Timo Selvaraj’s Post

By combining the generation capabilities of large language models (#LLMs)with a retrieval component typically using a #vector or #semantic search, #RAG #chatbots can provide informative and personalized responses backed by evidence from a supplied corpus of documents. However, the #performance of these #models hinges on properly processing and indexing the document corpus for efficient #retrieval. If the retrieval is a failure, then the chatbot responses will be incoherent to the users. https://lnkd.in/g5YYpsCU

What is Semantic Search with LLMs and RAG ? Semantic search is an advanced method of retrieving information that surpasses traditional keyword-based searches. It aims to comprehend the context and meaning behind user queries, By utilizing text embeddings — numerical representations of meaning generated by language models like BERT, GPT, and others — semantic search improves the accuracy and efficiency of information retrieval. Retrieval Augmented Generation (RAG) integrates large language models with external knowledge sources to address limitations in memory. in simpler words:  A smart way to search that understands what you mean, not just the exact words you use. Example: If you search for "best places to visit in summer," it knows you want vacation spots, not just the words "best," "places," "visit," and "summer." The Role of Embeddings in Semantic Search: Embeddings are like secret codes that turn words or sentences into numbers. These numbers represent the meaning of the text in a special way. They help computers understand relationships between words or phrases. Advantages of Semantic Search with LLMs: - Precision - Efficiency, - Multilingual Support, - Versatility Disadvantages of Semantic Search with LLMs: - Resource Intensive - Dependency on Quality Data - Semantic Ambiguity

Routing in RAG-Driven Applications 🌐 Routing Control in RAG Applications Routing control based on user query intent can significantly enhance the functionality of Retrieval Augmented Generation (RAG) applications. 🔄 It allows users to access a diverse array of data sources, including documents, images, and databases tailored to specific business domains like sales and accounting. 📊📁 📚 Diversity of Data Sources The diversity in data sources necessitates varied storage and access methods, ranging from vector storage and SQL databases to API calls for third-party systems. 🌍 Optimizing vector storage setups for different query types is crucial for enhancing query efficiency. ⚙️ 🔍 Component Routing Based on Queries Queries may be routed to appropriate components such as proxies or vector storage based on their nature, enhancing processing efficiency. 🛠️ Custom prompt templates and conditional If/Else logic are employed to navigate and manage the query flow effectively. 🧭 🗣️ Natural Language Routers Natural language routers decide routes based on language inputs and produce outputs in natural language, though their non-deterministic nature requires careful testing and best practices for robust RAG applications. 📡 These routers leverage LLM or machine learning algorithms to enhance decision-making processes. 🤖 🔀 Different Types of Natural Language Routers From LLM Completion and Function Call Routers to Semantic and Zero-shot Classification Routers, each type provides unique routing capabilities based on language processing. 🧠 Language Classification Routers efficiently identify the query's language to determine the most appropriate routing path. 🌐 🔧 Logical Routers Logical routers operate on discrete variables like string length and filenames, routing queries without needing to interpret natural language, relying solely on conditional logic. 📏 🔄 The Difference Between Proxies and Routers While both proxies and routers handle routing tasks, routers are specialized for this function, whereas proxies also manage logic operations and tool interactions within their processes. 🛠️ 🔗 Conclusions The development of natural language routers in RAG and LLM frameworks is expanding, becoming integral to building efficient and user-friendly applications. 📈 As routing concepts and technologies evolve, their importance in crafting useful RAG applications will grow, underscoring the need for innovative routing solutions. 🚀 #AI #MachineLearning #DataScience #NaturalLanguageProcessing #RAG

Separating Dual-Column PDF Documents for RAG   Why is this important? Retrieval-Augmented Generation (RAG) models, known for their unique blend of retrieval mechanisms (large databases) and generative capabilities like GPT (Generative Pretrained Transformer), thrive on well-curated data. In scenarios involving bilingual documents, particularly legal texts, having a clean separation of language content significantly enhances RAG system performance for the following reasons: - Embedding Space: Language models create embeddings, which are numerical representations of words and phrases. Embeddings from different languages occupy different regions within a vector space. Separating languages ensures that similar concepts in each language cluster together, allowing for accurate retrieval. - Query Specificity: When a user asks a question, it's likely in a single language. If the documents aren't separated, the RAG model may retrieve irrelevant text from the other language, reducing accuracy and usefulness. - Improved Accuracy: Separate language models and embeddings lead to better understanding of each language's nuances, resulting in more accurate retrieval and generation of relevant text. - Tailored Responses: The RAG model can provide responses in the same language as the query, ensuring a smoother user experience. - Reduced Noise: Prevents confusion that would arise from the model attempting to process two languages simultaneously. Our approach: Our process begins with the meticulous reading of dual-column PDF file, navigating through each page to extract text from predefined regions corresponding to the two columns. More detail can be found here: https://lnkd.in/gyWWGw8S. Who can benefit? Anyone working with RAG models, particularly those handling bilingual documents in legal, academic, or governmental contexts, will find this project interesting.

Gecko: Versatile Text Embeddings Distilled from Large Language Models DeepMind has made another notable advancement with their development of embedding representations, which are essential to the functionality of search and recommendation systems, question answering, and various other NLP-driven applications. The importance of minimizing the size of these embeddings cannot be overstated; doing so enhances system performance by reducing memory, computational, and storage demands. This is particularly crucial in applications processing millions of queries per second, where system latency must be kept within stringent limits (100-200 ms for complete tasks). The team at DeepMind has introduced a groundbreaking embedding model that outperforms those with larger dimensions. They've unveiled Gecko, a model that's both compact and adaptable, designed for text embedding. Gecko excels in retrieval tasks by adopting a novel strategy: it distills insights from large language models (LLMs) into a more focused retriever. This distillation occurs in two phases. Initially, it generates a wide array of synthetic paired data with an LLM. Then, it enhances the quality of this data by selecting a batch of candidate passages for every query. These are then relabeled, identifying the positive and challenging negative passages, with the assistance of the same LLM. The efficiency of their method is underscored by Gecko's compactness. On the Massive Text Embedding Benchmark (MTEB), a Gecko model with 256 embedding dimensions surpasses all competitors with 768-dimension embeddings. When scaled up to 768 dimensions, Gecko achieves an average score of 66.31, rivaling models that are seven times larger and have embeddings five times as dimensional. This achievement highlights DeepMind's contribution to making high-performance systems more efficient and effective. https://lnkd.in/gk822dgT Improving Text Embeddings with Large Language Models Recent studies have also made significant strides in the field of embedding generation, with one notable paper from Microsoft emphasizing the pursuit of high-quality embeddings, prioritizing their efficacy over compactness. This research introduces a more efficient training methodology that leans heavily on synthetic data, eschewing the traditional reliance on costly and time-consuming human-annotated datasets. Remarkably, without using annotated data, this approach has managed to produce embeddings of exceptional quality across more than 100 languages, setting new benchmarks on the BEIR and MTEB evaluations. Furthermore, Microsoft's method has significantly streamlined the typical embedding generation pipeline. By simplifying the process, they not only improve the practicality of embedding production but also enhance the accessibility and applicability of high-quality embeddings across a broader range of languages and applications. https://lnkd.in/gBjMvRNj and (another paper) https://lnkd.in/gnAzmvuq

Hypothetical Document Embeddings (HyDE) is an advanced technique designed to improve the effectiveness of Retrieval-Augmented Generation (RAG) systems. HyDE generates synthetic document embeddings from a query, which better represent the ideal answer. These embeddings guide the retrieval process towards more relevant documents, enhancing the overall quality of the generated responses. To implement HyDE, use a language model to create hypothetical documents based on the query, encode these documents into embeddings, and employ these embeddings to retrieve the most relevant information. This approach ensures that RAG systems can more accurately access and utilize external knowledge, especially in specialized or complex domains. Explore the full capabilities and setup instructions of HyDE on our latest blog for detailed guidance on integrating this technique into your RAG systems.

Leveraging Large Language Models to Populate Complex Knowledge Databases 💡 Knowledge bases are invaluable assets to any organization. They can contain comprehensive information about a particular domain and enable efficient access and analysis of that information and they can be used to enhance symbolically LLMs chatbot applications. However, constructing a knowledge base is an extensive task that often requires manual annotation or bespoke information extraction algorithms. Large Language Models (LLMs), like GPT-4 or Claude 2, can be leveraged to extract structured information from unstructured text and populate complex knowledge databases. A method called SPIRES (Structured Prompt Interrogation and Recursive Extraction of Semantics), which uses prompt engineering to perform zero-shot extraction and ground the extracted entities to ontology terms for normalization is here. Eager to delve deeper into this topic? Don't hesitate to read my detailed exploration in my Medium article! https://lnkd.in/eQW3n-pg Like and share to support my writing ✍️! PS : this is a continued serie of posts on this subject that I apply to decision-making support applications. https://lnkd.in/eBDfVbkb https://lnkd.in/eqz-EeHU https://lnkd.in/e9pqHxrz

🔍📚 Retrieval-augmented generation (RAG) is a powerful approach that combines large language models with external knowledge retrieval to generate more accurate and informative responses. However, effective query translation remains a crucial challenge in RAG systems. In this post, we'll explore several advanced techniques that can significantly improve query translation for RAG. Multi-Query Enhancement By generating multiple queries from a single input, we can tap into a broader information base, enhancing the retrieval quality and relevance, and ensuring our RAG systems are more robust and comprehensive. 🌍💡 RAG-Fusion This innovative approach merges retrieval and generation processes more tightly, enabling the model to dynamically interact with the retrieved documents. This fusion process not only improves context understanding but also enhances the relevance of generated responses. 🔄📖 Decomposition Breaking down complex queries into simpler, manageable sub-queries can significantly boost the effectiveness of the retrieval process. Each component addresses a specific aspect of the query, making the aggregation of results more precise and informative. 🧩✂️ Step-back Sometimes, looking at a problem from a different angle is all you need. The step-back method involves revisiting the query formulation stage after initial retrieval, allowing the system to refine its approach based on the context of the information already gathered. 🔙🔍 HyDE (Hybrid Decomposition and Enhancement) This technique combines the strengths of both decomposition and query enhancement. By decomposing a query into simpler parts and simultaneously enhancing those sub-queries, HyDE ensures a more thorough exploration of the knowledge base, leading to richer and more accurate outputs. 🔄🌐 By incorporating these advanced techniques, query translation in RAG systems can be significantly enhanced, leading to more accurate and informative generated responses. These approaches address various challenges, such as query diversity, context-aware fusion, complex question handling, query drift, and effective passage ranking. Image credits: https://lnkd.in/eBGbFxxi Github: https://lnkd.in/eBGbFxxi 🔖 #RAG #QueryTranslation #InformationRetrieval #NLProc #MachineLearning

Excited to present ArcheType, an investigation into the viability of large language models for complex real-world classification tasks. (Our focus was on zero-shot semantic column type annotation (CTA), an important task in data cleaning and discovery, but our take-aways are useful for anyone interested in LLM classification) We decomposed the problem into four key components: Context sampling: Selecting which samples to add to the LLM's prompt Prompt Serialization: AKA prompt engineering -- converting context and instructions into a model-ready prompt Model querying: Selecting a target LLM, querying it, and collecting the response (either locally or via an API) Label remapping: Error correction for when the LLM returns a response that was not in the set of classification options Here are a few interesting things we learned -- Context sampling and label remapping are the key components to consider when using LLMs for classification. We found that improved error correction and sampling strategies led to reliable improvements across models and datasets. Prompt serialization behaves more like a hyperparameter than a method. Current-generation LLM performance is extremely sensitive to prompt semantics; small changes in prompting can lead to large performance gains or losses. Unfortunately, this sensitivity behaves more like noise than signal when we look at multiple architectures. For example, randomizing the position of classnames in the prompt causes the entire probability distribution to change, and the changes differ depending on the LLM you use. Open-source models are highly competitive with closed-source models. In particular, encoder-decoder architectures, such as Google's T5 and UL2 models, are strong zero-shot classifiers. Using these and other insights, we built ArcheType, a new method for column type annotation which achieves state-of-the-art zero-shot CTA performance, and is highly competitive with the best performing methods on fine-tuned CTA as well. Thanks to my collaborator Yurong Liu, as well as the supervising faculty, Chinmay Hegde & Juliana Freire. https://lnkd.in/e5faGZi7

1. What is HyDE? HyDE stands for Hypothetical Document Embeddings. It is a technique used in Retrieval-Augmented Generation (RAG) to improve the document retrieval process by creating hypothetical embeddings that represent the ideal documents to answer a specific query. 2. The Need for HyDE Traditional RAG models use actual document embeddings based on similarity to retrieve information. However, these can often miss nuances in queries or fail in out-of-domain scenarios. HyDE addresses these issues by generating idealized, query-specific document embeddings, leading to more accurate and relevant retrievals. 3. How HyDE Works a. Generate Hypothetical Content: HyDE begins by using a language model (e.g., GPT-3.5) to generate text that hypothetically answers the query. b. Create Embeddings: These hypothetical texts are then transformed into embeddings using an embedding model. c. Retrieve Using Hypothetical Embeddings: These embeddings are used to find the most similar real documents in a knowledge base, rather than relying directly on the initial query embeddings. 4. Advantages of Using HyDE - Improved relevance of retrieved documents. - Enhanced performance of RAG systems, especially in complex or technical domains. - Better handling of out-of-domain queries. 5. Considerations for Effective Use The success of HyDE depends on the quality of the hypothetical document generation and the subsequent embeddings. It is crucial to tailor the HyDE process to the specific requirements and contexts of the application to maximize effectiveness. Read more in our latest blog post: https://lnkd.in/dmGXMaz5