieval and adopt a fine-tuning approach in the retriever, and we can relatively easy swap out or upgrade individual modules based on specific needs or advancements in technology.
The most common Retrieval-Augmented Generation (RAG) techniques used involve a combination of sophisticated retrieval mechanisms and advanced generative models. These techniques are designed to ensure that the most relevant information is retrieved and effectively utilized to generate high-quality responses. Here are some of the most common RAG techniques:
What are the most common RAG techniques?
The most common Retrieval-Augmented Generation (RAG) techniques used involve a combination of sophisticated retrieval mechanisms and advanced generative models. These techniques are designed to ensure that the most relevant information is retrieved and effectively utilized to generate high-quality responses, ensuring that the final output is both informative and contextually appropriate. Here are some of the most common RAG techniques:
Dense Vector Retrieval
This technique uses dense embeddings (vector representations) of text to find documents that are semantically similar to the input query. The biggest advantage of dense vector retrieval is that it captures deeper semantic meanings, providing more relevant results compared to traditional keyword-based retrieval.
Models Used: BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, or other transformer-based models.
TF-IDF and BM25
TF-IDF (Term Frequency-Inverse Document Frequency) and BM25 are traditional information retrieval methods that rank documents based on term frequency and inverse document frequency. The biggest advantage is simplicity – they are simple and effective for many use cases, especially when computational resources are limited.
Re-ranking
After initial retrieval, the documents are re-ranked based on their contextual relevance to the query using more sophisticated models. With neural re-rankers we can evaluate the relevance of each document in context. The biggest advantage is the improvement of the quality of the final retrieved set, ensuring the most relevant documents are prioritized.
Query Reformulation
This is a simple technique where we rephrase the input query or expand it to capture the user’s intent more accurately before retrieval. We do this by synonym expansion, context addition, or even by leveraging user history. By improving the initial query we can enhance the accuracy of the retrieval process.
Document Summarization
This technique summarizes retrieved documents to extract the most relevant information, making it easier for the generative model to utilize; techniques used are abstractive or extractive summarization methods. The biggest advantage is that this technique reduces noise and focuses on key information, improving the quality of the generated responses.
Prompt Compression
Prompt Compression compresses and refines the input prompt by removing irrelevant information and highlighting important passages. THis helps maintain the context within the model’s input limitations, reducing noise and enhancing response quality.
Cross-Encoder Models
These models evaluate the relevance of documents in relation to the query by encoding them together, capturing deeper contextual relationships. These models provide more precise relevance scoring compared to separate encoding, although they are computationally intensive.
Feedback Loops
:
Feedback Loops incorporates user feedback or automated evaluation metrics to refine the retrieval and generation processes over time. Through continuous learning from user interactions the system improves and adapts based on actual usage patterns and feedback and this ensures improvement.
Fine-Tuning with Retrieved Data
Fine-Tuning with Retrieved Data fine-tunes the generative model on datasets that include retrieved documents, helping it better integrate external information. This technique improves the model’s ability to generate contextually relevant responses by learning from examples where retrieval information is crucial.
FAISS (Facebook AI Similarity Search)
FAISS Efficiently searches and clusters dense vectors for quick retrieval of high-dimensional data. FAISS is highly efficient for large-scale retrieval tasks, ensuring fast and accurate similarity searches.
In conclusion
Retrieval-Augmented Generation (RAG) represents a powerful advancement in NLP by leveraging external information to enhance the quality of generated text. By combining sophisticated retrieval mechanisms, augmentation processes, and advanced generative models, RAG produces highly relevant and contextually enriched responses. This integration not only improves accuracy but also ensures the generated content is more informative and tailored to specific queries. As NLP continues to evolve, RAG stands out as a vital technique for creating more intelligent and responsive systems.