Add Go select-algorithm sample

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

@@ -0,0 +1,26 @@
+# Azure DocumentDB cluster name (find in Azure Portal > DocumentDB > Overview)
+MONGO_CLUSTER_NAME=your-cluster-name
+
+# Azure OpenAI embedding endpoint (find in Azure Portal > Azure OpenAI > Keys and Endpoint)
+AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+
+# Azure OpenAI embedding model deployment name
+AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+
+# Database name (default: Hotels)
+AZURE_DOCUMENTDB_DATABASENAME=Hotels
+
+# Path to pre-computed vectors JSON file (default: ../data/Hotels_Vector.json)
+DATA_FILE_WITH_VECTORS=../data/Hotels_Vector.json
+
+# Embedding dimensions (default: 1536)
+EMBEDDING_DIMENSIONS=1536
+
+# Field name containing embeddings in the data file
+EMBEDDED_FIELD=contentVector
+
+# Algorithm to test: all, diskann, hnsw, ivf (default: all)
+ALGORITHM=all
+
+# Similarity to test: COS, L2, IP (default: COS)
+SIMILARITY=COS

ai/select-algorithm-go/.gitignore

@@ -0,0 +1,19 @@
+# Build output
+build/
+*.exe
+*.exe~
+*.dll
+*.so
+*.dylib
+
+# Test binary
+*.test
+
+# Output
+*.out
+
+# Vendor
+vendor/
+
+# Environment
+.env

ai/select-algorithm-go/README.md

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+# Select algorithm - Go
+
+Compare DiskANN, HNSW, and IVF vector index algorithms across COS, L2, and IP similarity metrics using Azure DocumentDB.
+
+## Prerequisites
+
+- Go 1.22+
+- Azure DocumentDB cluster
+- Azure OpenAI resource with `text-embedding-3-small` deployment
+
+## Setup
+
+1. Copy `.env.example` to `.env` in this directory and fill in your values.
+2. Source the `.env` file into your shell (the app reads `os.Getenv` directly):
+
+```bash
+export $(grep -v '^#' .env | xargs)   # Linux / macOS
+```
+
+```powershell
+Get-Content .env | ForEach-Object { if ($_ -match '^\s*([^#][^=]+)=(.*)') { [System.Environment]::SetEnvironmentVariable($Matches[1].Trim(), $Matches[2].Trim()) } }
+```
+
+3. Install dependencies:
+
+```bash
+go mod download
+```
+
+## Usage
+
+### Compare all algorithms (default: COS similarity)
+
+```bash
+go run src/main.go
+```
+
+Set `ALGORITHM` and `SIMILARITY` env vars in `.env` to control which collections are queried:
+
+| ALGORITHM | SIMILARITY | Collections queried |
+|-----------|------------|---------------------|
+| `all`     | `COS`      | 3 (one per algorithm, COS) |
+| `all`     | `all`      | 9 (all combinations) |
+| `diskann` | `COS`      | 1 (hotels_diskann_cos) |
+| `diskann` | `all`      | 3 (diskann × all similarities) |
+
+## Architecture
+
+Creates collections per algorithm and similarity combination (3 algorithms × 3 distance metrics):
+
+| Algorithm | COS | L2 | IP |
+|-----------|-----|----|----|
+| DiskANN   | `hotels_diskann_cos` | `hotels_diskann_l2` | `hotels_diskann_ip` |
+| HNSW      | `hotels_hnsw_cos` | `hotels_hnsw_l2` | `hotels_hnsw_ip` |
+| IVF       | `hotels_ivf_cos` | `hotels_ivf_l2` | `hotels_ivf_ip` |
+
+Each collection gets its own vector index created via `db.RunCommand()` and data inserted via `InsertMany()`. The main script runs `$search` aggregation queries and prints a comparison table.
+
+## Expected output
+
+When you run the sample with `ALGORITHM=all` and `SIMILARITY=COS`, the console prints a comparison table similar to the following (exact timings and scores vary per run):
+
+```
+Vector Algorithm Comparison
+   Database: hotels
+   Algorithms: all
+   Similarity: COS
+   Collections to query: hotels_diskann_cos, hotels_hnsw_cos, hotels_ivf_cos
+   Search query: "luxury hotel with ocean view"
+
+Initializing MongoDB and Azure OpenAI clients...
+Loading data from data/hotels.json...
+Loaded 10 documents
+Generating query embedding...
+
+Processing collection: hotels_diskann_cos
+  Creating collection...
+  Creating vector index (diskann / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 42ms
+
+Processing collection: hotels_hnsw_cos
+  Creating collection...
+  Creating vector index (hnsw / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 38ms
+
+Processing collection: hotels_ivf_cos
+  Creating collection...
+  Creating vector index (ivf / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 35ms
+
++-------------------+-----------+--------+----------------------------------+
+| Collection        | Algorithm | Latency| Top Result                       |
++-------------------+-----------+--------+----------------------------------+
+| hotels_diskann_cos| diskann   |  42 ms | Oceanfront Resort (score: 0.87)  |
+| hotels_hnsw_cos   | hnsw      |  38 ms | Oceanfront Resort (score: 0.87)  |
+| hotels_ivf_cos    | ivf       |  35 ms | Oceanfront Resort (score: 0.86)  |
++-------------------+-----------+--------+----------------------------------+
+
+Done.
+```
+
+> Results vary based on cluster size, region latency, and data. The table format and column names are stable; the values are not.
+
+## Notes
+
+- Uses passwordless authentication with `DefaultAzureCredential` for both Azure OpenAI and DocumentDB
+- Environment variables are read directly via `os.Getenv`; source your `.env` file before running
+- Algorithm-specific index parameters:
+  - DiskANN: maxDegree=32, lBuild=50
+  - HNSW: m=16, efConstruction=64
+  - IVF: numLists=1
+- Algorithm-specific search parameters:
+  - DiskANN: lSearch=100
+  - HNSW: efSearch=80
+  - IVF: nProbes=1
+
+## Troubleshooting
+
+### Authentication with DefaultAzureCredential
+
+This sample uses `DefaultAzureCredential` for passwordless authentication to both Azure OpenAI and DocumentDB. Before running, sign in with the Azure CLI:
+
+```bash
+az login
+```
+
+`DefaultAzureCredential` tries multiple credential sources in order (environment variables, managed identity, Azure CLI, and others). For local development, the Azure CLI credential is typically used.
+
+### RBAC role requirements
+
+Your Azure identity needs the following roles:
+
+- **Cognitive Services OpenAI User** on the Azure OpenAI resource (for embedding generation).
+- A DocumentDB/Cosmos DB data-plane role that permits read and write operations on the target database. Consult your cluster's access control settings.
+
+### MongoDB OIDC connection errors
+
+If you see `MONGODB-OIDC` authentication failures:
+
+- Confirm the `MONGO_CLUSTER_NAME` environment variable is set correctly (cluster name only, not the full URI).
+- Verify your Azure identity has been granted access to the DocumentDB cluster.
+- Check that the token resource (`https://ossrdbms-aad.database.windows.net`) is correct for your cluster type.
+- Ensure network connectivity to the cluster (firewall rules, VNet configuration).
+
+### Common error codes
+
+| Error | Cause | Fix |
+|-------|-------|-----|
+| `failed to create Azure credential` | No valid Azure credential found | Run `az login` or configure a service principal |
+| `failed to connect to MongoDB` | Network or auth issue | Check cluster name, firewall rules, and RBAC |
+| `failed to generate embedding` | Azure OpenAI call failed | Verify endpoint URL, deployment name, and RBAC role |
+| `invalid ALGORITHM` / `invalid SIMILARITY` | Bad env var value | Use one of: `all`, `diskann`, `hnsw`, `ivf` / `all`, `COS`, `L2`, `IP` |

ai/select-algorithm-go/go.mod

@@ -0,0 +1,35 @@
+module documentdb-vector-samples
+
+go 1.22
+
+require (
+	github.com/Azure/azure-sdk-for-go/sdk/azcore v1.20.0
+	github.com/Azure/azure-sdk-for-go/sdk/azidentity v1.13.1
+	github.com/openai/openai-go/v3 v3.12.0
+	go.mongodb.org/mongo-driver v1.17.6
+)
+
+require (
+	github.com/Azure/azure-sdk-for-go/sdk/internal v1.11.2 // indirect
+	github.com/AzureAD/microsoft-authentication-library-for-go v1.6.0 // indirect
+	github.com/golang-jwt/jwt/v5 v5.3.0 // indirect
+	github.com/golang/snappy v1.0.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/klauspost/compress v1.18.2 // indirect
+	github.com/kylelemons/godebug v1.1.0 // indirect
+	github.com/montanaflynn/stats v0.7.1 // indirect
+	github.com/pkg/browser v0.0.0-20240102092130-5ac0b6a4141c // indirect
+	github.com/tidwall/gjson v1.18.0 // indirect
+	github.com/tidwall/match v1.2.0 // indirect
+	github.com/tidwall/pretty v1.2.1 // indirect
+	github.com/tidwall/sjson v1.2.5 // indirect
+	github.com/xdg-go/pbkdf2 v1.0.0 // indirect
+	github.com/xdg-go/scram v1.2.0 // indirect
+	github.com/xdg-go/stringprep v1.0.4 // indirect
+	github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78 // indirect
+	golang.org/x/crypto v0.46.0 // indirect
+	golang.org/x/net v0.48.0 // indirect
+	golang.org/x/sync v0.19.0 // indirect
+	golang.org/x/sys v0.39.0 // indirect
+	golang.org/x/text v0.32.0 // indirect
+)

ai/select-algorithm-go/go.sum

@@ -0,0 +1,96 @@
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+github.com/Azure/azure-sdk-for-go/sdk/azidentity v1.13.1 h1:Hk5QBxZQC1jb2Fwj6mpzme37xbCDdNTxU7O9eb5+LB4=
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