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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# E5 PyTerrier_DR Index for RAG Wikipedia Corpus\n",
"\n",
"This creates a dense index using [PyTerrier](https://github.com/terrier-org/pyterrier) and [PyTerrier_dr](https://github.com/terrierteam/pyterrier_dr) for the Wikipedia corpus used by Natural Questions and TextbookQuestionAnswering datasets.\n",
"\n",
"The corpus is downloaded from https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets/resolve/main/retrieval-corpus/wiki18_100w.zip by `\n",
"pt.get_dataset('rag:nq_wiki').get_corpus_iter()`.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pyterrier as pt\n",
"import pyterrier_rag\n",
"\n",
"# print pretty progress bars\n",
"pt.utils.set_tqdm('notebook')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ensure pyterrier_dr is installed"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\n",
"\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install -q pyterrier_dr\n",
"from pyterrier_dr import FlexIndex, E5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need an E5 model - this will transform the document text into document embeddings"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"e5 = E5()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's setup an indexing pipeline. Documents are encoded using `e5` before being stored in the FlexIndex emebdding store. The FlexIndex can be used for retrieval later."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
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"text/plain": [
"indexing: 0dvec [00:00, ?dvec/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"index = \"./nq_tctindex.flex\"\n",
"index = FlexIndex(index)\n",
"(e5 >> index).index(pt.get_dataset('rag:nq_wiki').get_corpus_iter())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we upload the index to Huggingface."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adding docnos.npids [207 B]\n",
"adding pt_meta.json [81 B]\n",
"adding vecs.f4 [60.1 GB]\n",
"starting segment 1\n",
"starting segment 2\n",
"starting segment 3\n",
"starting segment 4\n",
"starting segment 5\n",
"starting segment 6\n",
"starting segment 7\n",
"starting segment 8\n",
"starting segment 9\n",
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"starting segment 13\n"
]
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{
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"text": [
"\n",
"Artifact uploaded to https://huggingface.co/datasets/pyterrier/ragwiki-e5.flex/tree/main/\n",
"Consider editing the README.md to help explain this artifact to others.\n"
]
}
],
"source": [
"e5_emb_index.to_hf('pyterrier/ragwiki-e5.flex')"
]
}
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