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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval\n",
"\n",
"This notebook shows how to use an implementation of RAPTOR with llama-index, leveraging the RAPTOR llama-pack.\n",
"\n",
"RAPTOR works by recursively clustering and summarizing clusters in layers for retrieval.\n",
"\n",
"There two retrieval modes:\n",
"- tree_traversal -- traversing the tree of clusters, performing top-k at each level in the tree.\n",
"- collapsed -- treat the entire tree as a giant pile of nodes, perform simple top-k.\n",
"\n",
"See [the paper](https://arxiv.org/abs/2401.18059) for full algorithm details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install llama-index llama-index-packs-raptor llama-index-vector-stores-qdrant"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.packs.raptor import RaptorPack\n",
"\n",
"# optionally download the pack to inspect/modify it yourself!\n",
"# from llama_index.core.llama_pack import download_llama_pack\n",
"# RaptorPack = download_llama_pack(\"RaptorPack\", \"./raptor_pack\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Will not apply HSTS. The HSTS database must be a regular and non-world-writable file.\n",
"ERROR: could not open HSTS store at '/home/loganm/.wget-hsts'. HSTS will be disabled.\n",
"--2024-02-29 22:16:11-- https://arxiv.org/pdf/2401.18059.pdf\n",
"Resolving arxiv.org (arxiv.org)... 151.101.3.42, 151.101.195.42, 151.101.131.42, ...\n",
"Connecting to arxiv.org (arxiv.org)|151.101.3.42|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2547113 (2.4M) [application/pdf]\n",
"Saving to: ‘./raptor_paper.pdf’\n",
"\n",
"./raptor_paper.pdf 100%[===================>] 2.43M 12.5MB/s in 0.2s \n",
"\n",
"2024-02-29 22:16:12 (12.5 MB/s) - ‘./raptor_paper.pdf’ saved [2547113/2547113]\n",
"\n"
]
}
],
"source": [
"!wget https://arxiv.org/pdf/2401.18059.pdf -O ./raptor_paper.pdf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constructing the Clusters/Hierarchy Tree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core import SimpleDirectoryReader\n",
"\n",
"documents = SimpleDirectoryReader(input_files=[\"./raptor_paper.pdf\"]).load_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating embeddings for level 0.\n",
"Performing clustering for level 0.\n",
"Generating summaries for level 0 with 10 clusters.\n",
"Level 0 created summaries/clusters: 10\n",
"Generating embeddings for level 1.\n",
"Performing clustering for level 1.\n",
"Generating summaries for level 1 with 1 clusters.\n",
"Level 1 created summaries/clusters: 1\n",
"Generating embeddings for level 2.\n",
"Performing clustering for level 2.\n",
"Generating summaries for level 2 with 1 clusters.\n",
"Level 2 created summaries/clusters: 1\n"
]
}
],
"source": [
"from llama_index.core.node_parser import SentenceSplitter\n",
"from llama_index.llms.openai import OpenAI\n",
"from llama_index.embeddings.openai import OpenAIEmbedding\n",
"from llama_index.vector_stores.chroma import ChromaVectorStore\n",
"import chromadb\n",
"\n",
"client = chromadb.PersistentClient(path=\"./raptor_paper_db\")\n",
"collection = client.get_or_create_collection(\"raptor\")\n",
"\n",
"vector_store = ChromaVectorStore(chroma_collection=collection)\n",
"\n",
"raptor_pack = RaptorPack(\n",
" documents,\n",
" embed_model=OpenAIEmbedding(\n",
" model=\"text-embedding-3-small\"\n",
" ), # used for embedding clusters\n",
" llm=OpenAI(model=\"gpt-3.5-turbo\", temperature=0.1), # used for generating summaries\n",
" vector_store=vector_store, # used for storage\n",
" similarity_top_k=2, # top k for each layer, or overall top-k for collapsed\n",
" mode=\"collapsed\", # sets default mode\n",
" transformations=[\n",
" SentenceSplitter(chunk_size=400, chunk_overlap=50)\n",
" ], # transformations applied for ingestion\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retrieval"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"Specifically, RAPTOR’s F-1 scores are at least 1.8% points higher than DPR and at least 5.3% points\n",
"higher than BM25.\n",
"Retriever GPT-3 F-1 Match GPT-4 F-1 Match UnifiedQA F-1 Match\n",
"Title + Abstract 25.2 22.2 17.5\n",
"BM25 46.6 50.2 26.4\n",
"DPR 51.3 53.0 32.1\n",
"RAPTOR 53.1 55.7 36.6\n",
"Table 4: Comparison of accuracies on the QuAL-\n",
"ITY dev dataset for two different language mod-\n",
"els (GPT-3, UnifiedQA 3B) using various retrieval\n",
"methods. RAPTOR outperforms the baselines of\n",
"BM25 and DPR by at least 2.0% in accuracy.\n",
"Model GPT-3 Acc. UnifiedQA Acc.\n",
"BM25 57.3 49.9\n",
"DPR 60.4 53.9\n",
"RAPTOR 62.4 56.6\n",
"Table 5: Results on F-1 Match scores of various\n",
"models on the QASPER dataset.\n",
"Model F-1 Match\n",
"LongT5 XL (Guo et al., 2022) 53.1\n",
"CoLT5 XL (Ainslie et al., 2023) 53.9\n",
"RAPTOR + GPT-4 55.7Comparison to State-of-the-art Systems\n",
"Building upon our controlled comparisons,\n",
"we examine RAPTOR’s performance relative\n",
"to other state-of-the-art models.\n"
]
}
],
"source": [
"nodes = raptor_pack.run(\"What baselines is raptor compared against?\", mode=\"collapsed\")\n",
"print(len(nodes))\n",
"print(nodes[0].text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieved parent IDs from level 2: ['cc3b3f41-f4ca-4020-b11f-be7e0ce04c4f']\n",
"Retrieved 1 from parents at level 2.\n",
"Retrieved parent IDs from level 1: ['a4ca9426-a312-4a01-813a-c9b02aefc7e8']\n",
"Retrieved 2 from parents at level 1.\n",
"Retrieved parent IDs from level 0: ['63126782-2778-449f-99c0-1e8fd90caa36', 'd8f68d31-d878-41f1-aeb6-a7dde8ed5143']\n",
"Retrieved 4 from parents at level 0.\n",
"4\n",
"Specifically, RAPTOR’s F-1 scores are at least 1.8% points higher than DPR and at least 5.3% points\n",
"higher than BM25.\n",
"Retriever GPT-3 F-1 Match GPT-4 F-1 Match UnifiedQA F-1 Match\n",
"Title + Abstract 25.2 22.2 17.5\n",
"BM25 46.6 50.2 26.4\n",
"DPR 51.3 53.0 32.1\n",
"RAPTOR 53.1 55.7 36.6\n",
"Table 4: Comparison of accuracies on the QuAL-\n",
"ITY dev dataset for two different language mod-\n",
"els (GPT-3, UnifiedQA 3B) using various retrieval\n",
"methods. RAPTOR outperforms the baselines of\n",
"BM25 and DPR by at least 2.0% in accuracy.\n",
"Model GPT-3 Acc. UnifiedQA Acc.\n",
"BM25 57.3 49.9\n",
"DPR 60.4 53.9\n",
"RAPTOR 62.4 56.6\n",
"Table 5: Results on F-1 Match scores of various\n",
"models on the QASPER dataset.\n",
"Model F-1 Match\n",
"LongT5 XL (Guo et al., 2022) 53.1\n",
"CoLT5 XL (Ainslie et al., 2023) 53.9\n",
"RAPTOR + GPT-4 55.7Comparison to State-of-the-art Systems\n",
"Building upon our controlled comparisons,\n",
"we examine RAPTOR’s performance relative\n",
"to other state-of-the-art models.\n"
]
}
],
"source": [
"nodes = raptor_pack.run(\n",
" \"What baselines is raptor compared against?\", mode=\"tree_traversal\"\n",
")\n",
"print(len(nodes))\n",
"print(nodes[0].text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading\n",
"\n",
"Since we saved to a vector store, we can also use it again! (For local vector stores, there is a `persist` and `from_persist_dir` method on the retriever)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.packs.raptor import RaptorRetriever\n",
"\n",
"retriever = RaptorRetriever(\n",
" [],\n",
" embed_model=OpenAIEmbedding(\n",
" model=\"text-embedding-3-small\"\n",
" ), # used for embedding clusters\n",
" llm=OpenAI(model=\"gpt-3.5-turbo\", temperature=0.1), # used for generating summaries\n",
" vector_store=vector_store, # used for storage\n",
" similarity_top_k=2, # top k for each layer, or overall top-k for collapsed\n",
" mode=\"tree_traversal\", # sets default mode\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# if using a default vector store\n",
"# retriever.persist(\"./persist\")\n",
"# retriever = RaptorRetriever.from_persist_dir(\"./persist\", ...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Query Engine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core.query_engine import RetrieverQueryEngine\n",
"\n",
"query_engine = RetrieverQueryEngine.from_args(\n",
" retriever, llm=OpenAI(model=\"gpt-3.5-turbo\", temperature=0.1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = query_engine.query(\"What baselines was RAPTOR compared against?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"BM25 and DPR\n"
]
}
],
"source": [
"print(str(response))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llama-index-4aB9_5sa-py3.10",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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