使用Pinecone和OpenAI进行语义搜索
在本指南中，您将学习如何使用OpenAI Embedding API生成语言嵌入，并将这些嵌入索引到Pinecone向量数据库中，以实现快速且可扩展的向量搜索。
这是构建语义搜索、问答、威胁检测以及其他依赖NLP和对大量文本数据进行搜索的应用程序的强大且常见组合。
基本工作流程如下：
嵌入和索引
- 使用OpenAI Embedding API生成文档（或任何文本数据）的向量嵌入。
- 将这些向量嵌入上传到Pinecone中，Pinecone可以存储和索引数百万/数十亿个这些向量嵌入，并以超低延迟进行搜索。
搜索
- 再次通过OpenAI Embedding API传递您的查询文本或文档。
- 获取生成的向量嵌入，并将其作为查询发送到Pinecone。
- 获取语义上相似的文档，即使它们与查询不共享任何关键字。
让我们开始吧…
设置
我们首先需要设置我们的环境并获取OpenAI和Pinecone的API密钥。让我们从我们的环境开始，我们需要HuggingFace Datasets来获取我们的数据，以及OpenAI和Pinecone客户端：
!pip install -qU \
pinecone-client==3.0.2 \
openai==1.10.0 \
datasets==2.16.1
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 201.4/201.4 kB 2.0 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 225.1/225.1 kB 12.1 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 507.1/507.1 kB 12.4 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 75.9/75.9 kB 4.4 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 115.3/115.3 kB 9.7 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 134.8/134.8 kB 7.6 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━ ━━━━━━━━━━━━━━━━━ 76.9/76.9 kB 4.7 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 58.3/58.3 kB 3.8 MB/s eta 0:00:00
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 134.8/134.8 kB 5.2 MB/s eta 0:00:00
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
llmx 0.0.15a0 requires cohere, which is not installed.
llmx 0.0.15a0 requires tiktoken, which is not installed.
tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.
创建嵌入
然后我们初始化与OpenAI Embeddings和Pinecone向量数据库的连接。在OpenAI和Pinecone上注册获取API密钥。
from openai import OpenAI
client = OpenAI(
api_key="OPENAI_API_KEY"
) # 从platform.openai.com获取API密钥
我们现在可以像这样使用OpenAI Ada相似性模型创建嵌入：
MODEL = "text-embedding-3-small"
res = client.embeddings.create(
input=[
"Sample document text goes here",
"there will be several phrases in each batch"
], model=MODEL
)
res
CreateEmbeddingResponse(data=[Embedding(embedding=[-0.0007019874756224453, 0.017813093960285187, 0.028484342619776726, -0.01655358262360096, -0.04467806592583656, -0.03371616080403328, 0.02429058402776718, -0.015460160560905933, 0.0147542804479599, -0.006034583784639835, 0.03413138538599014, -0.010325227864086628, 0.004678186494857073, -0.006508630700409412, 0.06505170464515686, 0.07252573221921921, -0.004359848331660032, 0.013335599564015865, -0.026740403845906258, 0.03684417903423309, 0.004705868195742369, 0.04675418511033058, -0.021384017542004585, 0.03440820053219795, 0.012664321810007095, -0.021896127611398697, -0.040553510189056396, -0.024913419038057327, 0.05112787336111069, -0.06244963780045509, -0.01853281632065773, -0.04124554991722107, 0.01914181001484394, -0.03044973500072956, -0.040165968239307404, 0.054698795080184937, 0.06560533493757248, 0.0040449704974889755, -0.049799155443906784, -0.04819362610578537, -0.009563985280692577, -0.012491311877965927, 0.0549202486872673, 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print(f"vector 0: {len(res.data[0].embedding)}\nvector 1: {len(res.data[1].embedding)}")
vector 0: 1536
vector 1: 1536
# 我们可以将嵌入提取到一个列表 中
embeds = [record.embedding for record in res.data]
len(embeds)
2
接下来，我们将使用Pinecone 初始化我们的索引以存储向量嵌入。
len(embeds[0])
1536
初始化与Pinecone的连接，您可以在Pinecone仪表板中获取免费的API密钥。
from pinecone import Pinecone
pc = Pinecone(api_key="...")
import time
from pinecone import ServerlessSpec
spec = ServerlessSpec(cloud="aws", region="us-west-2")
index_name = 'semantic-search-openai'
# check if index already exists (if shouldn't if this is your first run)
if index_name not in pc.list_indexes().names():
# if does not exist, create index
pc.create_index(
index_name,
dimension=len(embeds[0]), # dimensionality of text-embed-3-small
metric='dotproduct',
spec=spec
)
# wait for index to be initialized
while not pc.describe_index(index_name).status['ready']:
time.sleep(1)
# 连接到索引
index = pc.Index(index_name)
time.sleep(1)
# 查看索引统计信息
index.describe_index_stats()
{'dimension': 1536,
'index_fullness': 0.0,
'namespaces': {},
'total_vector_count': 0}
填充索引
现在我们将从TREC数据集中获取1000个问题。
from datasets import load_dataset
# 加载TREC数据集的前1000行
trec = load_dataset('trec', split='train[:1000]')
trec
/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning:
The secret `HF_TOKEN` does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
warnings.warn(
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Dataset({
features: ['text', 'coarse_label', 'fine_label'],
num_rows: 1000
})
trec[0]
{'text': 'How did serfdom develop in and then leave Russia ?',
'coarse_label': 2,
'fine_label': 26}
然后我们使用OpenAI为每个短语创建一个向量嵌入，并将每个短语的ID、向量嵌入和原始文本“upsert”到Pinecone中。
from tqdm.auto import tqdm
count = 0 # we'll use the count to create unique IDs
batch_size = 32 # process everything in batches of 32
for i in tqdm(range(0, len(trec['text']), batch_size)):
# set end position of batch
i_end = min(i+batch_size, len(trec['text']))
# get batch of lines and IDs
lines_batch = trec['text'][i: i+batch_size]
ids_batch = [str(n) for n in range(i, i_end)]
# create embeddings
res = client.embeddings.create(input=lines_batch, model=MODEL)
embeds = [record.embedding for record in res.data]
# prep metadata and upsert batch
meta = [{'text': line} for line in lines_batch]
to_upsert = zip(ids_batch, embeds, meta)
# 插入或更新到 Pinecone
index.upsert(vectors=list(to_upsert))
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查询
现在我们的数据已经建立索引，我们准备开始执行搜索。这个过程与建立索引类似。我们从一个文本“查询”开始，希望用它来查找相似的句子。与之前一样，我们使用OpenAI的文本相似性Babbage模型对其进行编码，创建一个查询向量
xq。然后我们使用 xq 来查询Pinecone索引。
query = "What caused the 1929 Great Depression?"
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
现在查询…
res = index.query(vector=[xq], top_k=5, include_metadata=True)
res
{'matches': [{'id': '932',
'metadata': {'text': 'Why did the world enter a global '
'depression in 1929 ?'},
'score': 0.751888752,
'values': []},
{'id': '787',
'metadata': {'text': "When was `` the Great Depression '' ?"},
'score': 0.597448647,
'values': []},
{'id': '400',
'metadata': {'text': 'What crop failure caused the Irish Famine '
'?'},
'score': 0.367482603,
'values': []},
{'id': '835',
'metadata': {'text': 'What were popular songs and types of songs '
'in the 1920s ?'},
'score': 0.324545294,
'values': []},
{'id': '262',
'metadata': {'text': 'When did World War I start ?'},
'score': 0.320995867,
'values': []}],
'namespace': '',
'usage': {'read_units': 6}}
Pinecone的响应在metadata字段中包含了我们的原始文本，让我们打印出top_k个最相似的问题及它们各自的相似度分数。
for match in res['matches']:
print(f"{match['score']:.2f}: {match['metadata']['text']}")
0.75: Why did the world enter a global depression in 1929 ?
0.60: When was `` the Great Depression '' ?
0.37: What crop failure caused the Irish Famine ?
0.32: What were popular songs and types of songs in the 1920s ?
0.32: When did World War I start ?
看起来不错，让我们增加难度，将“抑郁症”替换为不正确的术语“经济衰退”。
query = "What was the cause of the major recession in the early 20th century?"
# 创建查询嵌入
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
# 查询，返回最相似的前5个结果
res = index.query(vector=[xq], top_k=5, include_metadata=True)
for match in res['matches']:
print(f"{match['score']:.2f}: {match['metadata']['text']}")
0.63: Why did the world enter a global depression in 1929 ?
0.55: When was `` the Great Depression '' ?
0.34: What were popular songs and types of songs in the 1920s ?
0.33: What crop failure caused the Irish Famine ?
0.29: What is considered the costliest disaster the insurance industry has ever faced ?
再次…
query = "Why was there a long-term economic downturn in the early 20th century?"
# 创建查询嵌入
xq = client.embeddings.create(input=query, model=MODEL).data[0].embedding
# 查询，返回最相似的前5个结果
res = index.query(vector=[xq], top_k=5, include_metadata=True)
for match in res['matches']:
print(f"{match['score']:.2f}: {match['metadata']['text']}")
0.62: Why did the world enter a global depression in 1929 ?
0.54: When was `` the Great Depression '' ?
0.34: What were popular songs and types of songs in the 1920s ?
0.33: What crop failure caused the Irish Famine ?
0.32: What do economists do ?
看起来很棒，我们的语义搜索流程明显能够识别每个查询之间的含义，并从已经索引的问题中返回最具语义相似性的问题。
完成索引后，我们会删除它以节省资源。
pc.delete_index(index_name)