Add Python select-algorithm sample

ai/select-algorithm-python/.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+.venv/
+venv/
+
+# Environment
+.env

ai/select-algorithm-python/README.md

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+# DocumentDB Vector Algorithm Comparison - Python
+
+Compare DocumentDB vector index algorithms (DiskANN, HNSW, IVF) across similarity functions (cosine, L2, inner product).
+
+## Prerequisites
+
+- Python 3.10+
+- Azure DocumentDB cluster with vector search enabled
+- Azure OpenAI service with embedding model deployed
+- Azure CLI authenticated (`az login`)
+- DocumentDB `dbOwner` role on the target database
+- `Cognitive Services OpenAI User` role on the Azure OpenAI resource
+
+## Setup
+
+1. Create and activate a virtual environment:
+```bash
+python -m venv .venv
+
+# Windows
+.venv\Scripts\activate
+
+# macOS/Linux
+source .venv/bin/activate
+```
+
+2. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+3. Configure environment variables:
+```bash
+cp .env.example .env
+# Edit .env with your Azure resource details
+```
+
+4. Ensure vector data exists:
+```bash
+# Data file should be at: ../../data/Hotels_Vector.json
+```
+
+## Usage
+
+Run all algorithms with all similarity functions:
+```bash
+python src/select_algorithm.py
+```
+
+Run specific algorithm:
+```bash
+ALGORITHM=diskann python src/select_algorithm.py
+```
+
+Run specific similarity function:
+```bash
+SIMILARITY=L2 python src/select_algorithm.py
+```
+
+Run specific combination:
+```bash
+ALGORITHM=hnsw SIMILARITY=IP python src/select_algorithm.py
+```
+
+## Environment Variables
+
+- `ALGORITHM`: Algorithm to test (`all`, `diskann`, `hnsw`, `ivf`)
+- `SIMILARITY`: Similarity function (`all`, `COS`, `L2`, `IP`)
+- `MONGO_CLUSTER_NAME`: DocumentDB cluster name
+- `AZURE_OPENAI_EMBEDDING_ENDPOINT`: Azure OpenAI endpoint
+- `AZURE_OPENAI_EMBEDDING_MODEL`: Embedding model deployment name
+
+## Expected output
+
+The script creates collections per algorithm/similarity combo, runs vector search, and prints a comparison table showing:
+- Algorithm and similarity function used
+- Top search result
+- Search score
+- Query latency in milliseconds
+
+```
+Vector Algorithm Comparison
+   Database: Hotels
+   Algorithms: all
+   Similarity: cos
+   Collections to query: hotels_diskann_cos, hotels_hnsw_cos, hotels_ivf_cos
+   Search query: "quintessential lodging near running trails, eateries, retail"
+
+Initializing MongoDB and Azure OpenAI clients...
+Loading data from ...
+Loaded [N] documents
+Generating query embedding...
+Query embedding: 1536 dimensions
+
+--- DiskANN / COS ---
+Collection: hotels_diskann_cos
+Created collection: hotels_diskann_cos
+Inserted: [N]/[N]
+Created vector index: vectorIndex_diskann_cos
+Executing vector search...
+Success: 5 results, [time]ms
+
+--- HNSW / COS ---
+...
+
+--- IVF / COS ---
+...
+
+=== Vector Search Comparison ===
+[Table of results with Algorithm, Similarity, Top Result, Score, and Latency columns]
+[Results vary based on data and cluster configuration]
+```
+
+## Troubleshooting
+
+### Authentication failures
+
+This sample uses `DefaultAzureCredential` for passwordless authentication. If you see authentication errors:
+
+- Run `az login` to authenticate with Azure CLI.
+- Verify your account has access to the DocumentDB cluster and Azure OpenAI resource.
+- If using a managed identity, ensure the identity is assigned to the resource.
+
+### Missing environment variables
+
+If the script fails at startup, verify all required variables are set in your `.env` file. Copy `.env.example` as a starting point and fill in each value.
+
+### pymongo connection issues
+
+- Verify the `MONGO_CLUSTER_NAME` value matches your DocumentDB cluster name.
+- Ensure network access is enabled for your IP in the DocumentDB cluster firewall settings.
+- Check that the cluster is running and not paused.
+
+### DocumentDB RBAC permissions
+
+Vector search requires read/write permissions on the target database. Ensure your identity has the appropriate DocumentDB RBAC role assigned, such as `dbOwner` or a custom role with `createCollection`, `createIndex`, and `find` actions.

ai/select-algorithm-python/pyproject.toml

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+[project]
+name = "documentdb-select-algorithm"
+version = "1.0.0"
+requires-python = ">=3.10"
+description = "Compare DocumentDB vector index algorithms"
+license = { text = "MIT" }
+authors = [{ name = "Microsoft Corporation" }]
+readme = "README.md"

ai/select-algorithm-python/requirements.txt

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+# MongoDB driver for connecting to Azure DocumentDB
+pymongo==4.6.0
+
+# Azure OpenAI SDK for generating embeddings
+openai==1.55.3
+
+# Azure authentication library for passwordless connection
+azure-identity==1.15.0
+
+# Environment variable management from .env files
+python-dotenv==1.0.0