Semantic search with Pinecone

Vector Databases for Embeddings with Pinecone

James Chapman

Curriculum Manager, DataCamp

Semantic search engines

Embed and ingest documents into a Pinecone index
Embed a user query
Query the index with the embedded user query

semantic search

Setting up Pinecone and OpenAI for semantic search

from openai import OpenAI
from pinecone import Pinecone, ServerlessSpec

client = OpenAI(api_key="OPENAI_API_KEY")
pc = Pinecone(api_key="PINECONE_API_KEY")


pc.create_index(
    name="semantic-search-datacamp",

    dimension=1536,

    spec=ServerlessSpec(cloud='aws', region='us-east-1')
)

index = pc.Index("semantic-search-datacamp")

Ingesting documents to Pinecone index

import pandas as pd
import numpy as np
from uuid import uuid4

df = pd.read_csv('squad_dataset.csv')

| id | text                                              | title             |
|----|---------------------------------------------------|-------------------|
| 1  | Architecturally, the school has a Catholic cha... | University of ... |
| 2  | The College of Engineering was established in.... | University of ... |
| 3  | Following the disbandment of Destiny's Child in.. | Beyonce           |
| 4  | Architecturally, the school has a Catholic cha... | University of ... |

Ingesting documents to Pinecone index

batch_limit = 100


for batch in np.array_split(df, len(df) / batch_limit):

    metadatas = [{"text_id": row['id'], "text": row['text'], "title": row['title']} 
                 for _, row in batch.iterrows()]

    texts = batch['text'].tolist()

    ids = [str(uuid4()) for _ in range(len(texts))]


    response = client.embeddings.create(input=texts, model="text-embedding-3-small")
    embeds = [np.array(x.embedding) for x in response.data]


    index.upsert(vectors=zip(ids, embeds, metadatas), namespace="squad_dataset")

Ingesting documents to Pinecone index

index.describe_index_stats()

{'dimension': 1536, 'index_fullness': 0.02,
 'namespaces': {'squad_dataset': {'vector_count': 2000}},
 'total_vector_count': 2000}

Querying with Pinecone

query = "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?"


query_response = client.embeddings.create(
    input=query,
    model="text-embedding-3-small")
query_emb = query_response.data[0].embedding


retrieved_docs = index.query(vector=query_emb, 
                             top_k=3, 
                             namespace=namespace,
                             include_metadata=True)

Querying with Pinecone

for result in retrieved_docs['matches']:
    print(f"{round(result['score'], 2)}: {result['metadata']['text']}")

0.41: Architecturally, the school has a Catholic character. Atop the Main Building
gold dome is a golden statue of the Virgin Mary...

0.3: Because of its Catholic identity, a number of religious buildings stand on 
campus. The Old College building has become one of two seminaries...

0.29: Within the white inescutcheon, the five quinas (small blue shields) with 
their five white bezants representing the five wounds...

Time to build!

Vector Databases for Embeddings with Pinecone