If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/CVUW8FVKgjAvi Chawla (@_avichawla):RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant https://t.co/Ad5ztdo7Lz ...

Technology & Software Development - Graph RAG is presented as a practical example for RAG over code, addressing limitations of naive chunking in handling codebases with long-range dependencies [1] - Graph-Code, a graph-driven RAG system, is introduced for analyzing Python codebases and enabling natural language querying [1] - Graph-Code extracts classes, functions, and relationships from code through deep code parsing [1] - Memgraph is utilized to store the codebase as a graph within the Graph-Code system [1] - Graph-Code parses pyproject files to understand external dependencies [1] - The system retrieves actual source code snippets for found functions [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/pxlp7JJJ4VAvi Chawla (@_avichawla):How to build a RAG app on AWS!The visual below shows the exact flow of how a simple RAG system works inside AWS, using services you already know.At its core, RAG is a two-stage pattern:- Ingestion (prepare knowledge)- Querying (use knowledge)Below is how each stage works https://t.co/YcTgvXbJlb ...

RAG Challenges & HyDE Solution - Traditional RAG faces challenges due to semantic dissimilarity between questions and answers, leading to irrelevant context retrieval [1] - HyDE addresses this by generating a hypothetical answer using an LLM, embedding it, and using the embedding to retrieve relevant context [2] - HyDE leverages contriever models trained with contrastive learning to filter out hallucinated details in the hypothetical answer [3] HyDE Performance & Trade-offs - Studies indicate HyDE improves retrieval performance compared to traditional embedding models [4] - HyDE implementation results in increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer (H) for the query (Q) [2] - The hypothetical answer is embedded using a contriever model to obtain embedding (E) [2] - Embedding (E) is used to query the vector database and retrieve relevant context (C) [2] - The hypothetical answer (H), retrieved context (C), and query (Q) are passed to the LLM to produce a final answer [3]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/DzDoKaIVcZAvi Chawla (@_avichawla):Traditional RAG vs. HyDE, visually explained!RAG is great, but it has a major problem:Questions are not semantically similar to their answers.Consider an example where you want to find context similar to "What is ML?"It is likely that "What is AI?" will appear more https://t.co/oZ7lttsZbG ...

RAG Challenges & HyDE Solution - Traditional RAG faces challenges due to semantic dissimilarity between questions and answers, leading to irrelevant context retrieval [1] - HyDE addresses this by generating a hypothetical answer to the query and embedding it to retrieve relevant context [2] - HyDE leverages contriever models trained with contrastive learning to filter out hallucinated details in the hypothetical answer [3] HyDE Performance & Trade-offs - Studies indicate HyDE improves retrieval performance compared to traditional embedding models [4] - The improvement in retrieval performance comes at the cost of increased latency and higher LLM usage [4] HyDE Implementation - HyDE involves using an LLM to generate a hypothetical answer, embedding the answer using a contriever model, querying the vector database, and passing the hypothetical answer, retrieved context, and query to the LLM for the final answer [2]

Performance Comparison - DDN Infinia writes chunks at 0041 seconds (4 milliseconds) per chunk, significantly faster than AWS [6] - AWS object store writes each chunk at 01169 seconds (112 milliseconds) per chunk [7] - DDN Infinia uploads a 628-chunk document in approximately 25 seconds, while AWS takes around 74 seconds [7] - DDN Infinia is approximately 285 times faster than AWS in document upload [7] - DDN Infinia retrieves chunks in 01600 seconds (160 milliseconds) total, averaging 32 milliseconds per chunk [13] - AWS retrieves chunks in 165 seconds, with each chunk taking 331 milliseconds [14] - DDN Infinia is 103 times faster than AWS in total query retrieval time [14] AI Pipeline Impact - With DDN Infinia, an analyst can upload and query an annual report in just 2 seconds [8] - A 30x performance advantage transforms the entire AI pipeline, making documents readily available for AI consumption [9] - Reduced latency with DDN Infinia can save significant time, potentially turning a 5-minute research task into 3 seconds [15] - Latency compounds across multiple users and sessions, impacting GPU economics and overall productivity [15]

Product Announcement - Gemini API 推出文件搜索工具，这是一个托管的 RAG 解决方案，提供免费存储和免费查询时间嵌入 [1] - 该方法旨在显著简化上下文感知 AI 系统的路径 [1]

AI Tools & Technologies - Six no-code LLM/RAG/Agent builder tools are available for AI engineers [1] - The tools are production-grade and 100% open-source [1]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/SvYt7PiJQxAvi Chawla (@_avichawla):6 no-code LLM/RAG/Agent builder tools for AI engineers.Production-grade and 100% open-source!(find the GitHub repos in the replies) https://t.co/It07fQRBL7 ...