Zilliz 和 OpenAI 入门指南
寻找下一本书
在本 notebook 中，我们将使用 OpenAI 为书籍描述生成 embeddings，并在 Zilliz 中使用这些 embeddings 来查找相关的书籍。本示例中的数据集来源于 HuggingFace datasets，包含 100 万多条标题-描述对。
让我们开始下载本 notebook 所需的库：
openai用于与 OpenAI embedding 服务通信pymilvus用于与 Zilliz 实例通信datasets用于下载数据集tqdm用于显示进度条
! pip install openai pymilvus datasets tqdm
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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要启动 Zilliz，请参阅 此处。设置好您的帐户和数据库后，请继续设置以下值：
- URI：您的数据库正在运行的 URI
- USER：您的数据库用户名
- PASSWORD：您的数据库密码
- COLLECTION_NAME：Zilliz 中集合的名称
- DIMENSION：embeddings 的维度
- OPENAI_ENGINE：要使用的 embedding 模型
- openai.api_key：您的 OpenAI 帐户密钥
- INDEX_PARAM：要用于集合的索引设置
- QUERY_PARAM：要使用的搜索参数
- BATCH_SIZE：一次嵌入和插入多少文本
import openai
URI = 'your_uri'
TOKEN = 'your_token' # TOKEN == user:password or api_key
COLLECTION_NAME = 'book_search'
DIMENSION = 1536
OPENAI_ENGINE = 'text-embedding-3-small'
openai.api_key = 'sk-your-key'
INDEX_PARAM = {
'metric_type':'L2',
'index_type':"AUTOINDEX",
'params':{}
}
QUERY_PARAM = {
"metric_type": "L2",
"params": {},
}
BATCH_SIZE = 1000
Zilliz
此部分涉及 Zilliz 以及为本用例设置数据库。在 Zilliz 中，我们需要设置一个集合并对其进行索引。
from pymilvus import connections, utility, FieldSchema, Collection, CollectionSchema, DataType
# 连接到 Zilliz 数据库
connections.connect(uri=URI, token=TOKEN)
# 如果集合已存在，则删除它
if utility.has_collection(COLLECTION_NAME):
utility.drop_collection(COLLECTION_NAME)
# 创建包含 id、title 和 embedding 的集合。
fields = [
FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
FieldSchema(name='title', 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)
# 在集合上创建索引并加载它。
collection.create_index(field_name="embedding", index_params=INDEX_PARAM)
collection.load()
数据集
Zilliz 运行正常后，我们就可以开始获取数据了。Hugging Face Datasets 是一个包含许多不同用户数据集的中心，在本示例中，我们使用的是 Skelebor 的书籍数据集。此数据集包含 100 万多本书的标题-描述对。我们将嵌入每个描述，并将其与标题一起存储在 Zilliz 中。
import datasets
# 下载数据集并仅使用 `train` 部分（文件大小约为 800Mb）
dataset = datasets.load_dataset('Skelebor/book_titles_and_descriptions_en_clean', split='train')
/Users/filiphaltmayer/miniconda3/envs/haystack/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Found cached dataset parquet (/Users/filiphaltmayer/.cache/huggingface/datasets/Skelebor___parquet/Skelebor--book_titles_and_descriptions_en_clean-3596935b1d8a7747/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)
插入数据
现在我们已经将数据加载到本地计算机上，可以开始嵌入数据并将其插入 Zilliz。嵌入函数接收文本并以列表格式返回 embeddings。
# 将文本转换为 embeddings 的简单函数
def embed(texts):
embeddings = openai.Embedding.create(
input=texts,
engine=OPENAI_ENGINE
)
return [x['embedding'] for x in embeddings['data']]
下一步是实际插入。由于数据点很多，如果您想立即进行测试，可以提前停止插入单元格并继续。这样做可能会降低结果的准确性，因为数据点较少，但仍然应该足够好。
from tqdm import tqdm
data = [
[], # title
[], # description
]
# 分批嵌入和插入
for i in tqdm(range(0, len(dataset))):
data[0].append(dataset[i]['title'])
data[1].append(dataset[i]['description'])
if len(data[0]) % BATCH_SIZE == 0:
data.append(embed(data[1]))
collection.insert(data)
data = [[],[]]
# 嵌入并插入剩余数据
if len(data[0]) != 0:
data.append(embed(data[1]))
collection.insert(data)
data = [[],[]]
0%| | 2999/1032335 [00:19<1:49:30, 156.66it/s]
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[10], line 14
12 if len(data[0]) % BATCH_SIZE == 0:
13 data.append(embed(data[1]))
---> 14 collection.insert(data)
15 data = [[],[]]
17 # Embed and insert the remainder
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/orm/collection.py:430, in Collection.insert(self, data, partition_name, timeout, **kwargs)
427 entities = Prepare.prepare_insert_data(data, self._schema)
429 conn = self._get_connection()
--> 430 res = conn.batch_insert(self._name, entities, partition_name,
431 timeout=timeout, schema=self._schema_dict, **kwargs)
433 if kwargs.get("_async", False):
434 return MutationFuture(res)
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:105, in error_handler.<locals>.wrapper.<locals>.handler(*args, **kwargs)
103 try:
104 record_dict["RPC start"] = str(datetime.datetime.now())
--> 105 return func(*args, **kwargs)
106 except MilvusException as e:
107 record_dict["RPC error"] = str(datetime.datetime.now())
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:136, in tracing_request.<locals>.wrapper.<locals>.handler(self, *args, **kwargs)
134 if req_id:
135 self.set_onetime_request_id(req_id)
--> 136 ret = func(self, *args, **kwargs)
137 return ret
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/decorators.py:50, in retry_on_rpc_failure.<locals>.wrapper.<locals>.handler(self, *args, **kwargs)
48 while True:
49 try:
--> 50 return func(self, *args, **kwargs)
51 except grpc.RpcError as e:
52 # DEADLINE_EXCEEDED means that the task wat not completed
53 # UNAVAILABLE means that the service is not reachable currently
54 # Reference: https://grpc.github.io/grpc/python/grpc.html#grpc-status-code
55 if e.code() != grpc.StatusCode.DEADLINE_EXCEEDED and e.code() != grpc.StatusCode.UNAVAILABLE:
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/pymilvus/client/grpc_handler.py:378, in GrpcHandler.batch_insert(self, collection_name, entities, partition_name, timeout, **kwargs)
375 f.add_callback(ts_utils.update_ts_on_mutation(collection_name))
376 return f
--> 378 response = rf.result()
379 if response.status.error_code == 0:
380 m = MutationResult(response)
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/grpc/_channel.py:733, in _MultiThreadedRendezvous.result(self, timeout)
728 """Returns the result of the computation or raises its exception.
729
730 See grpc.Future.result for the full API contract.
731 """
732 with self._state.condition:
--> 733 timed_out = _common.wait(self._state.condition.wait,
734 self._is_complete,
735 timeout=timeout)
736 if timed_out:
737 raise grpc.FutureTimeoutError()
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/grpc/_common.py:141, in wait(wait_fn, wait_complete_fn, timeout, spin_cb)
139 if timeout is None:
140 while not wait_complete_fn():
--> 141 _wait_once(wait_fn, MAXIMUM_WAIT_TIMEOUT, spin_cb)
142 else:
143 end = time.time() + timeout
File ~/miniconda3/envs/haystack/lib/python3.9/site-packages/grpc/_common.py:106, in _wait_once(wait_fn, timeout, spin_cb)
105 def _wait_once(wait_fn, timeout, spin_cb):
--> 106 wait_fn(timeout=timeout)
107 if spin_cb is not None:
108 spin_cb()
File ~/miniconda3/envs/haystack/lib/python3.9/threading.py:316, in Condition.wait(self, timeout)
314 else:
315 if timeout > 0:
--> 316 gotit = waiter.acquire(True, timeout)
317 else:
318 gotit = waiter.acquire(False)
KeyboardInterrupt:
查询数据库
我们的数据已安全地插入 Zilliz，现在我们可以执行查询了。查询接收一个或多个字符串并搜索它们。结果会打印出您提供的描述以及包含结果得分、结果标题和结果书籍描述的相关内容。
import textwrap
def query(queries, top_k = 5):
if type(queries) != list:
queries = [queries]
res = collection.search(embed(queries), anns_field='embedding', param=QUERY_PARAM, limit = top_k, output_fields=['title', 'description'])
for i, hit in enumerate(res):
print('Description:', queries[i])
print('Results:')
for ii, hits in enumerate(hit):
print('\t' + 'Rank:', ii + 1, 'Score:', hits.score, 'Title:', hits.entity.get('title'))
print(textwrap.fill(hits.entity.get('description'), 88))
print()
query('Book about a k-9 from europe')
Description: Book about a k-9 from europe
Results:
Rank: 1 Score: 0.3047754764556885 Title: Bark M For Murder
Who let the dogs out? Evildoers beware! Four of mystery fiction's top storytellers are
setting the hounds on your trail -- in an incomparable quartet of crime stories with a
canine edge. Man's (and woman's) best friends take the lead in this phenomenal
collection of tales tense and surprising, humorous and thrilling: New York
Timesbestselling author J.A. Jance's spellbinding saga of a scam-busting septuagenarian
and her two golden retrievers; Anthony Award winner Virginia Lanier's pureblood thriller
featuring bloodhounds and bloody murder; Chassie West's suspenseful stunner about a
life-saving German shepherd and a ghastly forgotten crime; rising star Lee Charles
Kelley's edge-of-your-seat yarn that pits an ex-cop/kennel owner and a yappy toy poodle
against a craven killer.
Rank: 2 Score: 0.3283390402793884 Title: Texas K-9 Unit Christmas: Holiday Hero\Rescuing Christmas
CHRISTMAS COMES WRAPPED IN DANGER Holiday Hero by Shirlee McCoy Emma Fairchild never
expected to find trouble in sleepy Sagebrush, Texas. But when she's attacked and left
for dead in her own diner, her childhood friend turned K-9 cop Lucas Harwood offers a
chance at justice--and love. Rescuing Christmas by Terri Reed She escaped a kidnapper,
but now a killer has set his sights on K-9 dog trainer Lily Anderson. When fellow
officer Jarrod Evans appoints himself her bodyguard, Lily knows more than her life is at
risk--so is her heart. Texas K-9 Unit: These lawmen solve the toughest cases with the
help of their brave canine partners
Rank: 3 Score: 0.33899369835853577 Title: Dogs on Duty: Soldiers' Best Friends on the Battlefield and Beyond
When the news of the raid on Osama Bin Laden's compound broke, the SEAL team member that
stole the show was a highly trained canine companion. Throughout history, dogs have been
key contributors to military units. Dorothy Hinshaw Patent follows man's best friend
onto the battlefield, showing readers why dogs are uniquely qualified for the job at
hand, how they are trained, how they contribute to missions, and what happens when they
retire. With full-color photographs throughout and sidebars featuring heroic canines
throughout history, Dogs on Duty provides a fascinating look at these exceptional
soldiers and companions.
Rank: 4 Score: 0.34207457304000854 Title: Toute Allure: Falling in Love in Rural France
After saying goodbye to life as a successful fashion editor in London, Karen Wheeler is
now happy in her small village house in rural France. Her idyll is complete when she
meets the love of her life - he has shaggy hair, four paws and a wet nose!
Rank: 5 Score: 0.343595951795578 Title: Otherwise Alone (Evan Arden, #1)
Librarian's note: This is an alternate cover edition for ASIN: B00AP5NNWC. Lieutenant
Evan Arden sits in a shack in the middle of nowhere, waiting for orders that will send
him back home - if he ever gets them. Other than his loyal Great Pyrenees, there's no
one around to break up the monotony. The tedium is excruciating, but it is suddenly
interrupted when a young woman stumbles up his path. "It's only 50-something pages, but
in that short amount of time, the author's awesome writing packs in a whole lotta
character detail. And sets the stage for the series, perfectly." -Maryse.net, 4.5 Stars
He has two choices - pick her off from a distance with his trusty sniper-rifle, or dare
let her approach his cabin and enter his life. Why not? It's been ages, and he is
otherwise alone...