Ollama Next.js App Ideas

let's brainstorm this ollama local LLM application that uses next.js

That sounds like an awesome project! Let's break it down and explore some potential use cases, tech stack decisions, and features for integrating Ollama (local LLM) with a Next.js application. --- ## **Application Ideas** Here are some possible applications you could build with Ollama and Next.js: ### 1. **AI-Powered Journaling App** - Users write journal entries, and Ollama provides feedback, summaries, or sentiment analysis. - Could integrate with a visualization tool to track emotional trends over time. - Markdown support with AI-assisted suggestions. ### 2. **Local AI-Powered Chatbot** - A privacy-focused chatbot that runs entirely locally, storing chat history in a database. - Can be used for research, customer service, or personal productivity. - Option to fine-tune responses based on user preferences. ### 3. **Code Review & Debugging Assistant** - Users paste code into a Next.js app, and Ollama provides suggestions, explains errors, or refactors the code. - Could integrate with GitHub API to analyze PRs and suggest improvements. ### 4. **AI-Powered Research Assistant** - Users upload PDFs, web links, or text, and Ollama extracts key insights or generates summaries. - Includes search functionality to find relevant information quickly. ### 5. **AI-Powered Knowledge Base / Docs Assistant** - Users can input a knowledge base (Markdown, Notion, or custom docs), and Ollama generates quick answers. - Includes an interactive Q&A interface. - Local retrieval-augmented generation (RAG) with ChromaDB for better context handling. ### 6. **AI-Powered SEO Tool** - Generates meta descriptions, blog post outlines, and content recommendations based on input topics. - Local and private, unlike cloud-based SEO tools. --- ## **Tech Stack** Here's a possible stack for the project: ### **Frontend: Next.js** - Server-side rendering (SSR) or static site generation (SSG) for performance. - Tailwind CSS or ShadCN for styling. - React Query (TanStack) for managing API calls. ### **Backend: Ollama + API Layer** - API Routes in Next.js for serving AI responses (`/api/chat` or `/api/llm`). - Run Ollama as a local service with a persistent context. - Could integrate with SQLite or PostgreSQL (Supabase) for storing user interactions. ### **Database & Storage** - SQLite for local storage (simpler setup). - Supabase or PostgreSQL for cloud-backed data persistence. - ChromaDB for vector search and RAG. ### **Other Enhancements** - **TTS (Text-to-Speech):** Convert AI responses into speech using Coqui AI or Piper. - **Offline Mode:** Fully local functionality with no external dependencies. - **Prompt Engineering UI:** Users can tweak prompts and test different settings. --- ## **Next Steps** 1. **Define MVP Features** – Start with a basic chatbot or journaling assistant. 2. **Set Up Next.js + Ollama API Route** – Ensure smooth communication between frontend and the local model. 3. **Add Local Storage (ChromaDB or SQLite)** – Keep conversation context or user data. 4. **Enhance UX/UI** – Clean, minimalistic interface with Tailwind CSS. 5. **Expand Features** – Depending on the application type (e.g., code analysis, document summarization). Would you like to focus on a specific use case, or do you want to experiment with multiple ones? 🚀

think of an idea which would use neo4j

### **Idea: AI-Powered Knowledge Graph with Next.js, Ollama, and Neo4j** A **local LLM-driven knowledge graph** that helps users visualize and explore relationships between concepts, people, or entities. --- ## **Concept: AI-Powered Personal Knowledge Graph** **Use Case:** Users input data (text, documents, or notes), and the system **dynamically builds a knowledge graph** using Neo4j. The AI (Ollama) extracts entities and relationships, then represents them as an interactive graph in the UI. ### **How It Works:** 1. **User Inputs Data** - Paste text, upload documents, or enter notes via the Next.js frontend. - Example input: _"Nikola Tesla worked with Thomas Edison before founding Tesla Electric Company in 1887."_ 2. **Ollama Extracts Entities & Relationships** - Identifies key entities (_Nikola Tesla_, _Thomas Edison_, _Tesla Electric Company_). - Determines relationships (_worked with_, _founded_). 3. **Neo4j Stores Graph Data** - Stores nodes (_people, companies, concepts, events_). - Creates edges (_"worked with"_, _"founded"_, _"related to"_). 4. **Next.js Visualizes the Graph** - Displays interactive relationships using **ReactFlow** or **D3.js**. - Users can click nodes to expand, edit, or refine the AI-generated graph. --- ## **Potential Features** - 🏗 **Auto-Building Knowledge Graphs** from user-provided text. - 🔎 **Search & Query:** Ask questions like _“Who are all the people connected to Tesla?”_ - 📝 **AI-Augmented Notes:** AI suggests connections between different topics. - 📂 **Import PDFs, Markdown, or Web Links** for automated knowledge extraction. - 🔄 **Self-Updating Graphs:** As more data is added, the graph expands dynamically. --- ## **Tech Stack** - **Frontend:** Next.js (ReactFlow/D3.js for graph visualization) - **Backend:** Neo4j (Graph database) - **AI Processing:** Ollama (Entity extraction & relation mapping) - **Vector Search (Optional):** ChromaDB for RAG --- ## **Possible Use Cases** - **Personal Knowledge Management** (PKM like Obsidian but AI-enhanced). - **Research Tool** (connects academic papers, topics, and authors). - **Business Intelligence** (maps companies, products, and market trends). Would this align with what you’re looking for, or do you want to tweak the concept? 🚀

no think of some other use cases

Here are some **alternative use cases** that combine **Neo4j, Next.js, and Ollama** in interesting ways: --- ### **1. AI-Powered Conspiracy / Influence Mapping** 🕵️‍♂️ **Use Case:** Users input names, organizations, or events, and the system **maps connections between people, entities, and influence networks**. **How It Works:** - Users enter text (news articles, tweets, or reports). - Ollama extracts key entities (people, organizations, events) and relationships. - Neo4j stores and visualizes these connections as a **conspiracy map**. - Users can **expand nodes** to see hidden connections. - Chat with an AI to **ask about connections** (_“How is X related to Y?”_). **Potential Use Cases:** - **Political influence tracking** (PACs, donors, lobbyists). - **Corporate ownership mapping** (Who really owns what?). - **Fake news detection** (Identifying common misinformation patterns). --- ### **2. AI-Powered Crime Data Visualization** 🚔 **Use Case:** A tool for **law enforcement, journalists, or researchers** to analyze crime patterns and connections between cases. **How It Works:** - Users input crime reports, articles, or police logs. - Ollama extracts suspects, locations, crime types, and events. - Neo4j stores this as a **criminal network graph**. - Next.js provides **interactive visualizations** (timeline, heatmaps). - Users can **query patterns** (_“Show all crimes involving person X in the last year”_). **Potential Use Cases:** - **Journalists analyzing corruption cases.** - **Police departments linking crimes across regions.** - **Researchers studying crime trends.** --- ### **3. AI-Powered Legal Case Navigator** ⚖️ **Use Case:** A **knowledge graph of legal cases, precedents, and laws** to help lawyers, students, and researchers find relevant legal arguments. **How It Works:** - Users input case names, legal texts, or descriptions. - Ollama extracts **judges, rulings, case citations, and legal principles**. - Neo4j builds a **precedent graph**, linking similar cases and rulings. - Next.js provides **a chat interface** for case lookup (_“What cases set a precedent for X?”_). **Potential Use Cases:** - **Law students studying case law.** - **Lawyers finding legal precedents.** - **AI-powered legal research assistants.** --- ### **4. AI-Powered Supply Chain Risk Analyzer** 📦 **Use Case:** A tool for companies to **map their supply chains and detect risks** using AI-powered relationship analysis. **How It Works:** - Users enter suppliers, locations, or product dependencies. - Ollama extracts **supply chain relationships, risks, and dependencies**. - Neo4j builds a **supply chain graph** showing weak points. - Next.js provides **visual analytics & alerts** (_“Show all suppliers affected by conflict in X country”_). **Potential Use Cases:** - **Companies analyzing supplier dependencies.** - **Risk management teams detecting weak points.** - **E-commerce companies optimizing logistics.** --- ### **5. AI-Powered Cybersecurity Threat Intelligence** 🔒 **Use Case:** A **cyber threat intelligence tool** that maps **attack vectors, hackers, vulnerabilities, and affected systems**. **How It Works:** - Users input security reports, logs, or threat feeds. - Ollama extracts **threat actors, attack techniques, vulnerabilities**. - Neo4j builds a **threat graph** to visualize cyberattacks. - Next.js provides **a dashboard for querying threats** (_“Which vulnerabilities are commonly exploited together?”_). **Potential Use Cases:** - **Cybersecurity teams tracking hacker groups.** - **Companies mapping potential attack surfaces.** - **Researchers analyzing cyberattack patterns.** --- Would any of these ideas interest you? Or do you want to refine one further? 🚀

Fake news detection (Identifying common misinformation patterns).

### **AI-Powered Fake News Detection & Misinformation Mapping** 🕵️‍♂️📰 A **local LLM-powered platform** that **analyzes, maps, and detects misinformation patterns** by identifying repeated claims, sources, and influence networks. --- ## **How It Works** ### **1. Users Input News Articles, Tweets, or URLs** - Paste text, upload documents, or enter social media links. - The system scrapes and cleans content. ### **2. Ollama Analyzes Content for Misinformation Patterns** - Extracts **key entities** (people, organizations, topics). - Identifies **recurring claims** across different sources. - Flags **potential misinformation** based on: - Fact-checker databases (optional integration). - Sentiment & exaggeration analysis. - Source credibility scoring. ### **3. Neo4j Builds a Fake News Influence Graph** - **Nodes:** Articles, authors, sources, organizations. - **Edges:** Shared claims, funding sources, re-posting chains. - Detects **who is repeating the same claims** across platforms. ### **4. Next.js Provides an Interactive Visualization** - Users can **search and explore** the misinformation network. - See how **stories evolve over time** (_who first posted it?_) - Click on nodes to see **fact-checking reports** (if available). --- ## **Features** 🔎 **Search & Track News Narratives** – See how a claim spreads. 📊 **Trend Analysis** – Detect emerging misinformation patterns. ⚖️ **Bias Analysis** – Check how different sources report the same event. 🔗 **Link Fact-Checkers** – Optionally integrate with Snopes, PolitiFact, etc. 📡 **Social Media Monitoring** – Detect bot networks amplifying news. --- ## **Tech Stack** - **Frontend:** Next.js (ReactFlow for graph visualization). - **LLM Processing:** Ollama (content analysis & entity extraction). - **Database:** Neo4j (storing misinformation networks). - **Optional:** ChromaDB for vector similarity search. --- ## **Use Cases** - **Journalists tracking misinformation trends.** - **Researchers studying how fake news spreads.** - **Users verifying news credibility before sharing.** - **Governments/NGOs monitoring election misinformation.** --- Would you like any tweaks, or does this sound good as a starting point? 🚀

no let's think of something that is funny

### **AI-Powered Conspiracy Theory Generator & Graph** 🛸👽🔍 A **hilarious misinformation sandbox** where users input any topic, and the AI **creates a wild conspiracy theory** with a visual **Neo4j-powered relationship graph**. --- ### **How It Works:** 1️⃣ **User Inputs a Topic** - Example: _"Pigeons are not real."_ - Example: _"Why does Jeff Bezos look like an egg?"_ 2️⃣ **Ollama Generates a Fake Conspiracy Theory** - **Entities:** Extracts key figures, places, and organizations. - **Claims:** Invents totally absurd connections (_"Bezos is a time traveler from the lizard dimension."_) - **Supporting ‘Evidence’**: AI fabricates fake but believable ‘historical facts.’ 3️⃣ **Neo4j Builds the Conspiracy Graph** - **Nodes:** People, organizations, events, and secret societies. - **Edges:** Relationships like _“is controlled by,” “was spotted with,” “funded by aliens.”_ - Example: _“Bezos 🡆 Funded by 🡆 The Illuminati 🡆 Who Are 🡆 Controlled by Pigeons”_ 4️⃣ **Next.js Displays an Interactive Graph & Explanation** - **Visual Conspiracy Board** (like a detective's red-string corkboard). - Users can **expand nodes** for AI-generated backstories. - AI **auto-generates fake tweets, leaked emails, and “news articles”** to support the theory. --- ### **Bonus Features** 🤣 **"Create Your Own Conspiracy" Mode** – Let users drag and drop connections. 📡 **Auto-Generated ‘Leaked Government Memos’** – AI writes realistic but absurd documents. 🎙️ **Deepfake Audio** – Generate fake speeches where "celebrities" reveal the truth. 🚀 **"Who’s Involved?" Button** – Auto-links celebrities and politicians to the theory. --- ### **Example Theories** 🛸 **“Elon Musk is a Martian Prince”** – SpaceX is just his way home. 🦴 **“Dinosaurs Never Went Extinct, They Run the Banks”** – Rothschilds = Velociraptors. 🥒 **“Pickles Are Sentient”** – The pickle industry is silencing them. --- ### **Tech Stack** - **Frontend:** Next.js (ReactFlow for conspiracy visualization). - **LLM:** Ollama (generating fake theories). - **Database:** Neo4j (storing & expanding theories). - **Optional:** ElevenLabs for deepfake audio clips. --- ### **Who Would Use This?** - **For fun** – Social media challenges, memes, party games. - **Educators** – Teaching media literacy in a hilarious way. - **Satirists & Comedians** – Endless content. --- Would you actually build this? Because this sounds legendary. 🚀😂

write a prompt for an IDE to auto complete this program as a series of terminal commands and descriptions of code

Here’s a **prompt** you can use in an IDE with an AI-assisted autocompletion tool (like Copilot or a local LLM in VSCode) to generate a **series of terminal commands and descriptions** for setting up and running the conspiracy generator: --- ### **Prompt for IDE Autocompletion** ```plaintext # Goal: Generate a Next.js + Neo4j + Ollama-powered conspiracy theory generator # The following is a step-by-step guide with terminal commands and descriptions of the code setup. ## Step 1: Create a Next.js Project # Initialize a new Next.js app npx create-next-app@latest conspiracy-generator cd conspiracy-generator ## Step 2: Install Dependencies # Install required packages npm install next react react-dom npm install @neo4j/graphql neo4j-driver reactflow # Install Ollama for local LLM inference # (Skip this step if Ollama is already installed) curl -fsSL https://ollama.com/install.sh | sh # Run Ollama and download a suitable LLM model ollama run mistral ## Step 3: Set Up Neo4j Database # Pull and run a Neo4j instance with Docker docker run -d --name neo4j -p 7687:7687 -p 7474:7474 \ -e NEO4J_AUTH=neo4j/password \ neo4j:latest # Verify Neo4j is running docker ps ## Step 4: Configure Neo4j in the Project # Create a .env.local file and add the following credentials: NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=password ## Step 5: Create the Neo4j Schema in JavaScript # Inside the Next.js app, create a `lib/neo4j.js` file: import { Neo4jGraphQL } from "@neo4j/graphql"; import { driver } from "neo4j-driver"; const neo4jDriver = driver( process.env.NEO4J_URI, neo4j.auth.basic(process.env.NEO4J_USER, process.env.NEO4J_PASSWORD) ); const typeDefs = ` type Conspiracy { id: ID! title: String! description: String! connections: [Connection!]! @relationship(type: "LINKED_TO", direction: OUT) } type Connection { id: ID! name: String! conspiracies: [Conspiracy!]! @relationship(type: "LINKED_TO", direction: IN) } `; export const neoSchema = new Neo4jGraphQL({ typeDefs, driver: neo4jDriver }); ## Step 6: Create an API Route to Generate Theories # Inside `pages/api/generate.js`, add: import ollama from "ollama"; import { neoSchema } from "../../lib/neo4j"; export default async function handler(req, res) { const { topic } = req.body; const response = await ollama.chat({ model: "mistral", messages: [ { role: "system", content: "Generate a wild conspiracy theory involving " + topic } ]}); const conspiracy = { title: topic, description: response.message.content }; // Save to Neo4j await neoSchema.executeMutation(` CREATE (c:Conspiracy {title: $title, description: $description}) RETURN c `, { title: conspiracy.title, description: conspiracy.description }); res.status(200).json(conspiracy); } ## Step 7: Create a Frontend Visualization # Inside `components/Graph.js`, use ReactFlow to display conspiracy nodes. ## Step 8: Start the Development Server npm run dev ## Step 9: Generate a Test Conspiracy curl -X POST http://localhost:3000/api/generate \ -H "Content-Type: application/json" \ -d '{"topic": "The moon is a hologram"}' # The AI-generated conspiracy will be stored in Neo4j and displayed in Next.js! ``` --- ### **How This Works** - The **IDE autocompletion** should continue generating code and filling in missing parts based on your local model. - The **sequence of commands** ensures a smooth setup, from project creation to running AI-generated conspiracy theories. - **Neo4j handles relationships** between conspiracies, people, and events. - **Ollama generates absurd AI-driven narratives.** - **ReactFlow visualizes** the conspiracy graphs. Would you like any refinements or extra steps in the prompt? 🚀😆

Below is an IDE prompt that outlines terminal commands and inline code descriptions to scaffold an “AI-Powered Conspiracy Theory Generator & Graph” project using Next.js, Neo4j, and a placeholder for the Ollama LLM. You can copy these steps into your IDE’s terminal and code editor to start building your project. --- ### **Step 1: Initialize the Next.js Project** ```bash # Create a new Next.js project with TypeScript support npx create-next-app@latest conspiracy-graph --typescript ``` *Description:* This command scaffolds a Next.js project named `conspiracy-graph` using TypeScript. --- ### **Step 2: Install Required Dependencies** ```bash # Move into the project directory cd conspiracy-graph # Install packages for Neo4j, graph visualization, and Tailwind CSS for styling npm install neo4j-driver reactflow tailwindcss postcss autoprefixer ``` *Description:* - **neo4j-driver:** Connect to your Neo4j database. - **reactflow:** For rendering the interactive conspiracy graph. - **tailwindcss, postcss, autoprefixer:** For rapid, utility-first styling. --- ### **Step 3: Set Up Tailwind CSS** ```bash # Initialize Tailwind CSS configuration npx tailwindcss init -p ``` *Description:* This command creates both `tailwind.config.js` and `postcss.config.js`. Now, update your Tailwind config (`tailwind.config.js`) with the proper content paths: ```js // tailwind.config.js module.exports = { content: [ "./pages/**/*.{js,ts,jsx,tsx}", "./components/**/*.{js,ts,jsx,tsx}", ], theme: { extend: {}, }, plugins: [], } ``` Then, add Tailwind’s directives to your global CSS (e.g. in `styles/globals.css`): ```css @tailwind base; @tailwind components; @tailwind utilities; ``` *Description:* This configures Tailwind CSS to scan your project files for class names and applies the framework’s base, components, and utility styles. --- ### **Step 4: Create an API Route for Generating Conspiracy Theories** Create a new file at `pages/api/generateConspiracy.ts` with the following content: ```ts // pages/api/generateConspiracy.ts import type { NextApiRequest, NextApiResponse } from 'next'; import { runOllamaConspiracy } from '../../lib/ollama'; export default async function handler(req: NextApiRequest, res: NextApiResponse) { if (req.method !== 'POST') { return res.status(405).json({ error: 'Method not allowed' }); } const { topic } = req.body; try { // Generate a conspiracy theory using the local LLM (Ollama) const conspiracyData = await runOllamaConspiracy(topic); res.status(200).json(conspiracyData); } catch (error) { res.status(500).json({ error: 'Failed to generate conspiracy theory' }); } } ``` *Description:* This API route listens for POST requests containing a topic, calls a helper function (to be implemented next) that interacts with Ollama, and returns a generated conspiracy theory along with nodes and edges for visualization. --- ### **Step 5: Create a Module for the Ollama LLM Interaction** Create a file at `lib/ollama.ts`: ```ts // lib/ollama.ts export async function runOllamaConspiracy(topic: string) { // Placeholder function simulating a call to a local LLM (Ollama) // Replace this with your actual integration logic return { topic, theory: `Conspiracy theory for "${topic}": Elon Musk is secretly a Martian prince controlled by alien pigeons.`, nodes: [ { id: '1', label: 'Elon Musk' }, { id: '2', label: 'Martian Prince' }, { id: '3', label: 'Alien Pigeons' }, ], edges: [ { source: '1', target: '2', label: 'is secretly' }, { source: '2', target: '3', label: 'controlled by' }, ], }; } ``` *Description:* This module defines a function that simulates generating a conspiracy theory. It returns a theory string plus a simple graph (nodes and edges) that your frontend can later visualize. --- ### **Step 6: Set Up Neo4j Connectivity** Create a file at `lib/neo4j.ts` to manage connections and save graph data: ```ts // lib/neo4j.ts import neo4j from 'neo4j-driver'; const driver = neo4j.driver( process.env.NEO4J_URI || 'bolt://localhost:7687', neo4j.auth.basic( process.env.NEO4J_USER || 'neo4j', process.env.NEO4J_PASSWORD || 'password' ) ); export const saveGraphData = async (nodes: any[], edges: any[]) => { const session = driver.session(); try { // Create nodes for (const node of nodes) { await session.run( 'MERGE (n:Entity {id: $id, label: $label})', { id: node.id, label: node.label } ); } // Create relationships for (const edge of edges) { await session.run( `MATCH (a:Entity {id: $source}), (b:Entity {id: $target}) MERGE (a)-[:RELATED {label: $label}]->(b)`, { source: edge.source, target: edge.target, label: edge.label } ); } } finally { await session.close(); } }; ``` *Description:* This module initializes a connection to Neo4j (using environment variables) and defines a helper function to persist nodes and relationships using Cypher queries. --- ### **Step 7: Build the Frontend Component for Graph Visualization** Create a React component at `components/ConspiracyGraph.tsx`: ```tsx // components/ConspiracyGraph.tsx import React from 'react'; import ReactFlow, { Elements } from 'reactflow'; import 'reactflow/dist/style.css'; interface ConspiracyGraphProps { nodes: any[]; edges: any[]; } const ConspiracyGraph: React.FC<ConspiracyGraphProps> = ({ nodes, edges }) => { // Convert nodes and edges into a format compatible with ReactFlow const elements: Elements = [ ...nodes.map(node => ({ id: node.id, data: { label: node.label }, position: { x: Math.random() * 400, y: Math.random() * 400 } })), ...edges.map(edge => ({ id: `e${edge.source}-${edge.target}`, source: edge.source, target: edge.target, label: edge.label })), ]; return <ReactFlow elements={elements} style={{ width: '100%', height: '500px' }} />; }; export default ConspiracyGraph; ``` *Description:* This component receives graph data (nodes and edges), transforms them into ReactFlow’s expected format, and renders an interactive graph. --- ### **Step 8: Create the Main Page to Tie Everything Together** Update `pages/index.tsx` with the following code: ```tsx // pages/index.tsx import { useState } from 'react'; import ConspiracyGraph from '../components/ConspiracyGraph'; export default function Home() { const [topic, setTopic] = useState(''); const [data, setData] = useState<any>(null); const handleSubmit = async (e: React.FormEvent) => { e.preventDefault(); const res = await fetch('/api/generateConspiracy', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify({ topic }), }); const json = await res.json(); // Optionally: Save graph data to Neo4j using your saveGraphData function setData(json); }; return ( <div className="container mx-auto p-4"> <h1 className="text-3xl font-bold mb-4">Conspiracy Theory Generator</h1> <form onSubmit={handleSubmit} className="mb-4"> <input type="text" value={topic} onChange={(e) => setTopic(e.target.value)} placeholder="Enter a topic..." className="border p-2 mr-2" /> <button type="submit" className="bg-blue-500 text-white p-2">Generate</button> </form> {data && ( <div> <h2 className="text-2xl font-semibold">Generated Theory</h2> <p>{data.theory}</p> <ConspiracyGraph nodes={data.nodes} edges={data.edges} /> </div> )} </div> ); } ``` *Description:* This page includes: - A form for entering a topic. - A fetch call to your API route that triggers the conspiracy theory generation. - Display of the generated text along with the interactive graph using the `ConspiracyGraph` component. --- ### **Step 9: Configure Environment Variables for Neo4j** Create a file named `.env.local` in your project root with the following content: ```env NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=your_password ``` *Description:* These environment variables allow your application to securely connect to the local Neo4j instance. --- ### **Step 10: Run Your Development Server** ```bash # Start the Next.js development server npm run dev ``` *Description:* Your app will now run on [http://localhost:3000](http://localhost:3000) where you can test the conspiracy theory generator and interact with the graph. --- This prompt provides a complete series of terminal commands and inline code descriptions to help your IDE autocomplete a working prototype of the project. Feel free to modify and extend each part as you refine the integration with your actual Ollama instance and further develop the Neo4j graph functionalities. Enjoy coding!