Quickstart Hub Install Details

docs/getting_started/quickstart.md

@@ -16,23 +16,40 @@ First, install Guardrails for your desired language:
 pip install guardrails-ai
 ```
 
+### Generate an API Key (required)
+Next, get a free API key from [Guardrails Hub](https://hub.guardrailsai.com/keys).  Copy and keep track of this API key because you'll use it in the next step to configure the CLI.
+
 ### Configure the Guardrails CLI (required)
 
-First, get a free auth key from [Guardrails Hub](https://hub.guardrailsai.com/keys). Then, configure the Guardrails CLI with the auth key.
+Configure the Guardrails CLI with the command:
 
 ```bash
 guardrails configure
 ```
 
-## Usage
+The configuration process will ask you three questions:
+
+1. Whether you want to enable metrics reporting.
+2. Whether you want to use our hosted remote inference endpoints for validators that utilize large ML models.
+3. To enter the API key you generated in the previous step.  This is required in order to install validators in the next step.
 
-1. Install a guardrail from [Guardrails Hub](https://hub.guardrailsai.com).
 
+### Install a Validator from the Guardrails Hub
+In order to perform any validation on LLM output with Guardrails, you will need to install an appropriate validator for your use case from the [Guardrails Hub](https://hub.guardrailsai.com).  Each validator has a unique and identifying Hub URI that can be used in the Guardrails CLI to install it into your current environment.  You can find the exact install command for a validator on it's details page in the [Guardrails Hub](https://hub.guardrailsai.com).  For example, the [Detect PII](https://hub.guardrailsai.com/validator/guardrails/detect_pii) validator can be installed via:
+
+    ```bash
+    guardrails hub install hub://guardrails/detect_pii
+    ```
+
+## Usage
+1. Create a Guard with an installed validator.
+    First, install the validator you want to use from the Guardrails Hub:
+
     ```bash
     guardrails hub install hub://guardrails/regex_match --quiet
     ```
-2. Create a Guard from the installed guardrail.
 
+    Next, you can import this validator from the `guardrails.hub` module and use it to construct a Guard.
     ```python
     # Import Guard and Validator
     from guardrails.hub import RegexMatch
@@ -49,14 +66,14 @@ guardrails configure
         .validation_passed
     )  # Guardrail Fails
     ```
-3. Run multiple guardrails within a Guard.
-    First, install the necessary guardrails from Guardrails Hub.
+2. Run multiple validators within a Guard.
+    First, install the necessary validators from Guardrails Hub.
 
     ```bash
-    guardrails hub install hub://guardrails/valid_length --quiet
+    guardrails hub install hub://guardrails/regex_match hub://guardrails/valid_length --quiet
     ```
 
-    Then, create a Guard from the installed guardrails.
+    Then, create a Guard from the installed validators.
 
     ```python
     from guardrails.hub import RegexMatch, ValidLength

docs/integrations/llama_index.ipynb

@@ -19,34 +19,14 @@
     "Install LlamaIndex and a version of Guardrails with LlamaIndex support."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found existing installation: guardrails-ai 0.6.0\n",
-      "Uninstalling guardrails-ai-0.6.0:\n",
-      "  Successfully uninstalled guardrails-ai-0.6.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "! pip uninstall guardrails-ai -y"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "! pip install llama-index -q\n",
-    "# ! pip install \"guardrails-ai>=0.6.1\"\n",
-    "! pip install /Users/calebcourier/Projects/guardrails -q"
+    "! pip install \"guardrails-ai>=0.6.1\" -q"
    ]
   },
   {