Added tests + docs + CI for Elasticsearch

docs/docs/providers/vector_io/remote_elasticsearch.mdx

@@ -0,0 +1,104 @@
+---
+description: |
+  [Elasticsearch](https://www.elastic.co/) is a vector database provider for Llama Stack.
+  It allows you to store and query vectors directly within an Elasticsearch database.
+  That means you're not limited to storing vectors in memory or in a separate service.
+
+  ## Features
+  Elasticsearch supports:
+  - Store embeddings and their metadata
+  - Vector search
+  - Full-text search
+  - Fuzzy search
+  - Hybrid search
+  - Document storage
+  - Metadata filtering
+  - Inference service
+  - Machine Learning integrations
+
+  ## Usage
+
+  To use Elasticsearch in your Llama Stack project, follow these steps:
+
+  1. Install the necessary dependencies.
+  2. Configure your Llama Stack project to use Elasticsearch.
+  3. Start storing and querying vectors.
+
+  ## Installation
+
+  You can test Elasticsearch locally by running this script in the terminal:
+
+  ```bash
+  curl -fsSL https://elastic.co/start-local | sh
+  ```
+
+  Or you can [start a free trial](https://www.elastic.co/cloud/cloud-trial-overview?utm_campaign=llama-stack-integration) on Elastic Cloud.
+  For more information on how to deploy Elasticsearch, see the [official documentation](https://www.elastic.co/docs/deploy-manage/deploy).
+
+  ## Documentation
+  See [Elasticsearch's documentation](https://www.elastic.co/docs/solutions/search) for more details about Elasticsearch in general.
+sidebar_label: Remote - Elasticsearch
+title: remote::elasticsearch
+---
+
+# remote::elasticsearch
+
+## Description
+
+
+[Elasticsearch](https://www.elastic.co/) is a vector database provider for Llama Stack.
+It allows you to store and query vectors directly within an Elasticsearch database.
+That means you're not limited to storing vectors in memory or in a separate service.
+
+## Features
+Elasticsearch supports:
+- Store embeddings and their metadata
+- Vector search
+- Full-text search
+- Fuzzy search
+- Hybrid search
+- Document storage
+- Metadata filtering
+- Inference service
+- Machine Learning integrations
+
+## Usage
+
+To use Elasticsearch in your Llama Stack project, follow these steps:
+
+1. Install the necessary dependencies.
+2. Configure your Llama Stack project to use Elasticsearch.
+3. Start storing and querying vectors.
+
+## Installation
+
+You can test Elasticsearch locally by running this script in the terminal:
+
+```bash
+curl -fsSL https://elastic.co/start-local | sh
+```
+
+Or you can [start a free trial](https://www.elastic.co/cloud/cloud-trial-overview?utm_campaign=llama-stack-integration) on Elastic Cloud.
+For more information on how to deploy Elasticsearch, see the [official documentation](https://www.elastic.co/docs/deploy-manage/deploy).
+
+## Documentation
+See [Elasticsearch's documentation](https://www.elastic.co/docs/solutions/search) for more details about Elasticsearch in general.
+
+
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `api_key` | `str \| None` | No |  | The API key for the Elasticsearch instance |
+| `hosts` | `str \| None` | No | localhost:9200 | The URL of the Elasticsearch instance |
+| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No |  | Config for KV store backend (SQLite only for now) |
+
+## Sample Configuration
+
+```yaml
+hosts: ${env.ELASTICSEARCH_API_KEY:=None}
+api_key: ${env.ELASTICSEARCH_URL:=localhost:9200}
+persistence:
+  namespace: vector_io::elasticsearch
+  backend: kv_default
+```