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add cell to top of notebooks
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‎docs/examples/bug_free_python_code.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://reflex/valid_python"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"\n",
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"In short, we want to make sure that the code can be executed without any errors.\n",
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"\n",
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"## Step 0: Install the necessary validators from Guardrails Hub\n",
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"\n",
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"We first have to install the ```ValidPython``` validator from Guardrails Hub:\n",
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"\n",
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"```guardrails hub install hub://reflex/valid_python```\n",
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"\n",
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"## Step 1: Generating `RAIL` Spec\n",
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"\n",
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"Ordinarily, we could create a separate `RAIL` spec in a file. However, for the sake of this example, we will generate the `RAIL` spec in the notebook as a string. We will also show the same RAIL spec in a code-first format using a Pydantic model."
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.17"
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"version": "3.12.2"
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},
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"orig_nbformat": 4,
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"vscode": {

‎docs/examples/competitors_check.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/competitor_check\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},

‎docs/examples/extracting_entities.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/valid_length\n",
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"!guardrails hub install hub://guardrails/two_words\n",
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"!guardrails hub install hub://guardrails/valid_range"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"\n",
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"We want to extract structured information about all fees and interest rates associated with the Chase credit card.\n",
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"\n",
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"## Step 0: Download PDF and load it as string and Install Validators from Guardrails Hub \n",
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"## Step 0: Download PDF and load it as string\n",
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"\n",
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"To get started, download the document from [here](https://github.com/ShreyaR/guardrails/blob/main/docs/examples/data/chase_card_agreement.pdf) and save it in `data/chase_card_agreement.pdf`.\n",
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"\n",
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"Guardrails has some built-in functions to help with common tasks. Here, we will use the `read_pdf` function to load the PDF as a string.\n",
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"\n",
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"We also need to install the necessary validators from Guardrails Hub: \n",
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"\n",
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"1. LowerCase: ```guardrails hub install hub://guardrails/lowercase```\n",
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"\n",
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"2. TwoWords: ```guardrails hub install hub://guardrails/two_words```\n",
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"\n",
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"3. OneLine: ```guardrails hub install hub://guardrails/one_line```"
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"Guardrails has some built-in functions to help with common tasks. Here, we will use the `read_pdf` function to load the PDF as a string."
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]
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},
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{

‎docs/examples/generate_structured_data.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://reflex/valid_python\n",
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"!guardrails hub install hub://guardrails/two_words\n",
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"!guardrails hub install hub://guardrails/valid_range"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"3. The number of orders associated with each user should be between 0 and 50.\n",
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"4. Each user should have a most recent order date.\n",
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"\n",
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"## Step 0: Install the necessary validators from Guardrails Hub\n",
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"\n",
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"We first have to install the necessary validators from Guardrails Hub:\n",
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"\n",
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"1. ValidLength: ```guardrails hub install hub://reflex/valid_python```\n",
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"\n",
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"2. TwoWords: ```guardrails hub install hub://guardrails/two_words```\n",
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"\n",
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"3. ValidRange: ```guardrails hub install hub://guardrails/valid_range```\n",
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"\n",
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"## Step 1: Generating `RAIL` Spec\n",
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"\n",
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"Ordinarily, we could create a separate `RAIL` spec in a file. However, for the sake of this example, we will generate the `RAIL` spec in the notebook as a string. We will also show the same RAIL spec in a code-first format using a Pydantic model."

‎docs/examples/guardrails_with_chat_models.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/lowercase\n",
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"!guardrails hub install hub://guardrails/two_words\n",
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"!guardrails hub install hub://guardrails/one_line"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"\n",
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"We retry the [entity extraction example](./extracting_entities.ipynb) using a chat model.\n",
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"\n",
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"## Step 0: Download PDF and load it as string and Install Validators from Guardrails Hub\n",
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"## Step 0: Download PDF and load it as string\n",
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"\n",
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"To get started, download the document from [here](https://github.com/guardrails-ai/guardrails/blob/main/docs/examples/data/chase_card_agreement.pdf) and save it in `data/chase_card_agreement.pdf`.\n",
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"\n",
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"Guardrails has some built-in functions to help with common tasks. Here, we will use the `read_pdf` function to load the PDF as a string.\n",
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"\n",
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"We also need to install the necessary validators from Guardrails Hub: \n",
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"\n",
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"1. LowerCase: ```guardrails hub install hub://guardrails/lowercase```\n",
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"\n",
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"2. TwoWords: ```guardrails hub install hub://guardrails/two_words```\n",
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"\n",
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"3. OneLine: ```guardrails hub install hub://guardrails/one_line```"
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"Guardrails has some built-in functions to help with common tasks. Here, we will use the `read_pdf` function to load the PDF as a string."
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]
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},
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{

‎docs/examples/input_validation.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/two_words"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Input Validation\n",
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"\n",
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"Guardrails supports validating inputs (prompts, instructions, msg_history) with string validators.\n",
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"\n",
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"We first have to install the ```TwoWords``` validator from Guardrails Hub: \n",
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"\n",
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"```guardrails hub install hub://guardrails/two_words```"
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"Guardrails supports validating inputs (prompts, instructions, msg_history) with string validators."
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]
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},
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{

‎docs/examples/provenance.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/provenance_embeddings\n",
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"!guardrails hub install hub://guardrails/provenance_llm"
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]
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},
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"attachments": {},
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"cell_type": "markdown",
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"For the sake of this post, let's use the following configuration for our validator:\n",
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"\n",
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"1. `sources`: List of strings, each representing a source.\n",
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"2. `embed function`: A function that takes a list of strings of length k and returns a 2D numpy array of shape (k, embedding_dim).\n",
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"\n",
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"We first have to install the ```ProvenanceEmbeddings``` validator from Guardrails Hub: \n",
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"\n",
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"```guardrails hub install hub://guardrails/provenance_embeddings```"
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"2. `embed function`: A function that takes a list of strings of length k and returns a 2D numpy array of shape (k, embedding_dim)."
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]
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},
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{
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"\n",
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"- Set the OPENAI_API_KEY environment variable: os.environ[\"OPENAI_API_KEY\"] = \"[OpenAI_API_KEY]\"\n",
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"- Set the OPENAI_API_KEY using openai.api_key=\"[OpenAI_API_KEY]\"\n",
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"- Pass the api_key as a parameter to the parse function as done below, in this example\n",
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"\n",
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"We first install the ```ProvenanceLLM``` validator from Guardrails Hub: \n",
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"\n",
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"```guardrails hub install hub://guardrails/provenance_llm```"
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"- Pass the api_key as a parameter to the parse function as done below, in this example"
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]
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},
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{

‎docs/examples/regex_validation.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/regex_match"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"regex = '\\\\d{3}-\\\\d{3}-\\\\d{4}'"
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]
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Install the ```RegexMatch``` validator from Guardrails Hub \n",
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"\n",
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"```guardrails hub install hub://guardrails/regex_match```"
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"cell_type": "markdown",
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"metadata": {},

‎docs/examples/response_is_on_topic.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://tryolabs/restricttotopic"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"The argument `model` indicates the model that will be used to classify the topic. See a list of all models [here](https://huggingface.co/models?pipeline_tag=zero-shot-classification&sort=trending)."
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install the ```RestrictToTopic``` validator from Guardrails Hub \n",
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"\n",
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"```guardrails hub install hub://tryolabs/restricttotopic```\n"
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"cell_type": "markdown",
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"metadata": {},

‎docs/examples/secrets_detection.ipynb‎

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"cells": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!guardrails hub install hub://guardrails/secrets_present"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"! pip install detect-secrets -q"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We also need to install the ```SecretsPresent``` validator from Guardrails Hub: \n",
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"\n",
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"```guardrails hub install hub://guardrails/secrets_present```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

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