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445 lines
22 KiB
Plaintext
445 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Filtered Search with Milvus and OpenAI\n",
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"### Finding your next movie\n",
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"\n",
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"In this notebook we will be going over generating embeddings of movie descriptions with OpenAI and using those embeddings within Milvus to find relevant movies. To narrow our search results and try something new, we are going to be using filtering to do metadata searches. The dataset in this example is sourced from HuggingFace datasets, and contains a little over 8 thousand movie entries.\n",
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"\n",
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"Lets begin by first downloading the required libraries for this notebook:\n",
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"- `openai` is used for communicating with the OpenAI embedding service\n",
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"- `pymilvus` is used for communicating with the Milvus server\n",
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"- `datasets` is used for downloading the dataset\n",
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"- `tqdm` is used for the progress bars\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install openai pymilvus datasets tqdm"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With the required packages installed we can get started. Lets begin by launching the Milvus service. The file being run is the `docker-compose.yaml` found in the folder of this file. This command launches a Milvus standalone instance which we will use for this test. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"E0317 14:06:38.344884000 140704629352640 fork_posix.cc:76] Other threads are currently calling into gRPC, skipping fork() handlers\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[1A\u001b[1B\u001b[0G\u001b[?25l[+] Running 1/0\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠋ Container milvus-etcd Creating 0.0s\n",
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"\u001b[0m\u001b[37m ⠋ Container milvus-minio Creating 0.0s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 1/3\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠙ Container milvus-etcd Creating 0.1s\n",
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"\u001b[0m\u001b[37m ⠙ Container milvus-minio Creating 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/3\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.2s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.2s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.3s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.3s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.4s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.4s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.5s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.5s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.6s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.6s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.7s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.7s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.8s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.8s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 2/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-etcd Starting 0.9s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-minio Starting 1.0s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Created 0.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.0s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.1s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.2s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.3s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.4s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Running 3/4\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[37m ⠿ Container milvus-standalone Starting 1.5s\n",
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"\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l\u001b[34m[+] Running 4/4\u001b[0m\n",
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"\u001b[34m ⠿ Network milvus Created 0.1s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-etcd Started 0.9s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-minio Started 1.0s\n",
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"\u001b[0m\u001b[34m ⠿ Container milvus-standalone Started 1.6s\n",
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"\u001b[0m\u001b[?25h"
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]
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}
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],
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"source": [
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"! docker compose up -d"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"With Milvus running we can setup our global variables:\n",
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"- HOST: The Milvus host address\n",
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"- PORT: The Milvus port number\n",
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"- COLLECTION_NAME: What to name the collection within Milvus\n",
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"- DIMENSION: The dimension of the embeddings\n",
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"- OPENAI_ENGINE: Which embedding model to use\n",
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"- openai.api_key: Your OpenAI account key\n",
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"- INDEX_PARAM: The index settings to use for the collection\n",
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"- QUERY_PARAM: The search parameters to use\n",
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"- BATCH_SIZE: How many movies to embed and insert at once"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"\n",
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"HOST = 'localhost'\n",
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"PORT = 19530\n",
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"COLLECTION_NAME = 'movie_search'\n",
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"DIMENSION = 1536\n",
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"OPENAI_ENGINE = 'text-embedding-3-small'\n",
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"openai.api_key = 'sk-your_key'\n",
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"\n",
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"INDEX_PARAM = {\n",
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" 'metric_type':'L2',\n",
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" 'index_type':\"HNSW\",\n",
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" 'params':{'M': 8, 'efConstruction': 64}\n",
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"}\n",
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"\n",
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"QUERY_PARAM = {\n",
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" \"metric_type\": \"L2\",\n",
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" \"params\": {\"ef\": 64},\n",
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"}\n",
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"\n",
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"BATCH_SIZE = 1000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pymilvus import connections, utility, FieldSchema, Collection, CollectionSchema, DataType\n",
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"\n",
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"# Connect to Milvus Database\n",
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"connections.connect(host=HOST, port=PORT)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Remove collection if it already exists\n",
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"if utility.has_collection(COLLECTION_NAME):\n",
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" utility.drop_collection(COLLECTION_NAME)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create collection which includes the id, title, and embedding.\n",
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"fields = [\n",
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" FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),\n",
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" FieldSchema(name='title', dtype=DataType.VARCHAR, max_length=64000),\n",
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" FieldSchema(name='type', dtype=DataType.VARCHAR, max_length=64000),\n",
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" FieldSchema(name='release_year', dtype=DataType.INT64),\n",
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" FieldSchema(name='rating', dtype=DataType.VARCHAR, max_length=64000),\n",
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" FieldSchema(name='description', dtype=DataType.VARCHAR, max_length=64000),\n",
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" FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)\n",
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"]\n",
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"schema = CollectionSchema(fields=fields)\n",
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"collection = Collection(name=COLLECTION_NAME, schema=schema)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create the index on the collection and load it.\n",
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"collection.create_index(field_name=\"embedding\", index_params=INDEX_PARAM)\n",
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"collection.load()"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Dataset\n",
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"With Milvus up and running we can begin grabbing our data. Hugging Face Datasets is a hub that holds many different user datasets, and for this example we are using HuggingLearners's netflix-shows dataset. This dataset contains movies and their metadata pairs for over 8 thousand movies. We are going to embed each description and store it within Milvus along with its title, type, release_year and rating."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Found cached dataset csv (/Users/filiphaltmayer/.cache/huggingface/datasets/hugginglearners___csv/hugginglearners--netflix-shows-03475319fc65a05a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)\n"
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]
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}
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],
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"source": [
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"import datasets\n",
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"\n",
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"# Download the dataset \n",
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"dataset = datasets.load_dataset('hugginglearners/netflix-shows', split='train')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Insert the Data\n",
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"Now that we have our data on our machine we can begin embedding it and inserting it into Milvus. The embedding function takes in text and returns the embeddings in a list format. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Simple function that converts the texts to embeddings\n",
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"def embed(texts):\n",
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" embeddings = openai.Embedding.create(\n",
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" input=texts,\n",
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" engine=OPENAI_ENGINE\n",
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" )\n",
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" return [x['embedding'] for x in embeddings['data']]\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This next step does the actual inserting. We iterate through all the entries and create batches that we insert once we hit our set batch size. After the loop is over we insert the last remaning batch if it exists. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 8807/8807 [00:31<00:00, 276.82it/s]\n"
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]
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}
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],
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"source": [
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"from tqdm import tqdm\n",
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"\n",
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"data = [\n",
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" [], # title\n",
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" [], # type\n",
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" [], # release_year\n",
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" [], # rating\n",
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" [], # description\n",
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"]\n",
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"\n",
|
|
"# Embed and insert in batches\n",
|
|
"for i in tqdm(range(0, len(dataset))):\n",
|
|
" data[0].append(dataset[i]['title'] or '')\n",
|
|
" data[1].append(dataset[i]['type'] or '')\n",
|
|
" data[2].append(dataset[i]['release_year'] or -1)\n",
|
|
" data[3].append(dataset[i]['rating'] or '')\n",
|
|
" data[4].append(dataset[i]['description'] or '')\n",
|
|
" if len(data[0]) % BATCH_SIZE == 0:\n",
|
|
" data.append(embed(data[4]))\n",
|
|
" collection.insert(data)\n",
|
|
" data = [[],[],[],[],[]]\n",
|
|
"\n",
|
|
"# Embed and insert the remainder \n",
|
|
"if len(data[0]) != 0:\n",
|
|
" data.append(embed(data[4]))\n",
|
|
" collection.insert(data)\n",
|
|
" data = [[],[],[],[],[]]\n"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Query the Database\n",
|
|
"With our data safely inserted in Milvus, we can now perform a query. The query takes in a tuple of the movie description you are searching for an the filter to use. More info about the filter can be found [here](https://milvus.io/docs/boolean.md). The search first prints out your description and filter expression. After that for each result we print the score, title, type, release year, rating, and description of the result movies. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Description: movie about a fluffly animal Expression: release_year < 2019 and rating like \"PG%\"\n",
|
|
"Results:\n",
|
|
"\tRank: 1 Score: 0.30083978176116943 Title: The Lamb\n",
|
|
"\t\tType: Movie Release Year: 2017 Rating: PG\n",
|
|
"A big-dreaming donkey escapes his menial existence and befriends some free-spirited\n",
|
|
"animal pals in this imaginative retelling of the Nativity Story.\n",
|
|
"\n",
|
|
"\tRank: 2 Score: 0.33528298139572144 Title: Puss in Boots\n",
|
|
"\t\tType: Movie Release Year: 2011 Rating: PG\n",
|
|
"The fabled feline heads to the Land of Giants with friends Humpty Dumpty and Kitty\n",
|
|
"Softpaws on a quest to nab its greatest treasure: the Golden Goose.\n",
|
|
"\n",
|
|
"\tRank: 3 Score: 0.33528298139572144 Title: Puss in Boots\n",
|
|
"\t\tType: Movie Release Year: 2011 Rating: PG\n",
|
|
"The fabled feline heads to the Land of Giants with friends Humpty Dumpty and Kitty\n",
|
|
"Softpaws on a quest to nab its greatest treasure: the Golden Goose.\n",
|
|
"\n",
|
|
"\tRank: 4 Score: 0.3414868116378784 Title: Show Dogs\n",
|
|
"\t\tType: Movie Release Year: 2018 Rating: PG\n",
|
|
"A rough and tough police dog must go undercover with an FBI agent as a prim and proper\n",
|
|
"pet at a dog show to save a baby panda from an illegal sale.\n",
|
|
"\n",
|
|
"\tRank: 5 Score: 0.3414868116378784 Title: Show Dogs\n",
|
|
"\t\tType: Movie Release Year: 2018 Rating: PG\n",
|
|
"A rough and tough police dog must go undercover with an FBI agent as a prim and proper\n",
|
|
"pet at a dog show to save a baby panda from an illegal sale.\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"import textwrap\n",
|
|
"\n",
|
|
"def query(query, top_k = 5):\n",
|
|
" text, expr = query\n",
|
|
" res = collection.search(embed(text), anns_field='embedding', expr = expr, param=QUERY_PARAM, limit = top_k, output_fields=['title', 'type', 'release_year', 'rating', 'description'])\n",
|
|
" for i, hit in enumerate(res):\n",
|
|
" print('Description:', text, 'Expression:', expr)\n",
|
|
" print('Results:')\n",
|
|
" for ii, hits in enumerate(hit):\n",
|
|
" print('\\t' + 'Rank:', ii + 1, 'Score:', hits.score, 'Title:', hits.entity.get('title'))\n",
|
|
" print('\\t\\t' + 'Type:', hits.entity.get('type'), 'Release Year:', hits.entity.get('release_year'), 'Rating:', hits.entity.get('rating'))\n",
|
|
" print(textwrap.fill(hits.entity.get('description'), 88))\n",
|
|
" print()\n",
|
|
"\n",
|
|
"my_query = ('movie about a fluffly animal', 'release_year < 2019 and rating like \\\"PG%\\\"')\n",
|
|
"\n",
|
|
"query(my_query)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "haystack",
|
|
"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": "3.9.16"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|