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"[![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:"
]
},
{
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"source": [
"!pip install -qU \\\n",
" \"pinecone-client[grpc]\"==2.2.1 \\\n",
" pinecone-datasets=='0.5.0rc11' \\\n",
" sentence-transformers==2.2.2"
]
},
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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",
"---"
]
},
{
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"metadata": {
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"source": [
"## Data Download"
]
},
{
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"id": "kujS_e8s55oJ"
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"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."
]
},
{
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" 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",
" 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": 3,
"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 sparse_values as they are not needed for this example\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": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "conuh2Uo-mwR",
"outputId": "9d2f9b69-c15f-4ba9-9003-464755c018e9"
},
"outputs": [
{
"data": {
"text/plain": [
"80000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(len(dataset))"
]
},
{
"attachments": {},
"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": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mc66NEBAcQHY",
"outputId": "3df89651-02ef-4af5-99ae-33d742c01e2d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.10/dist-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
" from tqdm.autonotebook import tqdm\n"
]
}
],
"source": [
"import os\n",
"import pinecone\n",
"\n",
"# get api key from app.pinecone.io\n",
"PINECONE_API_KEY = os.environ.get('PINECONE_API_KEY') or 'PINECONE_API_KEY'\n",
"# find your environment next to the api key in pinecone console\n",
"PINECONE_ENV = os.environ.get('PINECONE_ENVIRONMENT') or 'PINECONE_ENVIRONMENT'\n",
"\n",
"pinecone.init(\n",
" api_key=PINECONE_API_KEY,\n",
" environment=PINECONE_ENV\n",
")"
]
},
{
"attachments": {},
"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": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"index_name = 'semantic-search-fast'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "p1pyQh8gfm2-"
},
"outputs": [],
"source": [
"import time\n",
"\n",
"# only create index if it doesn't exist\n",
"if index_name not in pinecone.list_indexes():\n",
" pinecone.create_index(\n",
" name=index_name,\n",
" dimension=len(dataset.documents.iloc[0]['values']),\n",
" metric='cosine'\n",
" )\n",
" # wait a moment for the index to be fully initialized\n",
" time.sleep(1)\n",
"\n",
"# now connect to the index\n",
"index = pinecone.GRPCIndex(index_name)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "YUd1VGg6i108"
},
"source": [
"Upsert the data:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 99,
"referenced_widgets": [
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},
"id": "RhR6WOi1huXZ",
"outputId": "bf2ec9d1-9b47-401c-9258-200f94673852"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "df0a7ae2a8304da598f6488d51473f36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"sending upsert requests: 0%| | 0/80000 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "57de9e55c80c4134a9554fa2a1609b26",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"collecting async responses: 0%| | 0/160 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"upserted_count: 80000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for batch in dataset.iter_documents(batch_size=100):\n",
" index.upsert(batch)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "VrK_IN079Vuu"
},
"source": [
"## Making Queries"
]
},
{
"attachments": {},
"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": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 572,
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"id": "Fqo_hMRZiubM",
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{
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"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": 8,
"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('all-MiniLM-L6-v2', device=device)\n",
"model"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "MP2-unZ--XJ9"
},
"source": [
"Now let's query."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JWcO7jAK-N_1",
"outputId": "69eb2969-b6f8-4ca1-c69b-8235be9970ec"
},
"outputs": [
{
"data": {
"text/plain": [
"{'matches': [{'id': '210880',\n",
" 'metadata': {'text': ' Which is the most populated city in the '\n",
" 'world.?'},\n",
" 'score': 0.8828482,\n",
" 'sparse_values': {'indices': [], 'values': []},\n",
" 'values': []},\n",
" {'id': '197238',\n",
" 'metadata': {'text': ' What is the most populated city in the '\n",
" 'world?'},\n",
" 'score': 0.8788713,\n",
" 'sparse_values': {'indices': [], 'values': []},\n",
" 'values': []},\n",
" {'id': '239582',\n",
" 'metadata': {'text': ' Which is the largest city in the world?'},\n",
" 'score': 0.81348896,\n",
" 'sparse_values': {'indices': [], 'values': []},\n",
" 'values': []},\n",
" {'id': '216678',\n",
" 'metadata': {'text': ' Which is biggest city in the world?'},\n",
" 'score': 0.795525,\n",
" 'sparse_values': {'indices': [], 'values': []},\n",
" 'values': []},\n",
" {'id': '69331',\n",
" 'metadata': {'text': \" What's the world's largest city?\"},\n",
" 'score': 0.7856553,\n",
" 'sparse_values': {'indices': [], 'values': []},\n",
" 'values': []}],\n",
" 'namespace': ''}"
]
},
"execution_count": 9,
"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(xq, top_k=5, include_metadata=True)\n",
"xc"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "-XwOWcgo_QtI"
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"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": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gy7isg_f-vWg",
"outputId": "645f01a1-3110-48a9-edc0-29814687021b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.88: Which is the most populated city in the world.?\n",
"0.88: What is the most populated city in the world?\n",
"0.81: Which is the largest city in the world?\n",
"0.8: Which is biggest city in the world?\n",
"0.79: What's the world's largest city?\n"
]
}
],
"source": [
"for result in xc['matches']:\n",
" print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "1JK5yApl_5fE"
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"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": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dJbjE-iq_yMr",
"outputId": "3487d2f6-0aeb-43dd-886d-69ca4bbe942d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.73: Which is the most populated city in the world.?\n",
"0.72: What is the most populated city in the world?\n",
"0.67: What is the most isolated city in the world, with over a million metro area inhabitants?\n",
"0.64: What is the biggest city?\n",
"0.62: What is the city in the U.S. experiencing the fastest population growth as of 2016? Why?\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(xq, top_k=5, include_metadata=True)\n",
"for result in xc['matches']:\n",
" print(f\"{round(result['score'], 2)}: {result['metadata']['text']}\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "HIAxOPb-A2w_"
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"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": 12,
"metadata": {
"id": "-cWdeKzhAtww"
},
"outputs": [],
"source": [
"pinecone.delete_index(index_name)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "2B0zxR6hbf5d"
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"source": [
"---"
]
}
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