AppStoneLab Technologies LLP This workflow corresponds to n8n.io template #13977 — we link there as the canonical source.
This workflow follows the HTTP Request → Telegram recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"id": "7PVU0dnK3csdntrn",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "PageIndex RAG - Vectorless PDF Q&A",
"tags": [],
"nodes": [
{
"id": "cb4bce05-a6d3-40a6-8611-d1b5f9ecde1b",
"name": "Sticky Note - Summary",
"type": "n8n-nodes-base.stickyNote",
"position": [
4224,
2016
],
"parameters": {
"color": 7,
"width": 608,
"height": 576,
"content": "## \ud83e\udde0 Workflow Summary\n\n**PageIndex Vectorless RAG Bot**\n\n\nThis workflow builds a vectorless RAG system using [PageIndex](https://pageindex.ai) \u2014 no embeddings, no vector database. PageIndex builds a hierarchical tree index (like a Table of Contents) from your PDF, and the LLM reasons over that tree to find precise answers.\n\n\n---\n\n\n**\ud83d\udcc4 Flow 1 \u2014 PDF Upload (Run Once)**\nSend a PDF file to the Telegram bot. It downloads and indexes it on PageIndex cloud.\n\n**\ud83d\udcac Flow 2 \u2014 Q&A (Runs Every Time)**\nSend any question to the Telegram bot. It fetches all your indexed docs, passes them to PageIndex LLM reasoning, and returns a cited answer.\n\n\n---\n\n**\ud83d\udd11 Required Credentials**\n- Telegram Bot Token (via @BotFather)\n- PageIndex API Key (via dash.pageindex.ai)\n\n**\ud83d\udca1 How PageIndex Works**\nInstead of vector similarity search, PageIndex builds a tree of section summaries. The LLM reads the tree, decides which sections are relevant, retrieves only those, and generates the answer \u2014 with page citations included."
},
"typeVersion": 1
},
{
"id": "669aff95-ffe9-499d-97d0-5f9d3a02054f",
"name": "Sticky Note - Flow 1 Header",
"type": "n8n-nodes-base.stickyNote",
"position": [
4848,
2016
],
"parameters": {
"color": 5,
"width": 856,
"height": 96,
"content": "## \ud83d\udcc4 Flow 1 -> PDF Knowledge Upload\nRun this flow by sending a PDF file to the Telegram bot. The PDF is downloaded and indexed on PageIndex cloud. Run once per document."
},
"typeVersion": 1
},
{
"id": "e5526cd9-4df7-48a8-8098-77fb89f4188f",
"name": "Sticky Note - Receive PDF",
"type": "n8n-nodes-base.stickyNote",
"position": [
4848,
2128
],
"parameters": {
"width": 258,
"height": 472,
"content": "### \ud83d\udcce Receive PDF Document\n**Purpose:** Entry point for PDF upload flow. Listens for incoming Telegram messages that contain a file/document.\n\n**Input:** Telegram message with a PDF attachment\n**Output:** `message.document.file_id` used to download the file\n\n**Note:** Only send PDF files here. Other file types will cause errors downstream."
},
"typeVersion": 1
},
{
"id": "ebbd31aa-c73c-4489-ac06-563ed1107fd4",
"name": "Sticky Note - Download PDF",
"type": "n8n-nodes-base.stickyNote",
"position": [
5120,
2128
],
"parameters": {
"width": 274,
"height": 472,
"content": "### \u2b07\ufe0f Download PDF File\n**Purpose:** Downloads the PDF binary from Telegram's file storage using the `file_id`.\n\n**Input:** `message.document.file_id` from Telegram Trigger\n**Output:** Binary PDF file data\n\n**Note:** Telegram stores files temporarily. This node fetches the actual file content before it expires."
},
"typeVersion": 1
},
{
"id": "2a06e5cd-e74d-4ab1-a82a-a21b2ac9b1b0",
"name": "Sticky Note - Index PDF",
"type": "n8n-nodes-base.stickyNote",
"position": [
5408,
2128
],
"parameters": {
"width": 290,
"height": 472,
"content": "### \u2601\ufe0f Index PDF on PageIndex\n**Purpose:** Uploads the PDF to PageIndex cloud. PageIndex builds a hierarchical tree index (TOC) using LLM - no vectors.\n\n**Input:** Binary PDF data\n**Output:** `doc_id` (e.g. `pi-abc123`) assigned to this document\n\n**Note:** Processing takes 10\u201360s depending on PDF size. The doc is stored permanently."
},
"typeVersion": 1
},
{
"id": "57988369-c99a-4be1-b8c6-a8e80749f56d",
"name": "Receive PDF Document",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
4928,
2432
],
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "35a146e0-cba2-4e9a-a766-821f5becea38",
"name": "Download PDF File",
"type": "n8n-nodes-base.telegram",
"position": [
5216,
2432
],
"parameters": {
"fileId": "={{ $json.message.document.file_id }}",
"resource": "file",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "140249e8-c6f3-4e70-a6b7-e32dd3023320",
"name": "Index PDF on PageIndex",
"type": "n8n-nodes-base.httpRequest",
"position": [
5504,
2432
],
"parameters": {
"url": "https://api.pageindex.ai/doc/",
"method": "POST",
"options": {},
"sendBody": true,
"contentType": "multipart-form-data",
"sendHeaders": true,
"bodyParameters": {
"parameters": [
{
"name": "file",
"parameterType": "formBinaryData",
"inputDataFieldName": "data"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "api_key",
"value": "YOUR_PAGEINDEX_API_KEY"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "04057de2-5fec-45fb-8824-68a204192fd4",
"name": "Sticky Note - Flow 2 Header",
"type": "n8n-nodes-base.stickyNote",
"position": [
4224,
2624
],
"parameters": {
"color": 4,
"width": 1466,
"height": 80,
"content": "## \ud83d\udcac Flow 2 -> Q&A Chat\nRun this flow by sending any text message to the Telegram bot. It fetches all indexed docs, sends the question to PageIndex LLM reasoning, and returns a cited answer to the same user."
},
"typeVersion": 1
},
{
"id": "64697575-d379-4f4d-8d3e-2666019ebd6d",
"name": "Sticky Note - Receive Question",
"type": "n8n-nodes-base.stickyNote",
"position": [
4224,
2720
],
"parameters": {
"color": 6,
"width": 290,
"height": 519,
"content": "### \ud83d\udcac Receive User Question\n**Purpose:** Entry point for Q&A flow. Listens for text messages sent to the Telegram bot.\n\n**Input:** Telegram text message from user\n**Output:** `message.text` (the question) and `message.from.id` (for reply routing)\n\n**Note:** Any text message triggers this flow. The answer is always sent back to the same user."
},
"typeVersion": 1
},
{
"id": "622fbf87-1349-4de5-82ba-27fdf02e49d8",
"name": "Sticky Note - Fetch Docs",
"type": "n8n-nodes-base.stickyNote",
"position": [
4528,
2720
],
"parameters": {
"color": 6,
"width": 258,
"height": 519,
"content": "### \ud83d\udcda Fetch All Indexed Documents\n**Purpose:** Retrieves the list of all documents already indexed in your PageIndex account.\n\n**Input:** PageIndex API key (header)\n**Output:** Array of document objects including `id`, `name`, `status`, `pageNum`\n\n**Note:** Only documents with `status: completed` are ready for querying."
},
"typeVersion": 1
},
{
"id": "6fb9f267-cb04-4155-82f8-9eae020cefdb",
"name": "Sticky Note - Extract IDs",
"type": "n8n-nodes-base.stickyNote",
"position": [
4800,
2720
],
"parameters": {
"color": 6,
"width": 258,
"height": 519,
"content": "### \ud83d\udd11 Extract Document IDs\n**Purpose:** Maps the documents array into a clean array of `doc_id` strings for use in the chat API.\n\n**Input:** `documents` array from PageIndex\n**Output:** `doc_ids` array (e.g. `[\"pi-abc123\", \"pi-def456\"]`)\n\n**Note:** Passing multiple doc_ids allows PageIndex to reason across all your indexed documents at once."
},
"typeVersion": 1
},
{
"id": "534a8b8e-f17c-4042-a2ac-d2ed8706bb4d",
"name": "Sticky Note - LLM Reasoning",
"type": "n8n-nodes-base.stickyNote",
"position": [
5072,
2720
],
"parameters": {
"color": 6,
"width": 338,
"height": 519,
"content": "### \ud83e\udde0 LLM Reasoning over Document Tree\n**Purpose:** Core RAG step. Sends the user question + all doc_ids to PageIndex. The LLM reasons over the hierarchical tree index to find and retrieve the most relevant sections.\n\n**Input:** User question + `doc_ids` array\n**Output:** LLM-generated answer with page citations\n\n**Note:** No vector search involved. PageIndex uses tree traversal + LLM reasoning."
},
"typeVersion": 1
},
{
"id": "f4cc58b5-26fd-4096-bef2-785a5f294d90",
"name": "Sticky Note - Send Answer",
"type": "n8n-nodes-base.stickyNote",
"position": [
5424,
2720
],
"parameters": {
"color": 6,
"width": 274,
"height": 519,
"content": "### \ud83d\udce4 Send Answer to User\n**Purpose:** Delivers the LLM-generated answer back to the user on Telegram.\n\n**Input:** `choices[0].message.content` from PageIndex response\n**Output:** Text message sent to the user's Telegram chat\n\n**Note:** Reply is routed using `message.from.id` so each user gets their own response."
},
"typeVersion": 1
},
{
"id": "369a3577-6c59-4d02-a551-a6cab6308a20",
"name": "Receive User Question",
"type": "n8n-nodes-base.telegramTrigger",
"position": [
4320,
3040
],
"parameters": {
"updates": [
"message"
],
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "22cab621-7b73-465f-81e1-8531cb3fcfcc",
"name": "Fetch All Indexed Documents",
"type": "n8n-nodes-base.httpRequest",
"position": [
4608,
3040
],
"parameters": {
"url": "https://api.pageindex.ai/docs",
"options": {},
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "api_key",
"value": "YOUR_PAGEINDEX_API_KEY"
}
]
}
},
"typeVersion": 4.4
},
{
"id": "e320a50f-32a7-4bcc-a9c6-d9e09192652d",
"name": "Extract Document IDs",
"type": "n8n-nodes-base.set",
"position": [
4880,
3040
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "59845b9c-a89b-41a0-a038-ec0f8d06bf91",
"name": "doc_ids",
"type": "array",
"value": "={{ $json.documents.map(d => d.id) }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "b697c98a-4b45-446b-8a72-0765b411e305",
"name": "LLM Reasoning over Document Tree",
"type": "n8n-nodes-base.httpRequest",
"position": [
5200,
3040
],
"parameters": {
"url": "https://api.pageindex.ai/chat/completions",
"method": "POST",
"options": {},
"jsonBody": "={\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"{{ $('Receive User Question').item.json.message.text }}\"\n }\n ],\n \"stream\": false,\n \"doc_id\": {{ JSON.stringify($json.doc_ids) }},\n \"temperature\": 0.5,\n \"enable_citations\": false\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"headerParameters": {
"parameters": [
{
"name": "api_key",
"value": "YOUR_PAGEINDEX_API_KEY"
},
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "9632028b-74b7-46f2-897b-7b22ed91de11",
"name": "Send Answer to User",
"type": "n8n-nodes-base.telegram",
"position": [
5504,
3040
],
"parameters": {
"text": "={{ $json.choices[0].message.content }}",
"chatId": "={{ $('Receive User Question').item.json.message.from.id }}",
"additionalFields": {}
},
"credentials": {
"telegramApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
}
],
"active": false,
"settings": {
"binaryMode": "separate",
"availableInMCP": false,
"executionOrder": "v1"
},
"versionId": "d98e663c-e64f-4450-af7f-af70ecd02ef0",
"connections": {
"Download PDF File": {
"main": [
[
{
"node": "Index PDF on PageIndex",
"type": "main",
"index": 0
}
]
]
},
"Extract Document IDs": {
"main": [
[
{
"node": "LLM Reasoning over Document Tree",
"type": "main",
"index": 0
}
]
]
},
"Receive PDF Document": {
"main": [
[
{
"node": "Download PDF File",
"type": "main",
"index": 0
}
]
]
},
"Receive User Question": {
"main": [
[
{
"node": "Fetch All Indexed Documents",
"type": "main",
"index": 0
}
]
]
},
"Index PDF on PageIndex": {
"main": [
[]
]
},
"Fetch All Indexed Documents": {
"main": [
[
{
"node": "Extract Document IDs",
"type": "main",
"index": 0
}
]
]
},
"LLM Reasoning over Document Tree": {
"main": [
[
{
"node": "Send Answer to User",
"type": "main",
"index": 0
}
]
]
}
}
}
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telegramApi
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About this workflow
This template is built for developers, researchers, and automation builders who want to create a document Q&A system — without the complexity of vector databases, embeddings, or chunking pipelines.
Source: https://n8n.io/workflows/13977/ — original creator credit. Request a take-down →
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