Modular RAG and RAG Flow: Part Ⅰ
A compressive and high-level summarization of RAG .
In Part I, we will focus the concept and components of Modular RAG, containing 6 module types, 14 modules and 40+ operators.
Intro
Over the past year, the concept of Retrieval-Augmented Generation (RAG) as a method for implementing LLM applications has garnered considerable attention. We have authored a comprehensive survey on RAG , delving into the shift from Naive RAG to Advanced RAG and Modular RAG. However, the survey primarily scrutinized RAG technology through the lens of Augmentation (e.g. Augmentation Source/Stage/Process).
This piece will specifically center on the Modular RAG paradigm. We further defined a three-tier Modular RAG paradigm, comprising Module Type, Module, and Operator. Under this paradigm, we expound upon the core technologies within the current RAG system, encompassing 6 major Module Types, 14 Modules, and 40+Operators, aiming to provide a comprehensive understanding of RAG.
By orchestrating different operators, we can derive various RAG Flows, a concept we aim to elucidate in this article. Drawing from extensive research, we have distilled and summarized typical patterns, several specific implementation cases and best industry cases. (Due to space constraints, this part will be addressed in Part II.)
The objective of this article is to offer a more sophisticated comprehension of the present state of RAG development and to pave the way for future advancements. Modular RAG presents plenty opportunities, facilitating the definition of new operators, modules, and the configuration of new Flows.
What is Modular RAG？
The progress of RAG has brought about a more diverse and flexible process, as evidenced by the following crucial aspects:
- Enhanced Data Acquisition: RAG has expanded beyond traditional unstructured data and now includes semi-structured and structured data, with a focus on preprocessing structured data to improve retrieval and reduce the model’s dependence on external knowledge sources.
- Incorporated Techniques: RAG is integrating with other techniques, including the use of fine-tuning, adapter modules, and reinforcement learning to strengthen retrieval capabilities.
- Adaptable Retrieval Process: The retrieval process has evolved to support multi-round retrieval enhancement, using retrieved content to guide generation and vice versa. Additionally, autonomous judgment and the use of LLM have increased the efficiency of answering questions by determining the need for retrieval.
Definition of Modular RAG
Above, we can see that the rapid development of RAG has surpassed the Chain-style Advanced RAG paradigm, showcasing a modular characteristic. To address the current lack of organization and abstraction, we propose a Modular RAG approach that seamlessly integrates the development paradigms of Naive RAG and Advanced RAG.
Modular RAG presents a highly scalable paradigm, dividing the RAG system into a three-layer structure of Module Type, Modules, and Operators. Each Module Type represents a core process in the RAG system, containing multiple functional modules. Each functional module, in turn, includes multiple specific operators. The entire RAG system becomes a permutation and combination of multiple modules and corresponding operators, forming what we refer to as RAG Flow. Within the Flow, different functional modules can be selected in each module type, and within each functional module, one or more operators can be chosen.
The relationship with the previous paradigm
The Modular RAG organizes the RAG system in a multi-tiered modular form. Advanced RAG is a modular form of RAG, and Naive RAG is a special case of Advanced RAG. The relationship between the three paradigms is one of inheritance and development.
Opportunities in Modular RAG
The benefits of Modular RAG are evident, providing a fresh and comprehensive perspective on existing RAG-related work. Through modular organization, relevant technologies and methods are clearly summarized.
- Research perspective. Modular RAG is highly scalable, facilitating researchers to propose new Module Types, Modules, and operators based on a comprehensive understanding of the current RAG development.
- Application perspective. The design and construction of RAG systems become more convenient, allowing users to customize RAG Flow based on their existing data, usage scenarios, downstream tasks, and other requirements. Developers can also reference current Flow construction methods and define new flow and patterns based on different application scenarios and domains.
Module Type — Module — Operators
In this chapter, we will delve into the three-tier structure and constrcuct a technical roadmap for RAG. Due to space constraints, we will refrain from delving into technical specifics; however, comprehensive references will be provided for further reading.
1. Indexing
Indexing, the process of breaking down text into manageable chunks, is a crucial step in organizing the system, facing three main challenges:
- Incomplete Content Representation.The semantic information of chunks is influenced by the segmentation method, resulting in the loss or submergence of important information within longer contexts.
- Inaccurate Chunk Similarity Search. As data volume increases, noise in retrieval grows, leading to frequent matching with erroneous data, making the retrieval system fragile and unreliable.
- Unclear Reference Trajectory. The retrieved chunks may originate from any document, devoid of citation trails, potentially resulting in the presence of chunks from multiple different documents that, despite being semantically similar, contain content on entirely different topics.
Chunk Optimization
Larger chunks can capture more context, but they also generate more noise, requiring longer processing time and higher costs. While smaller chunks may not fully convey the necessary context, they do have less noise.
- Sliding Window
One simple way to balance these demands is to use overlapping chunks.By employing a sliding window, semantic transitions are enhanced. However, limitations exist, including imprecise control over context size, the risk of truncating words or sentences, and a lack of semantic considerations.
- Small-to-Big
The key idea is to separate the chunks used for retrieval from the chunks used for synthesis. Using smaller chunks can improve the accuracy of retrieval, while larger chunks can provide more context information.
Specifically, one approach could involve retrieving smaller chunks and then referencing parent IDs to return larger chunks. Alternatively, individual sentences could be retrieved, and the surrounding text window of the sentence returned.
Detailed information and LlamaIndex Implementation.
- Summary
It is akin to the Small-to-Big concept, where a summary of larger chunks is generated first, and the retrieval is performed on the summary. Subsequently, a secondary retrieval can be conducted on the larger chunks.
- Metadata Attachment
Chunks can be enriched with metadata information such as page number, file name, author, timestamp, summary, or the questions that the chunk can answer. Subsequently, retrieval can be filtered based on this metadata, limiting the scope of the search. See the implementation in LlamaIndex.
Structural Oraginzation
One effective method for enhancing information retrieval is to establish a hierarchical structure for the documents. By constructing chunks structure, RAG system can expedite the retrieval and processing of pertinent data.
- Hierarchical Index
In the hierarchical structure of documents, nodes are arranged in parent-child relationships, with chunks linked to them. Data summaries are stored at each node, aiding in the swift traversal of data and assisting the RAG system in determining which chunks to extract. This approach can also mitigate the illusion caused by block extraction issues.
The methods for constructing a structured index primarily include：
- Structural awareness.paragraph and sentence segmentation in docs
- Content awareness .inherent structure in PDF, HTML, Latex
- Semantic awareness.Semantic recognition and segmentation of text based on NLP techniques, such as leveraging NLTK.
Check Arcus’s hierarchical index at large-scale.
- KG Organization Docs
The utilization of Knowledge Graphs (KGs) in constructing the hierarchical structure of documents contributes to maintaining consistency. It delineates the connections between different concepts and entities, markedly reducing the potential for illusions.
Another advantage is the transformation of the information retrieval process into instructions that LLM can comprehend, thereby enhancing the accuracy of knowledge retrieval and enabling LLM to generate contextually coherent responses, thus improving the overall efficiency of the RAG system.
Check Neo4j implementation and LllmaIndex Neo4j query engine.
For organizing multiple documents using KG, you can refer to this research paper KGP:Knowledge Graph Prompting for Multi-Document Question Answering.
2. Pre-Retrieval
One of the primary challenges with Naive RAG is its direct reliance on the user’s orginal query as the basis for retrieval. Formulating a precise and clear question is difficult, and imprudent queries result in subpar retrieval effectiveness.
The primary challenges in this stage include:
- Poorly worded queries. The question itself is complex, and the language is not well-organized.
- language complexity & ambiguity.Language models often struggle when dealing with specialized vocabulary or ambiguous abbreviations with multiple meanings. For instance, they may not discern whether “LLM” refers to large language model or a Master of Laws in a legal context.
Query Expansion
Expanding a single query into multiple queries enriches the content of the query, providing further context to address any lack of specific nuances, thereby ensuring the optimal relevance of the generated answers.
- Multi-Query
By employing prompt engineering to expand queries via LLMs, these queries can then be executed in parallel. The expansion of queries is not random, but rather meticulously designed. Two crucial criteria for this design are the diversity and coverage of the queries.
One of the challenges of using multiple queries is the potential dilution of the user’s original intent. To mitigate this, we can instruct the model to assign greater weight to the original query in prompt engineering.
- Sub-Query
The process of sub-question planning represents the generation of the necessary sub-questions to contextualize and fully answer the original question when combined. This process of adding relevant context is, in principle, similar to query expansion. Specifically, a complex question can be decomposed into a series of simpler sub-questions using the least-to-most prompting method.
- CoVe
Another approach to query expansion involves the use of the Chain-of-Verification(CoVe) proposed by Meta AI. The expanded queries undergo validation by LLM to achieve the effect of reducing hallucinations. Validated expanded queries typically exhibit higher reliability.
Query Transformation
Retrieve and generate using a transformed query instead of the user’s original query.
- Rewrite
The original queries are not always optimal for LLM retrieval, especially in real-world scenarios. Therefore, we can prompt LLM to rewrite the queries. In addition to using LLM for query rewriting, specialized smaller language models, such as RRR（Rewrite-retrieve-read), can also be utilized.
The implementation of the Query Rewrite method in the Taobao promotion system, known as BEQUE:Query Rewriting for Retrieval-Augmented Large Language Models, has notably enhanced recall effectiveness for long-tail queries, resulting in a rise in GMV.
- HyDE
When responding to queries, LLM constructs hypothetical documents (assumed answers) instead of directly searching the query and its computed vectors in the vector database. It focuses on embedding similarity from answer to answer rather than seeking embedding similarity for the problem or query. In addition, it also includes Reverse HyDE, which focuses on retrieval from query to query.
- Step-back Prompting
Using the Step-back Prompting method proposed by Google DeepMind, the original query is abstracted to generate a high-level concept question (step-back question). In the RAG system, both the step-back question and the original query are used for retrieval, and both the results are utilized as the basis for language model answer generation.
Query Routing
Based on varying queries, routing to distinct RAG pipeline,which is suitable for a versatile RAG system designed to accommodate diverse scenarios.
- Metadata Router/ Filter
The first step involves extracting keywords (entity) from the query, followed by filtering based on the keywords and metadata within the chunks to narrow down the search scope.
- Semantic Router
Another method of routing involves leveraging the semantic information of the query. Specific apporch see Semantic Router.Certainly, a hybrid routing approach can also be employed, combining both semantic and metadata-based methods for enhanced query routing.
Check Semantic router repo.
Query Construction
Converting a user’s query into another query language for accessing alternative data sources. Common methods include:
- Text-to-Cypher
- Text-to-SQL
In many scenarios, structured query languages (e.g., SQL, Cypher) are often used in conjunction with semantic information and metadata to construct more complex queries. For specific details, please refer to the Langchain blog.
3 Retrieval
The retrieval process plays a crucial role in RAG. Leveraging powerful PLMs enables the effective representation of queries and text in latent spaces, facilitating the establishment of semantic similarity between questions and documents to support retrieval.
Three main considerations need to be taken into account :
- Retrieval Efficiency
- Embedding Quality
- Alignment of tasks , data and models
Retriver Selection
Since the release of ChatGPT, there has been a frenzy of development in embedding models.Hugging Face’s MTEB leaderboard evaluates nearly all available embedding models across 8 tasks — Clustering,Classification,Bitext Ming, Pair Classification, Reranking, Retrieval, Semantic Text Similarity (STS), and Summarization, covering 58 dataset Additionally, C-MTEB focuses on evaluating the capabilities of Chinese embedding models, covering 6 tasks and 35 datasets.
When constructing RAG applications, there is no one-size-fits-all answer to “which embedding model to use.” However, you may notice that specific embeddings are better suited for particular use cases.
Check the MTEB/C-MTEB Leaderboard.
- Sparse Retriever
While sparse encoding models may be considered a somewhat antiquated technique, often based on statistical methods such as word frequency statistics, they still hold a certain place due to their higher encoding efficiency and stability. Common coefficient encoding models include BM25 and TF-IDF.
- Dense Retriever
Neural network-based dense encoding models encompass several types:
- Encoder-Decoder language models built on the BERT architecture, such as ColBERT.
- Comprehensive multi-task fine-tuning models like BGE and Baichuan-Text-Embedding.
- Cloud API-based models such as OpenAI-Ada-002 and Cohere Embedding.
- Next-generation accelerated encoding framework Dragon+, designed for large-scale data applications.
- Mix/hybrid Retrieval
Two embedding approaches capture different relevance features and can benefit from each other by leveraging complementary relevance information. For instance, sparse retrieval models can be used to provide initial search results for training dense retrieval models. Additionally, PLMs can be utilized to learn term weights to enhance sparse retrieval. Specifically, it also demonstrates that sparse retrieval models can enhance the zero-shot retrieval capability of dense retrieval models and assist dense retrievers in handling queries containing rare entities, thereby improving robustness.
Retriever Fine-tuning
In cases where the context may diverge from what the pre-trained model deems similar in the embedding space, particularly in highly specialized fields like healthcare, law, and other domains abundant in proprietary terminology, adjusting the embedding model can address this issue. While this adjustment demands additional effort, it can substantially enhance retrieval efficiency and domain alignment.
- SFT
You can construct your own fine-tuning dataset based on domain-specific data, a task that can be swiftly accomplished using LlamaIndex.
- LSR (LM-supervised Retriever)
In contrast to directly constructing a fine-tuning dataset from the dataset, LSR utilizes the LM-generated results as supervisory signals to fine-tune the embedding model during the RAG process.
- RL(Reinforcement learning)
Inspired by RLHF(Reinforcement Learning fromHuman Feedback), utilizing LM-based feedback to reinforce the Retriever through reinforcement learning.
- Adapter
At times, fine-tuning an entire retriever can be costly, especially when dealing with API-based retrievers that cannot be directly fine-tuned. In such cases, we can mitigate this by incorporating an adapter module and conducting fine-tuning.Another benefit of adding an adapter is the ability to achieve better alignment with specific downstream tasks.
- Task Specific.PRCA: Fitting Black-Box Large Language Models for Retrieval QuestionAnswering via Pluggable Reward-Driven Contextual Adapter.
- Task Agnostic.The AAR(Augmentation -Adapted Retriver) introduces a universal adapter designed to accommodate multiple downstream tasks.
4 Post-Retrieval
Retrieving entire document chunks and feeding them directly into the LLM’s contextual environment is not an optimal choice. Post-processing the documents can aid LLM in better leveraging the contextual information.
The primary challenges include:
- Lost in the middle. Like humans, LLM tends to remember only the beginning and end of long texts, while forgetting the middle portion.
- Noise/anti-fact chunks. Retrieved noisy or factually contradictory documents can impact the final retrieval generation.
- Context Window. Despite retrieving a substantial amount of relevant content, the limitation on the length of contextual information in large models prevents the inclusion of all this content.
Rerank
Rerank the retrieved document chunks without altering their content or length, to enhance the visibility of the more crucial document chunks for LLM. In specific terms：
- Rule-base Rerank
According to certain rules, metrics are calculated to rerank chunks. Common metrics include:
- Diversity
- Relevance
- MRR (Maximal Marginal Relevance, 1998)
The idea behind MMR is to reduce redundancy and increase result diversity, and it is used for text summarization. MMR selects phrases in the final key phrase list based on a combined criterion of query relevance and information novelty.
Check there rerank implementation in HayStack
- Model-base Rerank
Utilize a language model to reorder the document chunks, with options including:
- Encoder-Decoder models from the BERT series, such as SpanBERT
- Specialized reranking models, such as Cohere rerank or bge-raranker-large
- General large language models, such as GPT-4
Compression and Selection
A common misconception in the RAG process is the belief that retrieving as many relevant documents as possible and concatenating them to form a lengthy retrieval prompt is beneficial. However, excessive context can introduce more noise, diminishing the LLM’s perception of key information and leading to issues such as “ lost in the middle” . A common approach to address this is to compress and select the retrieved content.
- （Long)LLMLingua
By utilizing aligned and trained small language models, such as GPT-2 Small or LLaMA-7B, the detection and removal of unimportant tokens from the prompt is achieved, transforming it into a form that is challenging for humans to comprehend but well understood by LLMs. This approach presents a direct and practical method for prompt compression, eliminating the need for additional training of LLMs while balancing language integrity and compression ratio.
check the LLMLingua project.
- Recomp
Recomp introduces two types of compressors: an extractive compressor that selects pertinent sentences from retrieved documents, and an abstractive compressor that produces concise summaries by amalgamating information from multiple documents. Both compressors are trained to enhance the performance of language models on end tasks when the generated summaries are prepended to the language models’ input, while ensuring the conciseness of the summary. In cases where the retrieved documents are irrelevant to the input or do not provide additional information to the language model, compressor can return an empty string, thereby implementing selective augmentation.
- Selective Context
By identifying and removing redundant content in the input context, the input can be streamlined, thus improving the language model’s reasoning efficiency. Selective Context is akin to a “stop-word removal” strategy. In practice, selective context assesses the information content of lexical units based on the self-information computed by the base language model. By retaining content with higher self-information, this method offers a more concise and efficient textual representation for language model processing, without compromising their performance across diverse applications. However, it overlooks the interdependence between compressed content and the alignment between the targeted language model and the small language model utilized for prompting compression.
- Tagging-Filter
Tagging is a relatively intuitive and straightforward approach. Specifically, the documents are first labeled, and then filtered based on the metadata of the query.
- LLM-Critique
Another straightforward and effective approach involves having the LLM evaluate the retrieved content before generating the final answer. This allows the LLM to filter out documents with poor relevance through LLM critique. For instance, in Chatlaw, the LLM is prompted to self-suggestion on the referenced legal provisions to assess their relevance.
5 Generation
Utilize the LLM to generate answers based on the user’s query and the retrieved context information.
Generator Selection
Depending on the scenario, the choice of LLM can be categorized into the following two types:
- Cloud API-base Generator
Cloud API-based Utilize third-party LLMs by invoking their APIs, such as OpenAI’s ChatGPT, GPT-4, and Anthropic Claude, among others. Benefits:
- No server pressure
- High concurrency
- Ability to use more powerful models
Drawbacks:
- Data passes through third parties, leading to data privacy concerns
- Inability to adjust the model (in the vast majority of cases)
- On-Premises
Locally deployed open-source or self-developed LLMs, such as the Llama series, GLM, and others.The advantages and disadvantages are opposite to those of Cloud API-based models. Locally deployed models offer greater flexibility and better privacy protection but require higher computational resources.
Generator Fine-tuning
In addition to directl LLM usage, targeted fine-tuning based on the scenario and data characteristics can yield better results. This is also one of the greatest advantages of using an on-premise setup. Common fine-tuning methods include the following:
- SFT
When LLMs lack data in a specific domain, additional knowledge can be provided to the LLM through fine-tuning. Huggingface’s fine-tuning data can also be used as an initial step.
Another benefit of fine-tuning is the ability to adjust the model’s input and output. For example, it can enable LLM to adapt to specific data formats and generate responses in a particular style as instructed.
- RL
Aligning LLM outputs with human or retriever preferences through reinforcement learning is a potential approach. For instance, manually annotating the final generated answers and then providing feedback through reinforcement learning. In addition to aligning with human preferences, it is also possible to align with the preferences of fine-tuned models and retrievers.
- Distillation
When circumstances prevent access to powerful proprietary models or larger parameter open-source models, a simple and effective method is to distill the more powerful models(e.g. GPT-4).
- Dual FT
Fine-tuning both Generator and Retriever to align their preferences. A typical approach, such as RA-DIT, aligns the scoring functions between Retriever and Generator using KL divergence.
6 Orchestration
Orchestration refers to the modules used to control the RAG process. RAG no longer follows a fixed process, and it involves making decisions at key points and dynamically selecting the next step based on the results. This is also one of the key features of modularized RAG compared to Naive RAG.
Scheduling
The Judge module assesses critical point in the RAG process, determining the need to retrieve external document repositories, the satisfaction of the answer, and the necessity of further exploration. It is typically used in recursive, iterative, and adaptive retrieval. Specifically, it mainly includes the following two operators:
- Rule-base
The next course of action is determined based on predefined rules. Typically, the generated answers are scored, and then the decision to continue or stop is made based on whether the scores meet predefined thresholds. Common thresholds include confidence levels for tokens.
- Prompt-base
LLM autonomously determines the next course of action. There are primarily two approaches to achieve this. The first involves prompting LLM to reflect or make judgments based on the conversation history, as seen in the ReACT framework. The benefit here is the elimination of the need for fine-tuning the model. However, the output format of the judgment depends on the LLM’s adherence to instructions. A prompt-base case is FLARE.
- Tuning-base
The second approach entails LLM generating specific tokens to trigger particular actions, a method that can be traced back to Toolformer and is applied in RAG, such as in Self-RAG.
Fusion
This concept originates from RAG Fusion. As mentioned in the previous section on Query Expansion, the current RAG process is no longer a singular pipeline. It often requires the expansion of retrieval scope or diversity through multiple branches. Therefore, following the expansion to multiple branches, the Fusion module is relied upon to merge multiple answers.
- Possibility Ensemble
The fusion method is based on the weighted values of different tokens generated from multiple beranches, leading to the comprehensive selection of the final output. Weighted averaging is predominantly employed. See REPLUG.
- RRF (Reciprocal Rank Fusion )
RRF, is a technique that combines the rankings of multiple search result lists to generate a single unified ranking. Developed in collaboration with the University of Waterloo (CAN) and Google, RRF produces results that are more effective than reordering chunks under any single branch.
Conclusion
The upcoming content on RAG Flow will be introduced in PART II, to be published soon.
As this is my first time publishing an article on Medium, I am still getting familiar with many features. Any feedback and criticism are welcome.
