Vibe Coding Session Building a Local LLM-Powered Knowledge Graph [post] deterministic


Brainstorming

Today I'm starting my vibe coding session with a full-on vibe for the brainstorming prompt below. I keep it fairly vague so that I can get a feel for what kind of things it will come up with. I'm going to try to keep it local and I'm building a graph. Let's see what today brings!

what are some vibe coding projects which are related to LLMs about building a graph, I want to build a graph, I want to vibe code, I want a blog post to be created about the whole thing, but I am going to write the blog post, what I want from you are ideas on what to build and the technologies used and then I want you to formulate several options with the technologies listed and allow me to choose one. One requirement is that I want everything to be local, the databases and inference are all done locally

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That prompt gave me a list of five ideas. I chose one that I liked and chatGPT even gave me some options which I gave in the following prompt along with the following:

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Now I want in .md form a full description, architecture and everything else needed to know in order to fill the context for the generation of the prompt I am going to give to CLIne, so I want full output from you, you are not creating a prompt for CLIne but you are just writing in .md form the full description of every aspect you can fit into your context.

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That outputted a document I went on to edit and include which is very long so I made it collapsable here:

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<details> <summary>Click to expand the full document</summary>

Mind Map AI — Full Project Specification

**Project:** Mind Map AI — LLM-powered Personal Knowledge Graph (All Local) **Target:** Local-only stack (Next.js frontend, FastAPI backend, local LLM, SQLite, NetworkX graph). **Purpose:** Convert notes/journals/markdown into a browsable, queryable, and editable knowledge graph; provide semantic search and visualization; all inference and storage stays local.

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Table of Contents

1. [Overview & Goals](#1-overview--goals) 2. [User Stories & Flows](#2-user-stories--flows) 3. [High-Level Architecture](#3-high-level-architecture) 4. [Technology Choices (Rationale)](#4-technology-choices-rationale) 5. [Data Models & Storage Design](#5-data-models--storage-design) 6. [LLM Strategy (Local Inference + Embeddings)](#6-llm-strategy) 7. [API Design (FastAPI)](#7-api-design) 8. [Frontend (Next.js)](#8-frontend) 9. [Graph Processing & Transformation Logic](#9-graph-processing--transformation-logic) 10. [Visualization Approach](#10-visualization-approach) 11. [File Structure & Example Files](#11-file-structure--example-files) 12. [Deployment / Local Dev Setup](#12-deployment--local-dev-setup) 13. [Testing & Validation Strategy](#13-testing--validation-strategy) 14. [Security & Privacy Considerations](#14-security--privacy-considerations) 15. [Performance & Scaling Notes](#15-performance--scaling-notes) 16. [Example Prompts & Extraction Templates](#16-example-prompts--extraction-templates) 17. [CLIne Handoff Notes](#17-cline-handoff-notes) 18. [Stretch Goals / Extensions](#18-stretch-goals--extensions)

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1. Overview & Goals

**What it does:** - Accepts local markdown/text notes (or pasted text) - Uses a locally-hosted LLM to extract entities, concepts, relationships, and sentiment - Stores raw notes in SQLite, embeddings in a local vector store, and graph relationships in a NetworkX graph persisted to disk - Exposes an API for ingestion, querying, and editing - Frontend (Next.js) provides an interactive visualization and editor for nodes/edges and a semantic search UI

**Constraints:** - Everything local: inference, DB, vector store, UI served locally - Offline-capable development workflow where possible - Auditable transformations — every extraction stores source text and provenance

**Primary users:** - You (the developer / blogger) building and experimenting; audience for blog: fellow vibe coders

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2. User Stories & Flows

**User Stories:** - As a user, I want to drop a folder of markdown into the app and have a graph generated automatically - As a user, I want to click on a node and see the source passages and the LLM's extraction/provenance - As a user, I want to semantically search my notes and get graph nodes as results - As a user, I want to edit nodes/edges manually and commit changes - As a user, I want exports: GraphML, GEXF, PNG snapshots

**Typical Flow:** 1. Drop or upload notes/folder or paste text 2. Backend reads files, extracts metadata, runs LLM extraction and embeddings 3. Save raw text to SQLite, embeddings to local vector store (Chroma or local Faiss), create/append nodes & edges to NetworkX graph 4. Frontend queries backend for graph and renders interactive visualization 5. User inspects nodes, opens provenance panel with source text and extracted labels 6. User edits a node/edge → backend updates NetworkX & SQLite 7. User exports or runs graph analytics (connected components, centrality)

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3. High-Level Architecture

[ Next.js (frontend) ] <---> [ FastAPI (backend) ] <---> [Local LLM runtime (Ollama/Llama)] |-- SQLite (raw notes + metadata) |-- Vector DB (local Chroma / Faiss) (embeddings) |-- NetworkX (graph persisted as .gpickle / GraphML)

**Components:** - **Frontend:** Next.js app (React). Interactive graph (react-cytoscapejs), note editor, search UI - **Backend:** FastAPI for ingestion, graph management, search endpoints, admin endpoints - **LLM runtime:** Ollama, Llama.cpp, or Dockerized local model backend (whichever you prefer). Used for extraction and for optional reasoning queries - **Embeddings:** local sentence-transformer model (e.g., all-MiniLM or similar) or Ollama embedding endpoint (local) - **Graph persistence:** NetworkX memory representation persisted to .gpickle / GraphML files, backed up in SQLite for quick metadata queries

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4. Technology Choices (Rationale)

• **Next.js:** you're familiar with it; great for building modern UIs, server-side rendering for initial page load; can run entirely locally with next dev or next start
• **FastAPI:** lightweight, async, great for building REST APIs; easy to integrate with Python graph code and LLM libraries
• **NetworkX:** excellent for in-memory graph algorithms and flexible node/edge attributes; easy persistence to gpickle or GraphML
• **SQLite:** simple, file-based database for raw text and provenance; ACID, portable
• **Local LLM (Ollama / Llama):** keeps inference local. Ollama provides an easy local server experience; alternatives: llama.cpp or locally run Mistral/Gemma via supported runtimes
• **Embeddings:** local sentence-transformers or Ollama embeddings. Useful for fast semantic search
• **Vector DB:** lightweight local Chroma or Faiss if you want faster vector search than scanning SQLite
• **Visualization:** Cytoscape (via react-cytoscapejs) — good UX for graph exploration

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5. Data Models & Storage Design

**SQLite Schema (Simplified):**

```sql -- notes table: raw source markdown / text CREATE TABLE notes ( id INTEGER PRIMARY KEY AUTOINCREMENT, filename TEXT, content TEXT, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, source_path TEXT, -- original path on disk if uploaded hash TEXT, -- content hash for dedup processed BOOLEAN DEFAULT 0 );

-- extracts table: store entity extracts & provenance CREATE TABLE extracts ( id INTEGER PRIMARY KEY AUTOINCREMENT, note_id INTEGER REFERENCES notes(id), extractor_model TEXT, extract_json TEXT, -- store raw JSON output from LLM (entities, relationships) score REAL, created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP );

-- metadata table (optional) CREATE TABLE metadata ( key TEXT PRIMARY KEY, value TEXT ); ```

**NetworkX Graph Model:** - **Node attributes:** - id (unique string; e.g., node:UUID or entity:<normalized_