AI For Enhancing Data Visualization

I built an AI Data Visualization AI Agent that writes its own code...🤯 And it's completely opensource. Here's what it can do: 1. Natural Language Analysis ↳ Upload any dataset ↳ Ask questions in plain English ↳ Get instant visualizations ↳ Follow up with more questions 2. Smart Viz Selection ↳ Automatically picks the right chart type ↳ Handles complex statistical plots ↳ Customizes formatting for clarity The AI agent: → Understands your question → Writes the visualization code → Creates the perfect chart → Explains what it found Choose the one that fits your needs: → Meta-Llama 3.1 405B for heavy lifting → DeepSeek V3 for deep insights → Qwen 2.5 7B for speed → Meta-Llama 3.3 70B for complex queries No more struggling with visualization libraries. No more debugging data processing code. No more switching between tools. The best part? I've included a step-by-step tutorial with 100% opensource code. Want to try it yourself? Link to the tutorial and GitHub repo in the comments. P.S. I create these tutorials and opensource them for free. Your 👍 like and ♻️ repost helps keep me going. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

Here's how I use AI to bootstrap a Wardley Map with capabilities—or at least get to a solid starting point. The *hard* works starts after this! 1. It starts with a prompt. I frame capabilities using "the ability to [blank]" and use GPT to break them down into sub-capabilities in JSON. (I built a tiny front-end for this, but totally optional.) Example: "Buy lunch for team" → breaks down into planning, sourcing ingredients, managing preferences, etc. 2. I then pull these into Obsidian—my tool of choice—to visualize and view the relationships. 3. Next, I run a second prompt to place each capability on the Y-axis (how close it is to the customer), using roles as a proxy: ops leaders, org designers, engineers, infra teams, etc. This helps with vertical positioning in the value chain. Tip: I always ask the model to explain why it placed something a certain way. Helps with tuning and building trust in the output. 4. Then I add richness: I use another prompt to identify relationships between capabilities—either functional similarity or one enabling another. These are returned in structured JSON. Think: "Analyze data insights" ↔ "Trend analysis" → Similar. This helps expand our graph. 5. To tie it all together: I feed the data into NetworkX (Python) to analyze clusters—kind of like social network graph analysis. The result? Capabilities grouped by both level and cluster. 6. The final output is a canvas in Obsidian—grouped, leveled, and linked. It's a decent kickoff point. From here, I’ll nerd out and go deep on the space I'm exploring. This isn’t a polished map. It’s a starting point for thinking, not a final artifact. If you’re using LLMs for systems thinking or capability modeling, I’d love to hear your process too.

This is when Graph RAG performs much better than naive RAG: When you want your LLM to understand the interconnection between your documents before arriving at its answer, Graph RAG becomes necessary. Graph RAG is not just useful for storing relationships in data. It can traverse multiple hops of connections and retrieve inferred context (e.g. Doc A to Doc B to Doc C) that wasn’t explicitly written in any single document. That’s what makes it powerful for reasoning and synthesis, not just retrieval. RAG returns search results based on semantic similarity. It doesn't consider this: If doc A is selected as highly relevant, the docs closely linked to A might also be essential to form the full context. This is where Graph RAG comes in. Search results from a graph are more likely to give a comprehensive view of the entity being searched and the information connected to it. Information on entities like people, organizations, products, or legal cases is often highly interconnected — and this might be true for your data too. Examples where Graph RAG works better than plain RAG: - Understanding customer support conversations where multiple tickets refer to the same issue or product. - Exploring research papers where concepts and citations form a dependency graph. - Retrieving facts in legal or compliance documents, where clauses refer to previous laws or definitions. - In company knowledge bases, where employee roles, teams, and projects are linked. - For supply chain analysis, where one entity’s data is tied to multiple suppliers or regions. In all these cases, naive RAG may miss key context that sits just one or two hops away, but Graph RAG connects those dots. ♻️ Share it with anyone who works with interconnected or relationship-heavy data :) I share tutorials on how to build + improve AI apps and agents, on my newsletter 𝑨𝑰 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑾𝒊𝒕𝒉 𝑺𝒂𝒓𝒕𝒉𝒂𝒌: https://lnkd.in/gaJTcZBR #AI #LLMs #RAG #AIAgents

A hidden layer of insight is waiting in your data  Here’s what most RAG setups miss  ☑ They fetch facts fast   ☑ They never see how those facts link together  That gap is why plain RAG stalls when you need the why. Enter GraphRAG and its next step, Agentic GraphRAG.  Instead of loose chunks, they stitch a graph:  ☑ Nodes are the entities you care about   ☑ Edges are the relationships between them  Your LLM now walks the graph and reasons over connections. A quick security incident example  It’s 3 AM. An alarm reports a breach.   Fifty CVE IDs pour in.  Which CVE is critical? Which is noise?  Agentic GraphRAG jumps in:  ☑ Pulls the incident data into the graph   ☑ Runs reasoning across known software, versions, past exploits   ☑ Ranks the threats and writes a short human readable note   ☑ Suggests next steps like a senior analyst would  The result feels like an autonomous analyst that understands relationships, reasons dynamically and even self corrects. If you want a RAG that does more than fetch, try building a knowledge graph first.  I’m Shrey Shah & I share daily guides on AI.  If this helped, hit the ♻️ reshare button to help someone else level up their AI.

From Tables to Graph: Missing Link Between Systems and Analytics There's a fundamental disconnect between how we build systems (as objects) and how we analyse them (i.e. going beyond tables). This gap makes seemingly simple questions surprisingly difficult to answer. 🐕 Working with financial services data, I've been using Data Object Graphs (DOGs) as an approach to bridge this divide: 🏗️First, we model the operational system domain using good old OOP/OOD concepts i.e classes. - This gives us an object model (e.g. PensionScheme, BlendedFund) that naturally reflects business process concepts. - Note we are modelling behaviour here, not just data, as we include the operations 📊Next we connect operational data using virtualised tables of the systems. These tables represent the system data from the business process - Note these can be any type of data accessed through a data fabric. 🔍Next, we use transformation (SQL, plus other methods e.g. python etc.), to transform the data into a Graph (DOG) that can be queried - This is a navigable graph that maintains all semantic relationships and operations (including new ones like metrics) - The transformation queries serve as the connective tissue, mapping operational tables to a rich semantic model while preserving all business rules and relationships. 🤖We then use AI to take natural language queries to ask business questions of our graph e.g. "What was the fund price last quarter?" from a complex join problem into a simple graph traversal. What makes this particularly powerful is how it creates a common language between IT systems and analytics. 📈Business analysts can work with familiar concepts rather than deciphering cryptic table structures. 🧪Data scientists can explore relationships without rebuilding domain knowledge that already exists in application code. 🧩Most organizations already have the core elements needed, well-designed object models and operational data. ✨The missing piece is the metadata-driven framework that connects them into a coherent graph. We can ask questions in business language rather than technical syntax, creating a continuous thread from system design through analytics and AI. Have you encountered similar challenges connecting your systems and analytics? DM me if you want to know how to use the approach to ask similar questions of your business. #DataModeling #GraphAnalytics #FinancialSystems #EnterpriseArchitecture

🧠 Simple Graphs, Multigraphs, Hypergraphs... Oh my! Not all graphs are created equal - and the structure you choose defines how much meaning your system can capture. Let’s break it down 👇 1️⃣ Simple Graphs Each edge connects two distinct nodes - nothing more, nothing less. Perfect for modeling pairwise relationships (e.g., person A is friends with person B). ➡️ Think social networks or basic link structures. 2️⃣ Multigraphs Now, we allow multiple edges between the same pair of nodes. Useful when relationships are repeated or redundant - like multiple communication channels between two servers. ➡️ Still pairwise, but richer in connection density. 3️⃣ Hypergraphs Here’s where things get interesting. A hyperedge can connect any number of nodes simultaneously - not just two. That means we can model higher-order relationships - multi-entity interactions that simple or multigraphs can’t capture. ➡️ Example: a single hyperedge could connect an actor, a director, and multiple movies - capturing the contextual meaning behind their collaboration. 💡 In Practice The majority of projects today still rely on simple graphs or multigraphs as the backbone for knowledge graphs and AI knowledge layers. These structures work - but they limit understanding to pairwise relationships. Hypergraphs go further, enabling AI systems to capture meaning, not just connection - modeling context, causality, and reasoning paths natively. 🔍 Why it matters for AI and Knowledge Systems Modern data isn’t pairwise - it’s contextual, overlapping, and compositional. This capability unlocks more accurate reasoning, better knowledge representation, and deeper insights in AI systems. In other words: Simple graphs connect data. Hypergraphs connect meaning.

This technical guide demonstrates a breakthrough two-phase methodology for constructing knowledge graphs using agentic AI systems. The approach seamlessly integrates structured data with unstructured text through specialized agents including Schema Proposal, Entity Extraction, and GraphRAG components. We detail advanced domain, lexical, and subject graph implementation with intelligent entity resolution using fuzzy string matching and similarity algorithms. The revolutionary multi-agent system automates complex schema generation, text chunking, and relationship mapping. #KnowledgeGraphs #AgenticAI #GraphRAG #BusinessIntelligence #DataIntegration #ConnectedData #AIAgents #DataAutomation #IntelligentSystems #DataDrivenDecisions

After burning through $40 worth of Gemini coding tokens, I finally got it working. I’ve been trying to get AI to not just answer a user’s enterprise data question, but to also pick the right visualization to explain it. AND for it to then justify that choice in plain English. Here's a breakdown of how it works: The Core Idea: An AI Data Visualization Expert Think of the system's AI as a data visualization expert. It's been trained not just on language, but on the principles of good data visualization. This is achieved through two core strategies: giving the AI specialized knowledge and forcing it to explain its reasoning. --- 1. How It Chooses the Right Chart The AI's smart selection comes from a combination of context and a specialized "rulebook" it must follow. a.  The Rulebook: The AI is given an internal guide on data visualization. This guide details every chart the system can create, explaining the ideal use case for each one. For instance, it instructs the AI that line charts are best for showing trends over time, while bar charts are ideal for comparing distinct categories. b.  The Context: When a user asks a question, the system bundles up the user's goal, a sample of the relevant data, and this "rulebook." This package gives the AI everything it needs to make an informed decision. c.  The Decision: Armed with this context, the AI matches the user's goal and the data's structure against its rulebook to select the most effective chart type. It then generates the precise configuration needed to display that chart. --- 2. How It Explains Its Thought Process Making the AI's thinking visible is key to building user trust. The system does this in two ways: by showing the final rationale and by revealing the live thought process. a.  The Rationale: The AI is required to include a simple, human-readable `rationale` with every chart it creates. This is a direct explanation of its choice, such as, "A bar chart was chosen to clearly compare values across different categories." This rationale is displayed to the user, turning a black box into a transparent partner. b.  Live Thinking Stream: The system can also ask the AI to "think out loud" as it works. As the AI analyzes the request, it sends a real-time stream of its internal monologue—like "Okay, I see time-series data, so a line chart is appropriate." The application can display this live feed, giving the user a behind-the-scenes look at the AI's reasoning as it happens. By combining this expert knowledge with a requirement for self-explanation, the system transforms a simple request into an insightful and trustworthy data visualization.