First commit

.editorconfig

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+# top-most EditorConfig file
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+indent_style = tab
+indent_size = 4
+tab_width = 4

.eslintignore

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+node_modules/
+
+main.js

.eslintrc

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+{
+    "root": true,
+    "parser": "@typescript-eslint/parser",
+    "env": { "node": true },
+    "plugins": [
+      "@typescript-eslint"
+    ],
+    "extends": [
+      "eslint:recommended",
+      "plugin:@typescript-eslint/eslint-recommended",
+      "plugin:@typescript-eslint/recommended"
+    ], 
+    "parserOptions": {
+        "sourceType": "module"
+    },
+    "rules": {
+      "no-unused-vars": "off",
+      "@typescript-eslint/no-unused-vars": ["error", { "args": "none" }],
+      "@typescript-eslint/ban-ts-comment": "off",
+      "no-prototype-builtins": "off",
+      "@typescript-eslint/no-empty-function": "off"
+    } 
+  }

.gitignore

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+# vscode
+.vscode 
+
+# Intellij
+*.iml
+.idea
+
+# npm
+node_modules
+
+# Don't include the compiled main.js file in the repo.
+# They should be uploaded to GitHub releases instead.
+main.js
+
+# Exclude sourcemaps
+*.map
+
+# obsidian
+data.json
+
+# Exclude macOS Finder (System Explorer) View States
+.DS_Store

.npmrc

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+tag-version-prefix=""

README.md

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+# Obsidian Vector Search Plugin
+
+This plugin adds semantic search capabilities to Obsidian using Ollama's embedding API. It allows you to find semantically similar notes based on content rather than just keyword matching.
+
+## Features
+
+-   🔍 Semantic search across your entire vault
+-   🤖 Powered by Ollama's embedding model
+-   📊 Configurable similarity threshold
+-   🚀 Fast local search once embeddings are generated
+
+## Prerequisites
+
+-   [Ollama](https://ollama.ai/) installed and running locally
+-   The `nomic-embed-text` model pulled in Ollama
+
+## Installation
+
+1. Clone this repo to your `.obsidian/plugins/` folder
+2. Install dependencies: `npm install`
+3. Build the plugin: `npm run dev`
+4. Enable the plugin in Obsidian's settings
+
+## Usage
+
+1. **Initial Setup**
+
+    - Go to Settings > Vector Search
+    - Configure your Ollama URL (default: http://localhost:11434)
+    - Set your desired similarity threshold (0-1)
+
+2. **Building the Index**
+
+    - Click the vector search icon in the ribbon
+    - Wait for all notes to be processed (progress will be shown)
+
+3. **Searching**
+    - Select any text in a note
+    - Use the command "Find Similar Notes" (or set up a hotkey)
+    - View results in the popup modal
+
+## How it Works
+
+1. The plugin creates vector embeddings for all your markdown notes using Ollama
+2. When you search, it:
+    - Creates an embedding for your selected text
+    - Uses cosine similarity to find the most similar notes
+    - Shows results above your configured threshold
+
+## Development
+
+-   `npm run dev` - Start compilation in watch mode
+-   `npm run build` - Build the plugin
+-   `npm test` - Run tests
+
+## Logs
+
+The plugin logs all API calls and operations to the Developer Console (Ctrl+Shift+I or Cmd+Option+I on Mac). This includes:
+
+-   Embedding API requests
+-   Index building progress
+-   File processing status
+-   Any errors or issues
+
+## Configuration
+
+```json
+{
+	"ollamaURL": "http://localhost:11434",
+	"searchThreshold": 0.7
+}
+```

esbuild.config.mjs

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+import esbuild from "esbuild";
+import process from "process";
+import builtins from "builtin-modules";
+
+const banner =
+`/*
+THIS IS A GENERATED/BUNDLED FILE BY ESBUILD
+if you want to view the source, please visit the github repository of this plugin
+*/
+`;
+
+const prod = (process.argv[2] === "production");
+
+const context = await esbuild.context({
+	banner: {
+		js: banner,
+	},
+	entryPoints: ["main.ts"],
+	bundle: true,
+	external: [
+		"obsidian",
+		"electron",
+		"@codemirror/autocomplete",
+		"@codemirror/collab",
+		"@codemirror/commands",
+		"@codemirror/language",
+		"@codemirror/lint",
+		"@codemirror/search",
+		"@codemirror/state",
+		"@codemirror/view",
+		"@lezer/common",
+		"@lezer/highlight",
+		"@lezer/lr",
+		...builtins],
+	format: "cjs",
+	target: "es2018",
+	logLevel: "info",
+	sourcemap: prod ? false : "inline",
+	treeShaking: true,
+	outfile: "main.js",
+	minify: prod,
+});
+
+if (prod) {
+	await context.rebuild();
+	process.exit(0);
+} else {
+	await context.watch();
+}

main.ts

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+import { 
+    App, 
+    Editor, 
+    MarkdownView, 
+    Modal, 
+    Notice, 
+    Plugin, 
+    PluginSettingTab, 
+    Setting,
+    TFile
+} from 'obsidian';
+
+interface MyPluginSettings {
+    ollamaURL: string;
+    searchThreshold: number;
+}
+
+const DEFAULT_SETTINGS: MyPluginSettings = {
+    ollamaURL: 'http://localhost:11434',
+    searchThreshold: 0.8
+}
+
+export default class VectorSearchPlugin extends Plugin {
+    settings: MyPluginSettings;
+    vectorStore: Map<string, number[]> = new Map();
+
+    async onload() {
+        await this.loadSettings();
+
+        // Add a ribbon icon for rebuilding the vector index
+        this.addRibbonIcon('refresh-cw', 'Rebuild Vector Index', async () => {
+            await this.buildVectorIndex();
+            new Notice('Vector index rebuilt!');
+        });
+
+        // Add a command to search similar notes
+        this.addCommand({
+            id: 'search-similar-notes',
+            name: 'Search Similar Notes',
+            editorCallback: async (editor: Editor, view: MarkdownView) => {
+                const selection = editor.getSelection();
+                if (selection) {
+                    await this.searchSimilarNotes(selection);
+                } else {
+                    new Notice('Please select some text to search');
+                }
+            }
+        });
+
+        // Add settings tab
+        this.addSettingTab(new VectorSearchSettingTab(this.app, this));
+    }
+
+    async onunload() {
+        // Clean up resources if needed
+    }
+
+    async loadSettings() {
+        this.settings = Object.assign({}, DEFAULT_SETTINGS, await this.loadData());
+    }
+
+    async saveSettings() {
+        await this.saveData(this.settings);
+    }
+
+    // Function to get embeddings from Ollama
+    async getEmbedding(text: string): Promise<number[]> {
+        try {
+            console.log(`[Vector Search] Requesting embedding for text of length ${text.length}`);
+            const response = await fetch(`${this.settings.ollamaURL}/api/embeddings`, {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                },
+                body: JSON.stringify({
+                    model: 'nomic-embed-text',
+                    prompt: text
+                })
+            });
+
+            const data = await response.json();
+            console.log(`[Vector Search] Successfully received embedding of length ${data.embedding.length}`);
+            return data.embedding;
+        } catch (error) {
+            console.error('[Vector Search] Error getting embedding:', error);
+            new Notice('Error getting embedding from Ollama');
+            return [];
+        }
+    }
+
+    // Calculate cosine similarity between two vectors
+    cosineSimilarity(vec1: number[], vec2: number[]): number {
+        const dotProduct = vec1.reduce((acc, val, i) => acc + val * vec2[i], 0);
+        const mag1 = Math.sqrt(vec1.reduce((acc, val) => acc + val * val, 0));
+        const mag2 = Math.sqrt(vec2.reduce((acc, val) => acc + val * val, 0));
+        return dotProduct / (mag1 * mag2);
+    }
+
+    async buildVectorIndex() {
+        console.log('[Vector Search] Starting vector index build');
+        this.vectorStore.clear();
+        const files = this.app.vault.getMarkdownFiles();
+        
+        let processed = 0;
+        const total = files.length;
+        
+        for (const file of files) {
+            console.log(`[Vector Search] Processing file: ${file.path}`);
+            const content = await this.app.vault.read(file);
+            const embedding = await this.getEmbedding(content);
+            this.vectorStore.set(file.path, embedding);
+            
+            processed++;
+            if (processed % 10 === 0) {
+                console.log(`[Vector Search] Progress: ${processed}/${total} files`);
+                new Notice(`Indexing progress: ${processed}/${total} files`);
+            }
+        }
+        console.log('[Vector Search] Completed vector index build');
+    }
+
+    async searchSimilarNotes(query: string) {
+        if (this.vectorStore.size === 0) {
+            new Notice('Vector index is empty. Please rebuild the index first.');
+            return;
+        }
+
+        const queryEmbedding = await this.getEmbedding(query);
+        const results: Array<{path: string, similarity: number}> = [];
+
+        for (const [path, embedding] of this.vectorStore.entries()) {
+            const similarity = this.cosineSimilarity(queryEmbedding, embedding);
+            if (similarity >= this.settings.searchThreshold) {
+                results.push({ path, similarity });
+            }
+        }
+
+        results.sort((a, b) => b.similarity - a.similarity);
+        
+        if (results.length > 0) {
+            new SimilarNotesModal(this.app, results).open();
+        } else {
+            new Notice('No similar notes found');
+        }
+    }
+}
+
+class SimilarNotesModal extends Modal {
+    results: Array<{path: string, similarity: number}>;
+
+    constructor(app: App, results: Array<{path: string, similarity: number}>) {
+        super(app);
+        this.results = results;
+    }
+
+    onOpen() {
+        const {contentEl} = this;
+        contentEl.empty();
+        
+        contentEl.createEl('h2', {text: 'Similar Notes'});
+        
+        const list = contentEl.createEl('ul');
+        for (const result of this.results) {
+            const item = list.createEl('li');
+            const link = item.createEl('a', {
+                text: `${result.path} (${(result.similarity * 100).toFixed(2)}%)`,
+                href: '#'
+            });
+            link.addEventListener('click', async (e) => {
+                e.preventDefault();
+                const file = this.app.vault.getAbstractFileByPath(result.path);
+                if (file instanceof TFile) {
+                    await this.app.workspace.activeLeaf.openFile(file);
+                    this.close();
+                }
+            });
+        }
+    }
+
+    onClose() {
+        const {contentEl} = this;
+        contentEl.empty();
+    }
+}
+
+class VectorSearchSettingTab extends PluginSettingTab {
+    plugin: VectorSearchPlugin;
+
+    constructor(app: App, plugin: VectorSearchPlugin) {
+        super(app, plugin);
+        this.plugin = plugin;
+    }
+
+    display(): void {
+        const {containerEl} = this;
+        containerEl.empty();
+
+        new Setting(containerEl)
+            .setName('Ollama URL')
+            .setDesc('URL of your Ollama server')
+            .addText(text => text
+                .setPlaceholder('http://localhost:11434')
+                .setValue(this.plugin.settings.ollamaURL)
+                .onChange(async (value) => {
+                    this.plugin.settings.ollamaURL = value;
+                    await this.plugin.saveSettings();
+                }));
+
+        new Setting(containerEl)
+            .setName('Search Threshold')
+            .setDesc('Minimum similarity score (0-1) for showing results')
+            .addSlider(slider => slider
+                .setLimits(0, 1, 0.1)
+                .setValue(this.plugin.settings.searchThreshold)
+                .setDynamicTooltip()
+                .onChange(async (value) => {
+                    this.plugin.settings.searchThreshold = value;
+                    await this.plugin.saveSettings();
+                }));
+    }
+}

manifest.json

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+{
+    "id": "obsidian-vector-search",
+    "name": "Vector Search",
+    "version": "1.0.0",
+    "minAppVersion": "0.15.0",
+    "description": "Semantic search for your notes using Ollama and nomic-embed-text embeddings",
+    "author": "Your Name",
+    "authorUrl": "https://github.com/yourusername",
+    "isDesktopOnly": true,
+    "dependencies": {}
+}

package.json

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+{
+	"name": "obsidian-sample-plugin",
+	"version": "1.0.0",
+	"description": "This is a sample plugin for Obsidian (https://obsidian.md)",
+	"main": "main.js",
+	"scripts": {
+		"dev": "node esbuild.config.mjs",
+		"build": "tsc -noEmit -skipLibCheck && node esbuild.config.mjs production",
+		"version": "node version-bump.mjs && git add manifest.json versions.json"
+	},
+	"keywords": [],
+	"author": "",
+	"license": "MIT",
+	"devDependencies": {
+		"@types/node": "^16.11.6",
+		"@typescript-eslint/eslint-plugin": "5.29.0",
+		"@typescript-eslint/parser": "5.29.0",
+		"builtin-modules": "3.3.0",
+		"esbuild": "0.17.3",
+		"obsidian": "latest",
+		"tslib": "2.4.0",
+		"typescript": "4.7.4"
+	}
+}

styles.css

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+/*
+
+This CSS file will be included with your plugin, and
+available in the app when your plugin is enabled.
+
+If your plugin does not need CSS, delete this file.
+
+*/

tsconfig.json

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+{
+  "compilerOptions": {
+    "baseUrl": ".",
+    "inlineSourceMap": true,
+    "inlineSources": true,
+    "module": "ESNext",
+    "target": "ES6",
+    "allowJs": true,
+    "noImplicitAny": true,
+    "moduleResolution": "node",
+    "importHelpers": true,
+    "isolatedModules": true,
+    "strictNullChecks": true,
+    "lib": [
+      "DOM",
+      "ES5",
+      "ES6",
+      "ES7"
+    ]
+  },
+  "include": [
+    "**/*.ts"
+  ]
+}

version-bump.mjs

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+import { readFileSync, writeFileSync } from "fs";
+
+const targetVersion = process.env.npm_package_version;
+
+// read minAppVersion from manifest.json and bump version to target version
+let manifest = JSON.parse(readFileSync("manifest.json", "utf8"));
+const { minAppVersion } = manifest;
+manifest.version = targetVersion;
+writeFileSync("manifest.json", JSON.stringify(manifest, null, "\t"));
+
+// update versions.json with target version and minAppVersion from manifest.json
+let versions = JSON.parse(readFileSync("versions.json", "utf8"));
+versions[targetVersion] = minAppVersion;
+writeFileSync("versions.json", JSON.stringify(versions, null, "\t"));

versions.json

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+{
+	"1.0.0": "0.15.0"
+}