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qdrant/README.md

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+# Qdrant
+
+High-performance vector database for AI/ML applications:
+
+- **Vector Search**: Fast similarity search with multiple distance metrics (Cosine, Euclidean, Dot Product)
+- **Rich Filtering**: Combine vector similarity with payload-based filtering
+- **Scalable**: Horizontal scaling for large-scale vector collections
+- **RESTful API**: Simple HTTP API for vector operations
+- **Secure Authentication**: API key-based authentication with Vault integration
+- **High Availability**: Built-in replication and fault tolerance
+
+## Installation
+
+```bash
+just qdrant::install
+```
+
+During installation, you will be prompted for:
+
+- **Qdrant host (FQDN)**: The domain name for accessing Qdrant (e.g., `qdrant.yourdomain.com`)
+
+The installation automatically:
+
+- Generates API keys (read-write and read-only)
+- Stores keys in Vault (if External Secrets is available) or Kubernetes Secrets
+- Configures Traefik ingress with TLS
+
+## Access
+
+Access Qdrant at `https://qdrant.yourdomain.com` using the API keys.
+
+### Get API Keys
+
+```bash
+# Get read-write API key
+just qdrant::get-api-key
+
+# Get read-only API key
+just qdrant::get-readonly-api-key
+```
+
+## Testing & Health Check
+
+Qdrant includes built-in testing recipes that use telepresence to access the service from your local machine.
+
+### Prerequisites
+
+Ensure telepresence is connected:
+
+```bash
+telepresence connect
+```
+
+### Health Check
+
+```bash
+just qdrant::health-check
+```
+
+Checks if Qdrant is running and responding to requests.
+
+### Vector Operations Test
+
+```bash
+just qdrant::test
+```
+
+Runs a complete test suite that:
+
+1. Creates a test collection with 4-dimensional vectors
+2. Adds sample points (cities with vector embeddings)
+3. Performs similarity search
+4. Cleans up the test collection
+
+Example output:
+
+```
+Testing Qdrant at http://qdrant.qdrant.svc.cluster.local:6333
+Using collection: test_collection_1760245249
+
+1. Creating collection...
+{
+  "result": true,
+  "status": "ok"
+}
+
+2. Adding test points...
+{
+  "result": {
+    "operation_id": 0,
+    "status": "completed"
+  },
+  "status": "ok"
+}
+
+3. Searching for similar vectors...
+{
+  "result": [
+    {
+      "id": 2,
+      "score": 0.99,
+      "payload": {"city": "London"}
+    }
+  ],
+  "status": "ok"
+}
+
+Test completed successfully!
+```
+
+## Using Qdrant
+
+### REST API
+
+Qdrant provides a RESTful API for all operations. Here are some common examples:
+
+#### Create a Collection
+
+```bash
+curl -X PUT "https://qdrant.yourdomain.com/collections/my_collection" \
+  -H "api-key: YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "vectors": {
+      "size": 384,
+      "distance": "Cosine"
+    }
+  }'
+```
+
+#### Insert Vectors
+
+```bash
+curl -X PUT "https://qdrant.yourdomain.com/collections/my_collection/points" \
+  -H "api-key: YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "points": [
+      {
+        "id": 1,
+        "vector": [0.1, 0.2, ...],
+        "payload": {"text": "example document"}
+      }
+    ]
+  }'
+```
+
+#### Search Similar Vectors
+
+```bash
+curl -X POST "https://qdrant.yourdomain.com/collections/my_collection/points/search" \
+  -H "api-key: YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "vector": [0.15, 0.25, ...],
+    "limit": 10
+  }'
+```
+
+### Python Client
+
+```python
+from qdrant_client import QdrantClient
+
+# Connect to Qdrant
+client = QdrantClient(
+    url="https://qdrant.yourdomain.com",
+    api_key="YOUR_API_KEY"
+)
+
+# Create collection
+client.create_collection(
+    collection_name="my_collection",
+    vectors_config={"size": 384, "distance": "Cosine"}
+)
+
+# Insert vectors
+client.upsert(
+    collection_name="my_collection",
+    points=[
+        {
+            "id": 1,
+            "vector": [0.1, 0.2, ...],
+            "payload": {"text": "example document"}
+        }
+    ]
+)
+
+# Search
+results = client.search(
+    collection_name="my_collection",
+    query_vector=[0.15, 0.25, ...],
+    limit=10
+)
+```
+
+### JupyterHub Integration
+
+Store your API key securely in Vault using the buunstack package:
+
+```python
+from buunstack import SecretStore
+
+secrets = SecretStore()
+secrets.put('qdrant', api_key='YOUR_API_KEY')
+
+# Later, retrieve it
+api_key = secrets.get('qdrant', field='api_key')
+```
+
+## Use Cases
+
+### Vector Embeddings Search
+
+Store and search document, image, or audio embeddings for:
+
+- Semantic search
+- Recommendation systems
+- Duplicate detection
+- Content-based filtering
+
+### RAG (Retrieval-Augmented Generation)
+
+Use Qdrant as the vector store for LLM applications:
+
+- Store document chunks with embeddings
+- Retrieve relevant context for LLM prompts
+- Build knowledge bases with semantic search
+
+### Similarity Matching
+
+Find similar items based on learned representations:
+
+- Image similarity search
+- Product recommendations
+- Anomaly detection
+- Clustering and classification
+
+## Cleanup
+
+To remove all Qdrant resources and secrets from Vault:
+
+```bash
+just qdrant::cleanup
+```
+
+This will prompt for confirmation before deleting the Vault secrets.
+
+## Uninstallation
+
+```bash
+just qdrant::uninstall
+```
+
+This will:
+
+- Uninstall the Qdrant Helm release
+- Delete API keys secrets
+- Remove the Qdrant namespace
+
+## Documentation
+
+For more information, see the official Qdrant documentation:
+
+- [Qdrant Documentation](https://qdrant.tech/documentation/)
+- [REST API Reference](https://qdrant.tech/documentation/api-reference/)
+- [Python Client](https://github.com/qdrant/qdrant-client)