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{
"cells": [
{
"cell_type": "markdown",
"id": "6a151ade-7d86-4a2e-bfe7-462089f4e04c",
"metadata": {},
"source": [
"# Approach\n",
"There are a number of aspects of choosing a vector db that might be unique to your situation. You should think through your HW, utilization, latency requirements, scale, etc before choosing. \n",
"\n",
"Im targeting a demo (low utilization, latency can be relaxed) that will live on a huggingface space. I have a small scale that could even fit in memory. I like [Qdrant](https://qdrant.tech) for this. "
]
},
{
"cell_type": "markdown",
"id": "b1b28232-b65d-41ce-88de-fd70b93a528d",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "88408486-566a-4791-8ef2-5ee3e6941156",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = 'all'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "abb5186b-ee67-4e1e-882d-3d8d5b4575d4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import pickle\n",
"\n",
"from tqdm.notebook import tqdm\n",
"from haystack.schema import Document\n",
"from qdrant_haystack import QdrantDocumentStore"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c4b82ea2-8b30-4c2e-99f0-9a30f2f1bfb7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/ec2-user/RAGDemo\n"
]
}
],
"source": [
"proj_dir = Path.cwd().parent\n",
"print(proj_dir)"
]
},
{
"cell_type": "markdown",
"id": "76119e74-f601-436d-a253-63c5a19d1c83",
"metadata": {},
"source": [
"# Config"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f6f74545-54a7-4f41-9f02-96964e1417f0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"file_in = proj_dir / 'data/processed/simple_wiki_embeddings.pkl'"
]
},
{
"cell_type": "markdown",
"id": "d2dd0df0-4274-45b3-9ee5-0205494e4d75",
"metadata": {
"tags": []
},
"source": [
"# Setup\n",
"Read in our list of dictionaries. This is the upper end for the machine Im using. This takes ~10GB of RAM. We could easily do this in batches of ~100k and be fine in most machines. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3c08e039-3686-4eca-9f87-7c469e3f19bc",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.6 s, sys: 2.25 s, total: 13.9 s\n",
"Wall time: 18.1 s\n"
]
}
],
"source": [
"%%time\n",
"with open(file_in, 'rb') as handle:\n",
" documents = pickle.load(handle)"
]
},
{
"cell_type": "markdown",
"id": "98aec715-8d97-439e-99c0-0eff63df386b",
"metadata": {},
"source": [
"Convert the dictionaries to `Documents`"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4821e3c1-697d-4b69-bae3-300168755df9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"documents = [Document.from_dict(d) for d in documents]"
]
},
{
"cell_type": "markdown",
"id": "676f644c-fb09-4d17-89ba-30c92aad8777",
"metadata": {},
"source": [
"Instantiate our `DocumentStore`. Note that Im saving this to disk, this is for portability which is good considering I want to move from this ec2 instance into a Hugging Face Space. \n",
"\n",
"Note that if you are doing this at scale, you should use a proper instance and not saving to file. You should also take a [measured ingestion](https://qdrant.tech/documentation/tutorials/bulk-upload/) approach instead of using a convenient loader. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e51b6e19-3be8-4cb0-8b65-9d6f6121f660",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"document_store = QdrantDocumentStore(\n",
" path=str(proj_dir/'Qdrant'),\n",
" index=\"RAGDemo\",\n",
" embedding_dim=768,\n",
" recreate_index=True,\n",
" hnsw_config={\"m\": 16, \"ef_construct\": 64} # Optional\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "55fbcd5d-922c-4e93-a37a-974ba84464ac",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"270000it [28:43, 156.68it/s] "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 13min 23s, sys: 48.6 s, total: 14min 12s\n",
"Wall time: 28min 43s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"%%time\n",
"document_store.write_documents(documents, batch_size=5_000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a073815-0191-48f7-890f-a4e4ecc0f9f1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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