mirror of
https://github.com/VectifyAI/PageIndex.git
synced 2026-04-24 23:56:21 +02:00
623 lines
421 KiB
Text
623 lines
421 KiB
Text
{
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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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"id": "TCh9BTedHJK1"
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},
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"source": [
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""
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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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"id": "nD0hb4TFHWTt"
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},
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"source": [
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" <p align=\"center\"><i>Reasoning-based RAG ✧ No Vector DB ✧ No Chunking ✧ Human-like Retrieval</i></p>\n",
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"\n",
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"<p align=\"center\">\n",
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" <a href=\"https://vectify.ai\">🏠 Homepage</a> • \n",
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" <a href=\"https://dash.pageindex.ai\">🖥️ Dashboard</a> • \n",
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" <a href=\"https://docs.pageindex.ai/quickstart\">📚 API Docs</a> • \n",
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" <a href=\"https://github.com/vectifyai/pageindex\">📦 GitHub</a> • \n",
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" <a href=\"https://discord.com/invite/VuXuf29EUj\">💬 Discord</a> • \n",
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" <a href=\"https://ii2abc2jejf.typeform.com/to/tK3AXl8T\">✉️ Contact</a> \n",
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"</p>"
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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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"id": "Ebvn5qfpcG1K"
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},
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"source": [
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"# 🧠 Simple Vectorless RAG with PageIndex\n",
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"\n",
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"PageIndex generates a searchable tree structure of documents, enabling reasoning-based retrieval through tree search — without vectors.\n",
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"\n",
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"- **No Vectors Needed**: Uses document structure and LLM reasoning for retrieval.\n",
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"- **No Chunking Needed**: Documents are organized into natural sections rather than artificial chunks.\n",
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"- **No Top-K Needed**: The LLM decides how many nodes need to be retrieved.\n",
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"- **Transparent Retrieval Process**: Retrieval based on reasoning — say goodbye to approximate semantic search ('vibe retrieval').\n",
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"\n",
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"# 📝 About this Notebook\n",
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"This notebook demonstrates a simple example of **vectorless RAG** with PageIndex. You will learn:\n",
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"- [x] How to generate PageIndex tree structure of a document.\n",
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"- [x] How to perform retrieval with tree search.\n",
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"- [x] How to generate the answer based on the retrieved context."
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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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"id": "7ziuTbbWcG1L"
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},
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"source": [
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"# Preparation\n",
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"\n"
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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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"id": "edTfrizMFK4c"
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},
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"source": [
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"## Install Dependencies"
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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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"collapsed": true,
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"id": "LaoB58wQFNDh"
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},
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"outputs": [],
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"source": [
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"%pip install -q --upgrade pageindex openai"
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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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"id": "WVEWzPKGcG1M"
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},
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"source": [
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"## Setup Environment"
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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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"id": "StvqfcK4cG1M"
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},
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"outputs": [],
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"source": [
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"import os, json, openai, requests\n",
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"from pageindex import PageIndexClient\n",
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"from pprint import pprint\n",
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"from IPython.display import Markdown, display\n",
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"\n",
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"PAGEINDEX_API_KEY = \"YOUR_PAGEINDEX_API_KEY\" # Get your PageIndex API key from https://dash.pageindex.ai/api-keys\n",
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"OPENAI_API_KEY = \"YOUR_OPENAI_API_KEY\"\n",
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"\n",
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"pi_client = PageIndexClient(api_key=PAGEINDEX_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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"id": "AR7PLeVbcG1N"
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},
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"source": [
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"## Define Utility Functions"
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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": 137,
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"metadata": {
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"id": "hmj3POkDcG1N"
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},
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"outputs": [],
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"source": [
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"async def call_llm(prompt, model=\"gpt-4.1\", temperature=0):\n",
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" client = openai.AsyncOpenAI(api_key=OPENAI_API_KEY)\n",
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" response = await client.chat.completions.create(\n",
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" model=model,\n",
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" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
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" temperature=temperature\n",
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" )\n",
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" return response.choices[0].message.content.strip()\n",
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"\n",
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"def remove_fields(data, fields=['text'], max_len=50):\n",
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" if isinstance(data, dict):\n",
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" return {k: remove_fields(v, fields)\n",
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" for k, v in data.items() if k not in fields}\n",
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" elif isinstance(data, list):\n",
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" return [remove_fields(item, fields) for item in data]\n",
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" elif isinstance(data, str):\n",
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" return (data[:max_len] + '...') if len(data) > max_len else data\n",
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" return data\n",
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"\n",
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"def print_tree(tree, exclude_fields=['text', 'page_index']):\n",
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" cleaned_tree = remove_fields(tree.copy(), exclude_fields)\n",
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" pprint(cleaned_tree, sort_dicts=False, width=150)\n",
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"\n",
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"def print_markdown(*lines):\n",
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" text = \"\\n\".join(lines)\n",
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" display(Markdown(text))\n",
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"\n",
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"def create_node_mapping(tree):\n",
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" \"\"\"Create a mapping of node_id to node for quick lookup\"\"\"\n",
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" def get_all_nodes(tree):\n",
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" if isinstance(tree, dict):\n",
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" return [tree] + [node for child in tree.get('nodes', []) for node in get_all_nodes(child)]\n",
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" elif isinstance(tree, list):\n",
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" return [node for item in tree for node in get_all_nodes(item)]\n",
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" return []\n",
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" return {node[\"node_id\"]: node for node in get_all_nodes(tree) if node.get(\"node_id\")}"
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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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"id": "heGtIMOVcG1N"
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},
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"source": [
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"# Step 1: PageIndex Tree Generation"
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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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"id": "Mzd1VWjwMUJL"
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},
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"source": [
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"## Submit a document with PageIndex SDK"
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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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "f6--eZPLcG1N",
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"outputId": "ca688cfd-6c4b-4a57-dac2-f3c2604c4112"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloaded https://arxiv.org/pdf/2501.12948.pdf\n",
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"Document Submitted: pi-cmek7luf400960ao3o0o8us4d\n"
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]
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}
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],
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"source": [
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"# You can also use our GitHub repo to generate PageIndex structure\n",
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"# https://github.com/VectifyAI/PageIndex\n",
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"\n",
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"pdf_url = \"https://arxiv.org/pdf/2501.12948.pdf\"\n",
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"pdf_path = os.path.join(\"../data\", pdf_url.split('/')[-1])\n",
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"os.makedirs(os.path.dirname(pdf_path), exist_ok=True)\n",
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"\n",
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"response = requests.get(pdf_url)\n",
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"with open(pdf_path, \"wb\") as f:\n",
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" f.write(response.content)\n",
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"print(f\"Downloaded {pdf_url}\")\n",
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"\n",
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"doc_id = pi_client.submit_document(pdf_path)[\"doc_id\"]\n",
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"print('Document Submitted:', doc_id)"
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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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"id": "4-Hrh0azcG1N"
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},
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"source": [
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"## Get the generated PageIndex tree structure"
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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": 138,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "b1Q1g6vrcG1O",
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"outputId": "dc944660-38ad-47ea-d358-be422edbae53"
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},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"## Simplified Tree Structure of the Document\n",
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"---"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'title': 'DeepSeek-R1: Incentivizing Reasoning Capability in...',\n",
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" 'node_id': '0000',\n",
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" 'prefix_summary': '# DeepSeek-R1: Incentivizing Reasoning Capability ...',\n",
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" 'nodes': [{'title': 'Abstract', 'node_id': '0001', 'summary': 'The partial document introduces two reasoning mode...'},\n",
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" {'title': 'Contents', 'node_id': '0002', 'summary': 'This partial document provides a detailed table of...'},\n",
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" {'title': '1. Introduction',\n",
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" 'node_id': '0003',\n",
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" 'prefix_summary': 'The partial document introduces recent advancement...',\n",
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" 'nodes': [{'title': '1.1. Contributions', 'node_id': '0004', 'summary': 'This partial document outlines the main contributi...'},\n",
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" {'title': '1.2. Summary of Evaluation Results',\n",
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" 'node_id': '0005',\n",
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" 'summary': 'The partial document provides a summary of evaluat...'}]},\n",
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" {'title': '2. Approach',\n",
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" 'node_id': '0006',\n",
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" 'prefix_summary': '## 2. Approach\\n',\n",
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" 'nodes': [{'title': '2.1. Overview', 'node_id': '0007', 'summary': '### 2.1. Overview\\n\\nPrevious work has heavily relie...'},\n",
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" {'title': '2.2. DeepSeek-R1-Zero: Reinforcement Learning on t...',\n",
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" 'node_id': '0008',\n",
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" 'prefix_summary': '### 2.2. DeepSeek-R1-Zero: Reinforcement Learning ...',\n",
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" 'nodes': [{'title': '2.2.1. Reinforcement Learning Algorithm',\n",
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" 'node_id': '0009',\n",
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" 'summary': 'This partial document describes the Group Relative...'},\n",
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" {'title': '2.2.2. Reward Modeling',\n",
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" 'node_id': '0010',\n",
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" 'summary': 'This partial document discusses the reward modelin...'},\n",
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" {'title': '2.2.3. Training Template',\n",
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" 'node_id': '0011',\n",
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" 'summary': '#### 2.2.3. Training Template\\n\\nTo train DeepSeek-R...'},\n",
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" {'title': '2.2.4. Performance, Self-evolution Process and Aha...',\n",
|
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" 'node_id': '0012',\n",
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" 'summary': 'This partial document discusses the performance, s...'}]},\n",
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" {'title': '2.3. DeepSeek-R1: Reinforcement Learning with Cold...',\n",
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" 'node_id': '0013',\n",
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" 'summary': 'This partial document describes the training pipel...'},\n",
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" {'title': '2.4. Distillation: Empower Small Models with Reaso...',\n",
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" 'node_id': '0014',\n",
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" 'summary': 'This partial document discusses the process of dis...'}]},\n",
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" {'title': '3. Experiment',\n",
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" 'node_id': '0015',\n",
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" 'prefix_summary': 'The partial document describes the experimental se...',\n",
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" 'nodes': [{'title': '3.1. DeepSeek-R1 Evaluation',\n",
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" 'node_id': '0016',\n",
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" 'summary': 'This partial document presents a comprehensive eva...'},\n",
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" {'title': '3.2. Distilled Model Evaluation',\n",
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" 'node_id': '0017',\n",
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" 'summary': 'This partial document presents an evaluation of va...'}]},\n",
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" {'title': '4. Discussion', 'node_id': '0018', 'summary': 'This partial document discusses the comparative ef...'},\n",
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" {'title': '5. Conclusion, Limitations, and Future Work',\n",
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" 'node_id': '0019',\n",
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" 'summary': 'This partial document presents the conclusion, lim...'},\n",
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" {'title': 'References', 'node_id': '0020', 'summary': 'The partial document consists of a comprehensive r...'},\n",
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" {'title': 'Appendix', 'node_id': '0021', 'summary': '## Appendix\\n'},\n",
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" {'title': 'A. Contributions and Acknowledgments',\n",
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" 'node_id': '0022',\n",
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" 'summary': 'This partial document section details the contribu...'}]}]\n"
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]
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}
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],
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"source": [
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"if pi_client.is_retrieval_ready(doc_id):\n",
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" tree = pi_client.get_tree(doc_id, node_summary=True)['result']\n",
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" print_markdown('## Simplified Tree Structure of the Document', '---')\n",
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" print_tree(tree)\n",
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"else:\n",
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" print(\"Processing document, please try again later...\")"
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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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"id": "USoCLOiQcG1O"
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},
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"source": [
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"# Step 2: Reasoning-Based Retrieval with Tree Search\n",
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"\n",
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"#### Use LLM to search the PageIndex tree and decide which nodes may contain the relevant context."
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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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"id": "LLHNJAtTcG1O"
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},
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"outputs": [],
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"source": [
|
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"query = \"What are the conclusions in this document?\"\n",
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"\n",
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"tree_without_text = remove_fields(tree.copy(), fields=['text'])\n",
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"\n",
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"search_prompt = f\"\"\"\n",
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"You are given a question and a tree structure of a document.\n",
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"Each node contains a node id, node title, and a corresponding summary.\n",
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"Your task is to find all nodes that are likely to contain the answer to the question.\n",
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"\n",
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"Question: {query}\n",
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"\n",
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"Document tree structure:\n",
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"{json.dumps(tree_without_text, indent=2)}\n",
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"\n",
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"Please reply in the following JSON format:\n",
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"{{\n",
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" \"thinking\": \"<Your thinking process on which nodes are relevant to the question>\",\n",
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" \"node_list\": [\"node_id_1\", \"node_id_2\", ..., \"node_id_n\"]\n",
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"}}\n",
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"Directly return the final JSON structure. Do not output anything else.\n",
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"\"\"\"\n",
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"\n",
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"tree_search_result = await call_llm(search_prompt)"
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]
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},
|
|
{
|
|
"cell_type": "code",
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|
"execution_count": null,
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|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 206
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|
},
|
|
"id": "P8DVUOuAen5u",
|
|
"outputId": "6bb6d052-ef30-4716-f88e-be98bcb7ebdb"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
"## Reasoning Process\n",
|
|
"---"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
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"text/markdown": [
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"The question asks for the conclusions in the document. The most direct and relevant node is '5. Conclusion, Limitations, and Future Work' (node_id: 0019), as it explicitly contains the conclusion section. Additionally, the 'Abstract' (node_id: 0001) often summarizes the main findings and conclusions, and the 'Discussion' (node_id: 0018) may also contain concluding remarks or synthesis of results. However, the primary and most comprehensive source for conclusions is node 0019."
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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|
"data": {
|
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"text/markdown": [
|
|
"## Retrieved Nodes\n",
|
|
"---"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
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"text": [
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"Node ID: 0019\t Page: 16\t Title: 5. Conclusion, Limitations, and Future Work\n"
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]
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}
|
|
],
|
|
"source": [
|
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"### Print retrieval nodes\n",
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"\n",
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"node_map = create_node_mapping(tree)\n",
|
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"tree_search_result_json = json.loads(tree_search_result)\n",
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"\n",
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"print_markdown('## Reasoning Process', '---')\n",
|
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"print_markdown(tree_search_result_json['thinking'])\n",
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"\n",
|
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"print_markdown('## Retrieved Nodes', '---')\n",
|
|
"for node_id in tree_search_result_json[\"node_list\"]:\n",
|
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" node = node_map[node_id]\n",
|
|
" print(f\"Node ID: {node['node_id']}\\t Page: {node['page_index']}\\t Title: {node['title']}\")"
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]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "10wOZDG_cG1O"
|
|
},
|
|
"source": [
|
|
"# Step 3: Answer Generation\n",
|
|
"\n",
|
|
"#### Extract context from relevant nodes and generate the final answer."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/",
|
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"height": 279
|
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},
|
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"id": "a7UCBnXlcG1O",
|
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"outputId": "8a026ea3-4ef3-473a-a57b-b4565409749e"
|
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},
|
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"outputs": [
|
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{
|
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"data": {
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"text/markdown": [
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"## Retrieved Context\n",
|
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"---"
|
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],
|
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"text/plain": [
|
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"<IPython.core.display.Markdown object>"
|
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]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/markdown": [
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"## 5. Conclusion, Limitations, and Future Work\n",
|
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"\n",
|
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"In this work, we share our journey in enhancing model reasoning abilities through reinforcement learning. DeepSeek-R1-Zero represents a pure RL approach without relying on cold-start data, achieving strong performance across various tasks. DeepSeek-R1 is more powerful, leveraging cold-start data alongside iterative RL fine-tuning. Ultimately, DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
|
|
"\n",
|
|
"We further explore distillation the reasoning capability to small dense models. We use DeepSeek-R1 as the teacher model to generate 800K training samples, and fine-tune several small dense models. The results are promising: DeepSeek-R1-Distill-Qwen-1.5B outperforms GPT-4o and Claude-3.5-Sonnet on math benchmarks with $28.9 \\%$ on AIME and $83.9 \\%$ on MATH. Other dense models also achieve impressive results, significantly outperforming other instructiontuned models based on the same underlying checkpoints.\n",
|
|
"\n",
|
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"In the fut ..."
|
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],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
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"output_type": "display_data"
|
|
}
|
|
],
|
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"source": [
|
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"# Prepare Retrieved Context\n",
|
|
"\n",
|
|
"node_list = json.loads(tree_search_result)[\"node_list\"]\n",
|
|
"relevant_content = \"\\n\\n\".join(node_map[node_id][\"text\"] for node_id in node_list)\n",
|
|
"print_markdown('## Retrieved Context', '---')\n",
|
|
"print_markdown(f'{relevant_content[:1000]} ...')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 210
|
|
},
|
|
"id": "tcp_PhHzcG1O",
|
|
"outputId": "187ff116-9bb0-4ab4-bacb-13944460b5ff"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
"## Generated Answer\n",
|
|
"---"
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
|
"text/markdown": [
|
|
"**Conclusions in this document:**\n",
|
|
"\n",
|
|
"- DeepSeek-R1-Zero, a pure reinforcement learning (RL) model without cold-start data, achieves strong performance across various tasks.\n",
|
|
"- DeepSeek-R1, which combines cold-start data with iterative RL fine-tuning, is even more powerful and achieves performance comparable to OpenAI-o1-1217 on a range of tasks.\n",
|
|
"- The reasoning capabilities of DeepSeek-R1 can be successfully distilled into smaller dense models, with DeepSeek-R1-Distill-Qwen-1.5B outperforming GPT-4o and Claude-3.5-Sonnet on math benchmarks.\n",
|
|
"- Other small dense models fine-tuned with DeepSeek-R1 data also significantly outperform other instruction-tuned models based on the same checkpoints."
|
|
],
|
|
"text/plain": [
|
|
"<IPython.core.display.Markdown object>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Generate Answer\n",
|
|
"\n",
|
|
"answer_prompt = f\"\"\"\n",
|
|
"Answer the question based on the context:\n",
|
|
"\n",
|
|
"Question: {query}\n",
|
|
"Context: {relevant_content}\n",
|
|
"\n",
|
|
"Provide a clear, concise answer based only on the context provided.\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"print_markdown('## Generated Answer', '---')\n",
|
|
"answer = await call_llm(answer_prompt)\n",
|
|
"print_markdown(answer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "_1kaGD3GcG1O"
|
|
},
|
|
"source": [
|
|
"# 🎯 What's Next\n",
|
|
"\n",
|
|
"This notebook has demonstrated a basic example of **reasoning-based**, **vectorless** RAG with PageIndex. The workflow illustrates the core idea:\n",
|
|
"> *Generating a hierarchical tree structure from a document, reasoning over that tree structure, and extracting relevant context without relying on a vector database or top-k similarity search*.\n",
|
|
"\n",
|
|
"While this notebook highlights a minimal workflow, the PageIndex framework is built to support **far more advanced** use cases. In upcoming tutorials, we will introduce:\n",
|
|
"* **Multi-node reasoning for complex query** — Scale tree search to handle queries that require context from multiple nodes.\n",
|
|
"* **Multi-document search** — Enable reasoning-based navigation across large document collections, extending beyond a single file.\n",
|
|
"* **Efficient Tree search** — Improve tree search efficiency for long documents with a large number of nodes.\n",
|
|
"* **Expert Knowledge Integration and Preference Alignment** — Incorporate user preferences or expert insights by adding knowledge directly into the LLM tree search, without the need for fine-tuning.\n",
|
|
"\n",
|
|
"# 🔎 Learn More About PageIndex\n",
|
|
" <a href=\"https://vectify.ai\">🏠 Homepage</a> • \n",
|
|
" <a href=\"https://dash.pageindex.ai\">🖥️ Dashboard</a> • \n",
|
|
" <a href=\"https://docs.pageindex.ai/quickstart\">📚 API Docs</a> • \n",
|
|
" <a href=\"https://github.com/vectifyai/pageindex\">📦 GitHub</a> • \n",
|
|
" <a href=\"https://discord.com/invite/VuXuf29EUj\">💬 Discord</a> • \n",
|
|
" <a href=\"https://ii2abc2jejf.typeform.com/to/tK3AXl8T\">✉️ Contact</a>\n",
|
|
"\n",
|
|
"<br>\n",
|
|
"\n",
|
|
"© 2025 [Vectify AI](https://vectify.ai)\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"provenance": []
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.11.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|