# AI Content Pipeline (Universal RAG Bundle) (`toumi.oussema.re/ai-content-pipeline`) Actor

Ingère une liste de sources mixtes (pages web, PDF, vidéos YouTube) en une seule fois, détecte automatiquement le type de chaque source, et retourne des chunks + embeddings prêts à indexer dans une base vectorielle — un seul Actor au lieu d'en orchestrer trois séparément.

- **URL**: https://apify.com/toumi.oussema.re/ai-content-pipeline.md
- **Developed by:** [Oussema Toumi](https://apify.com/toumi.oussema.re) (community)
- **Categories:** Agents, Automation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $25.00 / 1,000 source ingested (auto-detected type)s

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## AI Content Pipeline (Universal RAG Bundle)

Ingère une **liste de sources mixtes** (pages web, PDF, vidéos YouTube) en
une seule fois — le type de chaque source est détecté automatiquement à
partir de l'URL, aucun champ `sourceType` à préciser manuellement. Retourne
des chunks + embeddings prêts à indexer dans une base vectorielle. C'est le
même moteur que les Actors `web-to-markdown`, `rag-universal-ingestion`, et
`youtube-transcript-extractor` réunis dans un seul appel.

### Pourquoi ce bundle plutôt que les 3 Actors séparés ?

Un utilisateur qui doit ingérer 50 sources mixtes (30 pages web, 15 PDF, 5
vidéos) devrait sinon trier ses sources par type et faire 3 appels
différents. Ici il colle sa liste brute et l'Actor s'occupe du tri — c'est
cette commodité qui justifie un prix légèrement plus élevé par source que
`rag-universal-ingestion` seul.

### Input

| Champ | Type | Description |
|---|---|---|
| `sources` | array | URLs mixtes (web, PDF, YouTube) |
| `chunkSize` | int | Taille des chunks en caractères (défaut: 1000) |
| `chunkOverlap` | int | Chevauchement entre chunks (défaut: 150) |
| `generateEmbeddings` | bool | Générer les embeddings (défaut: true) |
| `embeddingModel` | string | Modèle OpenRouter (défaut: `openai/text-embedding-3-small`) |
| `openrouterApiKey` | string | Requise si `generateEmbeddings=true` |

### Output (dataset)

Chaque enregistrement contient: `source`, `sourceType` (auto-détecté),
`chunkIndex`, `text`, `charCount`, et si activé `embedding` +
`embeddingModel`.

### Facturation (pay-per-event)

- `source-ingested` — 0,025 $ — par source traitée, tous types confondus
  (vs 0,02 $ pour l'extraction seule sur `rag-universal-ingestion` — la
  différence reflète la détection automatique du type)
- `chunk-embedded` — 0,001 $ — par chunk avec embedding
- `chunk-only` — 0,0002 $ — par chunk sans embedding

Exemple: 50 sources mixtes, ~10 chunks chacune avec embeddings ≈
(50 × 0,025 $) + (500 × 0,001 $) = 1,25 $ + 0,50 $ = **1,75 $** facturés.

### Notes techniques

- Détection de type basée uniquement sur le motif de l'URL
  (`youtube.com/watch`, `.pdf`, sinon page web) — pas de vérification du
  Content-Type HTTP, donc une URL PDF sans extension `.pdf` sera traitée
  comme une page web (limite connue, acceptable pour la majorité des cas).
- Aucun GPU requis, mêmes garanties que `rag-universal-ingestion` :
  extraction et chunking en texte pur, embeddings délégués à OpenRouter.

# Actor input Schema

## `sources` (type: `array`):

URLs of web pages, PDFs, or YouTube videos — the type is auto-detected per source

## `chunkSize` (type: `integer`):

Target chunk size in characters

## `chunkOverlap` (type: `integer`):

Overlap between consecutive chunks in characters

## `generateEmbeddings` (type: `boolean`):

When enabled, generates embeddings for each chunk via OpenRouter

## `embeddingModel` (type: `string`):

OpenRouter embedding model id (e.g. openai/text-embedding-3-small)

## `openrouterApiKey` (type: `string`):

Required only if generateEmbeddings is enabled

## Actor input object example

```json
{
  "sources": [
    "https://en.wikipedia.org/wiki/Web_scraping"
  ],
  "chunkSize": 1000,
  "chunkOverlap": 150,
  "generateEmbeddings": false,
  "embeddingModel": "openai/text-embedding-3-small"
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("toumi.oussema.re/ai-content-pipeline").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 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);
});

// 📚 Want to learn more 📖? Go to → 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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("toumi.oussema.re/ai-content-pipeline").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call toumi.oussema.re/ai-content-pipeline --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,toumi.oussema.re/ai-content-pipeline"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/9ss8wbvdY7HVn2Tk7/builds/JIzNh1brA1P6qpWpd/openapi.json