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Docs: Create a Python version of our ET Recipe for Content Moderation [Fixes #9909]
GITHUB_PR_NUMBER: 9931 GITHUB_PR_URL: https://github.com/hasura/graphql-engine/pull/9931 PR-URL: https://github.com/hasura/graphql-engine-mono/pull/10384 Co-authored-by: Rutam Prita Mishra <47860497+Rutam21@users.noreply.github.com> GitOrigin-RevId: f8a96186ad2c10e7a9c237f146298f4167debf08
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@ -134,6 +134,15 @@ Below, we've written an example of webhook in JavaScript that uses `body-parser`
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earlier, this runs on port `4000`. If you're attempting to run this locally, follow the instructions below. If you're
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running this in a hosted environment, use this code as a guide to write your own webhook.
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<Tabs
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defaultValue="javascript"
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values={[
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{ label: 'JavaScript', value: 'javascript' },
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{ label: 'Python', value: 'python' },
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]}
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>
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<TabItem value="javascript">
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Init a new project with `npm init` and install the following dependencies:
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```bash
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@ -279,8 +288,175 @@ app.listen(4000, () => {
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</details>
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You can run the server by running `node index.js` in your terminal. If you see the message
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`Webhook server is running on port 4000`, you're good to go!
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You can run the server by running `node index.js` in your terminal.
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</TabItem>
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<TabItem value="python">
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Make sure you have the necessary dependencies installed. You can use pip to install them:
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```bash
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pip install Flask[async] openai requests
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```
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<details>
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<summary>
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Then, create a new file called <code>index.py</code> and add the following code:
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</summary>
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```python
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from flask import Flask, request, jsonify
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import openai
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import requests
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import json
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app = Flask(__name__)
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# Hasura and OpenAI config
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config = {
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'url': '<YOUR_PROJECT_ENDPOINT>',
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'secret': '<YOUR_ADMIN_SECRET>',
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'openAIKey': '<YOUR_OPENAI_KEY>',
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}
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# OpenAI API config and client
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openai.api_key = config["openAIKey"]
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prompt = (
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"You are a content moderator for SuperStore.com. A customer has left a review for a product they purchased. "
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'Your response should only be a JSON object with two properties: "feedback" and "is_appropriate". '
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'The "feedback" property should be a string containing your response to the customer only if the review "is_appropriate" value is false. '
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"The feedback should be on why their review was flagged as inappropriate, not a response to their review. "
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'The "is_appropriate" property should be a boolean indicating whether or not the review contains inappropriate content and it should be set by you.'
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'"is_appropriate" is set to TRUE for appropriate content and to FALSE for inappropriate content.'
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"The review is as follows:"
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)
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# Send a request to ChatGPT to see if the review contains inappropriate content
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def check_review_with_chat_gpt(review_text):
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try:
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": prompt},
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{"role": "user", "content": review_text},
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],
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)
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response_content = response["choices"][0]["message"]["content"]
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return json.loads(response_content)
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except Exception as e:
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print(f"Error evaluating content: {review_text}")
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print(str(e))
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return None
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# Mark their review as visible if there's no feedback
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async def mark_review_as_visible(user_review, review_id):
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response = requests.post(
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config["url"],
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json={
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"query": """
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mutation UpdateReviewToVisible($review_id: uuid!) {
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update_reviews_by_pk(pk_columns: {id: $review_id}, _set: {is_visible: true}) {
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id
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}
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}
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""",
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"variables": {
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"review_id": review_id,
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},
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},
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headers={
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"Content-Type": "application/json",
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"x-hasura-admin-secret": config["secret"],
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},
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)
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print(f"🎉 Review approved: {user_review}")
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data = response.json()
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return data.get("update_reviews_by_pk", None)
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# Send a notification to the user if their review is flagged
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def send_notification(user_review, user_id, review_feedback):
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query = """
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mutation InsertNotification($user_id: uuid!, $review_feedback: String!) {
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insert_notifications_one(object: {user_id: $user_id, message: $review_feedback}) {
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id
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}
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}
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"""
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variables = {"user_id": user_id, "review_feedback": review_feedback}
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headers = {
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"Content-Type": "application/json",
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"x-hasura-admin-secret": config["secret"],
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}
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url = config["url"]
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request_body = {"query": query, "variables": variables}
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try:
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response = requests.post(url, json=request_body, headers=headers)
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# Raise an error for bad responses
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response.raise_for_status()
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response_json = response.json()
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if "errors" in response_json:
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# Handle the case where there are errors in the response
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print(f"Failed to send a notification for: {user_review}")
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print(response_json)
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return None
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# Extract the updated data from the response
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data = response_json.get("data", {})
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notification = data.get("insert_notifications_one", {})
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print(
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f"🚩 Review flagged. This is not visible to users: {user_review}\n🔔 The user has received the following notification: {review_feedback}"
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)
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return notification
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except Exception as e:
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# Handle exceptions or network errors
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print(f"Error sending a notification for: {user_review}")
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print(str(e))
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return None
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@app.route("/check-review", methods=["POST"])
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async def check_review():
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auth_header = request.headers.get("secret-authorization-string")
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if auth_header != "super_secret_string_123":
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return jsonify({"message": "Unauthorized"}), 401
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# Parse the review from the event payload
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data = request.get_json()
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user_review = data["event"]["data"]["new"]["text"]
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user_id = data["event"]["data"]["new"]["user_id"]
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review_id = data["event"]["data"]["new"]["id"]
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# Check the review with ChatGPT
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moderation_report = check_review_with_chat_gpt(user_review)
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# If the review is appropriate, mark it as visible; if not, send a notification to the user
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if moderation_report["is_appropriate"]:
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await mark_review_as_visible(user_review, review_id)
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else:
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send_notification(user_review, user_id, moderation_report["feedback"])
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return jsonify({"GPTResponse": moderation_report})
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if __name__ == "__main__":
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app.run(port=4000)
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```
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</details>
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You can run the server by running `python3 index.py` in your terminal.
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</TabItem>
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</Tabs>
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If you see the message `Webhook server is running on port 4000`, you're good to go!
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## Step 4: Test the setup
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