### Develop RAG Playground Examples Locally

Source: https://github.com/poloclub/mememo/blob/main/README.md

These commands provide instructions for setting up and running the RAG Playground examples: cloning the repository, navigating to the example directory, installing its dependencies, and starting the development server.

```Bash
git clone git@github.com:poloclub/mememo.git
cd ./examples/rag-playground
npm install
npm run dev
```

--------------------------------

### Install RAG Playground Example Dependencies

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command installs all necessary Node.js dependencies specifically for the RAG Playground example application.

```bash
npm install
```

--------------------------------

### Run RAG Playground Development Server

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command starts the development server for the RAG Playground example, typically making it accessible via localhost:3000 in a web browser.

```bash
npm run dev
```

--------------------------------

### Develop MeMemo Library Locally

Source: https://github.com/poloclub/mememo/blob/main/README.md

These commands outline the steps to set up the MeMemo development environment: cloning the repository, installing dependencies, and running unit tests using Vitest.

```Bash
git clone git@github.com:poloclub/mememo.git
npm install
npm run test
```

--------------------------------

### Install MeMemo Development Dependencies

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command installs all required Node.js dependencies for developing the MeMemo library itself.

```bash
npm install
```

--------------------------------

### Navigate to RAG Playground Example Directory

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command changes the current working directory to the 'rag-playground' example folder within the MeMemo repository.

```bash
cd ./examples/rag-playground
```

--------------------------------

### Install MeMemo JavaScript Library

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command installs the MeMemo library using npm, making it available for use in both browser and Node.js environments.

```bash
npm install mememo
```

--------------------------------

### Install MeMemo Library

Source: https://github.com/poloclub/mememo/blob/main/README.md

This command installs the MeMemo JavaScript library using npm, making it available for both browser and Node.js environments.

```Bash
npm install mememo
```

--------------------------------

### Run MeMemo Unit Tests with Vitest

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command executes the unit tests for the MeMemo library using Vitest, ensuring the code's functionality and quality.

```bash
npm run test
```

--------------------------------

### Clone MeMemo GitHub Repository

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This command clones the entire MeMemo project repository from GitHub, providing access to the source code for development.

```bash
git clone git@github.com:poloclub/mememo.git
```

--------------------------------

### Perform Vector Search and Storage with HNSW

Source: https://github.com/poloclub/mememo/blob/main/README.md

This TypeScript code demonstrates how to import the HNSW class, create a new vector index, insert elements in batches, and query for k-nearest neighbors using a specified distance function (e.g., cosine).

```TypeScript
// Import the HNSW class from the MeMemo module
import { HNSW } from 'mememo';

// Creating a new index
const index = new HNSW({ distanceFunction: 'cosine' });

// Inserting elements into our index in batches
let keys: string[];
let values: number[][];
await index.bulkInsert(keys, values);

// Find k-nearest neighbors
let query: number[];
const { keys, distances } = await index.query(query, k);
```

--------------------------------

### Perform Vector Search and Storage with MeMemo HNSW

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This TypeScript code demonstrates how to initialize an HNSW index with a specified distance function, insert elements in batches, and perform approximate nearest neighbor queries to find similar vectors.

```typescript
// Import the HNSW class from the MeMemo module
import { HNSW } from 'mememo';

// Creating a new index
const index = new HNSW({ distanceFunction: 'cosine' });

// Inserting elements into our index in batches
let keys: string[];
let values: number[][];
await index.bulkInsert(keys, values);

// Find k-nearest neighbors
let query: number[];
const { keys, distances } = await index.query(query, k);
```

--------------------------------

### Cite MeMemo Research Paper

Source: https://github.com/poloclub/mememo/blob/main/README.md

This BibTeX entry provides the citation information for the MeMemo research paper published at SIGIR'24, useful for academic references.

```BibTeX
@inproceedings{wangMeMemoOndeviceRetrieval2024,
  title = {{{MeMemo}}: {{On-device Retrieval Augmentation}} for {{Private}} and {{Personalized Text Generation}}},
  booktitle = {Proceedings of the 47th {{International ACM SIGIR Conference}} on {{Research}} and {{Development}} in {{Information Retrieval}}},
  author = {Wang, Zijie J. and Chau, Duen Horng},
  year = {2024},
  urldate = {2024-06-26},
  langid = {english}
}
```

--------------------------------

### MeMemo Research Paper BibTeX Citation

Source: https://github.com/poloclub/mememo/blob/main/examples/rag-playground/README.md

This BibTeX entry provides the academic citation details for the MeMemo research paper published at SIGIR'24, suitable for inclusion in academic documents.

```bibtex
@inproceedings{wangMeMemoOndeviceRetrieval2024,
  title = {{{MeMemo}}: {{On-device Retrieval Augmentation}} for {{Private}} and {{Personalized Text Generation}}},
  booktitle = {Proceedings of the 47th {{International ACM SIGIR Conference}} on {{Research}} and {{Development}} in {{Information Retrieval}}},
  author = {Wang, Zijie J. and Chau, Duen Horng},
  year = {2024},
  urldate = {2024-06-26},
  langid = {english}
}
```

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