Finding your next movie
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.
Lets begin by first downloading the required libraries for this notebook:
openaiis used for communicating with the OpenAI embedding servicepymilvusis used for communicating with the Milvus serverdatasetsis used for downloading the datasettqdmis used for the progress bars
! pip install openai pymilvus datasets tqdmWith 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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[0m[?25hWith Milvus running we can setup our global variables:
- HOST: The Milvus host address
- PORT: The Milvus port number
- COLLECTION_NAME: What to name the collection within Milvus
- DIMENSION: The dimension of the embeddings
- OPENAI_ENGINE: Which embedding model to use
- openai.api_key: Your OpenAI account key
- INDEX_PARAM: The index settings to use for the collection
- QUERY_PARAM: The search parameters to use
- BATCH_SIZE: How many movies to embed and insert at once
import openai
HOST = 'localhost'
PORT = 19530
COLLECTION_NAME = 'movie_search'
DIMENSION = 1536
OPENAI_ENGINE = 'text-embedding-3-small'
openai.api_key = 'sk-your_key'
INDEX_PARAM = {
'metric_type':'L2',
'index_type':"HNSW",
'params':{'M': 8, 'efConstruction': 64}
}
QUERY_PARAM = {
"metric_type": "L2",
"params": {"ef": 64},
}
BATCH_SIZE = 1000from pymilvus import connections, utility, FieldSchema, Collection, CollectionSchema, DataType
# Connect to Milvus Database
connections.connect(host=HOST, port=PORT)# Remove collection if it already exists
if utility.has_collection(COLLECTION_NAME):
utility.drop_collection(COLLECTION_NAME)# Create collection which includes the id, title, and embedding.
fields = [
FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name='title', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='type', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='release_year', dtype=DataType.INT64),
FieldSchema(name='rating', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='description', dtype=DataType.VARCHAR, max_length=64000),
FieldSchema(name='embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION)
]
schema = CollectionSchema(fields=fields)
collection = Collection(name=COLLECTION_NAME, schema=schema)# Create the index on the collection and load it.
collection.create_index(field_name="embedding", index_params=INDEX_PARAM)
collection.load()Dataset
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.
import datasets
# Download the dataset
dataset = datasets.load_dataset('hugginglearners/netflix-shows', split='train')Found cached dataset csv (/Users/filiphaltmayer/.cache/huggingface/datasets/hugginglearners___csv/hugginglearners--netflix-shows-03475319fc65a05a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317)Insert the Data
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.
# Simple function that converts the texts to embeddings
def embed(texts):
embeddings = openai.Embedding.create(
input=texts,
engine=OPENAI_ENGINE
)
return [x['embedding'] for x in embeddings['data']]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.
from tqdm import tqdm
data = [
[], # title
[], # type
[], # release_year
[], # rating
[], # description
]
# Embed and insert in batches
for i in tqdm(range(0, len(dataset))):
data[0].append(dataset[i]['title'] or '')
data[1].append(dataset[i]['type'] or '')
data[2].append(dataset[i]['release_year'] or -1)
data[3].append(dataset[i]['rating'] or '')
data[4].append(dataset[i]['description'] or '')
if len(data[0]) % BATCH_SIZE == 0:
data.append(embed(data[4]))
collection.insert(data)
data = [[],[],[],[],[]]
# Embed and insert the remainder
if len(data[0]) != 0:
data.append(embed(data[4]))
collection.insert(data)
data = [[],[],[],[],[]]100%|██████████| 8807/8807 [00:31<00:00, 276.82it/s]Query the Database
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. 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.
import textwrap
def query(query, top_k = 5):
text, expr = query
res = collection.search(embed(text), anns_field='embedding', expr = expr, param=QUERY_PARAM, limit = top_k, output_fields=['title', 'type', 'release_year', 'rating', 'description'])
for i, hit in enumerate(res):
print('Description:', text, 'Expression:', expr)
print('Results:')
for ii, hits in enumerate(hit):
print('\t' + 'Rank:', ii + 1, 'Score:', hits.score, 'Title:', hits.entity.get('title'))
print('\t\t' + 'Type:', hits.entity.get('type'), 'Release Year:', hits.entity.get('release_year'), 'Rating:', hits.entity.get('rating'))
print(textwrap.fill(hits.entity.get('description'), 88))
print()
my_query = ('movie about a fluffly animal', 'release_year < 2019 and rating like \"PG%\"')
query(my_query)Description: movie about a fluffly animal Expression: release_year < 2019 and rating like "PG%"
Results:
Rank: 1 Score: 0.30083978176116943 Title: The Lamb
Type: Movie Release Year: 2017 Rating: PG
A big-dreaming donkey escapes his menial existence and befriends some free-spirited
animal pals in this imaginative retelling of the Nativity Story.
Rank: 2 Score: 0.33528298139572144 Title: Puss in Boots
Type: Movie Release Year: 2011 Rating: PG
The fabled feline heads to the Land of Giants with friends Humpty Dumpty and Kitty
Softpaws on a quest to nab its greatest treasure: the Golden Goose.
Rank: 3 Score: 0.33528298139572144 Title: Puss in Boots
Type: Movie Release Year: 2011 Rating: PG
The fabled feline heads to the Land of Giants with friends Humpty Dumpty and Kitty
Softpaws on a quest to nab its greatest treasure: the Golden Goose.
Rank: 4 Score: 0.3414868116378784 Title: Show Dogs
Type: Movie Release Year: 2018 Rating: PG
A rough and tough police dog must go undercover with an FBI agent as a prim and proper
pet at a dog show to save a baby panda from an illegal sale.
Rank: 5 Score: 0.3414868116378784 Title: Show Dogs
Type: Movie Release Year: 2018 Rating: PG
A rough and tough police dog must go undercover with an FBI agent as a prim and proper
pet at a dog show to save a baby panda from an illegal sale.