Add TypeScript select-algorithm sample

ai/select-algorithm-typescript/.env.example

@@ -0,0 +1,27 @@
+# ========================================
+# Azure OpenAI Embedding Settings
+# ========================================
+AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
+AZURE_OPENAI_EMBEDDING_ENDPOINT=https://<RESOURCE-NAME>.openai.azure.com
+
+# ========================================
+# Data File Paths and Vector Configuration
+# ========================================
+DATA_FILE_WITH_VECTORS=../data/Hotels_Vector.json
+EMBEDDED_FIELD=DescriptionVector
+EMBEDDING_DIMENSIONS=1536
+LOAD_SIZE_BATCH=100
+
+# ========================================
+# MongoDB/DocumentDB Connection Settings
+# ========================================
+MONGO_CLUSTER_NAME=<CLUSTER-NAME>
+
+# ========================================
+# Algorithm Selection (used by select-algorithm.ts)
+# ========================================
+# ALGORITHM: "all" | "diskann" | "hnsw" | "ivf"
+ALGORITHM=all
+# SIMILARITY: "all" | "COS" | "L2" | "IP"
+SIMILARITY=COS

ai/select-algorithm-typescript/README.md

@@ -0,0 +1,61 @@
+# Select Algorithm - TypeScript
+
+Compare DiskANN, HNSW, and IVF vector index algorithms across COS, L2, and IP similarity metrics using Azure DocumentDB.
+
+## Prerequisites
+
+- Node.js 20+
+- Azure DocumentDB cluster
+- Azure OpenAI resource with `text-embedding-3-small` deployment
+
+## Setup
+
+1. Copy `.env.example` to `../../.env` (repo root) and fill in your values.
+2. Install dependencies:
+
+```bash
+npm install
+```
+
+## Usage
+
+### Compare all algorithms (default: COS similarity)
+
+```bash
+npm start
+```
+
+Set `ALGORITHM` and `SIMILARITY` env vars in `.env` to control which collections are queried:
+
+| ALGORITHM | SIMILARITY | Collections queried |
+|-----------|------------|---------------------|
+| `all`     | `COS`      | 3 (one per algorithm, COS) |
+| `all`     | `all`      | 9 (all combinations) |
+| `diskann` | `COS`      | 1 (hotels_diskann_cos) |
+| `diskann` | `all`      | 3 (diskann × all similarities) |
+
+### Run single algorithm
+
+```bash
+npm run start:diskann
+npm run start:hnsw
+npm run start:ivf
+```
+
+### Verify indexes
+
+```bash
+npm run start:show-indexes
+```
+
+## Architecture
+
+Creates 9 collections (3 algorithms × 3 distance metrics):
+
+| Algorithm | COS | L2 | IP |
+|-----------|-----|----|----|
+| DiskANN   | `hotels_diskann_cos` | `hotels_diskann_l2` | `hotels_diskann_ip` |
+| HNSW      | `hotels_hnsw_cos` | `hotels_hnsw_l2` | `hotels_hnsw_ip` |
+| IVF       | `hotels_ivf_cos` | `hotels_ivf_l2` | `hotels_ivf_ip` |
+
+Each collection gets its own vector index created via `db.command()` and data inserted via `insertMany()`. The main script runs `$search` aggregation queries and prints a comparison table.