Ed Broussard’s Post

🌐 Traditional vs. Vector Databases 📊🤖 As data complexity and volume grow, the limitations of traditional databases are becoming evident, especially when dealing with unstructured data. Traditional databases are ideal for structured information—organized in rows and columns—where queries are based on exact matches (such as retrieving a record by ID). However, with the increasing use of AI-driven applications, data is now often unstructured, such as text embeddings, images, and other high-dimensional representations. Vector databases offer a powerful solution to this challenge. 💡 Key Differences: * Traditional Databases - Best suited for structured data and exact queries (e.g., SQL-based lookups). * Vector Databases - Tailored for unstructured data and similarity searches using vector representations (e.g., semantic search, recommendation systems). 🚀 Why Vector Databases Are Critical * Efficient Unstructured Data Handling - Optimized for high-dimensional data like images, documents, and embeddings. * Similarity Search Capabilities - Allows searches based on contextual meaning, not just exact keyword matches. * Scalable AI Applications - Essential for powering AI tasks such as semantic search, recommendation engines, and document classification. Vector databases are rapidly becoming indispensable in industries leveraging AI. They provide faster, more context-aware search capabilities, unlocking new potential for innovation and intelligent data retrieval. #AI #VectorDatabases #DataInnovation #MachineLearning #BigData

RAG Best Practices are very complex unless you break them down into simple steps ✅ Don't believe me? Let me simplify it for you! Before moving on, make sure to follow. I will soon be simplifying more complex AI concepts in the future! 📌A typical RAG workflow usually contains multiple intervening processing steps: ➔ query classification (determining whether retrieval is necessary for a given input query), ➔ retrieval (efficiently obtaining relevant documents for the query), ➔ reranking (refining the order of retrieved documents based on their relevance to the query), ➔ repacking (organizing the retrieved documents into a structured one for better generation), ➔ summarization (extracting key information for response generation from the repacked document and eliminating redundancies) And here is how each step can be optimized 1. Query Classification • Only look up information when necessary to save time and resources. • Train a classifier to automate decision-making. 2. Chunking Techniques • Find the right chunk size to clarify meaning without overwhelming the system. • Use Sentence-level chunking for simplicity and semantic preservation. • Chunk 256-512 provides better faithfulness and relevancy. • Sliding window does better over small2big. • Enhance response with metadata like titles, keywords, and hypothetical questions. 3. Embedding Model Selection • Pick models that work well and are not too resource-heavy. • Use MIT License LLM-Embedder for balanced performance and size 4. Vector Databases • Choose a flexible, scalable, cloud-native vector database with multiple indexes support • Milvus supports multiple indexes, billion-scale, hybrid search, and cloud-native • Qdrant misses on flexibility on indexes 5. Efficient Retrieval Methods • Balance speed and accuracy by using the right method for the task. • Hybrid search (sparse & dense retrieval) with HyDE is best but adds latency. Stick to Hybrid 6. Reranking Methods • Ensure the most important information comes first, even if it takes more computing power • Use advanced models to sort and rank the found information, so the most relevant pieces are on top • MonoT5 balances performance and efficiency • Has a critical role in producing quality responses 7. Document Repacking • Rearrange the found information so that the most relevant parts are closest to your question. • Place relevant info close to the query for best performance 8. Summarization Techniques • Reduce unnecessary details while keeping the main points • Long prompts and irrelevant information slow down the inference process • Use Recomp to pick out key sentences and rewrite the information in a shorter, clearer way 9. Generator Fine-tuning • Ensure the system can handle different information and still provide good answers 10. Multimodal Retrieval Extension • Integrate various data types to make the system more versatile and accurate If you want a further deep analysis you can visit #genai #rag #bestpractice

🚀 Dive into the world of analytics with our brand-new feature: "14 Best AI Tools for Data Analysis"! 🌟   https://lnkd.in/dryHxhQP   Unlock the potential of AI to transform your data into actionable insights. Our latest article unveils the most innovative AI tools that are reshaping data analysis. From uncovering hidden trends to predicting future outcomes, these tools offer a range of capabilities that cater to both data novices and seasoned analysts. Harness the power of AI to elevate your data game. Our comprehensive guide details the unique features, pricing, and practical applications of each tool, ensuring you find the perfect match for your analytical needs. Whether you're looking to optimize your business strategy or explore new data frontiers, these AI tools are your key to a smarter approach. Explore our expert analysis and join the data revolution today! 🧠✨   https://lnkd.in/dryHxhQP   #AI #AITools #AIGenerators #Generators #ArtificialIntelligence #Software #AISoftware #Innovation #DataAnalysis #DataAnalysisTools #AnswerRocket #JuliusAI #MonkeyLearn #MicrosoftPowerBI #DataRobot #Polymer #Tableau #Akkio #Qlik #BardeenAI #H2OAI #Sisense #SAPBusinessObjects #PowerBI

Learn how TigerGraph CoPilot uses LLM-friendly definitions to enhance user experience. This blog explains how incorporating large language models helps simplify complex graph concepts and makes graph data more accessible. By leveraging AI, CoPilot provides intuitive explanations and insights, making it easier for users to understand and interact with graph data. Read more: #GraphTechnology #AI #LLM #TigerGraph #DataAnalytics

When we introduced TigerGraph CoPilot to a few customers, we discovered a common theme: many had not previously utilized #generative #AI in conjunction with their #knowledgegraphs. Integrating generative AI requires the AI, through a large language model (#LLM), to "understand" your knowledge graph. Read this blog and explore how CoPilot is setting new standards in leveraging AI to enhance knowledge graph interactions. I had the fortune to work with many pioneers in creating the GJXDM and NIEM metadata registries. I was lucky to attend a formal training course on ISO/IEC Metadata Registry standards in the early 2000s. Interestingly, these standards are still relevant today. Remember the famous Jim Hendler quote, "A little semantics goes a long way." #AI #GenerativeAI #KnowledgeGraph #TechInnovation

This blog from Tomoro talks about several challenges in the retrieval stage of RAG.

As we introduced TigerGraph CoPilot to a few customers, we discovered a common theme: many had not previously utilized #generative #AI in conjunction with their #knowledgegraphs. Integrating generative AI requires the AI, through a large language model (#LLM), to "understand" your knowledge graph. Read this blog and explore how the CoPilot is setting new standards in leveraging AI to enhance knowledge graph interactions. #AI #GenerativeAI #KnowledgeGraph #TechInnovation

As we introduced TigerGraph CoPilot to a few customers, we discovered a common theme: many had not previously utilized #generative #AI in conjunction with their #knowledgegraphs. Integrating generative AI requires the AI, through a large language model (#LLM), to "understand" your knowledge graph. Read this blog and explore how the CoPilot is setting new standards in leveraging AI to enhance knowledge graph interactions. #AI #GenerativeAI #KnowledgeGraph #TechInnovation

As we introduced TigerGraph CoPilot to a few customers, we discovered a common theme: many had not previously utilized #generative #AI in conjunction with their #knowledgegraphs. Integrating generative AI requires the AI, through a large language model (#LLM), to "understand" your knowledge graph. Read this blog and explore how the CoPilot is setting new standards in leveraging AI to enhance knowledge graph interactions #AI #GenerativeAI #KnowledgeGraph #TechInnovation https://lnkd.in/gcV3Q6WJ Dan McCreary

As we introduced TigerGraph CoPilot to a few customers, we discovered a common theme: many had not previously utilized #generative #AI in conjunction with their #knowledgegraphs. Integrating generative AI requires the AI, through a large language model (#LLM), to "understand" your knowledge graph. Read this blog and explore how the CoPilot is setting new standards in leveraging AI to enhance knowledge graph interactions. #AI #GenerativeAI #KnowledgeGraph #TechInnovation