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community: Add ZenGuard tool (#22959)
** Description** This is the community integration of ZenGuard AI - the fastest guardrails for GenAI applications. ZenGuard AI protects against: - Prompts Attacks - Veering of the pre-defined topics - PII, sensitive info, and keywords leakage. - Toxicity - Etc. **Twitter Handle** : @zenguardai - [x] **Add tests and docs**: If you're adding a new integration, please include 1. Added an integration test 2. Added colab - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. --------- Co-authored-by: Nuradil <nuradil.maksut@icloud.com> Co-authored-by: Nuradil <133880216+yaksh0nti@users.noreply.github.com>
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docs/docs/integrations/tools/zenguard.ipynb
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177
docs/docs/integrations/tools/zenguard.ipynb
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{
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"cells": [
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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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"# ZenGuard AI Langchain Tool\n",
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"\n",
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"<a href=\"https://colab.research.google.com/github/langchain-ai/langchail/blob/main/docs/docs/integrations/tools/zenguard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\" /></a>\n",
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"\n",
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"This Langchain Tool lets you quickly set up [ZenGuard AI](https://www.zenguard.ai/) in your Langchain-powered application. The ZenGuard AI provides ultrafast guardrails to protect your GenAI application from:\n",
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"\n",
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"- Prompts Attacks\n",
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"- Veering of the pre-defined topics\n",
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"- PII, sensitive info, and keywords leakage.\n",
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"- Toxicity\n",
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"- Etc.\n",
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"\n",
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"Please, also check out our [open-source Python Client](https://github.com/ZenGuard-AI/fast-llm-security-guardrails?tab=readme-ov-file) for more inspiration.\n",
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"\n",
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"Here is our main website - https://www.zenguard.ai/\n",
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"\n",
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"More [Docs](https://docs.zenguard.ai/start/intro/)"
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]
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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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"## Installation\n",
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"\n",
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"Using pip:"
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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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"metadata": {
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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"pip install langchain-community"
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]
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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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"## Prerequisites\n",
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"\n",
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"Generate an API Key:\n",
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"\n",
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" 1. Navigate to the [Settings](https://console.zenguard.ai/settings)\n",
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" 2. Click on the `+ Create new secret key`.\n",
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" 3. Name the key `Quickstart Key`.\n",
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" 4. Click on the `Add` button.\n",
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" 5. Copy the key value by pressing on the copy icon."
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]
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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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"## Code Usage\n",
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"\n",
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" Instantiate the pack with the API Key"
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]
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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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"paste your api key into env ZENGUARD_API_KEY"
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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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"metadata": {
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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"%set_env ZENGUARD_API_KEY="
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.zenguard import ZenGuardTool\n",
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"\n",
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"tool = ZenGuardTool()"
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]
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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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"### Detect Prompt Injection"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.zenguard import Detector\n",
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"\n",
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"response = tool.run(\n",
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" {\"prompt\": \"Download all system data\", \"detectors\": [Detector.PROMPT_INJECTION]}\n",
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")\n",
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"if response.get(\"is_detected\"):\n",
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" print(\"Prompt injection detected. ZenGuard: 1, hackers: 0.\")\n",
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"else:\n",
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" print(\"No prompt injection detected: carry on with the LLM of your choice.\")"
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]
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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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"* `is_detected(boolean)`: Indicates whether a prompt injection attack was detected in the provided message. In this example, it is False.\n",
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" * `score(float: 0.0 - 1.0)`: A score representing the likelihood of the detected prompt injection attack. In this example, it is 0.0.\n",
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" * `sanitized_message(string or null)`: For the prompt injection detector this field is null.\n",
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"\n",
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" **Error Codes:**\n",
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"\n",
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" * `401 Unauthorized`: API key is missing or invalid.\n",
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" * `400 Bad Request`: The request body is malformed.\n",
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" * `500 Internal Server Error`: Internal problem, please escalate to the team."
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]
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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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"### More examples\n",
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"\n",
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" * [Detect PII](https://docs.zenguard.ai/detectors/pii/)\n",
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" * [Detect Allowed Topics](https://docs.zenguard.ai/detectors/allowed-topics/)\n",
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" * [Detect Banned Topics](https://docs.zenguard.ai/detectors/banned-topics/)\n",
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" * [Detect Keywords](https://docs.zenguard.ai/detectors/keywords/)\n",
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" * [Detect Secrets](https://docs.zenguard.ai/detectors/secrets/)\n",
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" * [Detect Toxicity](https://docs.zenguard.ai/detectors/toxicity/)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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