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llm-examples/integrations/openai/semantic_search_openai.ipynb
James Briggs 01623f28b4 added OpenAI example
2022-03-31 11:57:24 +01:00

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{
"cells": [
{
"cell_type": "markdown",
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"id": "izNeUi--hSSA"
},
"source": [
"# Semantic Search with Pinecone and OpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LI76-BMFhUmS"
},
"source": [
"In this notebook we will work through a demo example of building a SOTA semantic search tool with OpenAI vector embeddings and the Pinecone vector DB."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nXzzYE0hhU64"
},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FNk3hfyAtRNj"
},
"source": [
"We first need to setup our environment and retrieve API keys for OpenAI and Pinecone. Let's start with our environment, we need HuggingFace *Datasets* for our data, and the OpenAI and Pinecone clients:"
]
},
{
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"id": "SGQoz_JmtRNh",
"outputId": "f191c937-401d-4d74-8286-7f286a7d5d6c"
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]
}
],
"source": [
"!pip install pinecone-client openai datasets"
]
},
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"id": "IMp0TQ24tRNm"
},
"source": [
"### Creating Embeddings\n",
"\n",
"Then we initialize our connection to OpenAI Embeddings *and* Pinecone vector DB. Sign up for an API key over at [OpenAI](https://beta.openai.com/signup) and [Pinecone](https://app.pinecone.io)."
]
},
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import openai\n",
"import os\n",
"\n",
"openai.organization = \"<<YOUR_ORG_KEY>>\"\n",
"# get this from top-right dropdown on OpenAI under organization > settings\n",
"openai.api_key = \"<<YOUR_API_KEY>>\"\n",
"# get API key from top-right dropdown on OpenAI website\n",
"\n",
"openai.Engine.list() # check we have authenticated"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pw8cbO3HtRNo"
},
"source": [
"We can now create embeddings with the OpenAI Babbage similarity model like so:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Hg0yIGu3tRNo",
"outputId": "2dd5d247-cfe2-4087-cfe3-381b3cb71cd3"
},
"outputs": [
{
"data": {
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" -0.016164718195796013,\n",
" -0.014666353352367878\n",
" ],\n",
" \"index\": 1,\n",
" \"object\": \"embedding\"\n",
" }\n",
" ],\n",
" \"model\": \"text-similarity-babbage:001\",\n",
" \"object\": \"list\"\n",
"}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MODEL = \"text-similarity-babbage-001\"\n",
"\n",
"res = openai.Embedding.create(\n",
" input=[\n",
" \"Sample document text goes here\",\n",
" \"there will be several phrases in each batch\"\n",
" ], engine=MODEL\n",
")\n",
"res"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-Egz35DjtRNo",
"outputId": "8bca0c54-8473-489d-fbf7-ef97f761c265"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"vector 0: 2048\n",
"vector 1: 2048\n"
]
}
],
"source": [
"print(f\"vector 0: {len(res['data'][0]['embedding'])}\\nvector 1: {len(res['data'][1]['embedding'])}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZguN6obBtRNp",
"outputId": "099a7f35-a9db-447f-aecc-9f12c29d9725"
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we can extract embeddings to a list\n",
"embeds = [record['embedding'] for record in res['data']]\n",
"len(embeds)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U4WJwy58tRNp"
},
"source": [
"Next, we initialize our index to store vector embeddings with Pinecone."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "I2KLRbN-tRNq"
},
"outputs": [],
"source": [
"import pinecone\n",
"\n",
"# initialize connection to pinecone (get API key at app.pinecone.io)\n",
"pinecone.init(\n",
" api_key=\"<<YOUR_API_KEY>>\",\n",
" environment=\"us-west1-gcp\"\n",
")\n",
"# check if 'openai' index already exists (only create index if not)\n",
"if 'openai' not in pinecone.list_indexes():\n",
" pinecone.create_index('openai', dimension=len(embeds[0]))\n",
"# connect to index\n",
"index = pinecone.Index('openai')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TLCLmeZRtRNq"
},
"source": [
"## Populating the Index\n",
"\n",
"Now we will take 1K questions from the TREC dataset"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 354,
"referenced_widgets": [
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"8c09512a3f694974ac81eafe966b215d",
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"f4c890981eee4b309e74182d7c6efd7e",
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"b412fef729774d6486179efe1c67cc14",
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"ced90cd6e9674bb1bd67d960f9e84886",
"3807588b4c094ae1af577ba1812e2009",
"cc14577d86e24666af6de3703c9bb2f5",
"d8b927526d0247b082deac98155b64a2",
"484395f9d37e4ba082ca471fdd8bb16b",
"275f4d9a26654d84b24cf1d5e68be5d5",
"6571a9365a7d45a196f7893fc6a6816a",
"abffa382cf4643389c88025f50cf1f6d",
"d1d0a913d96e4ce09d71b222981da272",
"67bbd07770c246f7a3bd73de6f9d47f2",
"1ceefec6acb748239860ef5f0e226ab5",
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"e341077a664f4775953f18b87c58c7d2"
]
},
"id": "JiSfCTK-lhXx",
"outputId": "980c977a-77f2-4ae0-d257-0558bcb367ed"
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b7f88930c8374baca58ffc9f4ed2ead1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading builder script: 0%| | 0.00/2.22k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c09512a3f694974ac81eafe966b215d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading metadata: 0%| | 0.00/1.42k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using custom data configuration default\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset trec/default (download: 350.79 KiB, generated: 403.39 KiB, post-processed: Unknown size, total: 754.18 KiB) to /root/.cache/huggingface/datasets/trec/default/1.1.0/751da1ab101b8d297a3d6e9c79ee9b0173ff94c4497b75677b59b61d5467a9b9...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1f1468640aea4935b247acc8b329731a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data files: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3baa92a62d334dfd9321db79069c71f7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/336k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4fe63d87b66f484696bb466b42cae78a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data: 0%| | 0.00/23.4k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "84d64089fe4a4da0bf1c1151fa5ba336",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Extracting data files: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d4d209a4436d4f1a88d02bcab7534954",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0%| | 0/5452 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "cc14577d86e24666af6de3703c9bb2f5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating test split: 0%| | 0/500 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset trec downloaded and prepared to /root/.cache/huggingface/datasets/trec/default/1.1.0/751da1ab101b8d297a3d6e9c79ee9b0173ff94c4497b75677b59b61d5467a9b9. Subsequent calls will reuse this data.\n"
]
},
{
"data": {
"text/plain": [
"Dataset({\n",
" features: ['label-coarse', 'label-fine', 'text'],\n",
" num_rows: 1000\n",
"})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"# load the first 1K rows of the TREC dataset\n",
"trec = load_dataset('trec', split='train[:1000]')\n",
"trec"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2m7a_zl8lt2e",
"outputId": "6619adf5-bb60-432e-eec6-53c2678c1f3c"
},
"outputs": [
{
"data": {
"text/plain": [
"{'label-coarse': 0,\n",
" 'label-fine': 0,\n",
" 'text': 'How did serfdom develop in and then leave Russia ?'}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trec[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sJwnszP0ljN6"
},
"source": [
"Then we create a vector embedding for each phrase using OpenAI, and `upsert` the ID, vector embedding, and original text for each phrase to Pinecone."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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},
"id": "B80PgNyytRNq",
"outputId": "75167481-4cd0-4105-89c0-a471f383c0a1"
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"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4af75b670ad14d3181727f599eb2edfc",
"version_major": 2,
"version_minor": 0
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"text/plain": [
" 0%| | 0/32 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tqdm.auto import tqdm\n",
"\n",
"count = 0 # we'll use the count to create unique IDs\n",
"batch_size = 32 # process everything in batches of 32\n",
"for i in tqdm(range(0, len(trec['text']), batch_size)):\n",
" # set end position of batch\n",
" i_end = min(i+batch_size, len(trec['text']))\n",
" # get batch of lines and IDs\n",
" lines_batch = trec['text'][i: i+batch_size]\n",
" ids_batch = [str(n) for n in range(i, i_end)]\n",
" # create embeddings\n",
" res = openai.Embedding.create(input=lines_batch, engine=MODEL)\n",
" embeds = [record['embedding'] for record in res['data']]\n",
" # prep metadata and upsert batch\n",
" meta = [{'text': line} for line in lines_batch]\n",
" to_upsert = zip(ids_batch, embeds, meta)\n",
" # upsert to Pinecone\n",
" index.upsert(vectors=list(to_upsert))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iW_iM_M_tRNr"
},
"source": [
"---\n",
"\n",
"# Querying\n",
"\n",
"With our data indexed, we're now ready to move onto performing searches. This follows a similar process to indexing. We start with a text `query`, that we would like to use to find similar sentences. As before we encode this with OpenAI's text similarity Babbage model to create a *query vector* `xq`. We then use `xq` to query the Pinecone index."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "k80ASNMqtRNr"
},
"outputs": [],
"source": [
"query = \"What caused the 1929 Great Depression?\"\n",
"\n",
"xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RxCKv-UktRNs"
},
"source": [
"Now query..."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_cY3_rXytRNs",
"outputId": "11c8f66f-eb57-499c-ff9d-4706d9a24124"
},
"outputs": [
{
"data": {
"text/plain": [
"{'results': [{'matches': [{'id': '932',\n",
" 'metadata': {'text': 'Why did the world enter a '\n",
" 'global depression in 1929 ?'},\n",
" 'score': 0.948046744,\n",
" 'values': []},\n",
" {'id': '787',\n",
" 'metadata': {'text': 'When was `` the Great '\n",
" \"Depression '' ?\"},\n",
" 'score': 0.865037203,\n",
" 'values': []},\n",
" {'id': '400',\n",
" 'metadata': {'text': 'What crop failure caused the '\n",
" 'Irish Famine ?'},\n",
" 'score': 0.86109376,\n",
" 'values': []},\n",
" {'id': '481',\n",
" 'metadata': {'text': 'What caused the Lynmouth '\n",
" 'floods ?'},\n",
" 'score': 0.819413066,\n",
" 'values': []},\n",
" {'id': '233',\n",
" 'metadata': {'text': \"What caused Harry Houdini 's \"\n",
" 'death ?'},\n",
" 'score': 0.794981301,\n",
" 'values': []}],\n",
" 'namespace': ''}]}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = index.query([xq], top_k=5, include_metadata=True)\n",
"res"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I2iydlXVjmMZ"
},
"source": [
"The response from Pinecone includes our original text in the `metadata` field, let's print out the `top_k` most similar questions and their respective similarity scores."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WT1L2ogHjiO-",
"outputId": "2f31e353-c0f3-441f-eee6-359f6d25c386"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.95: Why did the world enter a global depression in 1929 ?\n",
"0.87: When was `` the Great Depression '' ?\n",
"0.86: What crop failure caused the Irish Famine ?\n",
"0.82: What caused the Lynmouth floods ?\n",
"0.79: What caused Harry Houdini 's death ?\n"
]
}
],
"source": [
"for match in res['results'][0]['matches']:\n",
" print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wu7LmvmIjqPq"
},
"source": [
"Looks good, let's make it harder and replace *\"depression\"* with the incorrect term *\"recession\"*."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SaT9yMj7jq0u",
"outputId": "bd41118d-69b4-4ac8-92f7-0b48c3d94355"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.92: Why did the world enter a global depression in 1929 ?\n",
"0.85: What crop failure caused the Irish Famine ?\n",
"0.83: When was `` the Great Depression '' ?\n",
"0.82: What are some of the significant historical events of the 1990s ?\n",
"0.82: What is considered the costliest disaster the insurance industry has ever faced ?\n"
]
}
],
"source": [
"query = \"What was the cause of the major recession in the early 20th century?\"\n",
"\n",
"# create the query embedding\n",
"xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']\n",
"\n",
"# query, returning the top 5 most similar results\n",
"res = index.query([xq], top_k=5, include_metadata=True)\n",
"\n",
"for match in res['results'][0]['matches']:\n",
" print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FXFhFri0jzkG"
},
"source": [
"And again..."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qOEvNweOj1AH",
"outputId": "bc0e980d-e045-4b63-f62b-8c5a33401c56"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.93: Why did the world enter a global depression in 1929 ?\n",
"0.83: What crop failure caused the Irish Famine ?\n",
"0.82: When was `` the Great Depression '' ?\n",
"0.82: How did serfdom develop in and then leave Russia ?\n",
"0.80: Why were people recruited for the Vietnam War ?\n"
]
}
],
"source": [
"query = \"Why was there a long-term economic downturn in the early 20th century?\"\n",
"\n",
"# create the query embedding\n",
"xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']\n",
"\n",
"# query, returning the top 5 most similar results\n",
"res = index.query([xq], top_k=5, include_metadata=True)\n",
"\n",
"for match in res['results'][0]['matches']:\n",
" print(f\"{match['score']:.2f}: {match['metadata']['text']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ItsvY1lej6dz"
},
"source": [
"Looks great, our semantic search pipeline is clearly able to identify the meaning between each of our queries and return the most semantically similar questions from the already indexed questions.\n",
"\n",
"---"
]
}
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