Semantic Search with Pinecone and OpenAI

This repository uses OpenAI Embedding API to generate language embeddings and store them in pinecone. Ref. ChatGPT Retrieval Plugin.

In this guide you will learn how to use the OpenAI Embedding API to generate language embeddings, and then index those embeddings in the Pinecone vector database for fast and scalable vector search.

This is a powerful and common combination for building semantic search, question-answering, threat-detection, and other applications that rely on NLP and search over a large corpus of text data.

The basic workflow looks like this:

Embed and index

• Use the OpenAI Embedding API to generate vector embeddings of your documents (or any text data).
• Upload those vector embeddings into Pinecone, which can store and index millions/billions of these vector embeddings, and search through them at ultra-low latencies.

Search

• Pass your query text or document through the OpenAI Embedding API again.
• Take the resulting vector embedding and send it as a query to Pinecone.
• Get back semantically similar documents, even if they don’t share any keywords with the query.

Architecture overview

Let’s get started…

Setup

We first need to setup our environment and retrieve API keys for OpenAI and Pinecone. Let’s start with our environment, we need HuggingFace Datasets for our data, and the OpenAI and Pinecone clients:

!pip install -qU pinecone-client openai datasets

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Creating Embeddings

Then we initialize our connection to OpenAI Embeddings and Pinecone vector DB. Sign up for an API key over at OpenAI and Pinecone.

import openai
import os

openai.api_key = "XXXXX"
# get API key from top-right dropdown on OpenAI website

openai.Engine.list()  # check we have authenticated

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}

We can now create embeddings with the OpenAI Ada similarity model like so:

MODEL = "text-embedding-ada-002"

res = openai.Embedding.create(
    input=[
        "Sample document text goes here",
        "there will be several phrases in each batch"
    ], engine=MODEL
)
res

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  "data": [
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      "index": 1,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-ada-002-v2",
  "object": "list",
  "usage": {
    "prompt_tokens": 13,
    "total_tokens": 13
  }
}

print(f"vector 0: {len(res['data'][0]['embedding'])}\nvector 1: {len(res['data'][1]['embedding'])}")

vector 0: 1536
vector 1: 1536

# we can extract embeddings to a list
embeds = [record['embedding'] for record in res['data']]
len(embeds)

2

Next, we initialize our index to store vector embeddings with Pinecone.

len(embeds[0])

1536

import pinecone

index_name = 'semantic-search-openai'

# initialize connection to pinecone (get API key at app.pinecone.io)
pinecone.init(
    api_key="XXX",
    environment="us-central1-gcp"  # find next to api key in console
)
print(pinecone.list_indexes())
# check if 'openai' index already exists (only create index if not)
if index_name not in pinecone.list_indexes():
    pinecone.create_index(index_name, dimension=len(embeds[0]))
# connect to index
index = pinecone.Index(index_name)

['openai-trec']

Populating the Index

Now we will take 1K questions from the TREC dataset

from datasets import load_dataset

# load the first 1K rows of the TREC dataset
trec = load_dataset('trec', split='train[:1000]')
trec

WARNING:datasets.builder:Found cached dataset trec (/root/.cache/huggingface/datasets/trec/default/2.0.0/f2469cab1b5fceec7249fda55360dfdbd92a7a5b545e91ea0f78ad108ffac1c2)

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}

Then we create a vector embedding for each phrase using OpenAI, and upsert the ID, vector embedding, and original text for each phrase to Pinecone.

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 = openai.Embedding.create(input=lines_batch, engine=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)
    # upsert to Pinecone
    index.upsert(vectors=list(to_upsert))

---

Querying

With our data indexed, we’re now ready to move onto performing searches. This follows a similar process to indexing. We start with a text query, that we would like to use to find similar sentences. As before we encode this with OpenAI’s text similarity Babbage model to create a query vector xq. We then use xq to query the Pinecone index.

query = "What caused the 1929 Great Depression?"

xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']

Now query…

res = index.query([xq], top_k=5, include_metadata=True)
res

{'matches': [{'id': '932',
              'metadata': {'text': 'Why did the world enter a global '
                                   'depression in 1929 ?'},
              'score': 0.918042541,
              'values': []},
             {'id': '787',
              'metadata': {'text': "When was `` the Great Depression '' ?"},
              'score': 0.871837378,
              'values': []},
             {'id': '400',
              'metadata': {'text': 'What crop failure caused the Irish Famine '
                                   '?'},
              'score': 0.812031388,
              'values': []},
             {'id': '775',
              'metadata': {'text': 'What historical event happened in Dogtown '
                                   'in 1899 ?'},
              'score': 0.79880774,
              'values': []},
             {'id': '481',
              'metadata': {'text': 'What caused the Lynmouth floods ?'},
              'score': 0.792296052,
              'values': []}],
 'namespace': ''}

The response from Pinecone includes our original text in the metadata field, let’s print out the top_k most similar questions and their respective similarity scores.

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")

0.92: Why did the world enter a global depression in 1929 ?
0.87: When was `` the Great Depression '' ?
0.81: What crop failure caused the Irish Famine ?
0.80: What historical event happened in Dogtown in 1899 ?
0.79: What caused the Lynmouth floods ?

Looks good, let’s make it harder and replace “depression” with the incorrect term “recession”.

query = "What was the cause of the major recession in the early 20th century?"

# create the query embedding
xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']

# query, returning the top 5 most similar results
res = index.query([xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")

0.88: Why did the world enter a global depression in 1929 ?
0.83: When was `` the Great Depression '' ?
0.81: What crop failure caused the Irish Famine ?
0.80: When did World War I start ?
0.80: What were popular songs and types of songs in the 1920s ?

And again…

query = "Why was there a long-term economic downturn in the early 20th century?"

# create the query embedding
xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']

# query, returning the top 5 most similar results
res = index.query([xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")

0.90: Why did the world enter a global depression in 1929 ?
0.84: When was `` the Great Depression '' ?
0.80: When did World War I start ?
0.80: What crop failure caused the Irish Famine ?
0.80: When did the Dow first reach ?

#Trying amazon document
query = "When Amazon will be net-zero?"

# create the query embedding
xq = openai.Embedding.create(input=query, engine=MODEL)['data'][0]['embedding']

# query, returning the top 5 most similar results
res = index.query([xq], top_k=5, include_metadata=True)

for match in res['matches']:
    print(f"{match['score']:.2f}: {match['metadata']['text']}")

0.85: Supporting  Nature-Based  Solutions Climate science is telling us  that, even with aggressive  decarbonization efforts, companies  will need to go beyond their  own operations to reach net- zero emissions by 2050. We  believe that companies, including  Amazon, can drastically reduce  their own emissions while also  investing outside their operations  in initiatives that significantly  contribute to achieving the Paris  Agreement. One way we are doing  this is by investing in nature-based  solutions that conserve, restore, and  improve land management activities  to increase carbon storage in a  variety of habitats.Our Approach In 2019, Amazon created the Right Now Climate Fund, a  $100 million fund for nature-based solutions to restore and  conserve forests, wetlands, and grasslands around the world.  Amazon uses nature-based solutions to mitigate carbon  emissions outside of our value chain and supplement the
0.85: demand signal that is shifting the market to meet not only  our needs, but also the needs of other companies around  the world.  We announced in November that, in the city of Paris,  two-thirds of our shipments are delivered using zero- emission transport such as EVs, electric cargo bikes, and  on-foot deliveries. We also are exploring green hydrogen technologies: Amazon’s Climate Pledge Fund—a corporate  venture fund that invests in sustainability—recently  announced it has invested in EH2 in the U.S. and Sunfire  in Germany, two companies helping to push the green  hydrogen industry forward. It will take time to remove carbon emissions from heavy  transportation systems, including ocean shipping, aviation,  and trucking. Governments and the private sector need  to come together on this important work. Amazon is at  the heart of such industry initiatives and government  partnerships, including the Cargo Owners for Zero Emission
0.85: Learn more about the science  and technology behind our carbon footprint in our  Carbon Methodology . Our carbon footprint , including Scope 1, Scope 2, and  Scope 3 greenhouse gas emissions, is available on page 97. Amazon co-founded The Climate Pledge—a commitment  to be net-zero carbon across our business by 2040. We have  a goal to deliver 50% of Amazon shipments with net-zero  carbon by 2030. We are also on a path to powering our  operations with 100% renewable energy by 2025—five  years ahead of our original target of 2030. See our public  methodology  for more on our approach.  As part of our commitment to The Climate Pledge, we are  proud to have joined the Science Based Targets initiative  (SBTi) and are working toward setting science-based targets.  This reaffirms our commitment to reduce carbon emissions
0.84: Green Building Council. In early 2022, our Amazon Fresh location in Seattle, Washington, became the world’s first grocery store to pursue Zero Carbon Certification from the International Living Future Institute.  Read more about how we incorporate sustainability into our buildings  on pages 22–23.Resource Efficiency; Energy Source;  Resilience Transportation Decarbonizing our transportation network is a key part of meeting The Climate Pledge by 2040 and making 50% of shipments net-zero carbon by 2030, which is why we are actively transforming our fleet network and operations. We are  deploying zero-emission vehicles, using low-carbon fuels, investing in emerging technologies, and collaborating with others to accelerate cross-sector innovation.  In 2019, we ordered 100,000 custom electric delivery vehicles from Rivian and thousands more from global manufacturers such as Stellantis and Mahindra Electric.
0.84: The summit also featured our second crowdsourcing  challenge for Amazon employees to present  innovative ideas on sustainability, which received  89 submissions. 1 Calculation based on CO 2e saved, using the U.S. Environmental Protection  Agency (EPA) carbon calculator .  2021 Sustainability Report       Introduction     I     Environment     I     Society     I     Governance     I     Appendix 23   Decarbonizing  Transportation Delivering for our global customers  requires Amazon to rely on a variety  of transportation solutions for long  and short distances. Decarbonizing  our transportation network is a  key part of meeting The Climate  Pledge by 2040 and making 50%  of Amazon shipments net-zero  carbon by 2030, which is why  we are actively transforming our  fleet network and operations.Our Approach One of the ways we are decarbonizing our transportation  network is by deploying technical solutions including zero-

Looks great, our semantic search pipeline is clearly able to identify the meaning between each of our queries and return the most semantically similar questions from the already indexed questions.

Once we’re finished with the index we delete it to save resources.

pinecone.delete_index(index_name)

---