# Ai Contextual Markdown Transformer (`audit-data-solutions/ai-contextual-markdown-transformer`) Actor
Convert messy HTML, broken data, and unformatted text into clean Markdown. Powered by Llama 3.3 via Groq. Expertly removes web-clutter and restores structure. Perfect for RAG, LLM training, and data cleaning. Ultra-fast, free, and accurate.
- **URL**: https://apify.com/audit-data-solutions/ai-contextual-markdown-transformer.md
- **Developed by:** [Quantix](https://apify.com/audit-data-solutions) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet
## Pricing
from $20.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## π AI Markdown Transformer β Professional Edition
β
**Apify Free Tier Friendly** | π° **$0.02 per result** | π€ **Llama 3.3 (70B) AI** | β‘ **Ultra-Fast**
---
#### π The "One-Click" Solution to Turn Scraper Data into Clean Reports
Modern teams drown in messy text: logs, scrapes, transcripts, and unstructured data. Whether you are using the **Google Maps Scraper**, **Website Content Crawler**, or **Social Media Scrapers**, the raw output is often a cluttered "data-dump."
The **AI Markdown Transformer** turns that chaos into clean, structured, **LLMβready Markdown** in milliseconds. Built for developers, data teams, and AI practitioners who need professional results without manual cleanup.
---
### π Use it for FREE every month
All Apify users receive **$5.00 in free monthly credits**. Weβve optimized this Actor to be extremely lean:
* **Startup cost:** Only **$0.01** (Flat rate)
* **Transformation cost:** Only **$0.02** per result
* **The Math:** You can process **200+ high-quality records** every month entirely within your free Apify allowance.
---
### β‘ Why This Tool Exists
Manual text cleaning is a bottleneck. This tool is the "final step" in your **web scraping pipeline**, bridging the gap between raw data and a professional AI-ready report.
#### βοΈ What It Does
* **Contextual Summarization:** Intelligently extracts the essence from Google Maps leads, TikTok transcripts, or Instagram captions.
* **Restores Structure:** Converts unorganized text into clean Markdown tables, headers, and lists.
* **RAG-Ready Output:** Strips HTML tags, UI clutter, and ads. Perfect for **Vector Databases** and AI knowledge bases.
* **MCP Compatible:** Seamlessly integrates with Model Context Protocol (MCP) workflows.
* **Ultra-Fast Performance:** Powered by **Groq LPU acceleration** for results in 20β80 ms.
---
### π Showcase: From Scraper Mess to Clean Data
**Input (Raw Unstructured Mess):**
> `[AD] [IMAGE: 4.5 Stars] "Best Pizza in NY" -- Open until 11PM -- "The crust was amazing!" (Review by John) -- Lat: 40.7128, Long: -74.0060 -- Phone: +1 212-555-0198 -- [BUTTON: ORDER NOW]`
**Output (Clean Professional Markdown):**
#### π Best Pizza in NY
| Rating | Status | Contact | Top Highlight |
| :--- | :--- | :--- | :--- |
| β 4.5 / 5 | Open until 23:00 | +1 212-555-0198 | "Amazing crust" |
---
### π Optimized for Top Scrapers
This Actor is specifically designed to post-process data from:
* π **Google Maps Scraper:** Clean B2B leads into executive-ready sales lists.
* π **Website Content Crawler:** Normalize scraped web pages for optimal RAG performance.
* π± **TikTok & Instagram Scrapers:** Clean up transcripts, captions, and comments by removing hashtags and emoji-clutter.
---
### π οΈ How to Integrate (Quick Start)
You can trigger this Actor manually via the Apify Console or programmatically via API.
#### Integration via Apify SDK (JavaScript)
Perfect for chaining this Actor to your existing scraper:
```javascript
const input = {
"task_prompt": "Clean this business data into a table",
"unstructured_text": "PASTE_YOUR_SCRAPER_OUTPUT_HERE"
};
// Run the Actor and wait for it to finish
const run = await client.actor("audit-data-solutions/ai-contextual-markdown-transformer").call(input);
// Fetch and print the clean Markdown results
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.markdown_output);
````
#### π Pro Tip: Automated Webhooks
Want to automate your workflow? Go to your **Source Scraper** (e.g., Google Maps Scraper) -> **Webhooks** -> **Ad-hoc Run** and set it to trigger this Transformer as soon as the scraping run succeeds!
***
### βοΈ Performance & Pricing
- **Inference**: Groq LPU acceleration (Llama-3.3-70B).
- **Speed**: Typically 20β80 ms per transformation.
- **Pricing**: $0.01 per start + $0.02 per result.
- **Free Tier**: Fully compatible with Apifyβs $5/month free credit.
### π¬ Support & Feedback
This tool is "Plug & Play". For the fastest experience, check the **Input** tab for examples. Found a bug or have a feature request? Please open an issue in the **Issues** tab.
### π License
**MIT License** β free for personal and commercial use.
***
The essential final step for every modern scraper user.
Clean your data. Save your time. Grow your business.
# Actor input Schema
## `content` (type: `string`):
Paste any messy data here: web scrapes, Google Maps leads, system logs, or unformatted notes.
## Actor input object example
```json
{
"content": "--- RAW DATA DUMP ---\n[AD] β 4.8 Stars | 'The Tech Hub' | Open until 8PM | Lat: 51.5074, Long: 0.1278 | Phone: +44 20 7946 0123\n\nStock Update Singapore Warehouse:\n- Quantum-X1 (SN-552) price: $1200 with 10% discount. Alex took 5 units.\n- Neural-Link v2 ($90 each) - 25 units sold today.\n\nWARNING: DB_SYNC_LATENCY_009 Detected at 14:00. Contact: admin@techhub.com\n----------------------"
}
```
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"content": `--- RAW DATA DUMP ---
[AD] β 4.8 Stars | 'The Tech Hub' | Open until 8PM | Lat: 51.5074, Long: 0.1278 | Phone: +44 20 7946 0123
Stock Update Singapore Warehouse:
- Quantum-X1 (SN-552) price: $1200 with 10% discount. Alex took 5 units.
- Neural-Link v2 ($90 each) - 25 units sold today.
WARNING: DB_SYNC_LATENCY_009 Detected at 14:00. Contact: admin@techhub.com
----------------------`
};
// Run the Actor and wait for it to finish
const run = await client.actor("audit-data-solutions/ai-contextual-markdown-transformer").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`πΎ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// π Want to learn more π? Go to β https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "content": """--- RAW DATA DUMP ---
[AD] β 4.8 Stars | 'The Tech Hub' | Open until 8PM | Lat: 51.5074, Long: 0.1278 | Phone: +44 20 7946 0123
Stock Update Singapore Warehouse:
- Quantum-X1 (SN-552) price: $1200 with 10% discount. Alex took 5 units.
- Neural-Link v2 ($90 each) - 25 units sold today.
WARNING: DB_SYNC_LATENCY_009 Detected at 14:00. Contact: admin@techhub.com
----------------------""" }
# Run the Actor and wait for it to finish
run = client.actor("audit-data-solutions/ai-contextual-markdown-transformer").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("πΎ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# π Want to learn more π? Go to β https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"content": "--- RAW DATA DUMP ---\\n[AD] β 4.8 Stars | '\''The Tech Hub'\'' | Open until 8PM | Lat: 51.5074, Long: 0.1278 | Phone: +44 20 7946 0123\\n\\nStock Update Singapore Warehouse:\\n- Quantum-X1 (SN-552) price: $1200 with 10% discount. Alex took 5 units.\\n- Neural-Link v2 ($90 each) - 25 units sold today.\\n\\nWARNING: DB_SYNC_LATENCY_009 Detected at 14:00. Contact: admin@techhub.com\\n----------------------"
}' |
apify call audit-data-solutions/ai-contextual-markdown-transformer --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=audit-data-solutions/ai-contextual-markdown-transformer",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Ai Contextual Markdown Transformer",
"description": "Convert messy HTML, broken data, and unformatted text into clean Markdown. Powered by Llama 3.3 via Groq. Expertly removes web-clutter and restores structure. Perfect for RAG, LLM training, and data cleaning. Ultra-fast, free, and accurate.",
"version": "1.0",
"x-build-id": "tiO3AGMBrDWKG2S1X"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/audit-data-solutions~ai-contextual-markdown-transformer/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-audit-data-solutions-ai-contextual-markdown-transformer",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/audit-data-solutions~ai-contextual-markdown-transformer/runs": {
"post": {
"operationId": "runs-sync-audit-data-solutions-ai-contextual-markdown-transformer",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/audit-data-solutions~ai-contextual-markdown-transformer/run-sync": {
"post": {
"operationId": "run-sync-audit-data-solutions-ai-contextual-markdown-transformer",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"content"
],
"properties": {
"content": {
"title": "Input Content",
"type": "string",
"description": "Paste any messy data here: web scrapes, Google Maps leads, system logs, or unformatted notes."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```