用 Milvus 推荐电影

在本笔记本中，我们将探讨如何使用 OpenAI 生成电影描述的 Embeddings，并在 Milvus 中利用这些 Embeddings 来推荐符合您偏好的电影。为了增强搜索结果，我们将利用过滤功能来执行元数据搜索。本示例中使用的数据集来自 HuggingFace 数据集，包含 8000 多个电影条目，为电影推荐提供了丰富的选择。

依赖项和环境

运行以下命令即可安装依赖项：

$ pip install openai pymilvus datasets tqdm

import os

os.environ["OPENAI_API_KEY"] = "sk-***********"

初始化 OpenAI 客户端和 Milvus

初始化 OpenAI 客户端。

from openai import OpenAI

openai_client = OpenAI()

为 Embeddings 设置 Collections 名称和维度。

COLLECTION_NAME = "movie_search"
DIMENSION = 1536

BATCH_SIZE = 1000

连接 Milvus。

from pymilvus import MilvusClient

# Connect to Milvus Database
client = MilvusClient("./milvus_demo.db")

# Remove collection if it already exists
if client.has_collection(COLLECTION_NAME):
    client.drop_collection(COLLECTION_NAME)

定义 Collections 的字段，包括 id、标题、类型、发布年份、评级和描述。

from pymilvus import DataType

# Create collection which includes the id, title, and embedding.

# 1. Create schema
schema = MilvusClient.create_schema(
    auto_id=True,
    enable_dynamic_field=False,
)

# 2. Add fields to schema
schema.add_field(field_name="id", datatype=DataType.INT64, is_primary=True)
schema.add_field(field_name="title", datatype=DataType.VARCHAR, max_length=64000)
schema.add_field(field_name="type", datatype=DataType.VARCHAR, max_length=64000)
schema.add_field(field_name="release_year", datatype=DataType.INT64)
schema.add_field(field_name="rating", datatype=DataType.VARCHAR, max_length=64000)
schema.add_field(field_name="description", datatype=DataType.VARCHAR, max_length=64000)
schema.add_field(field_name="embedding", datatype=DataType.FLOAT_VECTOR, dim=DIMENSION)

# 3. Create collection with the schema
client.create_collection(collection_name=COLLECTION_NAME, schema=schema)

在 Collections 上创建索引并加载。

# Create the index on the collection and load it.

# 1. Prepare index parameters
index_params = client.prepare_index_params()


# 2. Add an index on the embedding field
index_params.add_index(
    field_name="embedding", metric_type="IP", index_type="AUTOINDEX", params={}
)


# 3. Create index
client.create_index(collection_name=COLLECTION_NAME, index_params=index_params)


# 4. Load collection
client.load_collection(collection_name=COLLECTION_NAME, replica_number=1)

数据集

Milvus 启动并运行后，我们就可以开始抓取数据了。Hugging Face Datasets 是一个拥有许多不同用户数据集的集线器，在这个示例中，我们使用 HuggingLearners 的 netflix-shows 数据集。该数据集包含 8000 多部电影及其元数据对。我们将嵌入每条描述，并将其与标题、类型、发行年份和评分一起存储在 Milvus 中。

from datasets import load_dataset

dataset = load_dataset("hugginglearners/netflix-shows", split="train")

插入数据

现在我们的机器上已经有了数据，我们可以开始嵌入数据并将其插入 Milvus。Embeddings 函数接收文本，并以列表格式返回嵌入数据。

def emb_texts(texts):
    res = openai_client.embeddings.create(input=texts, model="text-embedding-3-small")
    return [res_data.embedding for res_data in res.data]

下一步是实际插入。我们会遍历所有条目，并创建批次，一旦达到设定的批次大小，就会插入这些条目。循环结束后，如果还存在最后一个批次，则插入该批次。

from tqdm import tqdm

# batch (data to be inserted) is a list of dictionaries
batch = []

# Embed and insert in batches
for i in tqdm(range(0, len(dataset))):
    batch.append(
        {
            "title": dataset[i]["title"] or "",
            "type": dataset[i]["type"] or "",
            "release_year": dataset[i]["release_year"] or -1,
            "rating": dataset[i]["rating"] or "",
            "description": dataset[i]["description"] or "",
        }
    )

    if len(batch) % BATCH_SIZE == 0 or i == len(dataset) - 1:
        embeddings = emb_texts([item["description"] for item in batch])

        for item, emb in zip(batch, embeddings):
            item["embedding"] = emb

        client.insert(collection_name=COLLECTION_NAME, data=batch)
        batch = []

查询数据库

数据安全地插入 Milvus 后，我们就可以执行查询了。查询将输入一个元组，其中包括要搜索的电影描述和要使用的过滤器。有关过滤器的更多信息，请点击此处。搜索首先会打印出描述和过滤器表达式。然后，我们会为每个结果打印分数、标题、类型、发行年份、评分和结果电影的描述。

import textwrap


def query(query, top_k=5):
    text, expr = query

    res = client.search(
        collection_name=COLLECTION_NAME,
        data=emb_texts(text),
        filter=expr,
        limit=top_k,
        output_fields=["title", "type", "release_year", "rating", "description"],
        search_params={
            "metric_type": "IP",
            "params": {},
        },
    )

    print("Description:", text, "Expression:", expr)

    for hit_group in res:
        print("Results:")
        for rank, hit in enumerate(hit_group, start=1):
            entity = hit["entity"]

            print(
                f"\tRank: {rank} Score: {hit['distance']:} Title: {entity.get('title', '')}"
            )
            print(
                f"\t\tType: {entity.get('type', '')} "
                f"Release Year: {entity.get('release_year', '')} "
                f"Rating: {entity.get('rating', '')}"
            )
            description = entity.get("description", "")
            print(textwrap.fill(description, width=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: 0.42213767766952515 Title: The Adventures of Tintin
        Type: Movie Release Year: 2011 Rating: PG
This 3-D motion capture adapts Georges Remi's classic comic strip about the adventures
of fearless young journalist Tintin and his trusty dog, Snowy.

    Rank: 2 Score: 0.4041026830673218 Title: Hedgehogs
        Type: Movie Release Year: 2016 Rating: PG
When a hedgehog suffering from memory loss forgets his identity, he ends up on a big
city journey with a pigeon to save his habitat from a human threat.

    Rank: 3 Score: 0.3980264663696289 Title: Osmosis Jones
        Type: Movie Release Year: 2001 Rating: PG
Peter and Bobby Farrelly outdo themselves with this partially animated tale about an
out-of-shape 40-year-old man who's the host to various organisms.

    Rank: 4 Score: 0.39479154348373413 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: 5 Score: 0.39370301365852356 Title: Open Season 2
        Type: Movie Release Year: 2008 Rating: PG
Elliot the buck and his forest-dwelling cohorts must rescue their dachshund pal from
some spoiled pets bent on returning him to domesticity.