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Pinecone Preprocessing Data for Vector Database
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README.txt
In this walkthrough we will see how to use Pinecone for semantic search.
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semantic_search.ipynb
{
"cells": [
{
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"metadata": {
"id": "k7Lc9I6taO3k"
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"source": [
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/docs/semantic-search.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/docs/semantic-search.ipynb)\n",
"\n",
"# Semantic Search\n",
"\n",
"In this walkthrough we will see how to use Pinecone for semantic search. To begin we must install the required prerequisite libraries:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"outputId": "19ee9678-91b3-4b53-ab2e-f1c6156fa01e"
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" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m192.6/192.6 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.9/34.9 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m346.6/346.6 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.5/73.5 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.4/73.4 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.1/11.1 MB\u001b[0m \u001b[31m58.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.8/143.8 kB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for pinecone-datasets (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"lida 0.0.10 requires fastapi, which is not installed.\n",
"lida 0.0.10 requires kaleido, which is not installed.\n",
"lida 0.0.10 requires python-multipart, which is not installed.\n",
"lida 0.0.10 requires uvicorn, which is not installed.\n",
"bigframes 0.18.0 requires pandas<2.1.4,>=1.5.0, but you have pandas 2.1.4 which is incompatible.\n",
"google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -qU \\\n",
" pinecone-client==3.1.0 \\\n",
" pinecone-datasets==0.7.0 \\\n",
" sentence-transformers==2.2.2"
]
},
{
"cell_type": "markdown",
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"source": [
"---\n",
"\n",
"🚨 _Note: the above `pip install` is formatted for Jupyter notebooks. If running elsewhere you may need to drop the `!`._\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hrSfFiIC5roI"
},
"source": [
"## Data Download"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kujS_e8s55oJ"
},
"source": [
"In this notebook we will skip the data preparation steps as they can be very time consuming and jump straight into it with the prebuilt dataset from *Pinecone Datasets*. If you'd rather see how it's all done, please refer to [this notebook](https://github.com/pinecone-io/examples/blob/master/learn/search/semantic-search/semantic-search.ipynb).\n",
"\n",
"Let's go ahead and download the dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "lOgjRG52Zqqz",
"outputId": "26a44f5f-5a6a-429f-8cec-72b86d99f380"
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"text/plain": [
" id values \\\n",
"240000 515997 [-0.00531694, 0.06937869, -0.0092854, 0.003286... \n",
"240001 515998 [-0.09243751, 0.065432355, -0.06946959, 0.0669... \n",
"240002 515999 [-0.021924071, 0.032280188, -0.020190848, 0.07... \n",
"240003 516000 [-0.120020054, 0.024080949, 0.10693012, -0.018... \n",
"240004 516001 [-0.095293395, -0.048446465, -0.017618902, -0.... \n",
"\n",
" sparse_values \\\n",
"240000 {'indices': [845, 1657, 13677, 20780, 27058, 2... \n",
"240001 {'indices': [2110, 6324, 9754, 13677, 15207, 2... \n",
"240002 {'indices': [2110, 4949, 23579, 23758, 27058, ... \n",
"240003 {'indices': [22014, 24734, 24773, 25791, 25991... \n",
"240004 {'indices': [307, 2110, 5785, 12969, 12971, 13... \n",
"\n",
" metadata \n",
"240000 {'text': ' Why is a \"law of sciences\" importan... \n",
"240001 {'text': ' Is it possible to format a BitLocke... \n",
"240002 {'text': ' Can formatting a hard drive stress ... \n",
"240003 {'text': ' Are the new Samsung Galaxy J7 and J... \n",
"240004 {'text': ' I just watched an add for Indonesia... "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pinecone_datasets import load_dataset\n",
"\n",
"dataset = load_dataset('quora_all-MiniLM-L6-bm25')\n",
"# we drop metadata as will use blob column\n",
"dataset.documents.drop(['metadata'], axis=1, inplace=True)\n",
"dataset.documents.rename(columns={'blob': 'metadata'}, inplace=True)\n",
"# we will use 80K rows of the dataset between rows 240K -> 320K\n",
"dataset.documents.drop(dataset.documents.index[320_000:], inplace=True)\n",
"dataset.documents.drop(dataset.documents.index[:240_000], inplace=True)\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "conuh2Uo-mwR",
"outputId": "13347486-2a77-47b6-f1c2-6b9287fdc867"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80000\n"
]
}
],
"source": [
"print(len(dataset))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ebd7XSamfMsC"
},
"source": [
"## Creating an Index\n",
"\n",
"Now the data is ready, we can set up our index to store it.\n",
"\n",
"We begin by initializing our connection to Pinecone. To do this we need a [free API key](https://app.pinecone.io)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "mc66NEBAcQHY"
},
"outputs": [],
"source": [
"import os\n",
"from pinecone import Pinecone\n",
"\n",
"# initialize connection to pinecone (get API key at app.pinecone.io)\n",
"api_key = os.environ.get('PINECONE_API_KEY') or 'PINECONE_API_KEY'\n",
"\n",
"# configure client\n",
"pc = Pinecone(api_key=api_key)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xknsDRh15-by"
},
"source": [
"Now we setup our index specification, this allows us to define the cloud provider and region where we want to deploy our index. You can find a list of all [available providers and regions here](https://docs.pinecone.io/docs/projects)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "m0BUsx0E590t"
},
"outputs": [],
"source": [
"from pinecone import ServerlessSpec\n",
"\n",
"cloud = os.environ.get('PINECONE_CLOUD') or 'aws'\n",
"region = os.environ.get('PINECONE_REGION') or 'us-east-1'\n",
"\n",
"spec = ServerlessSpec(cloud=cloud, region=region)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SdaTip6CfllN"
},
"source": [
"Now we create a new index called `semantic-search-fast`. It's important that we align the index `dimension` and `metric` parameters with those required by the `MiniLM-L6` model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PMmTOTzH6LNA",
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"index_name = 'semantic-search-fast'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kT8pfoO46Iwg",
"outputId": "0fec19be-c74d-4602-bec6-24d61cfc5bb4"
},
"outputs": [
{
"data": {
"text/plain": [
"{'dimension': 384,\n",
" 'index_fullness': 0.0,\n",
" 'namespaces': {},\n",
" 'total_vector_count': 0}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import time\n",
"\n",
"existing_indexes = [\n",
" index_info[\"name\"] for index_info in pc.list_indexes()\n",
"]\n",
"\n",
"# check if index already exists (it shouldn't if this is first time)\n",
"if index_name not in existing_indexes:\n",
" # if does not exist, create index\n",
" pc.create_index(\n",
" index_name,\n",
" dimension=384, # dimensionality of minilm\n",
" metric='dotproduct',\n",
" spec=spec\n",
" )\n",
" # wait for index to be initialized\n",
" while not pc.describe_index(index_name).status['ready']:\n",
" time.sleep(1)\n",
"\n",
"# connect to index\n",
"index = pc.Index(index_name)\n",
"time.sleep(1)\n",
"# view index stats\n",
"index.describe_index_stats()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YUd1VGg6i108"
},
"source": [
"Upsert the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"6c9efda7c7394404814e7466d4a9108c",
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]
},
"id": "RhR6WOi1huXZ",
"outputId": "9ae50bfe-3ebf-4d8b-e28e-3230aed0e1d7"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6c9efda7c7394404814e7466d4a9108c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/160 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm.auto import tqdm\n",
"\n",
"for batch in tqdm(dataset.iter_documents(batch_size=500), total=160):\n",
" index.upsert(batch)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VrK_IN079Vuu"
},
"source": [
"## Making Queries"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rr4unPAq9alb"
},
"source": [
"Now that our index is populated we can begin making queries. We are performing a semantic search for *similar questions*, so we should embed and search with another question. Let's begin."
]
},
{
"cell_type": "code",
"execution_count": null,
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"base_uri": "https://localhost:8080/",
"height": 679,
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},
"id": "Fqo_hMRZiubM",
"outputId": "de4b4489-6f66-4ea0-b373-df1b88260c7d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
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"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
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"data": {
"text/plain": [
"SentenceTransformer(\n",
" (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel \n",
" (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",
" (2): Normalize()\n",
")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sentence_transformers import SentenceTransformer\n",
"import torch\n",
"\n",
"device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
"\n",
"model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device=device)\n",
"model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MP2-unZ--XJ9"
},
"source": [
"Now let's query."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JWcO7jAK-N_1",
"outputId": "2bd676cb-a042-4695-e0d4-5ffb3b019f07"
},
"outputs": [
{
"data": {
"text/plain": [
"{'matches': [{'id': '69331',\n",
" 'metadata': {'text': \" What's the world's largest city?\"},\n",
" 'score': 0.7856552,\n",
" 'values': []},\n",
" {'id': '69332',\n",
" 'metadata': {'text': ' What is the biggest city?'},\n",
" 'score': 0.727139533,\n",
" 'values': []},\n",
" {'id': '84749',\n",
" 'metadata': {'text': \" What are the world's most advanced \"\n",
" 'cities?'},\n",
" 'score': 0.709211528,\n",
" 'values': []},\n",
" {'id': '109231',\n",
" 'metadata': {'text': ' Where is the most beautiful city in the '\n",
" 'world?'},\n",
" 'score': 0.696054935,\n",
" 'values': []},\n",
" {'id': '109230',\n",
" 'metadata': {'text': ' What is the greatest, most beautiful city '\n",
" 'in the world?'},\n",
" 'score': 0.657444596,\n",
" 'values': []}],\n",
" 'namespace': '',\n",
" 'usage': {'read_units': 5}}"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"query = \"which city has the highest population in the world?\"\n",
"\n",
"# create the query vector\n",
"xq = model.encode(query).tolist()\n",
"\n",
"# now query\n",
"xc = index.query(vector=xq, top_k=5, include_metadata=True)\n",
"xc"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-XwOWcgo_QtI"
},
"source": [
"In the returned response `xc` we can see the most relevant questions to our particular query — we don't have any exact matches but we can see that the returned questions are similar in the topics they are asking about. We can reformat this response to be a little easier to read:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gy7isg_f-vWg",
"outputId": "dbcc8119-28d2-41de-dd19-a1b181fe6800"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.79: What's the world's largest city?\n",
"0.73: What is the biggest city?\n",
"0.71: What are the world's most advanced cities?\n",
"0.7: Where is the most beautiful city in the world?\n",
"0.66: What is the greatest, most beautiful city in the world?\n"
]
}
],
"source": [
"for result in xc['matches']:\n",
" print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1JK5yApl_5fE"
},
"source": [
"These are good results, let's try and modify the words being used to see if we still surface similar results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dJbjE-iq_yMr",
"outputId": "ec6b9122-ff76-4696-8dfe-12a81cb81ead"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.64: What is the biggest city?\n",
"0.6: What is the most dangerous city in USA?\n",
"0.59: What's the world's largest city?\n",
"0.59: What is the most dangerous city in USA? Why?\n",
"0.58: What are the world's most advanced cities?\n"
]
}
],
"source": [
"query = \"which metropolis has the highest number of people?\"\n",
"\n",
"# create the query vector\n",
"xq = model.encode(query).tolist()\n",
"\n",
"# now query\n",
"xc = index.query(vector=xq, top_k=5, include_metadata=True)\n",
"for result in xc['matches']:\n",
" print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HIAxOPb-A2w_"
},
"source": [
"Here we used different terms in our query than that of the returned documents. We substituted **\"city\"** for **\"metropolis\"** and **\"populated\"** for **\"number of people\"**.\n",
"\n",
"Despite these very different terms and *lack* of term overlap between query and returned documents — we get highly relevant results — this is the power of *semantic search*.\n",
"\n",
"You can go ahead and ask more questions above. When you're done, delete the index to save resources:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-cWdeKzhAtww"
},
"outputs": [],
"source": [
"pc.delete_index(index_name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2B0zxR6hbf5d"
},
"source": [
"---"
]
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